diff --git a/examples/changepass.html b/examples/changepass.html
new file mode 100644
index 0000000..097f777
--- /dev/null
+++ b/examples/changepass.html
@@ -0,0 +1,25 @@
+<html><body><script>
+var generateHash = function (str, length=false){
+	var buffer = new TextEncoder("utf-8").encode(str);
+	return crypto.subtle.digest("SHA-256", buffer).then(
+		function (hash) {
+			hash = new Uint8Array(hash);
+			if (length){
+				hash = hash.slice(0, parseInt(parseInt(length)/2));
+			}
+			hash = toHexString(hash);
+			return hash;
+		}
+	);
+};
+function toHexString(byteArray){
+		return Array.prototype.map.call(byteArray, function(byte){
+			return ('0' + (byte & 0xFF).toString(16)).slice(-2);
+		}).join('');
+	}
+var password = prompt("Please enter the password");
+	
+generateHash(password + location.hostname, 4).then(function(hash) { // million to one error. 
+	alert("hash value: "+hash)
+});
+</script></body></html>
\ No newline at end of file
diff --git a/examples/dual.html b/examples/dual.html
new file mode 100644
index 0000000..d968048
--- /dev/null
+++ b/examples/dual.html
@@ -0,0 +1,331 @@
+<html>
+<head><title>Dual Input</title>
+<style>
+body{
+	padding:0;
+	margin:0;
+}
+iframe {
+	border:0;
+	margin:0;
+	padding:0;
+	display:block;
+	margin:0px;
+	min-height: 100px;
+    min-width: 100px;
+    max-height: 95%;
+    max-width: 99%%;
+    float: left;
+    position: fixed;
+}
+
+#viewlink {
+	width:400px;
+}
+
+
+input{
+	padding:5px;
+	margin:5px;
+}
+button{
+	padding:5px;
+	margin:5px;
+	position:relative;
+	
+}
+
+.menu {
+	z-index: 10;
+	float:right;
+	right: 20px;
+	color: #fff;
+}
+
+.close {
+	background-color: #d33;
+    color: #fff;
+}
+
+.reload {
+	background-color: #0a0;
+	color: #fff;
+}
+
+.popup {
+	z-index: 9;
+	background-color: #f1f1f1;
+	border: 1px solid #d3d3d3;
+	text-align: center;
+	min-height: 100px;
+	min-width: 100px;
+	max-height: 95%;
+    max-width: 99%;
+	scale: 0.5;
+}
+
+.popup {
+  position: absolute;
+  /*resize: both; !*enable this to css resize*! */
+  overflow: auto;
+}
+
+.popup-header {
+  cursor: move;
+  background-color: #2196f3;
+}
+
+
+
+.popup .resizer-right {
+  width: 5px;
+  height: 100%;
+  background: transparent;
+  position: absolute;
+  right: 0;
+  bottom: 0;
+  cursor: e-resize;
+}
+
+.popup .resizer-bottom {
+  width: 100%;
+  height: 5px;
+  background: transparent;
+  position: absolute;
+  right: 0;
+  bottom: 0;
+  cursor: n-resize;
+}
+
+.popup .resizer-both {
+  width: 5px;
+  height: 5px;
+  background: transparent;
+  z-index: 10;
+  position: absolute;
+  right: 0;
+  bottom: 0;
+  cursor: nw-resize;
+}
+
+
+/*NOSELECT*/
+
+.popup * {
+  -webkit-touch-callout: none; /* iOS Safari */
+  -webkit-user-select: none; /* Safari */
+  -khtml-user-select: none; /* Konqueror HTML */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* Internet Explorer/Edge */
+  user-select: none; /* Non-prefixed version, currently
+                                  supported by Chrome and Opera */
+}
+
+</style>
+</head>
+<body>
+
+<input placeholder="Enter an OBS.Ninja Room Link" id="viewlink" />
+
+<button onclick="loadIframe();">Load URL</button>You can drag and resize the generated windows; multiple can be created.
+
+<div id="container"></div>
+
+<script>
+var currentZIndex = 100;
+function initDragElement(popup){
+
+	var pos1 = 0,
+		pos2 = 0,
+		pos3 = 0,
+		pos4 = 0;
+
+	var elmnt = null;
+
+	var header = getHeader(popup);
+	var iframe = getIFrame(popup);
+
+	popup.onmousedown = function() {
+	  this.style.zIndex = "" + ++currentZIndex;
+	};
+
+	if (header) {
+	  header.parentPopup = popup;
+	  header.onmousedown = dragMouseDown;
+	}
+  
+
+  function dragMouseDown(e) {
+    elmnt = this.parentPopup;
+    elmnt.style.zIndex = "" + ++currentZIndex;
+
+    e = e || window.event;
+    // get the mouse cursor position at startup:
+    pos3 = e.clientX;
+    pos4 = e.clientY;
+    document.onmouseup = closeDragElement;
+    // call a function whenever the cursor moves:
+    document.onmousemove = elementDrag;
+  }
+
+  function elementDrag(e) {
+    if (!elmnt) {
+      return;
+    }
+
+    e = e || window.event;
+    // calculate the new cursor position:
+    pos1 = pos3 - e.clientX;
+    pos2 = pos4 - e.clientY;
+    pos3 = e.clientX;
+    pos4 = e.clientY;
+    // set the element's new position:
+    elmnt.style.top = elmnt.offsetTop - pos2 + "px";
+    elmnt.style.left = elmnt.offsetLeft - pos1 + "px";
+	
+	iframe.style.top = elmnt.offsetTop - pos2 + "px";
+	iframe.style.left = elmnt.offsetLeft - pos1 + "px";
+  }
+
+  function closeDragElement() {
+    /* stop moving when mouse button is released:*/
+    document.onmouseup = null;
+    document.onmousemove = null;
+  }
+
+  function getHeader(element) {
+    var headerItems = element.getElementsByClassName("popup-header");
+
+    if (headerItems.length === 1) {
+      return headerItems[0];
+    }
+
+    return null;
+  }
+  
+  function getIFrame(element) {
+    var headerItems = element.getElementsByTagName("iframe");
+
+    if (headerItems.length === 1) {
+      return headerItems[0];
+    }
+
+    return null;
+  }
+}
+
+function initResizeElement(p) {
+
+	var iframe = getIFrame(p);
+	var element = null;
+	var startX, startY, startWidth, startHeight;
+
+    var right = document.createElement("div");
+    right.className = "resizer-right";
+    p.appendChild(right);
+    right.addEventListener("mousedown", initDrag, false);
+    right.parentPopup = p;
+
+    var bottom = document.createElement("div");
+    bottom.className = "resizer-bottom";
+    p.appendChild(bottom);
+    bottom.addEventListener("mousedown", initDrag, false);
+    bottom.parentPopup = p;
+
+    var both = document.createElement("div");
+    both.className = "resizer-both";
+    p.appendChild(both);
+    both.addEventListener("mousedown", initDrag, false);
+    both.parentPopup = p;
+  
+
+  function initDrag(e) {
+    element = this.parentPopup;
+
+    startX = e.clientX;
+    startY = e.clientY;
+    startWidth = parseInt(
+      document.defaultView.getComputedStyle(element).width,
+      10
+    );
+    startHeight = parseInt(
+      document.defaultView.getComputedStyle(element).height,
+      10
+    );
+    document.documentElement.addEventListener("mousemove", doDrag, false);
+    document.documentElement.addEventListener("mouseup", stopDrag, false);
+	document.documentElement.addEventListener("click", stopDrag, false)
+  }
+
+  function doDrag(e) {
+	if (e.buttons==0){
+		stopDrag(e);
+		return false;
+	}
+  
+    element.style.width = startWidth + e.clientX - startX + "px";
+    element.style.height = startHeight + e.clientY - startY + "px";
+	
+	iframe.style.width = startWidth + e.clientX - startX + "px";
+	iframe.style.height = startHeight + e.clientY - startY + "px";
+  }
+
+  function stopDrag(e) {
+    document.documentElement.removeEventListener("mousemove", doDrag, false);
+    document.documentElement.removeEventListener("mouseup", stopDrag, false);
+  }
+
+  function getIFrame(element) {
+    var headerItems = element.getElementsByTagName("iframe");
+
+    if (headerItems.length === 1) {
+      return headerItems[0];
+    }
+
+    return null;
+  }
+}
+  
+function loadIframe(){
+
+	var iframeContainer = document.createElement("div");
+	iframeContainer.className="popup";
+	iframeContainer.style.zIndex = "" + ++currentZIndex;
+	iframeContainer.style.width="325px";
+	iframeContainer.style.height="420px";
+	
+	var button = document.createElement("button");
+	button.innerHTML = "Move";
+	button.className = "popup-header menu";
+	iframeContainer.appendChild(button);
+	
+	var button = document.createElement("button");
+	button.innerHTML = "Close";
+	button.className = "menu close";
+	button.onclick = function(){iframe.contentWindow.postMessage({"close":true}, '*');iframe.parentNode.parentNode.removeChild(iframeContainer);}
+	iframeContainer.appendChild(button);
+	
+	var button = document.createElement("button");
+	button.innerHTML = "Reload";
+	button.className = "menu reload";
+	button.onclick = function(){iframe.contentWindow.postMessage({"reload":true}, '*');}
+	iframeContainer.appendChild(button);
+
+	var iframe = document.createElement("iframe");
+	iframe.allow="autoplay";
+	iframe.src = document.getElementById("viewlink").value || "https://obs.ninja";
+	iframe.style.width="325px";
+	iframe.style.height="420px";
+	
+	iframeContainer.appendChild(iframe);
+	
+	document.getElementById("container").appendChild(iframeContainer);
+
+	initDragElement(iframeContainer);
+	initResizeElement(iframeContainer);
+	
+}
+
+</script>
+</body>
+</html>
\ No newline at end of file
diff --git a/examples/mini.css b/examples/mini.css
new file mode 100644
index 0000000..6a4100f
--- /dev/null
+++ b/examples/mini.css
@@ -0,0 +1,3 @@
+.tile {
+  max-width:200px !important;
+}
\ No newline at end of file
diff --git a/examples/status.html b/examples/status.html
new file mode 100644
index 0000000..e9eb89d
--- /dev/null
+++ b/examples/status.html
@@ -0,0 +1,172 @@
+<html>
+	<head>
+		<meta charset="utf8" />
+		<meta name="viewport" content="width=device-width, initial-scale=1" />
+		<title>OBSN Chat Overlay</title>
+		<style>
+		
+			@font-face {
+			  font-family: 'Cousine';
+			  src: url('fonts/Cousine-Bold.ttf') format('truetype');
+			}
+		
+			body {
+				margin:0;
+				padding:0 10px;
+				height:100%;
+				border: 0;
+				display: flex;
+				flex-direction: column-reverse;
+				position:absolute;
+				bottom:0;
+				overflow:hidden;
+				max-width:100%;
+			}
+
+			ul {
+				margin:0;
+				background-color: #0000;
+				color: white;
+				font-family: Cousine, monospace;
+				font-size: 3.2em;
+				line-height: 1.1em;
+				letter-spacing: 0.0em;
+				text-transform: uppercase;
+				padding: 0em;
+				text-shadow: 0.05em 0.05em 0px rgba(0,0,0,1);
+				max-width:100%;
+			}
+			
+			ul li { 
+				background-color: black; 
+				padding: 8px 8px 0px 8px;
+				margin:0;
+				word-wrap: break-word;
+				overflow-wrap: break-word;
+				word-wrap: break-word;
+				word-break: break-all;
+				hyphens: auto;
+				max-width:100%;
+			}
+			
+			a {
+				color:white;
+				font-size:1.2em;
+				text-transform: none;
+				word-wrap: break-word;
+				overflow-wrap: break-word;
+				word-wrap: break-word;
+				word-break: break-all;
+				hyphens: auto;
+			}
+			</style>
+		<script>
+		
+		
+			(function (w) {
+				w.URLSearchParams =
+					w.URLSearchParams ||
+					function (searchString) {
+						var self = this;
+						self.searchString = searchString;
+						self.get = function (name) {
+							var results = new RegExp("[\?&]" + name + "=([^&#]*)").exec(
+								self.searchString
+							);
+							if (results == null) {
+								return null;
+							} else {
+								return decodeURI(results[1]) || 0;
+							}
+						};
+					};
+			})(window);
+			var urlParams = new URLSearchParams(window.location.search);
+			
+
+			function loadIframe() {
+				
+				var iframe = document.createElement("iframe");
+
+				var view= "";
+				if (urlParams.has("view")) {
+					view = "&view="+(urlParams.get("view") || "");
+				} 
+				var room="";
+				if (urlParams.has("room")) {
+					room = "&room="+urlParams.get("room");
+				}
+				
+				var password="";
+				if (urlParams.has("password")) {
+					password = "&password="+urlParams.get("password");
+				}
+				
+				iframe.allow = "autoplay";
+				var srcString = "./?novideo&noaudio&label=chatOverlay&scene"+room+view+password;
+				
+				iframe.src = srcString;
+				iframe.style.width="0";
+				iframe.style.height="0";
+				iframe.style.border="0";
+				
+				document.body.appendChild(iframe);
+
+				////////////  LISTEN FOR EVENTS
+	
+				var eventMethod = window.addEventListener ? "addEventListener" : "attachEvent";
+				var eventer = window[eventMethod];
+				var messageEvent = eventMethod === "attachEvent" ? "onmessage" : "message";
+
+
+				/// If you have a routing system setup, you could have just one global listener for all iframes instead.
+				
+				eventer(messageEvent, function (e) {
+					if (e.source != iframe.contentWindow){return} // reject messages send from other iframes
+
+					console.log(e);
+					if ("gotChat" in e.data){
+						logData(e.data.gotChat.label,e.data.gotChat.msg);
+					}
+				});
+			}
+
+			function printValues(obj) {
+				var out = "";
+				for (var key in obj) {
+					if (typeof obj[key] === "object") {
+						out += "<br />";
+						out += printValues(obj[key]);
+					} else {
+						if (key.startsWith("_")) {
+						} else {
+							out += "<b>" + key + "</b>: " + obj[key] + "<br />";
+						}
+					}
+				}
+				return out;
+			}
+
+			function logData(type, data) {
+				var log = document.body.getElementsByTagName("ul")[0];
+				var entry = document.createElement('li');
+				if (type){
+					type = "<i>"+type+"</i>";
+				}
+				entry.innerHTML = type + data;
+				
+				//setTimeout(function(entry){ // hide message after 60 seconds
+			//		entry.innerHTML="";
+			//		entry.remove();
+			//	},60000,entry);
+				
+				log.appendChild(entry);
+			}
+		</script>
+	</head>
+	<body onload="loadIframe();">
+			<ul></ul>
+
+
+	</body>
+</html>
diff --git a/examples/webhid.html b/examples/webhid.html
new file mode 100644
index 0000000..9bda785
--- /dev/null
+++ b/examples/webhid.html
@@ -0,0 +1,133 @@
+<!DOCTYPE html>
+<html>
+<head>
+	<meta charset="utf-8" />
+	<title>WebHID Demo</title>
+	<style>
+	body {
+		text-align: center;
+    }
+  
+  .button {
+    background-color: black;
+    border: none;
+    color: #00FF00;
+    padding: 15px 32px;
+    text-align: center;
+    text-decoration: none;
+    display: inline-block;
+    margin: 4px 2px;
+    cursor: pointer;
+    font-size: 24px;
+  }
+  
+  #connected {
+	font-size: 24px;
+	max-height:700px;
+	overflow-y:scroll
+  }
+  #disconnectButton {
+    font-size: 24px;
+  }
+ 
+  </style>
+</head>
+<body>
+	<h1>STREAMDECK DEMO</h1>
+	<img src="./media/streamdeck.png" /><br />
+	<input class="button" type="button" id="connectButton" value="Connect" />
+	<input class="button" type="button" id="disconnectButton" style="display:none" value="Disconnect" />
+	<div id="connected" style>
+	</div>
+
+    <script>
+		const connectButton = document.getElementById("connectButton");
+		const disconnectButton = document.getElementById("disconnectButton");
+		const connect = document.getElementById("connect");
+		const deviceButtonPressed = document.getElementById("deviceButtonPressed");
+		var lastState = false;
+			//productId: 0x0060,
+			//class: models_1.StreamDeckOriginal,
+		
+			//productId: 0x0063,
+			//class: models_1.StreamDeckMini,
+		
+			//productId: 0x006c,
+			//class: models_1.StreamDeckXL,
+		
+			//productId: 0x006d,
+			//class: models_1.StreamDeckOriginalV2,
+				
+		document.addEventListener('DOMContentLoaded', async () => {
+		  let devices = await navigator.hid.getDevices();
+		  devices.forEach(device => {
+			console.log(`HID: ${device.productName}`);
+		  });
+		});
+		
+		function handleInputReport(e) {
+		  console.log(e.device.productName + ": got input report " + e.reportId);
+		  console.log(new Uint8Array(e.data.buffer));
+		  var data = new Uint8Array(e.data.buffer);
+		  if (lastState!==false){
+			for (var i=0;i<data.length;i++){
+				
+				if (parseInt(data[i])!=data[i]){continue;}
+				if (lastState[i]!==data[i]){
+					if (data[i]){
+						document.getElementById("connected").innerHTML = "<br />Button "+(i+1)+" Pressed"+document.getElementById("connected").innerHTML;
+					} else {
+						document.getElementById("connected").innerHTML = "<br />Button "+(i+1)+" Released"+document.getElementById("connected").innerHTML;
+					}
+				} else {
+					if (data[i]){
+						document.getElementById("connected").innerHTML = "<br />Button "+(i+1)+" Pressed"+document.getElementById("connected").innerHTML;
+					}
+				}
+			}
+		  }
+		  lastState = data;
+		}
+
+		let device;
+		
+		connectButton.onclick = async () => {
+		  navigator.hid.requestDevice({
+			filters: [{ vendorId: 0x0fd9}] // elgato?
+		  }).then((devices)=>{
+			console.log(devices);
+			
+			device = devices[0];
+			
+			console.log(`HID connected: ${device.productName}`);
+			document.getElementById("connected").innerHTML = "<br />Connected" +document.getElementById("connected").innerHTML;
+			document.getElementById("disconnectButton").style.display = "inline-block";
+			device.addEventListener("inputreport", handleInputReport);
+			
+			//device.sendReport(outputReportId, outputReport).then(() => {
+			//  console.log("Sent output report " + outputReportId);
+			//});
+			
+			if (!device.opened){
+				device.open().then(()=>{
+					window.addEventListener("onbeforeunload", async () => {
+					  await device.close();
+					});
+				}).catch(function(err){console.error(err);});
+			}
+		  }).catch(function(err){console.error(err);});
+		  
+		};
+
+		disconnectButton.onclick = async () => {
+		  await device.close();
+
+		  //connected.style.display = "none";
+		  //connectButton.style.display = "initial";
+		  disconnectButton.style.display = "none";
+		};
+		
+	</script>
+</body>
+</html>
+
diff --git a/examples/zoom.html b/examples/zoom.html
new file mode 100644
index 0000000..8c8969a
--- /dev/null
+++ b/examples/zoom.html
@@ -0,0 +1,182 @@
+
+<html>
+<head><style>
+span{margin:10px 0 0 0;display:block;} 
+body {
+	
+	background-color:#cdf;
+	padding:0;
+	width;100%;height:100%
+} 
+
+input{padding:5px;}
+
+button {margin:10px 3px;}
+
+#stream{
+	display:block;
+}
+
+ </style></head>
+<body id="main" style="margin:5%;"
+<meta charset="utf-8"/>
+
+<video id="video" autoplay="true" muted="true" playsinline style='height:420px;background-color:black;display:block;margin:0 0 10px 0;'></video>
+<div id="devices">
+  <div class="select">
+    <label for="videoSource">Video source: </label><select id="videoSource"></select>
+  </div>
+  <div class="select">
+    <label for="audioSource">Audio source: </label><select id="audioSource"></select>
+  </div>
+</div>
+<button onclick="fullwindow()">FULL WINDOW</button>
+<script>
+window.onerror = function backupErr(errorMsg, url=false, lineNumber=false) {
+	console.error(errorMsg);
+	console.error(lineNumber);
+	console.error("Unhandeled Error occured"); //or any message
+	return false;
+};
+
+function fullwindow(){
+	videoElement.style.width="100%";
+	videoElement.style.padding= "0";
+	videoElement.style.margin="0";
+	videoElement.style.height="100%";
+	videoElement.style.zIndex="5";
+	videoElement.style.position = "absolute";
+	videoElement.style.top="0px";
+	videoElement.style.left="0px";
+	document.getElementById("main").style.overflow = "hidden";
+	videoElement.style.overflow = "hidden"
+	document.getElementById("main").style.backgroundColor="#000";
+	videoElement.style.cursor="none";
+	document.getElementById("main").style.cursor="none";
+}
+
+var videoElement = document.getElementById("video");
+var gotDev = false;
+async function gotDevices() {
+ if (gotDev){return;}
+	gotDev=true;
+	await navigator.mediaDevices.getUserMedia({audio:true, video:true}); // is needed to ask for permissinos.
+	navigator.mediaDevices.enumerateDevices().then((deviceInfos)=>{
+	  for (let i = 0; i !== deviceInfos.length; ++i) {
+		var deviceInfo = deviceInfos[i];
+		var option = document.createElement("option");
+		option.value = deviceInfo.deviceId;
+		
+		if (deviceInfo.kind === "audioinput") {
+		  option.text = deviceInfo.label || "microphone " + (audioSelect.length + 1);
+		  audioSelect.appendChild(option);
+		  if (option.text.startsWith("CABLE")){
+			option.selected =true;
+		  }
+		} else if (deviceInfo.kind === "videoinput") {
+		  option.text = deviceInfo.label || "camera " + (videoSelect.length + 1);
+		  if (option.text.startsWith("NewTek")){
+			continue;
+		  }
+		  videoSelect.appendChild(option);
+		  if (option.text.startsWith("OBS")){
+			option.selected =true;
+		  }
+		}
+	  }
+	  getStream();
+  });
+}
+
+function getStream() {
+  if (window.stream) {
+	window.stream.getTracks().forEach(function (track) {
+		track.stop();
+		log("TRack stopping");
+	});
+  }
+	
+  const constraints = {
+    audio: {
+      deviceId: { exact: audioSelect.value },
+	  echoCancellation : false,
+	  autoGainControl : false,
+	  noiseSuppression : false
+    },
+    video: {
+      deviceId: { exact: videoSelect.value },
+		width: { min: 1280, ideal: 1920, max: 1920 },
+		height: { min: 720, ideal: 1080, max: 1080 }
+    }
+  };
+  return navigator.mediaDevices.getUserMedia(constraints)
+    .then(gotStream)
+    .catch(console.error);
+}
+
+
+function gotStream(stream) {
+	if (window.stream) {
+		window.stream = stream; // make stream available to console
+		videoElement.srcObject = stream;
+		var senders = session.pc.getSenders();
+		videoElement.srcObject.getVideoTracks().forEach((track)=>{
+			var added = false;
+			senders.forEach((sender) => { // I suppose there could be a race condition between negotiating and updating this. if joining at the same time as changnig streams?
+				if (sender.track) {
+					if (sender.track && sender.track.kind == "video") {
+						sender.replaceTrack(track); // replace may not be supported by all browsers.  eek.
+						track.enabled = notCensored;
+						added = true;
+					}
+				}
+			});
+			if (added==false){
+				session.pc.addTrack(track);
+				log("ADDED NOT REPLACED?");
+			}
+		});
+
+		videoElement.srcObject.getAudioTracks().forEach((track)=>{
+			var added = false;
+			senders.forEach((sender) => { // I suppose there could be a race condition between negotiating and updating this. if joining at the same time as changnig streams?
+				if (sender.track) {
+					if (sender.track && sender.track.kind == "audio") {
+						sender.replaceTrack(track); // replace may not be supported by all browsers.  eek.
+						track.enabled = notCensored;
+						added = true;
+					}
+				}
+			});
+			if (added==false){
+				session.pc.addTrack(track);
+				log("ADDED NOT REPLACED?");
+			}
+		});
+	} else {
+		window.stream = stream; // make stream available to console
+		videoElement.srcObject = stream;
+	}
+}
+
+
+var audioSelect = document.querySelector("select#audioSource");
+var videoSelect = document.querySelector("select#videoSource");
+audioSelect.onchange = getStream;
+videoSelect.onchange = getStream;
+gotDevices();
+
+
+</script>
+</body>
+</html>
+
+
+
+
+
+
+
+
+
+
diff --git a/index.html b/index.html
index a7985c4..24d779a 100644
--- a/index.html
+++ b/index.html
@@ -68,7 +68,7 @@
 			<link itemprop="url" href="./media/obsNinja_logo_full.png" />
 		</span>
 		<script  type="text/javascript" crossorigin="anonymous"  src="./thirdparty/CodecsHandler.js?ver=33"></script>
-		<script  type="text/javascript" crossorigin="anonymous" src="./webrtc.js?ver=207"></script>
+		<script  type="text/javascript" crossorigin="anonymous" src="./webrtc.js?ver=208"></script>
 		<input id="zoomSlider" type="range" style="display: none;" />
 		<div id="header">
 			
@@ -495,6 +495,8 @@
 							</option>
 						</select>
 					</span>
+					<br />
+					<span data-translate="application-audio-capture">For application-specific audio capture, <a href='https://docs.vdo.ninja/audio' style='color: #007AC8;'>see here</a></span>
 				</div>
 				<div class="outer close">
 					<div class="inner">
@@ -1338,7 +1340,7 @@
 				<button data-action-type="recorder-local" title="Start Recording this remote stream to this local drive. *experimental*'" onclick="recordLocalVideoToggle();">
 					<i class="las la-circle"></i>
 					<span data-translate="record-local"> Record</span>
-				</button>
+				</button> 
 				
 				<span>
 					<button style="width:34px;" data-action-type="order-down" title="Shift this Video Down in Order"  onclick="changeOrderDirector(-1);">
diff --git a/lib.js b/lib.js
index 84172e8..c1fca97 100644
--- a/lib.js
+++ b/lib.js
@@ -397,7 +397,11 @@ function warnUser(message, timeout=false){
 	}
 	
 	zindex = 31 + document.querySelectorAll('.alertModal').length;
-	message = message.replace(/\n/g,"<br />");
+	try{
+		message = message.replace(/\n/g,"<br />");
+	} catch(e){
+		errorlog(message);
+	}
 	modalTemplate =
 	`<div class="alertModal" id="alertModal"  style="z-index:${zindex + 2}">	
 		<div class="alertModalInner">
@@ -2437,6 +2441,14 @@ function updateUserList(){
 	},200);
 }
 
+
+function resetCanvas(){
+	session.streamSrc.getVideoTracks().forEach((track) => {
+		session.canvasSource.width = track.getSettings().width || 1280;
+		session.canvasSource.height = track.getSettings().height || 720;
+	});
+}
+
 var LaunchTFWorkerCallback = false;
 function TFLiteWorker(){
 	if (session.tfliteModule==false){
@@ -2471,6 +2483,8 @@ function TFLiteWorker(){
 	function process(){
 		if (session.tfliteModule.activelyProcessing){return;}
 		session.tfliteModule.activelyProcessing=true;
+
+
 		try{
 			segmentationMaskCtx.drawImage(
 				session.canvasSource,
@@ -3441,7 +3455,7 @@ function directorSendMessage(ele) {
 	inputField.style.margin = "5px 10px 5px 10px";
 	inputField.style.padding = "5px";
 
-	target.appendChild(inputField);
+	
 
 	var sendButton = document.createElement("button");
 	sendButton.innerHTML = "<i class='las la-reply'></i> send message ";
@@ -3501,6 +3515,7 @@ function directorSendMessage(ele) {
 	target.appendChild(closeButton);
 	target.appendChild(sendButton);
 	target.appendChild(overlayMsg);
+	target.appendChild(inputField);
 	ele.parentNode.appendChild(target);
 	inputField.focus();
 	inputField.select();
@@ -4661,7 +4676,7 @@ session.publishIFrame = function(iframeURL){
 	container.id = "container";
 	
 	var iframe = document.createElement("iframe");
-	iframe.allow = "autoplay;camera;microphone;fullscreen;picture-in-picture;transparency;";
+	iframe.allow = "autoplay;camera;microphone;fullscreen;picture-in-picture;";
 	iframe.src = session.iframeSrc;
 	iframe.id = "iframe_source"
 	session.iframeEle = iframe;
@@ -7706,17 +7721,17 @@ function resetupAudioOut() {
 
 function obfuscateURL(input) {
 	if (input.startsWith("https://obs.ninja/")) {
-		input = input.replace('https://obs.ninja/', '');
+		input = input.replace('https://obs.ninja/', 'obs.ninja/');
 	} else if (input.startsWith("http://obs.ninja/")) {
-		input = input.replace('http://obs.ninja/', '');
+		input = input.replace('http://obs.ninja/', 'obs.ninja/');
 	} else if (input.startsWith("obs.ninja/")) {
-		input = input.replace('obs.ninja/', '');
+		input = input.replace('obs.ninja/', 'obs.ninja/');
 	} else if (input.startsWith("https://vdo.ninja/")) {
-		input = input.replace('https://vdo.ninja/', '');
+		input = input.replace('https://vdo.ninja/', 'vdo.ninja/');
 	} else if (input.startsWith("http://vdo.ninja/")) {
-		input = input.replace('http://vdo.ninja/', '');
+		input = input.replace('http://vdo.ninja/', 'vdo.ninja/');
 	} else if (input.startsWith("vdo.ninja/")) {
-		input = input.replace('vdo.ninja/', '');
+		input = input.replace('vdo.ninja/', 'vdo.ninja/');
 	}
 
 	input = input.replace('&view=', '&v=');
@@ -7902,25 +7917,66 @@ if (navigator.userAgent.toLowerCase().indexOf(' electron/') > -1) {  // this ena
 		window.navigator.mediaDevices.getDisplayMedia = () => {
 		  return new Promise(async (resolve, reject) => {
 			try {
-			  
 			  if (session.autostart){
-				  var sscid = 0
-				  if (typeof session.screenshare === "number"){
-					  sscid = session.screenshare-1;
-					  if (sscid<0){sscid=0;}
+				  if (parseInt(session.screenshare)+"" === session.screenshare){
+					    var sscid = parseInt(session.screenshare)-1;
+					    if (sscid<0){sscid=0;}
+						const sources = await desktopCapturer.getSources({ types: ['screen'] });
+						const stream = await window.navigator.mediaDevices.getUserMedia({
+							audio: false,
+							video: {
+							  mandatory: {
+								chromeMediaSource: 'desktop',
+								chromeMediaSourceId: sources[sscid].id, 
+								maxFrameRate: 60
+							  }
+							}
+						});
+						resolve(stream);
+				  } else if (session.screenshare!==true){
+						var sscid=null;
+						const sources = await desktopCapturer.getSources({ types: ['window'] });
+						for (var i=0; i<sources.length;i++){
+							if (sources[i].name.startsWith(session.screenshare)){  // check if anythign starts with
+								sscid=i;
+								break;
+							}
+						}
+						if (sscid===null){
+							sscid = 0; // grab first window if nothing.
+							for (var i=0; i<sources.length;i++){
+								if (sources[i].name.includes(session.screenshare)){ // check if something includes the string; fallback
+									sscid=i;
+									break;
+								}
+							}
+						}
+						const stream = await window.navigator.mediaDevices.getUserMedia({
+							audio: false,
+							video: {
+							  mandatory: {
+								chromeMediaSource: 'desktop',
+								chromeMediaSourceId: sources[sscid].id, 
+								maxFrameRate: 60
+							  }
+							}
+						});
+						resolve(stream);
+				  } else {
+						var sscid = 0;
+						const sources = await desktopCapturer.getSources({ types: ['screen'] });
+						const stream = await window.navigator.mediaDevices.getUserMedia({
+							audio: false,
+							video: {
+							  mandatory: {
+								chromeMediaSource: 'desktop',
+								chromeMediaSourceId: sources[sscid].id, 
+								maxFrameRate: 60
+							  }
+							}
+						});
+						resolve(stream);
 				  }
-				  const sources = await desktopCapturer.getSources({ types: ['screen'] });
-				  const stream = await window.navigator.mediaDevices.getUserMedia({
-					audio: false,
-					video: {
-					  mandatory: {
-						chromeMediaSource: 'desktop',
-						chromeMediaSourceId: sources[sscid].id, 
-						maxFrameRate: 60
-					  }
-					}
-				  });
-				  resolve(stream)
 			  } else {
 				  const sources = await desktopCapturer.getSources({ types: ['screen', 'window'] });
 				  const selectionElem = document.createElement('div');
@@ -7984,7 +8040,7 @@ if (navigator.userAgent.toLowerCase().indexOf(' electron/') > -1) {  // this ena
 		}
 		ElectronDesktopCapture = true;
 	} catch(e){
-		warnlog("couldn't load electron's screen capture; you might need to decrease security permissions a bit.");
+		warnlog("Couldn't load electron's screen capture. Elevate the app's permission to allow it (right-click?)");
 	}
 }
 
@@ -10387,7 +10443,7 @@ function dragElement(elmnt) {
 function previewIframe(iframesrc) { // this is pretty important if you want to avoid camera permission popup problems.  You can also call it automatically via: <body onload=>loadIframe();"> , but don't call it before the page loads.
 
 	var iframe = document.createElement("iframe");
-	iframe.allow = "autoplay;camera;microphone;fullscreen;transparency;picture-in-picture;";
+	iframe.allow = "autoplay;camera;microphone;fullscreen;picture-in-picture;";
 	iframe.style.width = "100%";
 	iframe.style.height = "100%";
 	iframe.style.border = "10px dashed rgb(64 65 62)";
@@ -10437,7 +10493,7 @@ function previewIframe(iframesrc) { // this is pretty important if you want to a
 function loadIframe(iframesrc) { // this is pretty important if you want to avoid camera permission popup problems.  You can also call it automatically via: <body onload=>loadIframe();"> , but don't call it before the page loads.
 
 	var iframe = document.createElement("iframe");
-	iframe.allow = "autoplay;camera;microphone;fullscreen;transparency;picture-in-picture;";
+	iframe.allow = "autoplay;camera;microphone;fullscreen;picture-in-picture;";
 	iframe.style.width = "100%";
 	iframe.style.height = "100%";
 	iframe.style.border = "10px dashed rgb(64 65 62)";
@@ -12268,8 +12324,7 @@ function createIframePopup() {
 	}
 
 	var iframe = document.createElement("iframe");
-	iframe.allow = "autoplay;camera;microphone;fullscreen;picture-in-picture;transparency;";
-	iframe.allowtransparency = "true";
+	iframe.allow = "autoplay;camera;microphone;fullscreen;picture-in-picture;";
 	
 	var extras = "";
 	if (session.password){
@@ -13391,11 +13446,18 @@ function getChatMessage(msg, label = false, director = false, overlay = false) {
 
 function updateClosedCaptions(msg, label, UUID) {
 	msg.counter = parseInt(msg.counter);
-	var transcript = sanitizeChat(msg.transcript); // keep it clean.
+	var temp = document.createElement('div');
+	temp.innerText = msg.transcript;
+	temp.innerText = temp.innerHTML;
+	var transcript = temp.textContent || temp.innerText || "";
+
 	if (transcript == "") {
 		return;
 	}
-	transcript = transcript.toUpperCase();
+
+	transcript = transcript.charAt(0).toUpperCase() + transcript.slice(1);
+	//transcript = transcript.substr(-1, 5000); // keep it from being too long
+
 
 	if (label) {
 		label = sanitizeLabel(label);
@@ -14653,6 +14715,7 @@ function attemptTFLiteJsFileLoad(){
 	if (session.tfliteModule!==false){
 		return true;
 	}
+	warnUser("Loading effects model...");
 	TFLITELOADING=true;
 	session.tfliteModule={};
 		
@@ -14685,7 +14748,8 @@ async function changeTFLiteImage(ev, ele){
 
 
 async function loadTFLiteModel(){
-	try{
+	try {
+		
 		if (session.tfliteModule && (session.tfliteModule.img)){
 			var img = session.tfliteModule.img;
 			session.tfliteModule = await createTFLiteSIMDModule();
@@ -14702,6 +14766,7 @@ async function loadTFLiteModel(){
 		}
 	} catch(e){
 		warnlog("TF-LITE FAILED TO LOAD");
+		closeModal();
 		return;
 	}
 	const modelResponse = await fetch("./thirdparty/tflite/segm_full_v679.tflite");
@@ -14711,6 +14776,7 @@ async function loadTFLiteModel(){
 	session.tfliteModule._loadModel(session.tfliteModule.model.byteLength);
 	session.tfliteModule.activelyProcessing = false;
 	TFLITELOADING = false;
+	closeModal();
 	if ((session.effects>=3) && (session.effects<=5)){
 		if (document.getElementById("videosource")){
 			activatedPreview=false;
diff --git a/main.css b/main.css
index 09903b1..d7b1557 100644
--- a/main.css
+++ b/main.css
@@ -368,9 +368,6 @@ hr {
 	padding: 0;
 	overflow: hidden;
 	margin: var(--video-margin);
-	transition-property: all;
-    transition-duration: 1s;
-	transition-timing-function: ease-in;
 }
 
 #gridlayout {
diff --git a/main.js b/main.js
index 0779613..cef400b 100644
--- a/main.js
+++ b/main.js
@@ -286,7 +286,7 @@ async function main(){ // main asyncronous thread; mostly initializes the user s
 	} else if (urlParams.has('screenshare') || urlParams.has('ss')) {
 		session.screenshare = true;
 		if (urlParams.get('screenshare') || urlParams.get('ss')){
-			session.screenshare = parseInt(urlParams.get('screenshare'));
+			session.screenshare = urlParams.get('screenshare') || urlParams.get('ss');
 		}
 	} else if (urlParams.has('fileshare') || urlParams.has('fs')) {
 		getById("container-5").classList.remove('advanced');
@@ -2362,16 +2362,18 @@ async function main(){ // main asyncronous thread; mostly initializes the user s
 			document.head.appendChild(script4);
 		}
 		script4.onload = function() {
+			closeModal();
 			async function loadModel(){
 				model = await faceLandmarksDetection.load(faceLandmarksDetection.SupportedPackages.mediapipeFacemesh);
 				
 			}
 			loadModel();
 		}
-		script.src = "https://unpkg.com/@tensorflow/tfjs-core@2.4.0/dist/tf-core.js";
-		script2.src = "https://unpkg.com/@tensorflow/tfjs-converter@2.4.0/dist/tf-converter.js";
-		script3.src = "https://unpkg.com/@tensorflow/tfjs-backend-webgl@2.4.0/dist/tf-backend-webgl.js";
-		script4.src = "https://unpkg.com/@tensorflow-models/face-landmarks-detection@0.0.1/dist/face-landmarks-detection.js";
+		script.src = "./thirdparty/tfjs/tf-core.js";
+		script2.src = "./thirdparty/tfjs/tf-converter.js";
+		script3.src = "./thirdparty/tfjs/tf-backend-webgl.js";
+		script4.src = "./thirdparty/tfjs/face-landmarks-detection.js";
+		warnUser("Loading effects model...");
 		
 		script.type = 'text/javascript';script2.type = 'text/javascript';script3.type = 'text/javascript';script4.type = 'text/javascript';
 		document.head.appendChild(script);
diff --git a/thirdparty/ffmpeg.min.js b/thirdparty/ffmpeg.min.js
new file mode 100644
index 0000000..ce1a48c
--- /dev/null
+++ b/thirdparty/ffmpeg.min.js
@@ -0,0 +1,5 @@
+// https://unpkg.com/@ffmpeg/ffmpeg@0.10.1/dist/ffmpeg.min.js
+// MIT License - https://github.com/ffmpegwasm/ffmpeg.wasm
+// Copyright (c) 2019 Jerome Wu
+!function(e,t){"object"==typeof exports&&"object"==typeof module?module.exports=t():"function"==typeof define&&define.amd?define([],t):"object"==typeof exports?exports.FFmpeg=t():e.FFmpeg=t()}(self,(function(){return e={497:(e,t,r)=>{r(72);var n=r(306).devDependencies;e.exports={corePath:"https://unpkg.com/@ffmpeg/core@".concat(n["@ffmpeg/core"].substring(1),"/dist/ffmpeg-core.js")}},663:(e,t,r)=>{function n(e,t,r,n,o,i,a){try{var c=e[i](a),s=c.value}catch(e){return void r(e)}c.done?t(s):Promise.resolve(s).then(n,o)}var o=r(72),i=function(e){return new Promise((function(t,r){var n=new FileReader;n.onload=function(){t(n.result)},n.onerror=function(e){var t=e.target.error.code;r(Error("File could not be read! Code=".concat(t)))},n.readAsArrayBuffer(e)}))};e.exports=function(){var e,t=(e=regeneratorRuntime.mark((function e(t){var r,n;return regeneratorRuntime.wrap((function(e){for(;;)switch(e.prev=e.next){case 0:if(r=t,void 0!==t){e.next=3;break}return e.abrupt("return",new Uint8Array);case 3:if("string"!=typeof t){e.next=16;break}if(!/data:_data\/([a-zA-Z]*);base64,([^"]*)/.test(t)){e.next=8;break}r=atob(t.split(",")[1]).split("").map((function(e){return e.charCodeAt(0)})),e.next=14;break;case 8:return e.next=10,fetch(o(t));case 10:return n=e.sent,e.next=13,n.arrayBuffer();case 13:r=e.sent;case 14:e.next=20;break;case 16:if(!(t instanceof File||t instanceof Blob)){e.next=20;break}return e.next=19,i(t);case 19:r=e.sent;case 20:return e.abrupt("return",new Uint8Array(r));case 21:case"end":return e.stop()}}),e)})),function(){var t=this,r=arguments;return new Promise((function(o,i){var a=e.apply(t,r);function c(e){n(a,o,i,c,s,"next",e)}function s(e){n(a,o,i,c,s,"throw",e)}c(void 0)}))});return 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+//# sourceMappingURL=ffmpeg.min.js.map
\ No newline at end of file
diff --git a/thirdparty/tfjs/face-landmarks-detection.js b/thirdparty/tfjs/face-landmarks-detection.js
new file mode 100644
index 0000000..cb2f93d
--- /dev/null
+++ b/thirdparty/tfjs/face-landmarks-detection.js
@@ -0,0 +1,1497 @@
+/**
+    * @license
+    * Copyright 2021 Google LLC. All Rights Reserved.
+    * Licensed under the Apache License, Version 2.0 (the "License");
+    * you may not use this file except in compliance with the License.
+    * You may obtain a copy of the License at
+    *
+    * http://www.apache.org/licenses/LICENSE-2.0
+    *
+    * Unless required by applicable law or agreed to in writing, software
+    * distributed under the License is distributed on an "AS IS" BASIS,
+    * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+    * See the License for the specific language governing permissions and
+    * limitations under the License.
+    * =============================================================================
+    */
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('@tensorflow/tfjs-core'), require('@tensorflow/tfjs-converter')) :
+    typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs-core', '@tensorflow/tfjs-converter'], factory) :
+    (global = global || self, factory(global.faceLandmarksDetection = {}, global.tf, global.tf));
+}(this, (function (exports, tf, tfconv) { 'use strict';
+
+    /*! *****************************************************************************
+    Copyright (c) Microsoft Corporation.
+
+    Permission to use, copy, modify, and/or distribute this software for any
+    purpose with or without fee is hereby granted.
+
+    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+    REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
+    AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+    INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
+    LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
+    OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
+    PERFORMANCE OF THIS SOFTWARE.
+    ***************************************************************************** */
+
+    var __assign = function() {
+        __assign = Object.assign || function __assign(t) {
+            for (var s, i = 1, n = arguments.length; i < n; i++) {
+                s = arguments[i];
+                for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
+            }
+            return t;
+        };
+        return __assign.apply(this, arguments);
+    };
+
+    function __awaiter(thisArg, _arguments, P, generator) {
+        function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
+        return new (P || (P = Promise))(function (resolve, reject) {
+            function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+            function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+            function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
+            step((generator = generator.apply(thisArg, _arguments || [])).next());
+        });
+    }
+
+    function __generator(thisArg, body) {
+        var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+        return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+        function verb(n) { return function (v) { return step([n, v]); }; }
+        function step(op) {
+            if (f) throw new TypeError("Generator is already executing.");
+            while (_) try {
+                if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+                if (y = 0, t) op = [op[0] & 2, t.value];
+                switch (op[0]) {
+                    case 0: case 1: t = op; break;
+                    case 4: _.label++; return { value: op[1], done: false };
+                    case 5: _.label++; y = op[1]; op = [0]; continue;
+                    case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                    default:
+                        if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                        if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                        if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                        if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                        if (t[2]) _.ops.pop();
+                        _.trys.pop(); continue;
+                }
+                op = body.call(thisArg, _);
+            } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+            if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+        }
+    }
+
+    /**
+        * @license
+        * Copyright 2021 Google LLC. All Rights Reserved.
+        * Licensed under the Apache License, Version 2.0 (the "License");
+        * you may not use this file except in compliance with the License.
+        * You may obtain a copy of the License at
+        *
+        * http://www.apache.org/licenses/LICENSE-2.0
+        *
+        * Unless required by applicable law or agreed to in writing, software
+        * distributed under the License is distributed on an "AS IS" BASIS,
+        * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+        * See the License for the specific language governing permissions and
+        * limitations under the License.
+        * =============================================================================
+        */
+    function __awaiter$1(e,t,n,r){return new(n||(n=Promise))(function(o,i){function a(e){try{c(r.next(e));}catch(e){i(e);}}function s(e){try{c(r.throw(e));}catch(e){i(e);}}function c(e){var t;e.done?o(e.value):(t=e.value,t instanceof n?t:new n(function(e){e(t);})).then(a,s);}c((r=r.apply(e,t||[])).next());})}function __generator$1(e,t){var n,r,o,i,a={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return i={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(i[Symbol.iterator]=function(){return this}),i;function s(i){return function(s){return function(i){if(n)throw new TypeError("Generator is already executing.");for(;a;)try{if(n=1,r&&(o=2&i[0]?r.return:i[0]?r.throw||((o=r.return)&&o.call(r),0):r.next)&&!(o=o.call(r,i[1])).done)return o;switch(r=0,o&&(i=[2&i[0],o.value]),i[0]){case 0:case 1:o=i;break;case 4:return a.label++,{value:i[1],done:!1};case 5:a.label++,r=i[1],i=[0];continue;case 7:i=a.ops.pop(),a.trys.pop();continue;default:if(!(o=(o=a.trys).length>0&&o[o.length-1])&&(6===i[0]||2===i[0])){a=0;continue}if(3===i[0]&&(!o||i[1]>o[0]&&i[1]<o[3])){a.label=i[1];break}if(6===i[0]&&a.label<o[1]){a.label=o[1],o=i;break}if(o&&a.label<o[2]){a.label=o[2],a.ops.push(i);break}o[2]&&a.ops.pop(),a.trys.pop();continue}i=t.call(e,a);}catch(e){i=[6,e],r=0;}finally{n=o=0;}if(5&i[0])throw i[1];return {value:i[0]?i[1]:void 0,done:!0}}([i,s])}}}var disposeBox=function(e){e.startEndTensor.dispose(),e.startPoint.dispose(),e.endPoint.dispose();},createBox=function(e){return {startEndTensor:e,startPoint:tf.slice(e,[0,0],[-1,2]),endPoint:tf.slice(e,[0,2],[-1,2])}},scaleBox=function(e,t){var n=tf.mul(e.startPoint,t),r=tf.mul(e.endPoint,t),o=tf.concat2d([n,r],1);return createBox(o)},ANCHORS_CONFIG={strides:[8,16],anchors:[2,6]},NUM_LANDMARKS=6;function generateAnchors(e,t,n){for(var r=[],o=0;o<n.strides.length;o++)for(var i=n.strides[o],a=Math.floor((t+i-1)/i),s=Math.floor((e+i-1)/i),c=n.anchors[o],l=0;l<a;l++)for(var u=i*(l+.5),d=0;d<s;d++)for(var h=i*(d+.5),f=0;f<c;f++)r.push([h,u]);return r}function decodeBounds(e,t,n){var r=tf.slice(e,[0,1],[-1,2]),o=tf.add(r,t),i=tf.slice(e,[0,3],[-1,2]),a=tf.div(i,n),s=tf.div(o,n),c=tf.div(a,2),l=tf.sub(s,c),u=tf.add(s,c),d=tf.mul(l,n),h=tf.mul(u,n);return tf.concat2d([d,h],1)}function getInputTensorDimensions(e){return e instanceof tf.Tensor?[e.shape[0],e.shape[1]]:[e.height,e.width]}function flipFaceHorizontal(e,t){var n,r,o;if(e.topLeft instanceof tf.Tensor&&e.bottomRight instanceof tf.Tensor){var i=tf.tidy(function(){return [tf.concat([tf.slice(tf.sub(t-1,e.topLeft),0,1),tf.slice(e.topLeft,1,1)]),tf.concat([tf.sub(t-1,tf.slice(e.bottomRight,0,1)),tf.slice(e.bottomRight,1,1)])]});n=i[0],r=i[1],null!=e.landmarks&&(o=tf.tidy(function(){var n=tf.sub(tf.tensor1d([t-1,0]),e.landmarks),r=tf.tensor1d([1,-1]);return tf.mul(n,r)}));}else {var a=e.topLeft,s=a[0],c=a[1],l=e.bottomRight,u=l[0],d=l[1];n=[t-1-s,c],r=[t-1-u,d],null!=e.landmarks&&(o=e.landmarks.map(function(e){return [t-1-e[0],e[1]]}));}var h={topLeft:n,bottomRight:r};return null!=o&&(h.landmarks=o),null!=e.probability&&(h.probability=e.probability instanceof tf.Tensor?e.probability.clone():e.probability),h}function scaleBoxFromPrediction(e,t){return tf.tidy(function(){var n;return n=e.hasOwnProperty("box")?e.box:e,tf.squeeze(scaleBox(n,t).startEndTensor)})}var BlazeFaceModel=function(){function e(e,t,n,r,o,i){this.blazeFaceModel=e,this.width=t,this.height=n,this.maxFaces=r,this.anchorsData=generateAnchors(t,n,ANCHORS_CONFIG),this.anchors=tf.tensor2d(this.anchorsData),this.inputSizeData=[t,n],this.inputSize=tf.tensor1d([t,n]),this.iouThreshold=o,this.scoreThreshold=i;}return e.prototype.getBoundingBoxes=function(e,t,n){return void 0===n&&(n=!0),__awaiter$1(this,void 0,void 0,function(){var r,o,i,a,s,c,l,u,d,h,f,p,b,m,v=this;return __generator$1(this,function(y){switch(y.label){case 0:return r=tf.tidy(function(){var t=tf.image.resizeBilinear(e,[v.width,v.height]),n=tf.mul(tf.sub(tf.div(t,255),.5),2),r=v.blazeFaceModel.predict(n),o=tf.squeeze(r),i=decodeBounds(o,v.anchors,v.inputSize),a=tf.slice(o,[0,0],[-1,1]);return [o,i,tf.squeeze(tf.sigmoid(a))]}),o=r[0],i=r[1],a=r[2],s=console.warn,console.warn=function(){},c=tf.image.nonMaxSuppression(i,a,this.maxFaces,this.iouThreshold,this.scoreThreshold),console.warn=s,[4,c.array()];case 1:return l=y.sent(),c.dispose(),u=l.map(function(e){return tf.slice(i,[e,0],[1,-1])}),t?[3,3]:[4,Promise.all(u.map(function(e){return __awaiter$1(v,void 0,void 0,function(){var t;return __generator$1(this,function(n){switch(n.label){case 0:return [4,e.array()];case 1:return t=n.sent(),e.dispose(),[2,t]}})})}))];case 2:u=y.sent(),y.label=3;case 3:for(d=e.shape[1],h=e.shape[2],f=t?tf.div([h,d],this.inputSize):[h/this.inputSizeData[0],d/this.inputSizeData[1]],p=[],b=function(e){var r=u[e],i=tf.tidy(function(){var i=createBox(r instanceof tf.Tensor?r:tf.tensor2d(r));if(!n)return i;var s,c=l[e];return s=t?tf.slice(v.anchors,[c,0],[1,2]):v.anchorsData[c],{box:i,landmarks:tf.reshape(tf.squeeze(tf.slice(o,[c,NUM_LANDMARKS-1],[1,-1])),[NUM_LANDMARKS,-1]),probability:tf.slice(a,[c],[1]),anchor:s}});p.push(i);},m=0;m<u.length;m++)b(m);return i.dispose(),a.dispose(),o.dispose(),[2,{boxes:p,scaleFactor:f}]}})})},e.prototype.estimateFaces=function(e,t,n,r){return void 0===t&&(t=!1),void 0===n&&(n=!1),void 0===r&&(r=!0),__awaiter$1(this,void 0,void 0,function(){var o,i,a,s,c,l,u=this;return __generator$1(this,function(d){switch(d.label){case 0:return o=getInputTensorDimensions(e),i=o[1],a=tf.tidy(function(){return e instanceof tf.Tensor||(e=tf.browser.fromPixels(e)),tf.expandDims(tf.cast(e,"float32"),0)}),[4,this.getBoundingBoxes(a,t,r)];case 1:return s=d.sent(),c=s.boxes,l=s.scaleFactor,a.dispose(),t?[2,c.map(function(e){var t=scaleBoxFromPrediction(e,l),o={topLeft:tf.slice(t,[0],[2]),bottomRight:tf.slice(t,[2],[2])};if(r){var a=e,s=a.landmarks,c=a.probability,u=a.anchor,d=tf.mul(tf.add(s,u),l);o.landmarks=d,o.probability=c;}return n&&(o=flipFaceHorizontal(o,i)),o})]:[2,Promise.all(c.map(function(e){return __awaiter$1(u,void 0,void 0,function(){var t,o,a,s,c,u,d,h,f,p,b,m=this;return __generator$1(this,function(v){switch(v.label){case 0:return t=scaleBoxFromPrediction(e,l),r?[3,2]:[4,t.array()];case 1:return c=v.sent(),o={topLeft:c.slice(0,2),bottomRight:c.slice(2)},[3,4];case 2:return [4,Promise.all([e.landmarks,t,e.probability].map(function(e){return __awaiter$1(m,void 0,void 0,function(){return __generator$1(this,function(t){return [2,e.array()]})})}))];case 3:a=v.sent(),s=a[0],c=a[1],u=a[2],d=e.anchor,f=(h=l)[0],p=h[1],b=s.map(function(e){return [(e[0]+d[0])*f,(e[1]+d[1])*p]}),o={topLeft:c.slice(0,2),bottomRight:c.slice(2),landmarks:b,probability:u},disposeBox(e.box),e.landmarks.dispose(),e.probability.dispose(),v.label=4;case 4:return t.dispose(),n&&(o=flipFaceHorizontal(o,i)),[2,o]}})})}))]}})})},e}(),BLAZEFACE_MODEL_URL="https://tfhub.dev/tensorflow/tfjs-model/blazeface/1/default/1";function load(e){var t=void 0===e?{}:e,n=t.maxFaces,r=void 0===n?10:n,o=t.inputWidth,i=void 0===o?128:o,a=t.inputHeight,s=void 0===a?128:a,c=t.iouThreshold,l=void 0===c?.3:c,u=t.scoreThreshold,d=void 0===u?.75:u,h=t.modelUrl;return __awaiter$1(this,void 0,void 0,function(){var e;return __generator$1(this,function(t){switch(t.label){case 0:return null==h?[3,2]:[4,tfconv.loadGraphModel(h)];case 1:return e=t.sent(),[3,4];case 2:return [4,tfconv.loadGraphModel(BLAZEFACE_MODEL_URL,{fromTFHub:!0})];case 3:e=t.sent(),t.label=4;case 4:return [2,new BlazeFaceModel(e,i,s,r,l,d)]}})})}
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * https://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var MESH_ANNOTATIONS = {
+        silhouette: [
+            10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,
+            397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,
+            172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109
+        ],
+        lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],
+        lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
+        lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],
+        lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
+        rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
+        rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
+        rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
+        rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
+        rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
+        rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
+        rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
+        rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
+        rightEyebrowLower: [35, 124, 46, 53, 52, 65],
+        rightEyeIris: [473, 474, 475, 476, 477],
+        leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
+        leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
+        leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
+        leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
+        leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
+        leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
+        leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
+        leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
+        leftEyebrowLower: [265, 353, 276, 283, 282, 295],
+        leftEyeIris: [468, 469, 470, 471, 472],
+        midwayBetweenEyes: [168],
+        noseTip: [1],
+        noseBottom: [2],
+        noseRightCorner: [98],
+        noseLeftCorner: [327],
+        rightCheek: [205],
+        leftCheek: [425]
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * https://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function scaleBoxCoordinates(box, factor) {
+        var startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
+        var endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
+        return { startPoint: startPoint, endPoint: endPoint };
+    }
+    function getBoxSize(box) {
+        return [
+            Math.abs(box.endPoint[0] - box.startPoint[0]),
+            Math.abs(box.endPoint[1] - box.startPoint[1])
+        ];
+    }
+    function getBoxCenter(box) {
+        return [
+            box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
+            box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
+        ];
+    }
+    function cutBoxFromImageAndResize(box, image, cropSize) {
+        var h = image.shape[1];
+        var w = image.shape[2];
+        var boxes = [[
+                box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,
+                box.endPoint[0] / w
+            ]];
+        return tf.image.cropAndResize(image, boxes, [0], cropSize, 'bilinear' /* method */, 0 /* extrapolation value */);
+    }
+    /**
+     * Enlarges the box by the provided factor.
+     * @param box An object with startPoint and endPoint properties describing the
+     * outlines of the box to be enlarged.
+     * @param factor optional The enlargement factor. Defaults to 1.5
+     */
+    function enlargeBox(box, factor) {
+        if (factor === void 0) { factor = 1.5; }
+        var center = getBoxCenter(box);
+        var size = getBoxSize(box);
+        var newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];
+        var startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
+        var endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
+        return { startPoint: startPoint, endPoint: endPoint, landmarks: box.landmarks };
+    }
+    /**
+     * Squarifies the provided box by setting its length and height equal to
+     * max(length, height) while preserving its center point.
+     * @param box An object with startPoint and endPoint properties describing the
+     * outlines of the box to be squarified.
+     */
+    function squarifyBox(box) {
+        var centers = getBoxCenter(box);
+        var size = getBoxSize(box);
+        var maxEdge = Math.max.apply(Math, size);
+        var halfSize = maxEdge / 2;
+        var startPoint = [centers[0] - halfSize, centers[1] - halfSize];
+        var endPoint = [centers[0] + halfSize, centers[1] + halfSize];
+        return { startPoint: startPoint, endPoint: endPoint, landmarks: box.landmarks };
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * https://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];
+    /**
+     * Normalizes the provided angle to the range -pi to pi.
+     * @param angle The angle in radians to be normalized.
+     */
+    function normalizeRadians(angle) {
+        return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
+    }
+    /**
+     * Computes the angle of rotation between two anchor points.
+     * @param point1 First anchor point
+     * @param point2 Second anchor point
+     */
+    function computeRotation(point1, point2) {
+        var radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
+        return normalizeRadians(radians);
+    }
+    function buildTranslationMatrix(x, y) {
+        return [[1, 0, x], [0, 1, y], [0, 0, 1]];
+    }
+    function dot(v1, v2) {
+        var product = 0;
+        for (var i = 0; i < v1.length; i++) {
+            product += v1[i] * v2[i];
+        }
+        return product;
+    }
+    function getColumnFrom2DArr(arr, columnIndex) {
+        var column = [];
+        for (var i = 0; i < arr.length; i++) {
+            column.push(arr[i][columnIndex]);
+        }
+        return column;
+    }
+    function multiplyTransformMatrices(mat1, mat2) {
+        var product = [];
+        var size = mat1.length;
+        for (var row = 0; row < size; row++) {
+            product.push([]);
+            for (var col = 0; col < size; col++) {
+                product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));
+            }
+        }
+        return product;
+    }
+    function buildRotationMatrix(rotation, center) {
+        var cosA = Math.cos(rotation);
+        var sinA = Math.sin(rotation);
+        var rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
+        var translationMatrix = buildTranslationMatrix(center[0], center[1]);
+        var translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);
+        var negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);
+        return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);
+    }
+    function invertTransformMatrix(matrix) {
+        var rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
+        var translationComponent = [matrix[0][2], matrix[1][2]];
+        var invertedTranslation = [
+            -dot(rotationComponent[0], translationComponent),
+            -dot(rotationComponent[1], translationComponent)
+        ];
+        return [
+            rotationComponent[0].concat(invertedTranslation[0]),
+            rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]
+        ];
+    }
+    function rotatePoint(homogeneousCoordinate, rotationMatrix) {
+        return [
+            dot(homogeneousCoordinate, rotationMatrix[0]),
+            dot(homogeneousCoordinate, rotationMatrix[1])
+        ];
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * https://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LANDMARKS_COUNT = 468;
+    var UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD = 0.25;
+    var MESH_MOUTH_INDEX = 13;
+    var MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, MESH_ANNOTATIONS['midwayBetweenEyes'][0]];
+    var BLAZEFACE_MOUTH_INDEX = 3;
+    var BLAZEFACE_NOSE_INDEX = 2;
+    var BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];
+    var LEFT_EYE_OUTLINE = MESH_ANNOTATIONS['leftEyeLower0'];
+    var LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];
+    var RIGHT_EYE_OUTLINE = MESH_ANNOTATIONS['rightEyeLower0'];
+    var RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];
+    var IRIS_UPPER_CENTER_INDEX = 3;
+    var IRIS_LOWER_CENTER_INDEX = 4;
+    var IRIS_IRIS_INDEX = 71;
+    var IRIS_NUM_COORDINATES = 76;
+    // Factor by which to enlarge the box around the eye landmarks so the input
+    // region matches the expectations of the iris model.
+    var ENLARGE_EYE_RATIO = 2.3;
+    var IRIS_MODEL_INPUT_SIZE = 64;
+    // A mapping from facemesh model keypoints to iris model keypoints.
+    var MESH_TO_IRIS_INDICES_MAP = [
+        { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },
+        { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },
+        { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },
+        { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },
+        { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },
+        { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },
+        { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },
+        { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },
+        { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }
+    ];
+    // Replace the raw coordinates returned by facemesh with refined iris model
+    // coordinates.
+    // Update the z coordinate to be an average of the original and the new. This
+    // produces the best visual effect.
+    function replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {
+        for (var i = 0; i < MESH_TO_IRIS_INDICES_MAP.length; i++) {
+            var _a = MESH_TO_IRIS_INDICES_MAP[i], key = _a.key, indices = _a.indices;
+            var originalIndices = MESH_ANNOTATIONS["" + prefix + key];
+            var shouldReplaceAllKeys = keys == null;
+            if (shouldReplaceAllKeys || keys.includes(key)) {
+                for (var j = 0; j < indices.length; j++) {
+                    var index = indices[j];
+                    rawCoords[originalIndices[j]] = [
+                        newCoords[index][0], newCoords[index][1],
+                        (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2
+                    ];
+                }
+            }
+        }
+    }
+    // The Pipeline coordinates between the bounding box and skeleton models.
+    var Pipeline = /** @class */ (function () {
+        function Pipeline(boundingBoxDetector, meshDetector, meshWidth, meshHeight, maxContinuousChecks, maxFaces, irisModel) {
+            // An array of facial bounding boxes.
+            this.regionsOfInterest = [];
+            this.runsWithoutFaceDetector = 0;
+            this.boundingBoxDetector = boundingBoxDetector;
+            this.meshDetector = meshDetector;
+            this.irisModel = irisModel;
+            this.meshWidth = meshWidth;
+            this.meshHeight = meshHeight;
+            this.maxContinuousChecks = maxContinuousChecks;
+            this.maxFaces = maxFaces;
+        }
+        Pipeline.prototype.transformRawCoords = function (rawCoords, box, angle, rotationMatrix) {
+            var _this = this;
+            var boxSize = getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });
+            var scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];
+            var coordsScaled = rawCoords.map(function (coord) { return ([
+                scaleFactor[0] * (coord[0] - _this.meshWidth / 2),
+                scaleFactor[1] * (coord[1] - _this.meshHeight / 2), coord[2]
+            ]); });
+            var coordsRotationMatrix = buildRotationMatrix(angle, [0, 0]);
+            var coordsRotated = coordsScaled.map(function (coord) {
+                return (rotatePoint(coord, coordsRotationMatrix).concat([coord[2]]));
+            });
+            var inverseRotationMatrix = invertTransformMatrix(rotationMatrix);
+            var boxCenter = getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }).concat([
+                1
+            ]);
+            var originalBoxCenter = [
+                dot(boxCenter, inverseRotationMatrix[0]),
+                dot(boxCenter, inverseRotationMatrix[1])
+            ];
+            return coordsRotated.map(function (coord) { return ([
+                coord[0] + originalBoxCenter[0],
+                coord[1] + originalBoxCenter[1], coord[2]
+            ]); });
+        };
+        Pipeline.prototype.getLeftToRightEyeDepthDifference = function (rawCoords) {
+            var leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];
+            var rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];
+            return leftEyeZ - rightEyeZ;
+        };
+        // Returns a box describing a cropped region around the eye fit for passing to
+        // the iris model.
+        Pipeline.prototype.getEyeBox = function (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip) {
+            if (flip === void 0) { flip = false; }
+            var box = squarifyBox(enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), ENLARGE_EYE_RATIO));
+            var boxSize = getBoxSize(box);
+            var crop = tf.image.cropAndResize(face, [[
+                    box.startPoint[1] / this.meshHeight,
+                    box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,
+                    box.endPoint[0] / this.meshWidth
+                ]], [0], [IRIS_MODEL_INPUT_SIZE, IRIS_MODEL_INPUT_SIZE]);
+            if (flip) {
+                crop = tf.image.flipLeftRight(crop);
+            }
+            return { box: box, boxSize: boxSize, crop: crop };
+        };
+        // Given a cropped image of an eye, returns the coordinates of the contours
+        // surrounding the eye and the iris.
+        Pipeline.prototype.getEyeCoords = function (eyeData, eyeBox, eyeBoxSize, flip) {
+            if (flip === void 0) { flip = false; }
+            var eyeRawCoords = [];
+            for (var i = 0; i < IRIS_NUM_COORDINATES; i++) {
+                var x = eyeData[i * 3];
+                var y = eyeData[i * 3 + 1];
+                var z = eyeData[i * 3 + 2];
+                eyeRawCoords.push([
+                    (flip ? (1 - (x / IRIS_MODEL_INPUT_SIZE)) :
+                        (x / IRIS_MODEL_INPUT_SIZE)) *
+                        eyeBoxSize[0] +
+                        eyeBox.startPoint[0],
+                    (y / IRIS_MODEL_INPUT_SIZE) * eyeBoxSize[1] + eyeBox.startPoint[1], z
+                ]);
+            }
+            return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };
+        };
+        // The z-coordinates returned for the iris are unreliable, so we take the z
+        // values from the surrounding keypoints.
+        Pipeline.prototype.getAdjustedIrisCoords = function (rawCoords, irisCoords, direction) {
+            var upperCenterZ = rawCoords[MESH_ANNOTATIONS[direction + "EyeUpper0"][IRIS_UPPER_CENTER_INDEX]][2];
+            var lowerCenterZ = rawCoords[MESH_ANNOTATIONS[direction + "EyeLower0"][IRIS_LOWER_CENTER_INDEX]][2];
+            var averageZ = (upperCenterZ + lowerCenterZ) / 2;
+            // Iris indices:
+            // 0: center | 1: right | 2: above | 3: left | 4: below
+            return irisCoords.map(function (coord, i) {
+                var z = averageZ;
+                if (i === 2) {
+                    z = upperCenterZ;
+                }
+                else if (i === 4) {
+                    z = lowerCenterZ;
+                }
+                return [coord[0], coord[1], z];
+            });
+        };
+        /**
+         * Returns an array of predictions for each face in the input.
+         * @param input - tensor of shape [1, H, W, 3].
+         * @param predictIrises - Whether to return keypoints for the irises.
+         */
+        Pipeline.prototype.predict = function (input, predictIrises) {
+            return __awaiter(this, void 0, void 0, function () {
+                var returnTensors, annotateFace, _a, boxes, scaleFactor_1, scaledBoxes;
+                var _this = this;
+                return __generator(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            if (!this.shouldUpdateRegionsOfInterest()) return [3 /*break*/, 2];
+                            returnTensors = false;
+                            annotateFace = true;
+                            return [4 /*yield*/, this.boundingBoxDetector.getBoundingBoxes(input, returnTensors, annotateFace)];
+                        case 1:
+                            _a = _b.sent(), boxes = _a.boxes, scaleFactor_1 = _a.scaleFactor;
+                            if (boxes.length === 0) {
+                                this.regionsOfInterest = [];
+                                return [2 /*return*/, null];
+                            }
+                            scaledBoxes = boxes.map(function (prediction) {
+                                var predictionBoxCPU = {
+                                    startPoint: tf.squeeze(prediction.box.startPoint).arraySync(),
+                                    endPoint: tf.squeeze(prediction.box.endPoint).arraySync()
+                                };
+                                var scaledBox = scaleBoxCoordinates(predictionBoxCPU, scaleFactor_1);
+                                var enlargedBox = enlargeBox(scaledBox);
+                                var squarifiedBox = squarifyBox(enlargedBox);
+                                return __assign({}, squarifiedBox, { landmarks: prediction.landmarks.arraySync() });
+                            });
+                            boxes.forEach(function (box) {
+                                if (box != null && box.startPoint != null) {
+                                    box.startEndTensor.dispose();
+                                    box.startPoint.dispose();
+                                    box.endPoint.dispose();
+                                }
+                            });
+                            this.updateRegionsOfInterest(scaledBoxes);
+                            this.runsWithoutFaceDetector = 0;
+                            return [3 /*break*/, 3];
+                        case 2:
+                            this.runsWithoutFaceDetector++;
+                            _b.label = 3;
+                        case 3: return [2 /*return*/, tf.tidy(function () {
+                                return _this.regionsOfInterest.map(function (box, i) {
+                                    var angle = 0;
+                                    // The facial bounding box landmarks could come either from blazeface
+                                    // (if we are using a fresh box), or from the mesh model (if we are
+                                    // reusing an old box).
+                                    var boxLandmarksFromMeshModel = box.landmarks.length >= LANDMARKS_COUNT;
+                                    var indexOfMouth = MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES[0], indexOfForehead = MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES[1];
+                                    if (boxLandmarksFromMeshModel === false) {
+                                        indexOfMouth = BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES[0], indexOfForehead = BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES[1];
+                                    }
+                                    angle = computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);
+                                    var faceCenter = getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });
+                                    var faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];
+                                    var rotatedImage = input;
+                                    var rotationMatrix = IDENTITY_MATRIX;
+                                    if (angle !== 0) {
+                                        rotatedImage =
+                                            tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);
+                                        rotationMatrix = buildRotationMatrix(-angle, faceCenter);
+                                    }
+                                    var boxCPU = { startPoint: box.startPoint, endPoint: box.endPoint };
+                                    var face = tf.div(cutBoxFromImageAndResize(boxCPU, rotatedImage, [
+                                        _this.meshHeight, _this.meshWidth
+                                    ]), 255);
+                                    // The first returned tensor represents facial contours, which are
+                                    // included in the coordinates.
+                                    var _a = _this.meshDetector.predict(face), flag = _a[1], coords = _a[2];
+                                    var coordsReshaped = tf.reshape(coords, [-1, 3]);
+                                    var rawCoords = coordsReshaped.arraySync();
+                                    if (predictIrises) {
+                                        var _b = _this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true), leftEyeBox = _b.box, leftEyeBoxSize = _b.boxSize, leftEyeCrop = _b.crop;
+                                        var _c = _this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]), rightEyeBox = _c.box, rightEyeBoxSize = _c.boxSize, rightEyeCrop = _c.crop;
+                                        var eyePredictions = (_this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop])));
+                                        var eyePredictionsData = eyePredictions.dataSync();
+                                        var leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);
+                                        var _d = _this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true), leftEyeRawCoords = _d.rawCoords, leftIrisRawCoords = _d.iris;
+                                        var rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);
+                                        var _e = _this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize), rightEyeRawCoords = _e.rawCoords, rightIrisRawCoords = _e.iris;
+                                        var leftToRightEyeDepthDifference = _this.getLeftToRightEyeDepthDifference(rawCoords);
+                                        if (Math.abs(leftToRightEyeDepthDifference) <
+                                            30) { // User is looking straight ahead.
+                                            replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');
+                                            replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');
+                                        }
+                                        else if (leftToRightEyeDepthDifference < 1) { // User is looking
+                                            // towards the
+                                            // right.
+                                            // If the user is looking to the left or to the right, the iris
+                                            // coordinates tend to diverge too much from the mesh coordinates
+                                            // for them to be merged. So we only update a single contour line
+                                            // above and below the eye.
+                                            replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);
+                                        }
+                                        else { // User is looking towards the left.
+                                            replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);
+                                        }
+                                        var adjustedLeftIrisCoords = _this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');
+                                        var adjustedRightIrisCoords = _this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');
+                                        rawCoords = rawCoords.concat(adjustedLeftIrisCoords)
+                                            .concat(adjustedRightIrisCoords);
+                                    }
+                                    var transformedCoordsData = _this.transformRawCoords(rawCoords, box, angle, rotationMatrix);
+                                    var transformedCoords = tf.tensor2d(transformedCoordsData);
+                                    var landmarksBox = enlargeBox(_this.calculateLandmarksBoundingBox(transformedCoordsData));
+                                    var squarifiedLandmarksBox = squarifyBox(landmarksBox);
+                                    _this.regionsOfInterest[i] = __assign({}, squarifiedLandmarksBox, { landmarks: transformedCoords.arraySync() });
+                                    var prediction = {
+                                        coords: tf.tensor2d(rawCoords, [rawCoords.length, 3]),
+                                        scaledCoords: transformedCoords,
+                                        box: landmarksBox,
+                                        flag: tf.squeeze(flag)
+                                    };
+                                    return prediction;
+                                });
+                            })];
+                    }
+                });
+            });
+        };
+        // Updates regions of interest if the intersection over union between
+        // the incoming and previous regions falls below a threshold.
+        Pipeline.prototype.updateRegionsOfInterest = function (boxes) {
+            for (var i = 0; i < boxes.length; i++) {
+                var box = boxes[i];
+                var previousBox = this.regionsOfInterest[i];
+                var iou = 0;
+                if (previousBox && previousBox.startPoint) {
+                    var _a = box.startPoint, boxStartX = _a[0], boxStartY = _a[1];
+                    var _b = box.endPoint, boxEndX = _b[0], boxEndY = _b[1];
+                    var _c = previousBox.startPoint, previousBoxStartX = _c[0], previousBoxStartY = _c[1];
+                    var _d = previousBox.endPoint, previousBoxEndX = _d[0], previousBoxEndY = _d[1];
+                    var xStartMax = Math.max(boxStartX, previousBoxStartX);
+                    var yStartMax = Math.max(boxStartY, previousBoxStartY);
+                    var xEndMin = Math.min(boxEndX, previousBoxEndX);
+                    var yEndMin = Math.min(boxEndY, previousBoxEndY);
+                    var intersection = (xEndMin - xStartMax) * (yEndMin - yStartMax);
+                    var boxArea = (boxEndX - boxStartX) * (boxEndY - boxStartY);
+                    var previousBoxArea = (previousBoxEndX - previousBoxStartX) *
+                        (previousBoxEndY - boxStartY);
+                    iou = intersection / (boxArea + previousBoxArea - intersection);
+                }
+                if (iou < UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD) {
+                    this.regionsOfInterest[i] = box;
+                }
+            }
+            this.regionsOfInterest = this.regionsOfInterest.slice(0, boxes.length);
+        };
+        Pipeline.prototype.clearRegionOfInterest = function (index) {
+            if (this.regionsOfInterest[index] != null) {
+                this.regionsOfInterest = this.regionsOfInterest.slice(0, index).concat(this.regionsOfInterest.slice(index + 1));
+            }
+        };
+        Pipeline.prototype.shouldUpdateRegionsOfInterest = function () {
+            var roisCount = this.regionsOfInterest.length;
+            var noROIs = roisCount === 0;
+            if (this.maxFaces === 1 || noROIs) {
+                return noROIs;
+            }
+            return roisCount !== this.maxFaces &&
+                this.runsWithoutFaceDetector >= this.maxContinuousChecks;
+        };
+        Pipeline.prototype.calculateLandmarksBoundingBox = function (landmarks) {
+            var xs = landmarks.map(function (d) { return d[0]; });
+            var ys = landmarks.map(function (d) { return d[1]; });
+            var startPoint = [Math.min.apply(Math, xs), Math.min.apply(Math, ys)];
+            var endPoint = [Math.max.apply(Math, xs), Math.max.apply(Math, ys)];
+            return { startPoint: startPoint, endPoint: endPoint };
+        };
+        return Pipeline;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * https://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var UV_COORDS = [
+        [0.499976992607117, 0.652534008026123],
+        [0.500025987625122, 0.547487020492554],
+        [0.499974012374878, 0.602371990680695],
+        [0.482113003730774, 0.471979022026062],
+        [0.500150978565216, 0.527155995368958],
+        [0.499909996986389, 0.498252987861633],
+        [0.499523013830185, 0.40106201171875],
+        [0.289712011814117, 0.380764007568359],
+        [0.499954998493195, 0.312398016452789],
+        [0.499987006187439, 0.269918978214264],
+        [0.500023007392883, 0.107050001621246],
+        [0.500023007392883, 0.666234016418457],
+        [0.5000159740448, 0.679224014282227],
+        [0.500023007392883, 0.692348003387451],
+        [0.499976992607117, 0.695277988910675],
+        [0.499976992607117, 0.70593398809433],
+        [0.499976992607117, 0.719385027885437],
+        [0.499976992607117, 0.737019002437592],
+        [0.499967992305756, 0.781370997428894],
+        [0.499816000461578, 0.562981009483337],
+        [0.473773002624512, 0.573909997940063],
+        [0.104906998574734, 0.254140973091125],
+        [0.365929991006851, 0.409575998783112],
+        [0.338757991790771, 0.41302502155304],
+        [0.311120003461838, 0.409460008144379],
+        [0.274657994508743, 0.389131009578705],
+        [0.393361985683441, 0.403706014156342],
+        [0.345234006643295, 0.344011008739471],
+        [0.370094001293182, 0.346076011657715],
+        [0.319321990013123, 0.347265005111694],
+        [0.297903001308441, 0.353591024875641],
+        [0.24779200553894, 0.410809993743896],
+        [0.396889001131058, 0.842755019664764],
+        [0.280097991228104, 0.375599980354309],
+        [0.106310002505779, 0.399955987930298],
+        [0.2099249958992, 0.391353011131287],
+        [0.355807989835739, 0.534406006336212],
+        [0.471751004457474, 0.65040397644043],
+        [0.474155008792877, 0.680191993713379],
+        [0.439785003662109, 0.657229006290436],
+        [0.414617002010345, 0.66654098033905],
+        [0.450374007225037, 0.680860996246338],
+        [0.428770989179611, 0.682690978050232],
+        [0.374971002340317, 0.727805018424988],
+        [0.486716985702515, 0.547628998756409],
+        [0.485300987958908, 0.527395009994507],
+        [0.257764995098114, 0.314490020275116],
+        [0.401223003864288, 0.455172002315521],
+        [0.429818987846375, 0.548614978790283],
+        [0.421351999044418, 0.533740997314453],
+        [0.276895999908447, 0.532056987285614],
+        [0.483370006084442, 0.499586999416351],
+        [0.33721199631691, 0.282882988452911],
+        [0.296391993761063, 0.293242990970612],
+        [0.169294998049736, 0.193813979625702],
+        [0.447580009698868, 0.302609980106354],
+        [0.392390012741089, 0.353887975215912],
+        [0.354490011930466, 0.696784019470215],
+        [0.067304998636246, 0.730105042457581],
+        [0.442739009857178, 0.572826027870178],
+        [0.457098007202148, 0.584792017936707],
+        [0.381974011659622, 0.694710969924927],
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+        [0.607599973678589, 0.677703022956848],
+        [0.631937980651855, 0.663500010967255],
+        [0.752032995223999, 0.601315021514893],
+        [0.547226011753082, 0.420395016670227],
+        [0.563543975353241, 0.359827995300293],
+        [0.583841025829315, 0.368713974952698],
+        [0.586614012718201, 0.692366003990173],
+        [0.771915018558502, 0.683578014373779],
+        [0.531597018241882, 0.352482974529266],
+        [0.588370978832245, 0.804440975189209],
+        [0.52079701423645, 0.442565023899078],
+        [0.567984998226166, 0.493479013442993],
+        [0.543282985687256, 0.819254994392395],
+        [0.655317008495331, 0.745514988899231],
+        [0.621008992195129, 0.574018001556396],
+        [0.625559985637665, 0.78031200170517],
+        [0.680198013782501, 0.570719003677368],
+        [0.64276397228241, 0.604337990283966],
+        [0.704662978649139, 0.621529996395111],
+        [0.552012026309967, 0.862591981887817],
+        [0.589071989059448, 0.508637011051178],
+        [0.685944974422455, 0.775357007980347],
+        [0.645735025405884, 0.812640011310577],
+        [0.675342977046967, 0.703978002071381],
+        [0.810858011245728, 0.646304965019226],
+        [0.72012197971344, 0.714666962623596],
+        [0.866151988506317, 0.682704985141754],
+        [0.663187026977539, 0.644596993923187],
+        [0.570082008838654, 0.466325998306274],
+        [0.544561982154846, 0.548375964164734],
+        [0.562758982181549, 0.558784961700439],
+        [0.531987011432648, 0.530140042304993],
+        [0.585271000862122, 0.335177004337311],
+        [0.622952997684479, 0.32277899980545],
+        [0.655896008014679, 0.320163011550903],
+        [0.687132000923157, 0.322345972061157],
+        [0.716481983661652, 0.333200991153717],
+        [0.758756995201111, 0.382786989212036],
+        [0.897013008594513, 0.468769013881683],
+        [0.732392013072968, 0.424547016620636],
+        [0.70211398601532, 0.433162987232208],
+        [0.66652500629425, 0.433866024017334],
+        [0.633504986763, 0.426087975502014],
+        [0.603875994682312, 0.416586995124817],
+        [0.579657971858978, 0.409945011138916],
+        [0.992439985275269, 0.480777025222778],
+        [0.567192018032074, 0.569419980049133],
+        [0.54136598110199, 0.478899002075195],
+        [0.526564002037048, 0.546118021011353],
+        [0.523913025856018, 0.563830018043518],
+        [0.531529009342194, 0.555056989192963],
+        [0.566035985946655, 0.582329034805298],
+        [0.51631098985672, 0.563053965568542],
+        [0.5174720287323, 0.577877044677734],
+        [0.573594987392426, 0.389806985855103],
+        [0.560697972774506, 0.395331978797913],
+        [0.549755990505219, 0.399751007556915],
+        [0.710287988185883, 0.368252992630005],
+        [0.723330020904541, 0.363372981548309]
+    ];
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * https://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var FACEMESH_GRAPHMODEL_PATH = 'https://tfhub.dev/mediapipe/tfjs-model/facemesh/1/default/1';
+    var IRIS_GRAPHMODEL_PATH = 'https://tfhub.dev/mediapipe/tfjs-model/iris/1/default/2';
+    var MESH_MODEL_INPUT_WIDTH = 192;
+    var MESH_MODEL_INPUT_HEIGHT = 192;
+    var PREDICTION_VALUES = 'MediaPipePredictionValues';
+    var PREDICTION_TENSORS = 'MediaPipePredictionTensors';
+    /**
+     * Load the model.
+     *
+     * @param options - a configuration object with the following properties:
+     *  - `maxContinuousChecks` How many frames to go without running the bounding
+     * box detector. Only relevant if maxFaces > 1. Defaults to 5.
+     *  - `detectionConfidence` Threshold for discarding a prediction. Defaults to
+     * 0.9.
+     *  - `maxFaces` The maximum number of faces detected in the input. Should be
+     * set to the minimum number for performance. Defaults to 10.
+     *  - `iouThreshold` A float representing the threshold for deciding whether
+     * boxes overlap too much in non-maximum suppression. Must be between [0, 1].
+     * Defaults to 0.3.
+     *  - `scoreThreshold` A threshold for deciding when to remove boxes based
+     * on score in non-maximum suppression. Defaults to 0.75.
+     *  - `shouldLoadIrisModel` Whether to also load the iris detection model.
+     * Defaults to true.
+     *  - `modelUrl` Optional param for specifying a custom facemesh model url or
+     * a `tf.io.IOHandler` object.
+     *  - `detectorModelUrl` Optional param for specifying a custom blazeface model
+     * url or a `tf.io.IOHandler` object.
+     *  - `irisModelUrl` Optional param for specifying a custom iris model url or
+     * a `tf.io.IOHandler` object.
+     */
+    function load$1(config) {
+        return __awaiter(this, void 0, void 0, function () {
+            var _a, maxContinuousChecks, _b, detectionConfidence, _c, maxFaces, _d, iouThreshold, _e, scoreThreshold, _f, shouldLoadIrisModel, modelUrl, detectorModelUrl, irisModelUrl, models, faceMesh;
+            return __generator(this, function (_g) {
+                switch (_g.label) {
+                    case 0:
+                        _a = config.maxContinuousChecks, maxContinuousChecks = _a === void 0 ? 5 : _a, _b = config.detectionConfidence, detectionConfidence = _b === void 0 ? 0.9 : _b, _c = config.maxFaces, maxFaces = _c === void 0 ? 10 : _c, _d = config.iouThreshold, iouThreshold = _d === void 0 ? 0.3 : _d, _e = config.scoreThreshold, scoreThreshold = _e === void 0 ? 0.75 : _e, _f = config.shouldLoadIrisModel, shouldLoadIrisModel = _f === void 0 ? true : _f, modelUrl = config.modelUrl, detectorModelUrl = config.detectorModelUrl, irisModelUrl = config.irisModelUrl;
+                        if (!shouldLoadIrisModel) return [3 /*break*/, 2];
+                        return [4 /*yield*/, Promise.all([
+                                loadDetectorModel(detectorModelUrl, maxFaces, iouThreshold, scoreThreshold),
+                                loadMeshModel(modelUrl),
+                                loadIrisModel(irisModelUrl)
+                            ])];
+                    case 1:
+                        models = _g.sent();
+                        return [3 /*break*/, 4];
+                    case 2: return [4 /*yield*/, Promise.all([
+                            loadDetectorModel(detectorModelUrl, maxFaces, iouThreshold, scoreThreshold),
+                            loadMeshModel(modelUrl)
+                        ])];
+                    case 3:
+                        models = _g.sent();
+                        _g.label = 4;
+                    case 4:
+                        faceMesh = new FaceMesh(models[0], models[1], maxContinuousChecks, detectionConfidence, maxFaces, shouldLoadIrisModel ? models[2] : null);
+                        return [2 /*return*/, faceMesh];
+                }
+            });
+        });
+    }
+    function loadDetectorModel(modelUrl, maxFaces, iouThreshold, scoreThreshold) {
+        return __awaiter(this, void 0, void 0, function () {
+            return __generator(this, function (_a) {
+                return [2 /*return*/, load({ modelUrl: modelUrl, maxFaces: maxFaces, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold })];
+            });
+        });
+    }
+    function loadMeshModel(modelUrl) {
+        return __awaiter(this, void 0, void 0, function () {
+            return __generator(this, function (_a) {
+                if (modelUrl != null) {
+                    return [2 /*return*/, tfconv.loadGraphModel(modelUrl)];
+                }
+                return [2 /*return*/, tfconv.loadGraphModel(FACEMESH_GRAPHMODEL_PATH, { fromTFHub: true })];
+            });
+        });
+    }
+    function loadIrisModel(modelUrl) {
+        return __awaiter(this, void 0, void 0, function () {
+            return __generator(this, function (_a) {
+                if (modelUrl != null) {
+                    return [2 /*return*/, tfconv.loadGraphModel(modelUrl)];
+                }
+                return [2 /*return*/, tfconv.loadGraphModel(IRIS_GRAPHMODEL_PATH, { fromTFHub: true })];
+            });
+        });
+    }
+    function getInputTensorDimensions$1(input) {
+        return input instanceof tf.Tensor ? [input.shape[0], input.shape[1]] :
+            [input.height, input.width];
+    }
+    function flipFaceHorizontal$1(face, imageWidth) {
+        if (face.mesh instanceof tf.Tensor) {
+            var _a = tf.tidy(function () {
+                var subtractBasis = tf.tensor1d([imageWidth - 1, 0, 0]);
+                var multiplyBasis = tf.tensor1d([1, -1, 1]);
+                return tf.tidy(function () {
+                    return [
+                        tf.concat([
+                            tf.sub(imageWidth - 1, tf.slice(face.boundingBox.topLeft, 0, 1)),
+                            tf.slice(face.boundingBox.topLeft, 1, 1)
+                        ]),
+                        tf.concat([
+                            tf.sub(imageWidth - 1, tf.slice(face.boundingBox.bottomRight, 0, 1)),
+                            tf.slice(face.boundingBox.bottomRight, 1, 1)
+                        ]),
+                        tf.mul(tf.sub(subtractBasis, face.mesh), multiplyBasis),
+                        tf.mul(tf.sub(subtractBasis, face.scaledMesh), multiplyBasis)
+                    ];
+                });
+            }), topLeft = _a[0], bottomRight = _a[1], mesh = _a[2], scaledMesh = _a[3];
+            return Object.assign({}, face, { boundingBox: { topLeft: topLeft, bottomRight: bottomRight }, mesh: mesh, scaledMesh: scaledMesh });
+        }
+        return Object.assign({}, face, {
+            boundingBox: {
+                topLeft: [
+                    imageWidth - 1 - face.boundingBox.topLeft[0],
+                    face.boundingBox.topLeft[1]
+                ],
+                bottomRight: [
+                    imageWidth - 1 - face.boundingBox.bottomRight[0],
+                    face.boundingBox.bottomRight[1]
+                ]
+            },
+            mesh: (face.mesh).map(function (coord) {
+                var flippedCoord = coord.slice(0);
+                flippedCoord[0] = imageWidth - 1 - coord[0];
+                return flippedCoord;
+            }),
+            scaledMesh: face.scaledMesh.map(function (coord) {
+                var flippedCoord = coord.slice(0);
+                flippedCoord[0] = imageWidth - 1 - coord[0];
+                return flippedCoord;
+            })
+        });
+    }
+    var FaceMesh = /** @class */ (function () {
+        function FaceMesh(blazeFace, blazeMeshModel, maxContinuousChecks, detectionConfidence, maxFaces, irisModel) {
+            this.kind = 'MediaPipeFaceMesh';
+            this.pipeline = new Pipeline(blazeFace, blazeMeshModel, MESH_MODEL_INPUT_WIDTH, MESH_MODEL_INPUT_HEIGHT, maxContinuousChecks, maxFaces, irisModel);
+            this.detectionConfidence = detectionConfidence;
+        }
+        FaceMesh.getAnnotations = function () {
+            return MESH_ANNOTATIONS;
+        };
+        /**
+         * Returns an array of UV coordinates for the 468 facial keypoint vertices in
+         * mesh_map.jpg. Can be used to map textures to the facial mesh.
+         */
+        FaceMesh.getUVCoords = function () {
+            return UV_COORDS;
+        };
+        /**
+         * Returns an array of faces in an image.
+         *
+         * @param input The image to classify. Can be a tensor, DOM element image,
+         * video, or canvas.
+         * @param returnTensors (defaults to `false`) Whether to return tensors as
+         * opposed to values.
+         * @param flipHorizontal Whether to flip/mirror the facial keypoints
+         * horizontally. Should be true for videos that are flipped by default (e.g.
+         * webcams).
+         * @param predictIrises
+         *
+         * @return An array of AnnotatedPrediction objects.
+         */
+        FaceMesh.prototype.estimateFaces = function (config) {
+            return __awaiter(this, void 0, void 0, function () {
+                var _a, returnTensors, _b, flipHorizontal, _c, predictIrises, input, _d, width, image, predictions, savedWebglPackDepthwiseConvFlag;
+                var _this = this;
+                return __generator(this, function (_e) {
+                    switch (_e.label) {
+                        case 0:
+                            _a = config.returnTensors, returnTensors = _a === void 0 ? false : _a, _b = config.flipHorizontal, flipHorizontal = _b === void 0 ? false : _b, _c = config.predictIrises, predictIrises = _c === void 0 ? true : _c;
+                            input = config.input;
+                            if (predictIrises && this.pipeline.irisModel == null) {
+                                throw new Error('The iris model was not loaded as part of facemesh. ' +
+                                    'Please initialize the model with ' +
+                                    'facemesh.load({shouldLoadIrisModel: true}).');
+                            }
+                            _d = getInputTensorDimensions$1(input), width = _d[1];
+                            image = tf.tidy(function () {
+                                if (!(input instanceof tf.Tensor)) {
+                                    input = tf.browser.fromPixels(input);
+                                }
+                                return tf.expandDims(tf.cast(input, 'float32'), 0);
+                            });
+                            if (!(tf.getBackend() === 'webgl')) return [3 /*break*/, 2];
+                            savedWebglPackDepthwiseConvFlag = tf.env().get('WEBGL_PACK_DEPTHWISECONV');
+                            tf.env().set('WEBGL_PACK_DEPTHWISECONV', true);
+                            return [4 /*yield*/, this.pipeline.predict(image, predictIrises)];
+                        case 1:
+                            predictions = _e.sent();
+                            tf.env().set('WEBGL_PACK_DEPTHWISECONV', savedWebglPackDepthwiseConvFlag);
+                            return [3 /*break*/, 4];
+                        case 2: return [4 /*yield*/, this.pipeline.predict(image, predictIrises)];
+                        case 3:
+                            predictions = _e.sent();
+                            _e.label = 4;
+                        case 4:
+                            image.dispose();
+                            if (predictions != null && predictions.length > 0) {
+                                return [2 /*return*/, Promise.all(predictions.map(function (prediction, i) { return __awaiter(_this, void 0, void 0, function () {
+                                        var coords, scaledCoords, box, flag, tensorsToRead, tensorValues, flagValue, annotatedPrediction_1, _a, coordsArr, coordsArrScaled, annotatedPrediction, annotations, key;
+                                        var _this = this;
+                                        return __generator(this, function (_b) {
+                                            switch (_b.label) {
+                                                case 0:
+                                                    coords = prediction.coords, scaledCoords = prediction.scaledCoords, box = prediction.box, flag = prediction.flag;
+                                                    tensorsToRead = [flag];
+                                                    if (!returnTensors) {
+                                                        tensorsToRead = tensorsToRead.concat([coords, scaledCoords]);
+                                                    }
+                                                    return [4 /*yield*/, Promise.all(tensorsToRead.map(function (d) { return __awaiter(_this, void 0, void 0, function () { return __generator(this, function (_a) {
+                                                            return [2 /*return*/, d.array()];
+                                                        }); }); }))];
+                                                case 1:
+                                                    tensorValues = _b.sent();
+                                                    flagValue = tensorValues[0];
+                                                    flag.dispose();
+                                                    if (flagValue < this.detectionConfidence) {
+                                                        this.pipeline.clearRegionOfInterest(i);
+                                                    }
+                                                    if (returnTensors) {
+                                                        annotatedPrediction_1 = {
+                                                            kind: PREDICTION_TENSORS,
+                                                            faceInViewConfidence: flagValue,
+                                                            mesh: coords,
+                                                            scaledMesh: scaledCoords,
+                                                            boundingBox: {
+                                                                topLeft: tf.tensor1d(box.startPoint),
+                                                                bottomRight: tf.tensor1d(box.endPoint)
+                                                            }
+                                                        };
+                                                        if (flipHorizontal) {
+                                                            return [2 /*return*/, flipFaceHorizontal$1(annotatedPrediction_1, width)];
+                                                        }
+                                                        return [2 /*return*/, annotatedPrediction_1];
+                                                    }
+                                                    _a = tensorValues.slice(1), coordsArr = _a[0], coordsArrScaled = _a[1];
+                                                    scaledCoords.dispose();
+                                                    coords.dispose();
+                                                    annotatedPrediction = {
+                                                        kind: PREDICTION_VALUES,
+                                                        faceInViewConfidence: flagValue,
+                                                        boundingBox: { topLeft: box.startPoint, bottomRight: box.endPoint },
+                                                        mesh: coordsArr,
+                                                        scaledMesh: coordsArrScaled
+                                                    };
+                                                    if (flipHorizontal) {
+                                                        annotatedPrediction =
+                                                            flipFaceHorizontal$1(annotatedPrediction, width);
+                                                    }
+                                                    annotations = {};
+                                                    for (key in MESH_ANNOTATIONS) {
+                                                        if (predictIrises || key.includes('Iris') === false) {
+                                                            annotations[key] = MESH_ANNOTATIONS[key].map(function (index) { return annotatedPrediction.scaledMesh[index]; });
+                                                        }
+                                                    }
+                                                    annotatedPrediction['annotations'] = annotations;
+                                                    return [2 /*return*/, annotatedPrediction];
+                                            }
+                                        });
+                                    }); }))];
+                            }
+                            return [2 /*return*/, []];
+                    }
+                });
+            });
+        };
+        return FaceMesh;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * https://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    (function (SupportedPackages) {
+        SupportedPackages["mediapipeFacemesh"] = "mediapipe-facemesh";
+    })(exports.SupportedPackages || (exports.SupportedPackages = {}));
+    /**
+     * Load face-landmarks-detection.
+     *
+     * @param pkg - The name of the package to load, e.g. 'mediapipe-facemesh'.
+     * @param config - a configuration object with the following properties:
+     *  - `maxContinuousChecks` How many frames to go without running the bounding
+     * box detector. Only relevant if maxFaces > 1. Defaults to 5.
+     *  - `detectionConfidence` Threshold for discarding a prediction. Defaults to
+     * 0.9.
+     *  - `maxFaces` The maximum number of faces detected in the input. Should be
+     * set to the minimum number for performance. Defaults to 10.
+     *  - `iouThreshold` A float representing the threshold for deciding whether
+     * boxes overlap too much in non-maximum suppression. Must be between [0, 1].
+     * Defaults to 0.3.
+     *  - `scoreThreshold` A threshold for deciding when to remove boxes based
+     * on score in non-maximum suppression. Defaults to 0.75.
+     *  - `shouldLoadIrisModel` Whether to also load the iris detection model.
+     * Defaults to true.
+     */
+    function load$2(pkg, config) {
+        if (pkg === void 0) { pkg = exports.SupportedPackages.mediapipeFacemesh; }
+        if (config === void 0) { config = {}; }
+        return __awaiter(this, void 0, void 0, function () {
+            return __generator(this, function (_a) {
+                if (pkg === exports.SupportedPackages.mediapipeFacemesh) {
+                    return [2 /*return*/, load$1(config)];
+                }
+                else {
+                    throw new Error(pkg + " is not a valid package name.");
+                }
+            });
+        });
+    }
+
+    exports.load = load$2;
+
+    Object.defineProperty(exports, '__esModule', { value: true });
+
+})));
diff --git a/thirdparty/tfjs/tf-backend-webgl.js b/thirdparty/tfjs/tf-backend-webgl.js
new file mode 100644
index 0000000..7d7611e
--- /dev/null
+++ b/thirdparty/tfjs/tf-backend-webgl.js
@@ -0,0 +1,16300 @@
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('@tensorflow/tfjs-core')) :
+    typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs-core'], factory) :
+    (global = global || self, factory(global.tf = global.tf || {}, global.tf));
+}(this, (function (exports, tf) { 'use strict';
+
+    /*! *****************************************************************************
+    Copyright (c) Microsoft Corporation.
+
+    Permission to use, copy, modify, and/or distribute this software for any
+    purpose with or without fee is hereby granted.
+
+    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+    REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
+    AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+    INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
+    LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
+    OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
+    PERFORMANCE OF THIS SOFTWARE.
+    ***************************************************************************** */
+    /* global Reflect, Promise */
+
+    var extendStatics = function(d, b) {
+        extendStatics = Object.setPrototypeOf ||
+            ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+            function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+        return extendStatics(d, b);
+    };
+
+    function __extends(d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    }
+
+    function __awaiter(thisArg, _arguments, P, generator) {
+        function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
+        return new (P || (P = Promise))(function (resolve, reject) {
+            function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+            function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+            function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
+            step((generator = generator.apply(thisArg, _arguments || [])).next());
+        });
+    }
+
+    function __generator(thisArg, body) {
+        var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+        return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+        function verb(n) { return function (v) { return step([n, v]); }; }
+        function step(op) {
+            if (f) throw new TypeError("Generator is already executing.");
+            while (_) try {
+                if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+                if (y = 0, t) op = [op[0] & 2, t.value];
+                switch (op[0]) {
+                    case 0: case 1: t = op; break;
+                    case 4: _.label++; return { value: op[1], done: false };
+                    case 5: _.label++; y = op[1]; op = [0]; continue;
+                    case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                    default:
+                        if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                        if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                        if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                        if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                        if (t[2]) _.ops.pop();
+                        _.trys.pop(); continue;
+                }
+                op = body.call(thisArg, _);
+            } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+            if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var contexts = {};
+    var WEBGL_ATTRIBUTES = {
+        alpha: false,
+        antialias: false,
+        premultipliedAlpha: false,
+        preserveDrawingBuffer: false,
+        depth: false,
+        stencil: false,
+        failIfMajorPerformanceCaveat: true
+    };
+    function setWebGLContext(webGLVersion, gl) {
+        contexts[webGLVersion] = gl;
+    }
+    function getWebGLContext(webGLVersion) {
+        if (!(webGLVersion in contexts)) {
+            var newCtx = getWebGLRenderingContext(webGLVersion);
+            if (newCtx !== null) {
+                contexts[webGLVersion] = newCtx;
+            }
+            else {
+                console.log('Could not get context for WebGL version', webGLVersion);
+                return null;
+            }
+        }
+        var gl = contexts[webGLVersion];
+        if (gl.isContextLost()) {
+            delete contexts[webGLVersion];
+            return getWebGLContext(webGLVersion);
+        }
+        gl.disable(gl.DEPTH_TEST);
+        gl.disable(gl.STENCIL_TEST);
+        gl.disable(gl.BLEND);
+        gl.disable(gl.DITHER);
+        gl.disable(gl.POLYGON_OFFSET_FILL);
+        gl.disable(gl.SAMPLE_COVERAGE);
+        gl.enable(gl.SCISSOR_TEST);
+        gl.enable(gl.CULL_FACE);
+        gl.cullFace(gl.BACK);
+        return contexts[webGLVersion];
+    }
+    function createCanvas(webGLVersion) {
+        if (typeof OffscreenCanvas !== 'undefined' && webGLVersion === 2) {
+            return new OffscreenCanvas(300, 150);
+        }
+        else if (typeof document !== 'undefined') {
+            return document.createElement('canvas');
+        }
+        else {
+            throw new Error('Cannot create a canvas in this context');
+        }
+    }
+    function getWebGLRenderingContext(webGLVersion) {
+        if (webGLVersion !== 1 && webGLVersion !== 2) {
+            throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');
+        }
+        var canvas = createCanvas(webGLVersion);
+        canvas.addEventListener('webglcontextlost', function (ev) {
+            ev.preventDefault();
+            delete contexts[webGLVersion];
+        }, false);
+        if (webGLVersion === 1) {
+            return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||
+                canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));
+        }
+        return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var PackingScheme;
+    (function (PackingScheme) {
+        /**
+         * All values in a single texel are densely packed without any constraints.
+         *
+         * This is how the shader encodes a tensor with shape = [2, 3, 4]
+         * (indices are [batch, row, col]).
+         *
+         * 000|001   010|011   020|021
+         * -------   -------   -------
+         * 002|003   012|013   022|023
+         *
+         * 100|101   110|111   120|121
+         * -------   -------   -------
+         * 102|103   112|113   122|123
+         *
+         */
+        PackingScheme[PackingScheme["DENSE"] = 0] = "DENSE";
+        /**
+         * Single texels contain only values from the same batch, and from adjacent
+         * rows and columns.
+         *
+         * This is how the shader encodes a tensor with shape = [2, 3, 5]
+         * (indices are [batch, row, col]).
+         *
+         * 000|001   002|003   004|xxx   020|021   022|023   024|xxx
+         * -------   -------   -------   -------   -------   -------
+         * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx
+         *
+         * 100|101   102|103   104|xxx   120|121   122|123   124|xxx
+         * -------   -------   -------   -------   -------   -------
+         * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx
+         *
+         */
+        PackingScheme[PackingScheme["SHARED_BATCH"] = 1] = "SHARED_BATCH";
+    })(PackingScheme || (PackingScheme = {}));
+    var TextureUsage;
+    (function (TextureUsage) {
+        TextureUsage[TextureUsage["RENDER"] = 0] = "RENDER";
+        TextureUsage[TextureUsage["UPLOAD"] = 1] = "UPLOAD";
+        TextureUsage[TextureUsage["PIXELS"] = 2] = "PIXELS";
+        TextureUsage[TextureUsage["DOWNLOAD"] = 3] = "DOWNLOAD";
+    })(TextureUsage || (TextureUsage = {}));
+    var PhysicalTextureType;
+    (function (PhysicalTextureType) {
+        PhysicalTextureType[PhysicalTextureType["UNPACKED_FLOAT16"] = 0] = "UNPACKED_FLOAT16";
+        PhysicalTextureType[PhysicalTextureType["UNPACKED_FLOAT32"] = 1] = "UNPACKED_FLOAT32";
+        PhysicalTextureType[PhysicalTextureType["PACKED_4X1_UNSIGNED_BYTE"] = 2] = "PACKED_4X1_UNSIGNED_BYTE";
+        PhysicalTextureType[PhysicalTextureType["PACKED_2X2_FLOAT32"] = 3] = "PACKED_2X2_FLOAT32";
+        PhysicalTextureType[PhysicalTextureType["PACKED_2X2_FLOAT16"] = 4] = "PACKED_2X2_FLOAT16";
+    })(PhysicalTextureType || (PhysicalTextureType = {}));
+    function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {
+        return [columns, rows];
+    }
+    function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {
+        return matrixSize * channelsPerTexture;
+    }
+    /**
+     * Get shape for densely packed RGBA texture.
+     */
+    function getDenseTexShape(shape) {
+        var size = tf.util.sizeFromShape(shape);
+        var texelsNeeded = Math.ceil(size / 4);
+        return tf.util.sizeToSquarishShape(texelsNeeded);
+    }
+    function getPackedMatrixTextureShapeWidthHeight(rows, columns) {
+        return [
+            Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))
+        ];
+    }
+    function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {
+        var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), w = _a[0], h = _a[1];
+        return w * h * 4;
+    }
+    function getTextureConfig(
+    // tslint:disable-next-line:no-any
+    gl, textureHalfFloatExtension) {
+        // tslint:disable-next-line:no-any
+        var glany = gl;
+        var internalFormatFloat;
+        var internalFormatHalfFloat;
+        var internalFormatPackedHalfFloat;
+        var internalFormatPackedFloat;
+        var textureFormatFloat;
+        var downloadTextureFormat;
+        var downloadUnpackNumChannels;
+        var defaultNumChannels;
+        var textureTypeHalfFloat;
+        var textureTypeFloat;
+        if (tf.env().getNumber('WEBGL_VERSION') === 2) {
+            internalFormatFloat = glany.R32F;
+            internalFormatHalfFloat = glany.R16F;
+            internalFormatPackedHalfFloat = glany.RGBA16F;
+            internalFormatPackedFloat = glany.RGBA32F;
+            textureFormatFloat = glany.RED;
+            downloadUnpackNumChannels = 4;
+            defaultNumChannels = 1;
+            textureTypeHalfFloat = glany.HALF_FLOAT;
+            textureTypeFloat = glany.FLOAT;
+        }
+        else {
+            internalFormatFloat = gl.RGBA;
+            internalFormatHalfFloat = gl.RGBA;
+            internalFormatPackedHalfFloat = gl.RGBA;
+            internalFormatPackedFloat = glany.RGBA;
+            textureFormatFloat = gl.RGBA;
+            downloadUnpackNumChannels = 4;
+            defaultNumChannels = 4;
+            textureTypeHalfFloat = textureHalfFloatExtension != null ?
+                textureHalfFloatExtension.HALF_FLOAT_OES :
+                null;
+            textureTypeFloat = gl.FLOAT;
+        }
+        downloadTextureFormat = gl.RGBA;
+        return {
+            internalFormatFloat: internalFormatFloat,
+            internalFormatHalfFloat: internalFormatHalfFloat,
+            internalFormatPackedHalfFloat: internalFormatPackedHalfFloat,
+            internalFormatPackedFloat: internalFormatPackedFloat,
+            textureFormatFloat: textureFormatFloat,
+            downloadTextureFormat: downloadTextureFormat,
+            downloadUnpackNumChannels: downloadUnpackNumChannels,
+            defaultNumChannels: defaultNumChannels,
+            textureTypeHalfFloat: textureTypeHalfFloat,
+            textureTypeFloat: textureTypeFloat
+        };
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function callAndCheck(gl, func) {
+        var returnValue = func();
+        if (tf.env().getBool('DEBUG')) {
+            checkWebGLError(gl);
+        }
+        return returnValue;
+    }
+    function checkWebGLError(gl) {
+        var error = gl.getError();
+        if (error !== gl.NO_ERROR) {
+            throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));
+        }
+    }
+    // https://en.wikipedia.org/wiki/Half-precision_floating-point_format
+    var MIN_FLOAT16 = 5.96e-8;
+    var MAX_FLOAT16 = 65504;
+    function canBeRepresented(num) {
+        if (tf.env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||
+            (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {
+            return true;
+        }
+        return false;
+    }
+    function getWebGLErrorMessage(gl, status) {
+        switch (status) {
+            case gl.NO_ERROR:
+                return 'NO_ERROR';
+            case gl.INVALID_ENUM:
+                return 'INVALID_ENUM';
+            case gl.INVALID_VALUE:
+                return 'INVALID_VALUE';
+            case gl.INVALID_OPERATION:
+                return 'INVALID_OPERATION';
+            case gl.INVALID_FRAMEBUFFER_OPERATION:
+                return 'INVALID_FRAMEBUFFER_OPERATION';
+            case gl.OUT_OF_MEMORY:
+                return 'OUT_OF_MEMORY';
+            case gl.CONTEXT_LOST_WEBGL:
+                return 'CONTEXT_LOST_WEBGL';
+            default:
+                return "Unknown error code " + status;
+        }
+    }
+    function getExtensionOrThrow(gl, extensionName) {
+        return throwIfNull(gl, function () { return gl.getExtension(extensionName); }, 'Extension "' + extensionName + '" not supported on this browser.');
+    }
+    function createVertexShader(gl, vertexShaderSource) {
+        var vertexShader = throwIfNull(gl, function () { return gl.createShader(gl.VERTEX_SHADER); }, 'Unable to create vertex WebGLShader.');
+        callAndCheck(gl, function () { return gl.shaderSource(vertexShader, vertexShaderSource); });
+        callAndCheck(gl, function () { return gl.compileShader(vertexShader); });
+        if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {
+            console.log(gl.getShaderInfoLog(vertexShader));
+            throw new Error('Failed to compile vertex shader.');
+        }
+        return vertexShader;
+    }
+    function createFragmentShader(gl, fragmentShaderSource) {
+        var fragmentShader = throwIfNull(gl, function () { return gl.createShader(gl.FRAGMENT_SHADER); }, 'Unable to create fragment WebGLShader.');
+        callAndCheck(gl, function () { return gl.shaderSource(fragmentShader, fragmentShaderSource); });
+        callAndCheck(gl, function () { return gl.compileShader(fragmentShader); });
+        if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {
+            logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));
+            throw new Error('Failed to compile fragment shader.');
+        }
+        return fragmentShader;
+    }
+    var lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;
+    function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {
+        var lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);
+        if (lineNumberRegexResult == null) {
+            console.log("Couldn't parse line number in error: " + shaderInfoLog);
+            console.log(shaderSource);
+            return;
+        }
+        var lineNumber = +lineNumberRegexResult[1];
+        var shaderLines = shaderSource.split('\n');
+        var pad = shaderLines.length.toString().length + 2;
+        var linesWithLineNumbers = shaderLines.map(function (line, lineNumber) {
+            return tf.util.rightPad((lineNumber + 1).toString(), pad) + line;
+        });
+        var maxLineLength = 0;
+        for (var i = 0; i < linesWithLineNumbers.length; i++) {
+            maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);
+        }
+        var beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);
+        var errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);
+        var afterErrorLines = linesWithLineNumbers.slice(lineNumber);
+        console.log(beforeErrorLines.join('\n'));
+        console.log(shaderInfoLog.split('\n')[0]);
+        console.log("%c " + tf.util.rightPad(errorLine[0], maxLineLength), 'border:1px solid red; background-color:#e3d2d2; color:#a61717');
+        console.log(afterErrorLines.join('\n'));
+    }
+    function createProgram(gl) {
+        return throwIfNull(gl, function () { return gl.createProgram(); }, 'Unable to create WebGLProgram.');
+    }
+    function linkProgram(gl, program) {
+        callAndCheck(gl, function () { return gl.linkProgram(program); });
+        if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {
+            console.log(gl.getProgramInfoLog(program));
+            throw new Error('Failed to link vertex and fragment shaders.');
+        }
+    }
+    function validateProgram(gl, program) {
+        callAndCheck(gl, function () { return gl.validateProgram(program); });
+        if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {
+            console.log(gl.getProgramInfoLog(program));
+            throw new Error('Shader program validation failed.');
+        }
+    }
+    function createStaticVertexBuffer(gl, data) {
+        var buffer = throwIfNull(gl, function () { return gl.createBuffer(); }, 'Unable to create WebGLBuffer');
+        callAndCheck(gl, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, buffer); });
+        callAndCheck(gl, function () { return gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW); });
+        return buffer;
+    }
+    function createStaticIndexBuffer(gl, data) {
+        var buffer = throwIfNull(gl, function () { return gl.createBuffer(); }, 'Unable to create WebGLBuffer');
+        callAndCheck(gl, function () { return gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer); });
+        callAndCheck(gl, function () { return gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW); });
+        return buffer;
+    }
+    function getNumChannels() {
+        if (tf.env().getNumber('WEBGL_VERSION') === 2) {
+            return 1;
+        }
+        return 4;
+    }
+    function createTexture(gl) {
+        return throwIfNull(gl, function () { return gl.createTexture(); }, 'Unable to create WebGLTexture.');
+    }
+    function validateTextureSize(width, height) {
+        var maxTextureSize = tf.env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
+        if ((width <= 0) || (height <= 0)) {
+            var requested = "[" + width + "x" + height + "]";
+            throw new Error('Requested texture size ' + requested + ' is invalid.');
+        }
+        if ((width > maxTextureSize) || (height > maxTextureSize)) {
+            var requested = "[" + width + "x" + height + "]";
+            var max = "[" + maxTextureSize + "x" + maxTextureSize + "]";
+            throw new Error('Requested texture size ' + requested +
+                ' greater than WebGL maximum on this browser / GPU ' + max + '.');
+        }
+    }
+    function createFramebuffer(gl) {
+        return throwIfNull(gl, function () { return gl.createFramebuffer(); }, 'Unable to create WebGLFramebuffer.');
+    }
+    function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {
+        var loc = gl.getAttribLocation(program, attribute);
+        if (loc === -1) {
+            // The GPU compiler decided to strip out this attribute because it's unused,
+            // thus no need to bind.
+            return false;
+        }
+        callAndCheck(gl, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, buffer); });
+        callAndCheck(gl, function () { return gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes); });
+        callAndCheck(gl, function () { return gl.enableVertexAttribArray(loc); });
+        return true;
+    }
+    function bindTextureUnit(gl, texture, textureUnit) {
+        validateTextureUnit(gl, textureUnit);
+        callAndCheck(gl, function () { return gl.activeTexture(gl.TEXTURE0 + textureUnit); });
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, texture); });
+    }
+    function unbindTextureUnit(gl, textureUnit) {
+        validateTextureUnit(gl, textureUnit);
+        callAndCheck(gl, function () { return gl.activeTexture(gl.TEXTURE0 + textureUnit); });
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
+    }
+    function getProgramUniformLocationOrThrow(gl, program, uniformName) {
+        return throwIfNull(gl, function () { return gl.getUniformLocation(program, uniformName); }, 'uniform "' + uniformName + '" not present in program.');
+    }
+    function getProgramUniformLocation(gl, program, uniformName) {
+        return gl.getUniformLocation(program, uniformName);
+    }
+    function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {
+        callAndCheck(gl, function () { return bindTextureUnit(gl, texture, textureUnit); });
+        callAndCheck(gl, function () { return gl.uniform1i(uniformSamplerLocation, textureUnit); });
+    }
+    function bindCanvasToFramebuffer(gl) {
+        callAndCheck(gl, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, null); });
+        callAndCheck(gl, function () { return gl.viewport(0, 0, gl.canvas.width, gl.canvas.height); });
+        callAndCheck(gl, function () { return gl.scissor(0, 0, gl.canvas.width, gl.canvas.height); });
+    }
+    function bindColorTextureToFramebuffer(gl, texture, framebuffer) {
+        callAndCheck(gl, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer); });
+        callAndCheck(gl, function () { return gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); });
+    }
+    function unbindColorTextureFromFramebuffer(gl, framebuffer) {
+        callAndCheck(gl, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer); });
+        callAndCheck(gl, function () { return gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0); });
+    }
+    function validateFramebuffer(gl) {
+        var status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);
+        if (status !== gl.FRAMEBUFFER_COMPLETE) {
+            throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));
+        }
+    }
+    function getFramebufferErrorMessage(gl, status) {
+        switch (status) {
+            case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
+                return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';
+            case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
+                return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';
+            case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
+                return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';
+            case gl.FRAMEBUFFER_UNSUPPORTED:
+                return 'FRAMEBUFFER_UNSUPPORTED';
+            default:
+                return "unknown error " + status;
+        }
+    }
+    function throwIfNull(gl, returnTOrNull, failureMessage) {
+        var tOrNull = callAndCheck(gl, function () { return returnTOrNull(); });
+        if (tOrNull == null) {
+            throw new Error(failureMessage);
+        }
+        return tOrNull;
+    }
+    function validateTextureUnit(gl, textureUnit) {
+        var maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;
+        var glTextureUnit = textureUnit + gl.TEXTURE0;
+        if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {
+            var textureUnitRange = "[gl.TEXTURE0, gl.TEXTURE" + maxTextureUnit + "]";
+            throw new Error("textureUnit must be in " + textureUnitRange + ".");
+        }
+    }
+    function getBatchDim(shape, dimsToSkip) {
+        if (dimsToSkip === void 0) { dimsToSkip = 2; }
+        return tf.util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));
+    }
+    function getRowsCols(shape) {
+        if (shape.length === 0) {
+            throw Error('Cannot get rows and columns of an empty shape array.');
+        }
+        return [
+            shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]
+        ];
+    }
+    function getShapeAs3D(shape) {
+        var shapeAs3D = [1, 1, 1];
+        var isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);
+        if (!isScalar) {
+            shapeAs3D =
+                [getBatchDim(shape)].concat(getRowsCols(shape));
+        }
+        return shapeAs3D;
+    }
+    function getTextureShapeFromLogicalShape(logShape, isPacked) {
+        var _a;
+        if (isPacked === void 0) { isPacked = false; }
+        var maxTexSize = tf.env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
+        if (isPacked) {
+            maxTexSize = maxTexSize * 2;
+            // This logic ensures we accurately count the number of packed texels needed
+            // to accommodate the tensor. We can only pack values in the same texel if
+            // they are from adjacent pairs of rows/cols within the same batch. So if a
+            // tensor has 3 rows, we pretend it has 4 rows in order to account for the
+            // fact that the texels containing the third row are half empty.
+            logShape = logShape.map(function (d, i) { return i >= logShape.length - 2 ?
+                tf.util.nearestLargerEven(logShape[i]) :
+                logShape[i]; });
+            // Packed texture height is at least 2 (the channel height of a single
+            // texel).
+            if (logShape.length === 1) {
+                logShape = [2, logShape[0]];
+            }
+        }
+        // If logical shape is 2, we don't squeeze, since we want to match physical.
+        if (logShape.length !== 2) {
+            var squeezeResult = tf.util.squeezeShape(logShape);
+            logShape = squeezeResult.newShape;
+        }
+        var size = tf.util.sizeFromShape(logShape);
+        if (logShape.length <= 1 && size <= maxTexSize) {
+            return [1, size];
+        }
+        else if (logShape.length === 2 && logShape[0] <= maxTexSize &&
+            logShape[1] <= maxTexSize) {
+            return logShape;
+        }
+        else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&
+            logShape[2] <= maxTexSize) {
+            return [logShape[0] * logShape[1], logShape[2]];
+        }
+        else if (logShape.length === 3 && logShape[0] <= maxTexSize &&
+            logShape[1] * logShape[2] <= maxTexSize) {
+            return [logShape[0], logShape[1] * logShape[2]];
+        }
+        else if (logShape.length === 4 &&
+            logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&
+            logShape[3] <= maxTexSize) {
+            return [logShape[0] * logShape[1] * logShape[2], logShape[3]];
+        }
+        else if (logShape.length === 4 && logShape[0] <= maxTexSize &&
+            logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {
+            return [logShape[0], logShape[1] * logShape[2] * logShape[3]];
+        }
+        else {
+            if (isPacked) {
+                // For packed textures size equals the number of channels required to
+                // accommodate the texture data. However in order to squarify such that
+                // inner dimensions stay even, we rewrite size to equal the number of
+                // texels. Then in the return statement we rehydrate the squarified
+                // dimensions to channel units.
+                var batchDim = getBatchDim(logShape);
+                var rows = 2, cols = 2;
+                if (logShape.length) {
+                    _a = getRowsCols(logShape), rows = _a[0], cols = _a[1];
+                }
+                size = batchDim * (rows / 2) * (cols / 2);
+                return tf.util.sizeToSquarishShape(size).map(function (d) { return d * 2; });
+            }
+            return tf.util.sizeToSquarishShape(size);
+        }
+    }
+    function isEven(n) {
+        return n % 2 === 0;
+    }
+    /**
+     * This determines whether reshaping a packed texture requires rearranging
+     * the data within the texture, assuming 2x2 packing.
+     */
+    function isReshapeFree(shape1, shape2) {
+        shape1 = shape1.slice(-2);
+        shape2 = shape2.slice(-2);
+        if (tf.util.arraysEqual(shape1, shape2)) {
+            return true;
+        }
+        if (!shape1.length || !shape2.length) { // One of the shapes is a scalar.
+            return true;
+        }
+        if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||
+            shape2[1] === 0) {
+            return true;
+        }
+        if (shape1.length !== shape2.length) { // One of the shapes is a vector.
+            var shape1Cols = shape1.slice(-1)[0];
+            var shape2Cols = shape2.slice(-1)[0];
+            if (shape1Cols === shape2Cols) {
+                return true;
+            }
+            if (isEven(shape1Cols) && isEven(shape2Cols) &&
+                (shape1[0] === 1 || shape2[0] === 1)) {
+                return true;
+            }
+        }
+        return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);
+    }
+    // We cache webgl params because the environment gets reset between
+    // unit tests and we don't want to constantly query the WebGLContext for
+    // MAX_TEXTURE_SIZE.
+    var MAX_TEXTURE_SIZE;
+    var MAX_TEXTURES_IN_SHADER;
+    function getWebGLMaxTextureSize(webGLVersion) {
+        if (MAX_TEXTURE_SIZE == null) {
+            var gl = getWebGLContext(webGLVersion);
+            MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);
+        }
+        return MAX_TEXTURE_SIZE;
+    }
+    function resetMaxTextureSize() {
+        MAX_TEXTURE_SIZE = null;
+    }
+    function resetMaxTexturesInShader() {
+        MAX_TEXTURES_IN_SHADER = null;
+    }
+    function getMaxTexturesInShader(webGLVersion) {
+        if (MAX_TEXTURES_IN_SHADER == null) {
+            var gl = getWebGLContext(webGLVersion);
+            MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);
+        }
+        // We cap at 16 to avoid spurious runtime "memory exhausted" error.
+        return Math.min(16, MAX_TEXTURES_IN_SHADER);
+    }
+    function getWebGLDisjointQueryTimerVersion(webGLVersion) {
+        if (webGLVersion === 0) {
+            return 0;
+        }
+        var queryTimerVersion;
+        var gl = getWebGLContext(webGLVersion);
+        if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&
+            webGLVersion === 2) {
+            queryTimerVersion = 2;
+        }
+        else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {
+            queryTimerVersion = 1;
+        }
+        else {
+            queryTimerVersion = 0;
+        }
+        return queryTimerVersion;
+    }
+    function hasExtension(gl, extensionName) {
+        var ext = gl.getExtension(extensionName);
+        return ext != null;
+    }
+    function isWebGLVersionEnabled(webGLVersion) {
+        try {
+            var gl = getWebGLContext(webGLVersion);
+            if (gl != null) {
+                return true;
+            }
+        }
+        catch (e) {
+            console.log('Error when getting WebGL context: ', e);
+            return false;
+        }
+        return false;
+    }
+    function isCapableOfRenderingToFloatTexture(webGLVersion) {
+        if (webGLVersion === 0) {
+            return false;
+        }
+        var gl = getWebGLContext(webGLVersion);
+        if (webGLVersion === 1) {
+            if (!hasExtension(gl, 'OES_texture_float')) {
+                return false;
+            }
+        }
+        else {
+            if (!hasExtension(gl, 'EXT_color_buffer_float')) {
+                return false;
+            }
+        }
+        var isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
+        return isFrameBufferComplete;
+    }
+    /**
+     * Check if we can download values from a float/half-float texture.
+     *
+     * Note that for performance reasons we use binding a texture to a framebuffer
+     * as a proxy for ability to download float values later using readPixels. The
+     * texture params of this texture will not match those in readPixels exactly
+     * but if we are unable to bind some kind of float texture to the frameBuffer
+     * then we definitely will not be able to read float values from it.
+     */
+    function isDownloadFloatTextureEnabled(webGLVersion) {
+        if (webGLVersion === 0) {
+            return false;
+        }
+        var gl = getWebGLContext(webGLVersion);
+        if (webGLVersion === 1) {
+            if (!hasExtension(gl, 'OES_texture_float')) {
+                return false;
+            }
+            if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {
+                return false;
+            }
+        }
+        else {
+            if (hasExtension(gl, 'EXT_color_buffer_float')) {
+                return createFloatTextureAndBindToFramebuffer(gl);
+            }
+            var COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';
+            if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {
+                var textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
+                return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);
+            }
+            return false;
+        }
+        var isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
+        return isFrameBufferComplete;
+    }
+    function createFloatTextureAndBindToFramebuffer(gl) {
+        var texConfig = getTextureConfig(gl);
+        var texture = gl.createTexture();
+        gl.bindTexture(gl.TEXTURE_2D, texture);
+        var width = 1;
+        var height = 1;
+        gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);
+        var frameBuffer = gl.createFramebuffer();
+        gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
+        gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+        var isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
+        gl.bindTexture(gl.TEXTURE_2D, null);
+        gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+        gl.deleteTexture(texture);
+        gl.deleteFramebuffer(frameBuffer);
+        return isFrameBufferComplete;
+    }
+    function createHalfFloatTextureAndBindToFramebuffer(
+    // tslint:disable-next-line:no-any
+    gl, textureHalfFloatExtension) {
+        var texConfig = getTextureConfig(gl, textureHalfFloatExtension);
+        var texture = gl.createTexture();
+        gl.bindTexture(gl.TEXTURE_2D, texture);
+        var width = 1;
+        var height = 1;
+        gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);
+        var frameBuffer = gl.createFramebuffer();
+        gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
+        gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+        var isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
+        gl.bindTexture(gl.TEXTURE_2D, null);
+        gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+        gl.deleteTexture(texture);
+        gl.deleteFramebuffer(frameBuffer);
+        return isFrameBufferComplete;
+    }
+    function isWebGLFenceEnabled(webGLVersion) {
+        if (webGLVersion !== 2) {
+            return false;
+        }
+        var gl = getWebGLContext(webGLVersion);
+        // tslint:disable-next-line:no-any
+        var isEnabled = gl.fenceSync != null;
+        return isEnabled;
+    }
+    function assertNotComplex(tensor, opName) {
+        if (!Array.isArray(tensor)) {
+            tensor = [tensor];
+        }
+        tensor.forEach(function (t) {
+            if (t != null) {
+                tf.util.assert(t.dtype !== 'complex64', function () { return opName + " does not support complex64 tensors " +
+                    'in the WebGL backend.'; });
+            }
+        });
+    }
+
+    var webgl_util = {
+        __proto__: null,
+        callAndCheck: callAndCheck,
+        canBeRepresented: canBeRepresented,
+        getWebGLErrorMessage: getWebGLErrorMessage,
+        getExtensionOrThrow: getExtensionOrThrow,
+        createVertexShader: createVertexShader,
+        createFragmentShader: createFragmentShader,
+        createProgram: createProgram,
+        linkProgram: linkProgram,
+        validateProgram: validateProgram,
+        createStaticVertexBuffer: createStaticVertexBuffer,
+        createStaticIndexBuffer: createStaticIndexBuffer,
+        getNumChannels: getNumChannels,
+        createTexture: createTexture,
+        validateTextureSize: validateTextureSize,
+        createFramebuffer: createFramebuffer,
+        bindVertexBufferToProgramAttribute: bindVertexBufferToProgramAttribute,
+        bindTextureUnit: bindTextureUnit,
+        unbindTextureUnit: unbindTextureUnit,
+        getProgramUniformLocationOrThrow: getProgramUniformLocationOrThrow,
+        getProgramUniformLocation: getProgramUniformLocation,
+        bindTextureToProgramUniformSampler: bindTextureToProgramUniformSampler,
+        bindCanvasToFramebuffer: bindCanvasToFramebuffer,
+        bindColorTextureToFramebuffer: bindColorTextureToFramebuffer,
+        unbindColorTextureFromFramebuffer: unbindColorTextureFromFramebuffer,
+        validateFramebuffer: validateFramebuffer,
+        getFramebufferErrorMessage: getFramebufferErrorMessage,
+        getBatchDim: getBatchDim,
+        getRowsCols: getRowsCols,
+        getShapeAs3D: getShapeAs3D,
+        getTextureShapeFromLogicalShape: getTextureShapeFromLogicalShape,
+        isReshapeFree: isReshapeFree,
+        getWebGLMaxTextureSize: getWebGLMaxTextureSize,
+        resetMaxTextureSize: resetMaxTextureSize,
+        resetMaxTexturesInShader: resetMaxTexturesInShader,
+        getMaxTexturesInShader: getMaxTexturesInShader,
+        getWebGLDisjointQueryTimerVersion: getWebGLDisjointQueryTimerVersion,
+        hasExtension: hasExtension,
+        isWebGLVersionEnabled: isWebGLVersionEnabled,
+        isCapableOfRenderingToFloatTexture: isCapableOfRenderingToFloatTexture,
+        isDownloadFloatTextureEnabled: isDownloadFloatTextureEnabled,
+        isWebGLFenceEnabled: isWebGLFenceEnabled,
+        assertNotComplex: assertNotComplex
+    };
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ENV = tf.env();
+    /**
+     * This file contains WebGL-specific flag registrations.
+     */
+    /**
+     * True if WebGL is supported.
+     */
+    ENV.registerFlag('HAS_WEBGL', function () { return ENV.getNumber('WEBGL_VERSION') > 0; });
+    /** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */
+    ENV.registerFlag('WEBGL_VERSION', function () {
+        if (isWebGLVersionEnabled(2)) {
+            return 2;
+        }
+        else if (isWebGLVersionEnabled(1)) {
+            return 1;
+        }
+        return 0;
+    });
+    /** Whether to check for numerical representation problems. */
+    ENV.registerFlag('WEBGL_CHECK_NUMERICAL_PROBLEMS', function () { return false; });
+    ENV.registerFlag('WEBGL_BUFFER_SUPPORTED', function () { return ENV.get('WEBGL_VERSION') === 2; });
+    /** Whether the WebGL backend will sometimes forward ops to the CPU. */
+    ENV.registerFlag('WEBGL_CPU_FORWARD', function () { return true; });
+    /** Whether the WebGL backend will always use f16 textures for rendering. */
+    ENV.registerFlag('WEBGL_FORCE_F16_TEXTURES', function () { return false; });
+    /** Whether to turn all packing related flags on. */
+    ENV.registerFlag('WEBGL_PACK', function () { return ENV.getBool('HAS_WEBGL'); });
+    /** Whether we will pack the batchnormalization op. */
+    ENV.registerFlag('WEBGL_PACK_NORMALIZATION', function () { return ENV.getBool('WEBGL_PACK'); });
+    /** Whether we will pack the clip op. */
+    ENV.registerFlag('WEBGL_PACK_CLIP', function () { return ENV.getBool('WEBGL_PACK'); });
+    /** Whether we will pack the depthwise conv op. */
+    ENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', function () { return ENV.getBool('WEBGL_PACK'); });
+    /** Whether we will pack binary ops. */
+    ENV.registerFlag('WEBGL_PACK_BINARY_OPERATIONS', function () { return ENV.getBool('WEBGL_PACK'); });
+    /** Whether we will pack unary ops. */
+    ENV.registerFlag('WEBGL_PACK_UNARY_OPERATIONS', function () { return ENV.getBool('WEBGL_PACK'); });
+    /** Whether we will pack array ops. */
+    ENV.registerFlag('WEBGL_PACK_ARRAY_OPERATIONS', function () { return ENV.getBool('WEBGL_PACK'); });
+    /** Whether we will pack image ops. */
+    ENV.registerFlag('WEBGL_PACK_IMAGE_OPERATIONS', function () { return ENV.getBool('WEBGL_PACK'); });
+    /** Whether we will pack reduce ops. */
+    ENV.registerFlag('WEBGL_PACK_REDUCE', function () { return ENV.getBool('WEBGL_PACK'); });
+    /** Whether packed WebGL kernels lazily unpack their outputs. */
+    ENV.registerFlag('WEBGL_LAZILY_UNPACK', function () { return ENV.getBool('WEBGL_PACK'); });
+    /** Whether we will use the im2col algorithm to speed up convolutions. */
+    ENV.registerFlag('WEBGL_CONV_IM2COL', function () { return ENV.getBool('WEBGL_PACK'); });
+    /** The maximum texture dimension. */
+    ENV.registerFlag('WEBGL_MAX_TEXTURE_SIZE', function () { return getWebGLMaxTextureSize(ENV.getNumber('WEBGL_VERSION')); });
+    /** The maximum texture dimension. */
+    ENV.registerFlag('WEBGL_MAX_TEXTURES_IN_SHADER', function () { return getMaxTexturesInShader(ENV.getNumber('WEBGL_VERSION')); });
+    /**
+     * The disjoint_query_timer extension version.
+     * 0: disabled, 1: EXT_disjoint_timer_query, 2:
+     * EXT_disjoint_timer_query_webgl2.
+     * In Firefox with WebGL 2.0,
+     * EXT_disjoint_timer_query_webgl2 is not available, so we must use the
+     * WebGL 1.0 extension.
+     */
+    ENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', function () {
+        var webGLVersion = ENV.getNumber('WEBGL_VERSION');
+        if (webGLVersion === 0) {
+            return 0;
+        }
+        return getWebGLDisjointQueryTimerVersion(webGLVersion);
+    });
+    /**
+     * Whether the timer object from the disjoint_query_timer extension gives
+     * timing information that is reliable.
+     */
+    ENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', function () { return ENV.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&
+        !tf.device_util.isMobile(); });
+    /**
+     * Whether the device is physically capable of rendering to float32 textures.
+     */
+    ENV.registerFlag('WEBGL_RENDER_FLOAT32_CAPABLE', function () { return isCapableOfRenderingToFloatTexture(ENV.getNumber('WEBGL_VERSION')); });
+    /**
+     * Whether rendering to float32 textures is enabled. If disabled, renders to
+     * float16 textures.
+     */
+    ENV.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', function () {
+        return ENV.getBool('WEBGL_FORCE_F16_TEXTURES') ?
+            false :
+            ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');
+    });
+    /**
+     * Whether downloading float textures is enabled (16 or 32 bit). If disabled,
+     * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.
+     */
+    ENV.registerFlag('WEBGL_DOWNLOAD_FLOAT_ENABLED', function () { return isDownloadFloatTextureEnabled(ENV.getNumber('WEBGL_VERSION')); });
+    /** Whether the fence API is available. */
+    ENV.registerFlag('WEBGL_FENCE_API_ENABLED', function () { return isWebGLFenceEnabled(ENV.getNumber('WEBGL_VERSION')); });
+    /**
+     * Tensors with size <= than this will be uploaded as uniforms, not textures.
+     */
+    ENV.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', function () {
+        // Use uniform uploads only when 32bit floats are supported. In
+        // 16bit
+        // environments there are problems with comparing a 16bit texture value
+        // with a 32bit uniform value.
+        var useUniforms = ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED');
+        return useUniforms ? 4 : 0;
+    });
+    /**
+     * If the total number of bytes allocated on the GPU is greater than this
+     * number, we will aggressively delete textures upon disposal with
+     * gl.deleteMatrixTexture, rather than making them available for reuse.
+     *
+     * Default value -1 indicates that we will never aggressively delete textures.
+     */
+    ENV.registerFlag('WEBGL_DELETE_TEXTURE_THRESHOLD', function () {
+        return -1;
+    }, function (threshold) {
+        if (threshold < 0 && threshold !== -1) {
+            throw new Error("WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never " +
+                ("delete) or at least 0, but got " + threshold + "."));
+        }
+    });
+    /**
+     * Trigger a manual GL command flush if the threshold of time has passed since
+     * previous Kernel execution. This can be useful for Andorid device where GL
+     * command flush are delayed un til the end of javascript task. This value is
+     * measured in millisecond. Typically you want to set this value to close to 1.
+     *
+     * Default value 1 for mobile chrome, and -1 for rest cases. -1 indicates that
+     * we will not enforce manual flush and depend on system default flush schedule.
+     */
+    ENV.registerFlag('WEBGL_FLUSH_THRESHOLD', function () {
+        return tf.device_util.isMobile() && ENV.getBool('IS_CHROME') ? 1 : -1;
+    }, function (threshold) {
+        if (threshold < 0 && threshold !== -1) {
+            throw new Error("WEBGL_FLUSH_THRESHOLD must be -1 (indicating never " +
+                ("manual flush) or at least 0, but got " + threshold + "."));
+        }
+    });
+    /**
+     * Threshold for input tensor size that determines whether WebGL backend will
+     * delegate computation to CPU.
+     *
+     * Default value is 128.
+     */
+    ENV.registerFlag('CPU_HANDOFF_SIZE_THRESHOLD', function () { return 128; });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function getGlslDifferences() {
+        var version;
+        var attribute;
+        var varyingVs;
+        var varyingFs;
+        var texture2D;
+        var output;
+        var defineOutput;
+        var defineSpecialNaN;
+        var defineSpecialInf;
+        var defineRound;
+        if (tf.env().getNumber('WEBGL_VERSION') === 2) {
+            version = '#version 300 es';
+            attribute = 'in';
+            varyingVs = 'out';
+            varyingFs = 'in';
+            texture2D = 'texture';
+            output = 'outputColor';
+            defineOutput = 'out vec4 outputColor;';
+            // Use custom isnan definition to work across differences between
+            // implementations on various platforms. While this should happen in ANGLE
+            // we still see differences between android and windows (on chrome) when
+            // using isnan directly.
+            defineSpecialNaN = "\n      bool isnan_custom(float val) {\n        return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    ";
+            // In webgl 2 we do not need to specify a custom isinf so there is no
+            // need for a special INFINITY constant.
+            defineSpecialInf = "";
+            defineRound = "\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    ";
+        }
+        else {
+            version = '';
+            attribute = 'attribute';
+            varyingVs = 'varying';
+            varyingFs = 'varying';
+            texture2D = 'texture2D';
+            output = 'gl_FragColor';
+            defineOutput = '';
+            // WebGL1 has no built in isnan so we define one here.
+            defineSpecialNaN = "\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    ";
+            defineSpecialInf = "\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    ";
+            defineRound = "\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    ";
+        }
+        return {
+            version: version,
+            attribute: attribute,
+            varyingVs: varyingVs,
+            varyingFs: varyingFs,
+            texture2D: texture2D,
+            output: output,
+            defineOutput: defineOutput,
+            defineSpecialNaN: defineSpecialNaN,
+            defineSpecialInf: defineSpecialInf,
+            defineRound: defineRound
+        };
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Produces GLSL code that derives logical coordinates from a flat
+     * index. The code performs integer division with each stride and decrements
+     * the index until the index equals the final dimension coordinate.
+     */
+    function getLogicalCoordinatesFromFlatIndex(coords, shape, index) {
+        if (index === void 0) { index = 'index'; }
+        var strides = tf.util.computeStrides(shape);
+        return strides
+            .map(function (stride, i) {
+            var line1 = "int " + coords[i] + " = " + index + " / " + stride;
+            var line2 = i === strides.length - 1 ?
+                "int " + coords[i + 1] + " = " + index + " - " + coords[i] + " * " + stride :
+                "index -= " + coords[i] + " * " + stride;
+            return line1 + "; " + line2 + ";";
+        })
+            .join('');
+    }
+    /**
+     * Produces GLSL that computes the flat index from 3D coordinates.
+     */
+    function getFlatIndexFrom3D(shape) {
+        var strides = tf.util.computeStrides(shape).map(function (d) { return d.toString(); });
+        return "\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * " + strides[0] + " + coords.y * " + strides[1] + " + coords.z;\n  }\n";
+    }
+    var ENCODE_FLOAT_SNIPPET = "\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n";
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var DecodeMatrixProgram = /** @class */ (function () {
+        function DecodeMatrixProgram(outputShape) {
+            this.variableNames = ['A'];
+            this.packedInputs = false;
+            this.packedOutput = true;
+            this.outPackingScheme = PackingScheme.DENSE;
+            var texShape = getDenseTexShape(outputShape);
+            var glsl = getGlslDifferences();
+            this.outputShape = outputShape;
+            this.userCode = "\n      ivec3 outCoordsFromFlatIndex(int index) {\n        " + getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape) + "\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(" + texShape[0] + ", " + texShape[1] + "));\n        int index = 4 * (resTexRC.x * " + texShape[1] + " + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        " + glsl.output + " = result;\n      }\n    ";
+        }
+        return DecodeMatrixProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var DecodeMatrixPackedProgram = /** @class */ (function () {
+        function DecodeMatrixPackedProgram(outputShape) {
+            this.variableNames = ['A'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outPackingScheme = PackingScheme.DENSE;
+            var texShape = getDenseTexShape(outputShape);
+            var glsl = getGlslDifferences();
+            this.outputShape = outputShape;
+            this.userCode = "\n      ivec3 outCoordsFromFlatIndex(int index) {\n        " + getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape) + "\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(" + texShape[0] + ", " + texShape[1] + "));\n        int index = 4 * (resTexRC.x * " + texShape[1] + " + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        " + glsl.output + " = result;\n      }\n    ";
+        }
+        return DecodeMatrixPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var EncodeFloatProgram = /** @class */ (function () {
+        function EncodeFloatProgram(outputShape) {
+            this.variableNames = ['A'];
+            this.outTexUsage = TextureUsage.DOWNLOAD;
+            var glsl = getGlslDifferences();
+            this.outputShape = outputShape;
+            this.userCode = "\n      " + ENCODE_FLOAT_SNIPPET + "\n\n      void main() {\n        float x = getAAtOutCoords();\n        " + glsl.output + " = encode_float(x);\n      }\n    ";
+        }
+        return EncodeFloatProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var EncodeFloatPackedProgram = /** @class */ (function () {
+        function EncodeFloatPackedProgram(outputShape) {
+            this.variableNames = ['A'];
+            this.packedInputs = true;
+            this.packedOutput = false;
+            this.outTexUsage = TextureUsage.DOWNLOAD;
+            var glsl = getGlslDifferences();
+            this.outputShape = outputShape;
+            this.userCode = "\n      " + ENCODE_FLOAT_SNIPPET + "\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        " + glsl.output + " = encode_float(x);\n      }\n    ";
+        }
+        return EncodeFloatPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var EncodeMatrixProgram = /** @class */ (function () {
+        function EncodeMatrixProgram(outputShape, texShape, inputIsUnsignedByte) {
+            if (inputIsUnsignedByte === void 0) { inputIsUnsignedByte = false; }
+            this.variableNames = ['A'];
+            var glsl = getGlslDifferences();
+            var height = texShape[0], width = texShape[1];
+            this.outputShape = outputShape;
+            var output = "result";
+            if (inputIsUnsignedByte) {
+                output = "floor(result * 255. + 0.5)";
+            }
+            this.userCode = "\n      " + getFlatIndexFrom3D(outputShape) + "\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / " + width + ";\n        int c = imod(flatIndex, " + width + ");\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(" + width + ".0, " + height + ".0);\n        vec4 values = " + glsl.texture2D + "(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        " + glsl.output + " = vec4(" + output + ", 0., 0., 0.);\n      }\n    ";
+        }
+        return EncodeMatrixProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /*
+    This is how the shader encodes a tensor with shape = [2, 3, 5]
+    (indices are [batch, row, col]).
+
+    000|001   002|003   004|xxx   020|021   022|023   024|xxx
+    -------   -------   -------   -------   -------   -------
+    010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx
+
+    100|101   102|103   104|xxx   120|121   122|123   124|xxx
+    -------   -------   -------   -------   -------   -------
+    110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx
+
+    Single texels contain only values from the same batch, and from adjacent rows
+    and columns.
+     */
+    var EncodeMatrixPackedProgram = /** @class */ (function () {
+        function EncodeMatrixPackedProgram(outputShape, texShape, inputIsUnsignedByte) {
+            if (inputIsUnsignedByte === void 0) { inputIsUnsignedByte = false; }
+            this.variableNames = ['A'];
+            this.packedInputs = false;
+            this.packedOutput = true;
+            var glsl = getGlslDifferences();
+            var height = texShape[0], width = texShape[1];
+            this.outputShape = outputShape;
+            var mainLoop = '';
+            var output = 'result';
+            if (inputIsUnsignedByte) {
+                output = 'floor(result * 255. + 0.5)';
+            }
+            for (var row = 0; row <= 1; row++) {
+                for (var col = 0; col <= 1; col++) {
+                    var channel = row * 2 + col;
+                    mainLoop += "\n          localCoords = coords;\n          if(localCoords[2] + " + col + " < " + outputShape[2] + ") {\n            localCoords[2] += " + col + ";\n            if(localCoords[1] + " + row + " < " + outputShape[1] + ") {\n              localCoords[1] += " + row + ";\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n\n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / " + width + ";\n              c = imod(flatIndex, " + width + ");\n              uv = (vec2(c, r) + halfCR) / vec2(" + width + ".0, " + height + ".0);\n              values = " + glsl.texture2D + "(A, uv);\n\n              if(offset == 0) {\n                result[" + channel + "] = values[0];\n              } else if(offset == 1) {\n                result[" + channel + "] = values[1];\n              } else if(offset == 2) {\n                result[" + channel + "] = values[2];\n              } else {\n                result[" + channel + "] = values[3];\n              }\n            }\n          }\n        ";
+                }
+            }
+            this.userCode = "\n      " + getFlatIndexFrom3D(outputShape) + "\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n\n        " + mainLoop + "\n\n        " + glsl.output + " = " + output + ";\n      }\n    ";
+        }
+        return EncodeMatrixPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function createVertexShader$1(gl) {
+        var glsl = getGlslDifferences();
+        var vertexShaderSource = glsl.version + "\n    precision highp float;\n    " + glsl.attribute + " vec3 clipSpacePos;\n    " + glsl.attribute + " vec2 uv;\n    " + glsl.varyingVs + " vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }";
+        return createVertexShader(gl, vertexShaderSource);
+    }
+    function createVertexBuffer(gl) {
+        // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]
+        var vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
+        return createStaticVertexBuffer(gl, vertexArray);
+    }
+    function createIndexBuffer(gl) {
+        // OpenGL (and WebGL) have "CCW == front" winding
+        var triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);
+        return createStaticIndexBuffer(gl, triangleVertexIndices);
+    }
+    function createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {
+        validateTextureSize(width, height);
+        var texture = createTexture(gl);
+        var tex2d = gl.TEXTURE_2D;
+        callAndCheck(gl, function () { return gl.bindTexture(tex2d, texture); });
+        callAndCheck(gl, function () { return gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); });
+        callAndCheck(gl, function () { return gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); });
+        callAndCheck(gl, function () { return gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST); });
+        callAndCheck(gl, function () { return gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST); });
+        callAndCheck(gl, function () { return gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null); });
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
+        return texture;
+    }
+    function getInternalFormatForFloat32MatrixTexture(textureConfig) {
+        return textureConfig.internalFormatFloat;
+    }
+    function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {
+        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+        return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);
+    }
+    function getInternalFormatForFloat16MatrixTexture(textureConfig) {
+        return textureConfig.internalFormatHalfFloat;
+    }
+    function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {
+        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+        return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);
+    }
+    function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {
+        return textureConfig.downloadTextureFormat;
+    }
+    function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {
+        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+        return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);
+    }
+    function getInternalFormatForPackedMatrixTexture(textureConfig) {
+        return textureConfig.internalFormatPackedFloat;
+    }
+    function createPackedMatrixTexture(gl, rows, columns, textureConfig) {
+        var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+        return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);
+    }
+    function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {
+        return textureConfig.internalFormatPackedHalfFloat;
+    }
+    function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {
+        var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+        return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);
+    }
+    function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {
+        var posOffset = 0; // x is the first buffer element
+        var uvOffset = 3 * 4; // uv comes after [x y z]
+        var stride = (3 * 4) + (2 * 4); // xyz + uv, each entry is 4-byte float.
+        callAndCheck(gl, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); });
+        var success = bindVertexBufferToProgramAttribute(gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);
+        return success &&
+            bindVertexBufferToProgramAttribute(gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);
+    }
+    function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, texture); });
+        var dataForUpload, texelDataType, internalFormat;
+        if (data instanceof Uint8Array) {
+            dataForUpload = new Uint8Array(width * height * 4);
+            texelDataType = gl.UNSIGNED_BYTE;
+            internalFormat = gl.RGBA;
+        }
+        else {
+            dataForUpload = new Float32Array(width * height * 4);
+            texelDataType = gl.FLOAT;
+            internalFormat = textureConfig.internalFormatPackedFloat;
+        }
+        dataForUpload.set(data);
+        callAndCheck(gl, function () { return gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload); });
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
+    }
+    function uploadPixelDataToTexture(gl, texture, pixels) {
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, texture); });
+        if (pixels.data instanceof Uint8Array) {
+            callAndCheck(gl, function () { return gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data); });
+        }
+        else {
+            callAndCheck(gl, function () { return gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels); });
+        }
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
+    }
+    function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {
+        // Create and bind the buffer.
+        var buffer = gl2.createBuffer();
+        callAndCheck(gl2, function () { return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer); });
+        // Initialize the buffer to the size of the texture in bytes.
+        var bytesPerFloat = 4;
+        var valuesPerTexel = 4;
+        var bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;
+        callAndCheck(gl2, function () { return gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ); });
+        // Enqueue a command on the GPU command queue to copy of texture into the
+        // buffer.
+        callAndCheck(gl2, function () { return gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0); });
+        callAndCheck(gl2, function () { return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null); });
+        return buffer;
+    }
+    function downloadFloat32MatrixFromBuffer(gl, buffer, size) {
+        var gl2 = gl;
+        var downloadTarget = new Float32Array(size);
+        gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);
+        gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
+        gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
+        return downloadTarget;
+    }
+    function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {
+        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), w = _a[0], h = _a[1];
+        var numChannels = 4;
+        var downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));
+        callAndCheck(gl, function () { return gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget); });
+        // By wrapping the buffer in a Float32Array, we use native browser IEEE 754
+        // decoding of the 4 bytes that back each 32 bit float.
+        return new Float32Array(downloadTarget.buffer);
+    }
+    function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {
+        var gl2 = gl;
+        var downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));
+        gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);
+        gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
+        gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
+        return downloadTarget;
+    }
+    function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {
+        var packedRGBA = new Float32Array(physicalRows * physicalCols * 4);
+        callAndCheck(gl, function () { return gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA); });
+        return packedRGBA;
+    }
+
+    var gpgpu_util = {
+        __proto__: null,
+        createVertexShader: createVertexShader$1,
+        createVertexBuffer: createVertexBuffer,
+        createIndexBuffer: createIndexBuffer,
+        getInternalFormatForFloat32MatrixTexture: getInternalFormatForFloat32MatrixTexture,
+        createFloat32MatrixTexture: createFloat32MatrixTexture,
+        getInternalFormatForFloat16MatrixTexture: getInternalFormatForFloat16MatrixTexture,
+        createFloat16MatrixTexture: createFloat16MatrixTexture,
+        getInternalFormatForUnsignedBytesMatrixTexture: getInternalFormatForUnsignedBytesMatrixTexture,
+        createUnsignedBytesMatrixTexture: createUnsignedBytesMatrixTexture,
+        getInternalFormatForPackedMatrixTexture: getInternalFormatForPackedMatrixTexture,
+        createPackedMatrixTexture: createPackedMatrixTexture,
+        getInternalFormatForFloat16PackedMatrixTexture: getInternalFormatForFloat16PackedMatrixTexture,
+        createFloat16PackedMatrixTexture: createFloat16PackedMatrixTexture,
+        bindVertexProgramAttributeStreams: bindVertexProgramAttributeStreams,
+        uploadDenseMatrixToTexture: uploadDenseMatrixToTexture,
+        uploadPixelDataToTexture: uploadPixelDataToTexture,
+        createBufferFromOutputTexture: createBufferFromOutputTexture,
+        downloadFloat32MatrixFromBuffer: downloadFloat32MatrixFromBuffer,
+        downloadByteEncodedFloatMatrixFromOutputTexture: downloadByteEncodedFloatMatrixFromOutputTexture,
+        downloadPackedMatrixFromBuffer: downloadPackedMatrixFromBuffer,
+        downloadMatrixFromPackedOutputTexture: downloadMatrixFromPackedOutputTexture
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var GPGPUContext = /** @class */ (function () {
+        function GPGPUContext(gl) {
+            this.outputTexture = null;
+            this.program = null;
+            this.disposed = false;
+            this.vertexAttrsAreBound = false;
+            this.itemsToPoll = [];
+            var glVersion = tf.env().getNumber('WEBGL_VERSION');
+            if (gl != null) {
+                this.gl = gl;
+                setWebGLContext(glVersion, gl);
+            }
+            else {
+                this.gl = getWebGLContext(glVersion);
+            }
+            // WebGL 2.0 enables texture floats without an extension.
+            var COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';
+            var COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';
+            if (tf.env().getNumber('WEBGL_VERSION') === 1) {
+                var TEXTURE_FLOAT = 'OES_texture_float';
+                var TEXTURE_HALF_FLOAT = 'OES_texture_half_float';
+                this.textureFloatExtension =
+                    getExtensionOrThrow(this.gl, TEXTURE_FLOAT);
+                if (hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {
+                    this.textureHalfFloatExtension =
+                        getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);
+                }
+                else if (tf.env().get('WEBGL_FORCE_F16_TEXTURES')) {
+                    throw new Error('GL context does not support half float textures, yet the ' +
+                        'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');
+                }
+                this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
+                if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
+                    this.colorBufferHalfFloatExtension =
+                        getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);
+                }
+                else if (tf.env().get('WEBGL_FORCE_F16_TEXTURES')) {
+                    throw new Error('GL context does not support color renderable half floats, yet ' +
+                        'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');
+                }
+            }
+            else {
+                COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';
+                if (hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {
+                    this.colorBufferFloatExtension =
+                        this.gl.getExtension(COLOR_BUFFER_FLOAT);
+                }
+                else if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
+                    this.colorBufferHalfFloatExtension =
+                        this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
+                }
+                else {
+                    throw new Error('GL context does not support color renderable floats');
+                }
+            }
+            this.vertexBuffer = createVertexBuffer(this.gl);
+            this.indexBuffer = createIndexBuffer(this.gl);
+            this.framebuffer = createFramebuffer(this.gl);
+            this.textureConfig =
+                getTextureConfig(this.gl, this.textureHalfFloatExtension);
+        }
+        Object.defineProperty(GPGPUContext.prototype, "debug", {
+            get: function () {
+                return tf.env().getBool('DEBUG');
+            },
+            enumerable: true,
+            configurable: true
+        });
+        GPGPUContext.prototype.dispose = function () {
+            var _this = this;
+            if (this.disposed) {
+                return;
+            }
+            if (this.program != null) {
+                console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +
+                    ' This is probably a resource leak, delete the program with ' +
+                    'GPGPUContext.deleteProgram before disposing.');
+            }
+            if (this.outputTexture != null) {
+                console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +
+                    'texture.  This is probably a resource leak, delete the output ' +
+                    'matrix texture with GPGPUContext.deleteMatrixTexture before ' +
+                    'disposing.');
+            }
+            var gl = this.gl;
+            callAndCheck(gl, function () { return gl.finish(); });
+            callAndCheck(gl, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, null); });
+            callAndCheck(gl, function () { return gl.deleteFramebuffer(_this.framebuffer); });
+            callAndCheck(gl, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, null); });
+            callAndCheck(gl, function () { return gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null); });
+            callAndCheck(gl, function () { return gl.deleteBuffer(_this.indexBuffer); });
+            this.disposed = true;
+        };
+        GPGPUContext.prototype.createFloat32MatrixTexture = function (rows, columns) {
+            this.throwIfDisposed();
+            return createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);
+        };
+        GPGPUContext.prototype.createFloat16MatrixTexture = function (rows, columns) {
+            this.throwIfDisposed();
+            return createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);
+        };
+        GPGPUContext.prototype.createUnsignedBytesMatrixTexture = function (rows, columns) {
+            this.throwIfDisposed();
+            return createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);
+        };
+        GPGPUContext.prototype.uploadPixelDataToTexture = function (texture, pixels) {
+            this.throwIfDisposed();
+            uploadPixelDataToTexture(this.gl, texture, pixels);
+        };
+        GPGPUContext.prototype.uploadDenseMatrixToTexture = function (texture, width, height, data) {
+            this.throwIfDisposed();
+            uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);
+        };
+        GPGPUContext.prototype.createFloat16PackedMatrixTexture = function (rows, columns) {
+            this.throwIfDisposed();
+            return createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
+        };
+        GPGPUContext.prototype.createPackedMatrixTexture = function (rows, columns) {
+            this.throwIfDisposed();
+            return createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
+        };
+        GPGPUContext.prototype.deleteMatrixTexture = function (texture) {
+            var _this = this;
+            this.throwIfDisposed();
+            if (this.outputTexture === texture) {
+                unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
+                this.outputTexture = null;
+            }
+            callAndCheck(this.gl, function () { return _this.gl.deleteTexture(texture); });
+        };
+        GPGPUContext.prototype.downloadByteEncodedFloatMatrixFromOutputTexture = function (texture, rows, columns) {
+            var _this = this;
+            return this.downloadMatrixDriver(texture, function () { return downloadByteEncodedFloatMatrixFromOutputTexture(_this.gl, rows, columns, _this.textureConfig); });
+        };
+        GPGPUContext.prototype.downloadPackedMatrixFromBuffer = function (buffer, batch, rows, columns, physicalRows, physicalCols) {
+            return downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);
+        };
+        GPGPUContext.prototype.downloadFloat32MatrixFromBuffer = function (buffer, size) {
+            return downloadFloat32MatrixFromBuffer(this.gl, buffer, size);
+        };
+        GPGPUContext.prototype.createBufferFromTexture = function (texture, rows, columns) {
+            this.bindTextureToFrameBuffer(texture);
+            var result = createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);
+            this.unbindTextureToFrameBuffer();
+            return result;
+        };
+        GPGPUContext.prototype.createAndWaitForFence = function () {
+            var fenceContext = this.createFence(this.gl);
+            return this.pollFence(fenceContext);
+        };
+        GPGPUContext.prototype.createFence = function (gl) {
+            var _this = this;
+            var query;
+            var isFencePassed;
+            if (tf.env().getBool('WEBGL_FENCE_API_ENABLED')) {
+                var gl2_1 = gl;
+                var sync_1 = gl2_1.fenceSync(gl2_1.SYNC_GPU_COMMANDS_COMPLETE, 0);
+                gl.flush();
+                isFencePassed = function () {
+                    var status = gl2_1.clientWaitSync(sync_1, 0, 0);
+                    return status === gl2_1.ALREADY_SIGNALED ||
+                        status === gl2_1.CONDITION_SATISFIED;
+                };
+                query = sync_1;
+            }
+            else if (tf.env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {
+                query = this.beginQuery();
+                this.endQuery();
+                isFencePassed = function () { return _this.isQueryAvailable(query, tf.env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')); };
+            }
+            else {
+                // If we have no way to fence, return true immediately. This will fire in
+                // WebGL 1.0 when there is no disjoint query timer. In this case, because
+                // the fence passes immediately, we'll immediately ask for a download of
+                // the texture, which will cause the UI thread to hang.
+                isFencePassed = function () { return true; };
+            }
+            return { query: query, isFencePassed: isFencePassed };
+        };
+        GPGPUContext.prototype.downloadMatrixFromPackedTexture = function (texture, physicalRows, physicalCols) {
+            var _this = this;
+            return this.downloadMatrixDriver(texture, function () { return downloadMatrixFromPackedOutputTexture(_this.gl, physicalRows, physicalCols); });
+        };
+        GPGPUContext.prototype.createProgram = function (fragmentShaderSource) {
+            var _this = this;
+            this.throwIfDisposed();
+            var gl = this.gl;
+            var fragmentShader = createFragmentShader(gl, fragmentShaderSource);
+            if (this.vertexShader == null) {
+                this.vertexShader = createVertexShader$1(gl);
+            }
+            var program = createProgram(gl);
+            callAndCheck(gl, function () { return gl.attachShader(program, _this.vertexShader); });
+            callAndCheck(gl, function () { return gl.attachShader(program, fragmentShader); });
+            linkProgram(gl, program);
+            if (this.debug) {
+                validateProgram(gl, program);
+            }
+            if (!this.vertexAttrsAreBound) {
+                this.setProgram(program);
+                this.vertexAttrsAreBound = bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);
+            }
+            return program;
+        };
+        GPGPUContext.prototype.deleteProgram = function (program) {
+            var _this = this;
+            this.throwIfDisposed();
+            if (program === this.program) {
+                this.program = null;
+            }
+            if (program != null) {
+                callAndCheck(this.gl, function () { return _this.gl.deleteProgram(program); });
+            }
+        };
+        GPGPUContext.prototype.setProgram = function (program) {
+            var _this = this;
+            this.throwIfDisposed();
+            this.program = program;
+            if ((this.program != null) && this.debug) {
+                validateProgram(this.gl, this.program);
+            }
+            callAndCheck(this.gl, function () { return _this.gl.useProgram(program); });
+        };
+        GPGPUContext.prototype.getUniformLocation = function (program, uniformName, shouldThrow) {
+            if (shouldThrow === void 0) { shouldThrow = true; }
+            this.throwIfDisposed();
+            if (shouldThrow) {
+                return getProgramUniformLocationOrThrow(this.gl, program, uniformName);
+            }
+            else {
+                return getProgramUniformLocation(this.gl, program, uniformName);
+            }
+        };
+        GPGPUContext.prototype.getAttributeLocation = function (program, attribute) {
+            var _this = this;
+            this.throwIfDisposed();
+            return callAndCheck(this.gl, function () { return _this.gl.getAttribLocation(program, attribute); });
+        };
+        GPGPUContext.prototype.getUniformLocationNoThrow = function (program, uniformName) {
+            this.throwIfDisposed();
+            return this.gl.getUniformLocation(program, uniformName);
+        };
+        GPGPUContext.prototype.setInputMatrixTexture = function (inputMatrixTexture, uniformLocation, textureUnit) {
+            this.throwIfDisposed();
+            this.throwIfNoProgram();
+            bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);
+        };
+        GPGPUContext.prototype.setOutputMatrixTexture = function (outputMatrixTexture, rows, columns) {
+            this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);
+        };
+        GPGPUContext.prototype.setOutputPackedMatrixTexture = function (outputPackedMatrixTexture, rows, columns) {
+            this.throwIfDisposed();
+            var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+            this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);
+        };
+        GPGPUContext.prototype.setOutputMatrixWriteRegion = function (startRow, numRows, startColumn, numColumns) {
+            this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);
+        };
+        GPGPUContext.prototype.setOutputPackedMatrixWriteRegion = function (startRow, numRows, startColumn, numColumns) {
+            throw new Error('setOutputPackedMatrixWriteRegion not implemented.');
+        };
+        GPGPUContext.prototype.debugValidate = function () {
+            if (this.program != null) {
+                validateProgram(this.gl, this.program);
+            }
+            validateFramebuffer(this.gl);
+        };
+        GPGPUContext.prototype.executeProgram = function () {
+            this.throwIfDisposed();
+            this.throwIfNoProgram();
+            var gl = this.gl;
+            if (this.debug) {
+                this.debugValidate();
+            }
+            callAndCheck(gl, function () { return gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0); });
+        };
+        GPGPUContext.prototype.blockUntilAllProgramsCompleted = function () {
+            var _this = this;
+            this.throwIfDisposed();
+            callAndCheck(this.gl, function () { return _this.gl.finish(); });
+        };
+        GPGPUContext.prototype.getQueryTimerExtension = function () {
+            if (this.disjointQueryTimerExtension == null) {
+                this.disjointQueryTimerExtension =
+                    getExtensionOrThrow(this.gl, tf.env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?
+                        'EXT_disjoint_timer_query_webgl2' :
+                        'EXT_disjoint_timer_query');
+            }
+            return this.disjointQueryTimerExtension;
+        };
+        GPGPUContext.prototype.getQueryTimerExtensionWebGL2 = function () {
+            return this.getQueryTimerExtension();
+        };
+        GPGPUContext.prototype.getQueryTimerExtensionWebGL1 = function () {
+            return this.getQueryTimerExtension();
+        };
+        GPGPUContext.prototype.beginQuery = function () {
+            if (tf.env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {
+                var gl2 = this.gl;
+                var ext_1 = this.getQueryTimerExtensionWebGL2();
+                var query_1 = gl2.createQuery();
+                gl2.beginQuery(ext_1.TIME_ELAPSED_EXT, query_1);
+                return query_1;
+            }
+            var ext = this.getQueryTimerExtensionWebGL1();
+            var query = ext.createQueryEXT();
+            ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);
+            return query;
+        };
+        GPGPUContext.prototype.endQuery = function () {
+            if (tf.env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {
+                var gl2 = this.gl;
+                var ext_2 = this.getQueryTimerExtensionWebGL2();
+                gl2.endQuery(ext_2.TIME_ELAPSED_EXT);
+                return;
+            }
+            var ext = this.getQueryTimerExtensionWebGL1();
+            ext.endQueryEXT(ext.TIME_ELAPSED_EXT);
+        };
+        GPGPUContext.prototype.waitForQueryAndGetTime = function (query) {
+            return __awaiter(this, void 0, void 0, function () {
+                var _this = this;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, tf.util.repeatedTry(function () { return _this.disposed || // while testing contexts are created / disposed
+                                // in rapid succession, so without this check we
+                                // may poll for the query timer indefinitely
+                                _this.isQueryAvailable(query, tf.env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')); })];
+                        case 1:
+                            _a.sent();
+                            return [2 /*return*/, this.getQueryTime(query, tf.env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'))];
+                    }
+                });
+            });
+        };
+        GPGPUContext.prototype.getQueryTime = function (query, queryTimerVersion) {
+            if (queryTimerVersion === 0) {
+                return null;
+            }
+            if (queryTimerVersion === 2) {
+                var gl2 = this.gl;
+                var timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);
+                // Return milliseconds.
+                return timeElapsedNanos / 1000000;
+            }
+            else {
+                var ext = this.getQueryTimerExtensionWebGL1();
+                var timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);
+                // Return milliseconds.
+                return timeElapsedNanos / 1000000;
+            }
+        };
+        GPGPUContext.prototype.isQueryAvailable = function (query, queryTimerVersion) {
+            if (queryTimerVersion === 0) {
+                return true;
+            }
+            if (queryTimerVersion === 2) {
+                var gl2 = this.gl;
+                var ext = this.getQueryTimerExtensionWebGL2();
+                var available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);
+                if (this.disjoint == null) {
+                    this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
+                }
+                return available && !this.disjoint;
+            }
+            else {
+                var ext = this.getQueryTimerExtensionWebGL1();
+                var available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);
+                if (this.disjoint == null) {
+                    this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
+                }
+                return available && !this.disjoint;
+            }
+        };
+        GPGPUContext.prototype.pollFence = function (fenceContext) {
+            var _this = this;
+            return new Promise(function (resolve) {
+                _this.addItemToPoll(function () { return fenceContext.isFencePassed(); }, function () { return resolve(); });
+            });
+        };
+        GPGPUContext.prototype.pollItems = function () {
+            // Find the last query that has finished.
+            var index = linearSearchLastTrue(this.itemsToPoll.map(function (x) { return x.isDoneFn; }));
+            for (var i = 0; i <= index; ++i) {
+                var resolveFn = this.itemsToPoll[i].resolveFn;
+                resolveFn();
+            }
+            this.itemsToPoll = this.itemsToPoll.slice(index + 1);
+        };
+        GPGPUContext.prototype.addItemToPoll = function (isDoneFn, resolveFn) {
+            var _this = this;
+            this.itemsToPoll.push({ isDoneFn: isDoneFn, resolveFn: resolveFn });
+            if (this.itemsToPoll.length > 1) {
+                // We already have a running loop that polls.
+                return;
+            }
+            // Start a new loop that polls.
+            tf.util.repeatedTry(function () {
+                _this.pollItems();
+                // End the loop if no more items to poll.
+                return _this.itemsToPoll.length === 0;
+            });
+        };
+        GPGPUContext.prototype.bindTextureToFrameBuffer = function (texture) {
+            this.throwIfDisposed();
+            bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);
+            if (this.debug) {
+                validateFramebuffer(this.gl);
+            }
+        };
+        GPGPUContext.prototype.unbindTextureToFrameBuffer = function () {
+            if (this.outputTexture != null) {
+                bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);
+                if (this.debug) {
+                    validateFramebuffer(this.gl);
+                }
+            }
+            else {
+                unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
+            }
+        };
+        GPGPUContext.prototype.downloadMatrixDriver = function (texture, downloadAndDecode) {
+            this.bindTextureToFrameBuffer(texture);
+            var result = downloadAndDecode();
+            this.unbindTextureToFrameBuffer();
+            return result;
+        };
+        GPGPUContext.prototype.setOutputMatrixTextureDriver = function (outputMatrixTextureMaybePacked, width, height) {
+            this.throwIfDisposed();
+            var gl = this.gl;
+            bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);
+            if (this.debug) {
+                validateFramebuffer(gl);
+            }
+            this.outputTexture = outputMatrixTextureMaybePacked;
+            callAndCheck(gl, function () { return gl.viewport(0, 0, width, height); });
+            callAndCheck(gl, function () { return gl.scissor(0, 0, width, height); });
+        };
+        GPGPUContext.prototype.setOutputMatrixWriteRegionDriver = function (x, y, width, height) {
+            var _this = this;
+            this.throwIfDisposed();
+            callAndCheck(this.gl, function () { return _this.gl.scissor(x, y, width, height); });
+        };
+        GPGPUContext.prototype.throwIfDisposed = function () {
+            if (this.disposed) {
+                throw new Error('Attempted to use disposed GPGPUContext.');
+            }
+        };
+        GPGPUContext.prototype.throwIfNoProgram = function () {
+            if (this.program == null) {
+                throw new Error('No GPU program is currently set.');
+            }
+        };
+        return GPGPUContext;
+    }());
+    /**
+     * Finds the index of the last true element using linear search.
+     * Note: We can't do binary search because Chrome expects us to explicitly
+     * test all fences before download:
+     * https://github.com/tensorflow/tfjs/issues/1145
+     */
+    function linearSearchLastTrue(arr) {
+        var i = 0;
+        for (; i < arr.length; ++i) {
+            var isDone = arr[i]();
+            if (!isDone) {
+                break;
+            }
+        }
+        return i - 1;
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var getBroadcastDims = tf.backend_util.getBroadcastDims;
+    function makeShader(inputsInfo, outputShape, userCode, usesPackedTextures) {
+        var prefixSnippets = [];
+        inputsInfo.forEach(function (x) {
+            var size = tf.util.sizeFromShape(x.shapeInfo.logicalShape);
+            // Snippet when we decided to upload the values as uniform.
+            if (x.shapeInfo.isUniform) {
+                prefixSnippets.push("uniform float " + x.name + (size > 1 ? "[" + size + "]" : '') + ";");
+            }
+            else {
+                prefixSnippets.push("uniform sampler2D " + x.name + ";");
+                prefixSnippets.push("uniform int offset" + x.name + ";");
+            }
+        });
+        var inputPrefixSnippet = prefixSnippets.join('\n');
+        var inputSamplingSnippet = inputsInfo
+            .map(function (x) { return getInputSamplingSnippet(x, outputShape, usesPackedTextures); })
+            .join('\n');
+        var outTexShape = outputShape.texShape;
+        var glsl = getGlslDifferences();
+        var floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);
+        var outputSamplingSnippet;
+        var floatTextureSetOutputSnippet;
+        var shaderPrefix = getShaderPrefix(glsl);
+        if (outputShape.isPacked) {
+            outputSamplingSnippet =
+                getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);
+            floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);
+        }
+        else {
+            outputSamplingSnippet =
+                getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);
+            floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);
+        }
+        if (usesPackedTextures) {
+            shaderPrefix += SHADER_PACKED_PREFIX;
+        }
+        var source = [
+            shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,
+            inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode
+        ].join('\n');
+        return source;
+    }
+    function getSamplerFromInInfo(inInfo) {
+        var shape = inInfo.shapeInfo.logicalShape;
+        switch (shape.length) {
+            case 0:
+                return getSamplerScalar(inInfo);
+            case 1:
+                return getSampler1D(inInfo);
+            case 2:
+                return getSampler2D(inInfo);
+            case 3:
+                return getSampler3D(inInfo);
+            case 4:
+                return getSampler4D(inInfo);
+            case 5:
+                return getSampler5D(inInfo);
+            case 6:
+                return getSampler6D(inInfo);
+            default:
+                throw new Error(shape.length + "-D input sampling" +
+                    " is not yet supported");
+        }
+    }
+    function getPackedSamplerFromInInfo(inInfo) {
+        var shape = inInfo.shapeInfo.logicalShape;
+        switch (shape.length) {
+            case 0:
+                return getPackedSamplerScalar(inInfo);
+            case 1:
+                return getPackedSampler1D(inInfo);
+            case 2:
+                return getPackedSampler2D(inInfo);
+            case 3:
+                return getPackedSampler3D(inInfo);
+            default:
+                return getPackedSamplerND(inInfo);
+        }
+    }
+    function getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures) {
+        if (usesPackedTextures === void 0) { usesPackedTextures = false; }
+        var res = '';
+        if (usesPackedTextures) {
+            res += getPackedSamplerFromInInfo(inInfo);
+        }
+        else {
+            res += getSamplerFromInInfo(inInfo);
+        }
+        var inShape = inInfo.shapeInfo.logicalShape;
+        var outShape = outShapeInfo.logicalShape;
+        if (inShape.length <= outShape.length) {
+            if (usesPackedTextures) {
+                res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);
+            }
+            else {
+                res += getSamplerAtOutputCoords(inInfo, outShapeInfo);
+            }
+        }
+        return res;
+    }
+    function getPackedOutputSamplingSnippet(outShape, outTexShape) {
+        switch (outShape.length) {
+            case 0:
+                return getOutputScalarCoords();
+            case 1:
+                return getOutputPacked1DCoords(outShape, outTexShape);
+            case 2:
+                return getOutputPacked2DCoords(outShape, outTexShape);
+            case 3:
+                return getOutputPacked3DCoords(outShape, outTexShape);
+            default:
+                return getOutputPackedNDCoords(outShape, outTexShape);
+        }
+    }
+    function getOutputSamplingSnippet(outShape, outTexShape) {
+        switch (outShape.length) {
+            case 0:
+                return getOutputScalarCoords();
+            case 1:
+                return getOutput1DCoords(outShape, outTexShape);
+            case 2:
+                return getOutput2DCoords(outShape, outTexShape);
+            case 3:
+                return getOutput3DCoords(outShape, outTexShape);
+            case 4:
+                return getOutput4DCoords(outShape, outTexShape);
+            case 5:
+                return getOutput5DCoords(outShape, outTexShape);
+            case 6:
+                return getOutput6DCoords(outShape, outTexShape);
+            default:
+                throw new Error(outShape.length + "-D output sampling is not yet supported");
+        }
+    }
+    function getFloatTextureSampleSnippet(glsl) {
+        return "\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return " + glsl.texture2D + "(textureSampler, uv).r;\n    }\n  ";
+    }
+    function getFloatTextureSetRSnippet(glsl) {
+        return "\n    void setOutput(float val) {\n      " + glsl.output + " = vec4(val, 0, 0, 0);\n    }\n  ";
+    }
+    function getFloatTextureSetRGBASnippet(glsl) {
+        return "\n    void setOutput(vec4 val) {\n      " + glsl.output + " = val;\n    }\n  ";
+    }
+    function getShaderPrefix(glsl) {
+        var SHADER_PREFIX = glsl.version + "\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    " + glsl.varyingFs + " vec2 resultUV;\n    " + glsl.defineOutput + "\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    " + glsl.defineSpecialNaN + "\n    " + glsl.defineSpecialInf + "\n    " + glsl.defineRound + "\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    " + SAMPLE_1D_SNIPPET + "\n    " + SAMPLE_2D_SNIPPET + "\n    " + SAMPLE_3D_SNIPPET + "\n  ";
+        return SHADER_PREFIX;
+    }
+    var SAMPLE_1D_SNIPPET = "\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+    var SAMPLE_2D_SNIPPET = "\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+    var SAMPLE_3D_SNIPPET = "\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+    var SHADER_PACKED_PREFIX = "\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";
+    function getOutputScalarCoords() {
+        return "\n    int getOutputCoords() {\n      return 0;\n    }\n  ";
+    }
+    function getOutputPacked1DCoords(shape, texShape) {
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        if (packedTexShape[0] === 1) {
+            return "\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * " + packedTexShape[1] + ".0);\n      }\n    ";
+        }
+        if (packedTexShape[1] === 1) {
+            return "\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * " + packedTexShape[0] + ".0);\n      }\n    ";
+        }
+        return "\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      return 2 * (resTexRC.x * " + packedTexShape[1] + " + resTexRC.y);\n    }\n  ";
+    }
+    function getOutput1DCoords(shape, texShape) {
+        if (texShape[0] === 1) {
+            return "\n      int getOutputCoords() {\n        return int(resultUV.x * " + texShape[1] + ".0);\n      }\n    ";
+        }
+        if (texShape[1] === 1) {
+            return "\n      int getOutputCoords() {\n        return int(resultUV.y * " + texShape[0] + ".0);\n      }\n    ";
+        }
+        return "\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + texShape[0] + ", " + texShape[1] + "));\n      return resTexRC.x * " + texShape[1] + " + resTexRC.y;\n    }\n  ";
+    }
+    function getOutputPacked3DCoords(shape, texShape) {
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        var texelsInLogicalRow = Math.ceil(shape[2] / 2);
+        var texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);
+        return "\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n\n      int b = index / " + texelsInBatch + ";\n      index -= b * " + texelsInBatch + ";\n\n      int r = 2 * (index / " + texelsInLogicalRow + ");\n      int c = imod(index, " + texelsInLogicalRow + ") * 2;\n\n      return ivec3(b, r, c);\n    }\n  ";
+    }
+    function getOutput3DCoords(shape, texShape) {
+        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);
+        return "\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n      " + coordsFromIndexSnippet + "\n      return ivec3(r, c, d);\n    }\n  ";
+    }
+    function getOutputPackedNDCoords(shape, texShape) {
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        var texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);
+        var texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);
+        var texelsInBatchN = texelsInBatch;
+        var batches = "";
+        var coords = 'b, r, c';
+        for (var b = 2; b < shape.length - 1; b++) {
+            texelsInBatchN *= shape[shape.length - b - 1];
+            batches = "\n      int b" + b + " = index / " + texelsInBatchN + ";\n      index -= b" + b + " * " + texelsInBatchN + ";\n    " + batches;
+            coords = "b" + b + ", " + coords;
+        }
+        return "\n    ivec" + shape.length + " getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n\n      " + batches + "\n\n      int b = index / " + texelsInBatch + ";\n      index -= b * " + texelsInBatch + ";\n\n      int r = 2 * (index / " + texelsInLogicalRow + ");\n      int c = imod(index, " + texelsInLogicalRow + ") * 2;\n\n      return ivec" + shape.length + "(" + coords + ");\n    }\n  ";
+    }
+    function getOutput4DCoords(shape, texShape) {
+        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);
+        return "\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n      " + coordsFromIndexSnippet + "\n      return ivec4(r, c, d, d2);\n    }\n  ";
+    }
+    function getOutput5DCoords(shape, texShape) {
+        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);
+        return "\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(" + texShape[0] + ",\n                             " + texShape[1] + "));\n\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n\n      " + coordsFromIndexSnippet + "\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  ";
+    }
+    function getOutput6DCoords(shape, texShape) {
+        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);
+        return "\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n\n      " + coordsFromIndexSnippet + "\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  ";
+    }
+    function getOutputPacked2DCoords(shape, texShape) {
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        if (tf.util.arraysEqual(shape, texShape)) {
+            return "\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      }\n    ";
+        }
+        // texels needed to accommodate a logical row
+        var texelsInLogicalRow = Math.ceil(shape[1] / 2);
+        /**
+         * getOutputCoords
+         *
+         * resTexRC: The rows and columns of the texels. If you move over one
+         * texel to the right in the packed texture, you are moving over one column
+         * (not two).
+         *
+         * index: The texel index
+         */
+        return "\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n      int r = 2 * (index / " + texelsInLogicalRow + ");\n      int c = imod(index, " + texelsInLogicalRow + ") * 2;\n\n      return ivec2(r, c);\n    }\n  ";
+    }
+    function getOutput2DCoords(shape, texShape) {
+        if (tf.util.arraysEqual(shape, texShape)) {
+            return "\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(" + texShape[0] + ", " + texShape[1] + "));\n      }\n    ";
+        }
+        if (shape[1] === 1) {
+            return "\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(" + texShape[0] + ", " + texShape[1] + "));\n        int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ";
+        }
+        if (shape[0] === 1) {
+            return "\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(" + texShape[0] + ", " + texShape[1] + "));\n        int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ";
+        }
+        return "\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n      int r = index / " + shape[1] + ";\n      int c = index - r * " + shape[1] + ";\n      return ivec2(r, c);\n    }\n  ";
+    }
+    function getFlatOffsetUniformName(texName) {
+        return "offset" + texName;
+    }
+    function getPackedSamplerScalar(inputInfo) {
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var glsl = getGlslDifferences();
+        return "\n    vec4 " + funcName + "() {\n      return " + glsl.texture2D + "(" + texName + ", halfCR);\n    }\n  ";
+    }
+    function getSamplerScalar(inputInfo) {
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        if (inputInfo.shapeInfo.isUniform) {
+            return "float " + funcName + "() {return " + texName + ";}";
+        }
+        var _a = inputInfo.shapeInfo.texShape, texNumR = _a[0], texNumC = _a[1];
+        if (texNumR === 1 && texNumC === 1) {
+            return "\n      float " + funcName + "() {\n        return sampleTexture(" + texName + ", halfCR);\n      }\n    ";
+        }
+        var _b = inputInfo.shapeInfo.texShape, tNumR = _b[0], tNumC = _b[1];
+        var offset = getFlatOffsetUniformName(texName);
+        return "\n    float " + funcName + "() {\n      vec2 uv = uvFromFlat(" + tNumR + ", " + tNumC + ", " + offset + ");\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    function getPackedSampler1D(inputInfo) {
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var texShape = inputInfo.shapeInfo.texShape;
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        var glsl = getGlslDifferences();
+        return "\n    vec4 " + funcName + "(int index) {\n      vec2 uv = packedUVfrom1D(\n        " + packedTexShape[0] + ", " + packedTexShape[1] + ", index);\n      return " + glsl.texture2D + "(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSampler1D(inputInfo) {
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        if (inputInfo.shapeInfo.isUniform) {
+            // Uniform arrays will be less than 65505 (no risk of float16 overflow).
+            return "\n      float " + funcName + "(int index) {\n        " + getUniformSampler(inputInfo) + "\n      }\n    ";
+        }
+        var texShape = inputInfo.shapeInfo.texShape;
+        var tNumR = texShape[0];
+        var tNumC = texShape[1];
+        if (tNumC === 1 && tNumR === 1) {
+            return "\n      float " + funcName + "(int index) {\n        return sampleTexture(" + texName + ", halfCR);\n      }\n    ";
+        }
+        var offset = getFlatOffsetUniformName(texName);
+        if (tNumC === 1) {
+            return "\n      float " + funcName + "(int index) {\n        vec2 uv = vec2(0.5, (float(index + " + offset + ") + 0.5) / " + tNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        if (tNumR === 1) {
+            return "\n      float " + funcName + "(int index) {\n        vec2 uv = vec2((float(index + " + offset + ") + 0.5) / " + tNumC + ".0, 0.5);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        return "\n    float " + funcName + "(int index) {\n      vec2 uv = uvFromFlat(" + tNumR + ", " + tNumC + ", index + " + offset + ");\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    function getPackedSampler2D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var texShape = inputInfo.shapeInfo.texShape;
+        var texNumR = texShape[0];
+        var texNumC = texShape[1];
+        var glsl = getGlslDifferences();
+        if (texShape != null && tf.util.arraysEqual(shape, texShape)) {
+            return "\n      vec4 " + funcName + "(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(" + texNumC + ".0, " + texNumR + ".0);\n\n        return " + glsl.texture2D + "(" + texName + ", uv);\n      }\n    ";
+        }
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        var valuesPerRow = Math.ceil(shape[1] / 2);
+        return "\n    vec4 " + funcName + "(int row, int col) {\n      vec2 uv = packedUVfrom2D(" + valuesPerRow + ", " + packedTexShape[0] + ", " + packedTexShape[1] + ", row, col);\n      return " + glsl.texture2D + "(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSampler2D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var texShape = inputInfo.shapeInfo.texShape;
+        if (texShape != null && tf.util.arraysEqual(shape, texShape)) {
+            var texNumR_1 = texShape[0];
+            var texNumC_1 = texShape[1];
+            return "\n    float " + funcName + "(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(" + texNumC_1 + ".0, " + texNumR_1 + ".0);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+        }
+        var _a = tf.util.squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+        var squeezedShape = newShape;
+        if (squeezedShape.length < shape.length) {
+            var newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+            var params = ['row', 'col'];
+            return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
+        }
+        if (inputInfo.shapeInfo.isUniform) {
+            // Uniform arrays will be less than 65505 (no risk of float16 overflow).
+            return "\n      float " + funcName + "(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(" + shape[1] + ", 1)));\n        " + getUniformSampler(inputInfo) + "\n      }\n    ";
+        }
+        var texNumR = texShape[0];
+        var texNumC = texShape[1];
+        var offset = getFlatOffsetUniformName(texName);
+        if (texNumC === 1) {
+            // index is used directly as physical (no risk of float16 overflow).
+            return "\n    float " + funcName + "(int row, int col) {\n      float index = dot(vec3(row, col, " + offset + "), vec3(" + shape[1] + ", 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / " + texNumR + ".0);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+        }
+        if (texNumR === 1) {
+            // index is used directly as physical (no risk of float16 overflow).
+            return "\n    float " + funcName + "(int row, int col) {\n      float index = dot(vec3(row, col, " + offset + "), vec3(" + shape[1] + ", 1, 1));\n      vec2 uv = vec2((index + 0.5) / " + texNumC + ".0, 0.5);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+        }
+        return "\n  float " + funcName + "(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * " + shape[1] + " + col + " + offset + ";\n    vec2 uv = uvFromFlat(" + texNumR + ", " + texNumC + ", index);\n    return sampleTexture(" + texName + ", uv);\n  }\n";
+    }
+    function getPackedSampler3D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var texShape = inputInfo.shapeInfo.texShape;
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        if (shape[0] === 1) {
+            var squeezedShape = shape.slice(1);
+            var keptDims = [1, 2];
+            var newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+            var params = ['b', 'row', 'col'];
+            return "\n        " + getPackedSamplerFromInInfo(newInputInfo) + "\n        vec4 " + funcName + "(int b, int row, int col) {\n          return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n        }\n      ";
+        }
+        var texNumR = packedTexShape[0];
+        var texNumC = packedTexShape[1];
+        var valuesPerRow = Math.ceil(shape[2] / 2);
+        var texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);
+        var glsl = getGlslDifferences();
+        return "\n    vec4 " + funcName + "(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        " + texNumR + ", " + texNumC + ", " + texelsInBatch + ", " + valuesPerRow + ", b, row, col);\n      return " + glsl.texture2D + "(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSampler3D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var stride0 = shape[1] * shape[2];
+        var stride1 = shape[2];
+        var _a = tf.util.squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+        var squeezedShape = newShape;
+        if (squeezedShape.length < shape.length) {
+            var newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+            var params = ['row', 'col', 'depth'];
+            return "\n        " + getSamplerFromInInfo(newInputInfo) + "\n        float " + funcName + "(int row, int col, int depth) {\n          return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n        }\n      ";
+        }
+        if (inputInfo.shapeInfo.isUniform) {
+            // Uniform arrays will be less than 65505 (no risk of float16 overflow).
+            return "\n      float " + funcName + "(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(" + stride0 + ", " + stride1 + ", 1)));\n        " + getUniformSampler(inputInfo) + "\n      }\n    ";
+        }
+        var texShape = inputInfo.shapeInfo.texShape;
+        var texNumR = texShape[0];
+        var texNumC = texShape[1];
+        var flatOffset = inputInfo.shapeInfo.flatOffset;
+        if (texNumC === stride0 && flatOffset == null) {
+            // texC is used directly as physical (no risk of float16 overflow).
+            return "\n        float " + funcName + "(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(" + stride1 + ", 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(" + texNumC + ".0, " + texNumR + ".0);\n          return sampleTexture(" + texName + ", uv);\n        }\n      ";
+        }
+        if (texNumC === stride1 && flatOffset == null) {
+            // texR is used directly as physical (no risk of float16 overflow).
+            return "\n    float " + funcName + "(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(" + shape[1] + ", 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + texNumC + ".0, " + texNumR + ".0);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+        }
+        var offset = getFlatOffsetUniformName(texName);
+        return "\n      float " + funcName + "(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * " + stride0 + " + col * " + stride1 + " + depth + " + offset + ";\n        vec2 uv = uvFromFlat(" + texNumR + ", " + texNumC + ", index);\n        return sampleTexture(" + texName + ", uv);\n      }\n  ";
+    }
+    function getPackedSamplerND(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var rank = shape.length;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var texShape = inputInfo.shapeInfo.texShape;
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        var texNumR = packedTexShape[0];
+        var texNumC = packedTexShape[1];
+        var valuesPerRow = Math.ceil(shape[rank - 1] / 2);
+        var texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);
+        var params = "int b, int row, int col";
+        var index = "b * " + texelsInBatch + " + (row / 2) * " + valuesPerRow + " + (col / 2)";
+        for (var b = 2; b < rank - 1; b++) {
+            params = "int b" + b + ", " + params;
+            texelsInBatch *= shape[rank - b - 1];
+            index = "b" + b + " * " + texelsInBatch + " + " + index;
+        }
+        var glsl = getGlslDifferences();
+        return "\n    vec4 " + funcName + "(" + params + ") {\n      int index = " + index + ";\n      int texR = index / " + texNumC + ";\n      int texC = index - texR * " + texNumC + ";\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + texNumC + ", " + texNumR + ");\n      return " + glsl.texture2D + "(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSampler4D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var stride2 = shape[3];
+        var stride1 = shape[2] * stride2;
+        var stride0 = shape[1] * stride1;
+        var _a = tf.util.squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+        if (newShape.length < shape.length) {
+            var newInputInfo = squeezeInputInfo(inputInfo, newShape);
+            var params = ['row', 'col', 'depth', 'depth2'];
+            return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
+        }
+        if (inputInfo.shapeInfo.isUniform) {
+            // Uniform arrays will be less than 65505 (no risk of float16 overflow).
+            return "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(" + stride0 + ", " + stride1 + ", " + stride2 + ", 1)));\n        " + getUniformSampler(inputInfo) + "\n      }\n    ";
+        }
+        var flatOffset = inputInfo.shapeInfo.flatOffset;
+        var texShape = inputInfo.shapeInfo.texShape;
+        var texNumR = texShape[0];
+        var texNumC = texShape[1];
+        if (texNumC === stride0 && flatOffset == null) {
+            // texC is used directly as physical (no risk of float16 overflow).
+            return "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(" + stride1 + ", " + stride2 + ", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        if (texNumC === stride2 && flatOffset == null) {
+            // texR is used directly as physical (no risk of float16 overflow).
+            return "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(" + shape[1] * shape[2] + ", " + shape[2] + ", 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        var offset = getFlatOffsetUniformName(texName);
+        return "\n    float " + funcName + "(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * " + stride0 + " + col * " + stride1 + " +\n          depth * " + stride2 + " + depth2;\n      vec2 uv = uvFromFlat(" + texNumR + ", " + texNumC + ", index + " + offset + ");\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSampler5D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var stride3 = shape[4];
+        var stride2 = shape[3] * stride3;
+        var stride1 = shape[2] * stride2;
+        var stride0 = shape[1] * stride1;
+        var _a = tf.util.squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+        if (newShape.length < shape.length) {
+            var newInputInfo = squeezeInputInfo(inputInfo, newShape);
+            var params = ['row', 'col', 'depth', 'depth2', 'depth3'];
+            return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
+        }
+        if (inputInfo.shapeInfo.isUniform) {
+            // Uniform arrays will be less than 65505 (no risk of float16 overflow).
+            return "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + stride0 + ", " + stride1 + ", " + stride2 + ", " + stride3 + ")) +\n          depth3;\n        " + getUniformSampler(inputInfo) + "\n      }\n    ";
+        }
+        var flatOffset = inputInfo.shapeInfo.flatOffset;
+        var texShape = inputInfo.shapeInfo.texShape;
+        var texNumR = texShape[0];
+        var texNumC = texShape[1];
+        if (texNumC === stride0 && flatOffset == null) {
+            // texC is used directly as physical (no risk of float16 overflow).
+            return "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(" + stride1 + ", " + stride2 + ", " + stride3 + ", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        if (texNumC === stride3 && flatOffset == null) {
+            // texR is used directly as physical (no risk of float16 overflow).
+            return "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + shape[1] * shape[2] * shape[3] + ",\n               " + shape[2] * shape[3] + ", " + shape[3] + ", 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        var offset = getFlatOffsetUniformName(texName);
+        return "\n    float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * " + stride0 + " + col * " + stride1 + " + depth * " + stride2 + " +\n          depth2 * " + stride3 + " + depth3 + " + offset + ";\n      vec2 uv = uvFromFlat(" + texNumR + ", " + texNumC + ", index);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSampler6D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var _a = tf.util.squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+        if (newShape.length < shape.length) {
+            var newInputInfo = squeezeInputInfo(inputInfo, newShape);
+            var params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];
+            return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
+        }
+        var stride4 = shape[5];
+        var stride3 = shape[4] * stride4;
+        var stride2 = shape[3] * stride3;
+        var stride1 = shape[2] * stride2;
+        var stride0 = shape[1] * stride1;
+        if (inputInfo.shapeInfo.isUniform) {
+            // Uniform arrays will be less than 65505 (no risk of float16 overflow).
+            return "\n      float " + funcName + "(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + stride0 + ", " + stride1 + ", " + stride2 + ", " + stride3 + ")) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(" + stride4 + ", 1)));\n        " + getUniformSampler(inputInfo) + "\n      }\n    ";
+        }
+        var flatOffset = inputInfo.shapeInfo.flatOffset;
+        var texShape = inputInfo.shapeInfo.texShape;
+        var texNumR = texShape[0];
+        var texNumC = texShape[1];
+        if (texNumC === stride0 && flatOffset == null) {
+            // texC is used directly as physical (no risk of float16 overflow).
+            return "\n      float " + funcName + "(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(" + stride1 + ", " + stride2 + ", " + stride3 + ", " + stride4 + ")) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        if (texNumC === stride4 && flatOffset == null) {
+            // texR is used directly as physical (no risk of float16 overflow).
+            return "\n      float " + funcName + "(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(" + shape[1] * shape[2] * shape[3] * shape[4] + ",\n               " + shape[2] * shape[3] * shape[4] + ",\n               " + shape[3] * shape[4] + ",\n               " + shape[4] + ")) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        var offset = getFlatOffsetUniformName(texName);
+        return "\n    float " + funcName + "(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * " + stride0 + " + col * " + stride1 + " + depth * " + stride2 + " +\n          depth2 * " + stride3 + " + depth3 * " + stride4 + " + depth4 + " + offset + ";\n      vec2 uv = uvFromFlat(" + texNumR + ", " + texNumC + ", index);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    function getUniformSampler(inputInfo) {
+        var texName = inputInfo.name;
+        var inSize = tf.util.sizeFromShape(inputInfo.shapeInfo.logicalShape);
+        if (inSize < 2) {
+            return "return " + texName + ";";
+        }
+        return "\n    for (int i = 0; i < " + inSize + "; i++) {\n      if (i == index) {\n        return " + texName + "[i];\n      }\n    }\n  ";
+    }
+    function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {
+        var texName = inputInfo.name;
+        var texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
+        var funcName = 'get' + texFuncSnippet + 'AtOutCoords';
+        var inRank = inputInfo.shapeInfo.logicalShape.length;
+        var outRank = outShapeInfo.logicalShape.length;
+        var broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
+        var type = getCoordsDataType(outRank);
+        var rankDiff = outRank - inRank;
+        var coordsSnippet;
+        var fields = ['x', 'y', 'z', 'w', 'u', 'v'];
+        if (inRank === 0) {
+            coordsSnippet = '';
+        }
+        else if (outRank < 2 && broadcastDims.length >= 1) {
+            coordsSnippet = 'coords = 0;';
+        }
+        else {
+            coordsSnippet =
+                broadcastDims.map(function (d) { return "coords." + fields[d + rankDiff] + " = 0;"; })
+                    .join('\n');
+        }
+        var unpackedCoordsSnippet = '';
+        if (outRank < 2 && inRank > 0) {
+            unpackedCoordsSnippet = 'coords';
+        }
+        else {
+            unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape
+                .map(function (s, i) { return "coords." + fields[i + rankDiff]; })
+                .join(', ');
+        }
+        var output = "return outputValue;";
+        var inSize = tf.util.sizeFromShape(inputInfo.shapeInfo.logicalShape);
+        var isInputScalar = inSize === 1;
+        var outSize = tf.util.sizeFromShape(outShapeInfo.logicalShape);
+        var isOutputScalar = outSize === 1;
+        if (inRank === 1 && !isInputScalar && !isOutputScalar) {
+            output = "\n      return vec4(outputValue.xy, outputValue.xy);\n    ";
+        }
+        else if (isInputScalar && !isOutputScalar) {
+            if (outRank === 1) {
+                output = "\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      ";
+            }
+            else {
+                output = "\n        return vec4(outputValue.x);\n      ";
+            }
+        }
+        else if (broadcastDims.length) {
+            var rows = inRank - 2;
+            var cols = inRank - 1;
+            if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {
+                output = "return vec4(outputValue.x);";
+            }
+            else if (broadcastDims.indexOf(rows) > -1) {
+                output = "return vec4(outputValue.x, outputValue.y, " +
+                    "outputValue.x, outputValue.y);";
+            }
+            else if (broadcastDims.indexOf(cols) > -1) {
+                output = "return vec4(outputValue.xx, outputValue.zz);";
+            }
+        }
+        return "\n    vec4 " + funcName + "() {\n      " + type + " coords = getOutputCoords();\n      " + coordsSnippet + "\n      vec4 outputValue = get" + texFuncSnippet + "(" + unpackedCoordsSnippet + ");\n      " + output + "\n    }\n  ";
+    }
+    function getSamplerAtOutputCoords(inputInfo, outShapeInfo) {
+        var texName = inputInfo.name;
+        var texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
+        var funcName = 'get' + texFuncSnippet + 'AtOutCoords';
+        var outTexShape = outShapeInfo.texShape;
+        var inTexShape = inputInfo.shapeInfo.texShape;
+        var inRank = inputInfo.shapeInfo.logicalShape.length;
+        var outRank = outShapeInfo.logicalShape.length;
+        if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&
+            inputInfo.shapeInfo.flatOffset == null &&
+            tf.util.arraysEqual(inTexShape, outTexShape)) {
+            return "\n      float " + funcName + "() {\n        return sampleTexture(" + texName + ", resultUV);\n      }\n    ";
+        }
+        var type = getCoordsDataType(outRank);
+        var broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
+        var rankDiff = outRank - inRank;
+        var coordsSnippet;
+        var fields = ['x', 'y', 'z', 'w', 'u', 'v'];
+        if (inRank === 0) {
+            coordsSnippet = '';
+        }
+        else if (outRank < 2 && broadcastDims.length >= 1) {
+            coordsSnippet = 'coords = 0;';
+        }
+        else {
+            coordsSnippet =
+                broadcastDims.map(function (d) { return "coords." + fields[d + rankDiff] + " = 0;"; })
+                    .join('\n');
+        }
+        var unpackedCoordsSnippet = '';
+        if (outRank < 2 && inRank > 0) {
+            unpackedCoordsSnippet = 'coords';
+        }
+        else {
+            unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape
+                .map(function (s, i) { return "coords." + fields[i + rankDiff]; })
+                .join(', ');
+        }
+        return "\n    float " + funcName + "() {\n      " + type + " coords = getOutputCoords();\n      " + coordsSnippet + "\n      return get" + texFuncSnippet + "(" + unpackedCoordsSnippet + ");\n    }\n  ";
+    }
+    function getCoordsDataType(rank) {
+        if (rank <= 1) {
+            return 'int';
+        }
+        else if (rank === 2) {
+            return 'ivec2';
+        }
+        else if (rank === 3) {
+            return 'ivec3';
+        }
+        else if (rank === 4) {
+            return 'ivec4';
+        }
+        else if (rank === 5) {
+            return 'ivec5';
+        }
+        else if (rank === 6) {
+            return 'ivec6';
+        }
+        else {
+            throw Error("GPU for rank " + rank + " is not yet supported");
+        }
+    }
+    /** Returns a new input info (a copy) that has a squeezed logical shape. */
+    function squeezeInputInfo(inInfo, squeezedShape) {
+        // Deep copy.
+        var newInputInfo = JSON.parse(JSON.stringify(inInfo));
+        newInputInfo.shapeInfo.logicalShape = squeezedShape;
+        return newInputInfo;
+    }
+    function getSqueezedParams(params, keptDims) {
+        return keptDims.map(function (d) { return params[d]; }).join(', ');
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function compileProgram(gpgpu, program, inputs, output) {
+        var userCode = program.userCode;
+        var inputInfos = inputs.map(function (input, i) {
+            var shapeInfo = {
+                logicalShape: input.shape,
+                texShape: input.isUniform ? null : input.texData.texShape,
+                isUniform: input.isUniform,
+                isPacked: input.isUniform ? false : input.texData.isPacked,
+                flatOffset: null
+            };
+            if (input.texData != null && input.texData.slice != null &&
+                input.texData.slice.flatOffset > 0) {
+                shapeInfo.flatOffset = input.texData.slice.flatOffset;
+            }
+            return { name: program.variableNames[i], shapeInfo: shapeInfo };
+        });
+        var inShapeInfos = inputInfos.map(function (x) { return x.shapeInfo; });
+        var outShapeInfo = {
+            logicalShape: output.shape,
+            texShape: output.texData.texShape,
+            isUniform: false,
+            isPacked: output.texData.isPacked,
+            flatOffset: null
+        };
+        var source = makeShader(inputInfos, outShapeInfo, userCode, program.packedInputs);
+        var webGLProgram = gpgpu.createProgram(source);
+        // Add special uniforms (NAN, INFINITY)
+        var infLoc = null;
+        var nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);
+        if (tf.env().getNumber('WEBGL_VERSION') === 1) {
+            infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);
+        }
+        // Add user-defined uniforms
+        var uniformLocations = {};
+        for (var i = 0; i < program.variableNames.length; i++) {
+            var varName = program.variableNames[i];
+            var shouldThrow = false;
+            uniformLocations[varName] =
+                gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);
+            uniformLocations["offset" + varName] =
+                gpgpu.getUniformLocation(webGLProgram, "offset" + varName, shouldThrow);
+        }
+        return {
+            program: program,
+            source: source,
+            webGLProgram: webGLProgram,
+            uniformLocations: uniformLocations,
+            inShapeInfos: inShapeInfos,
+            outShapeInfo: outShapeInfo,
+            infLoc: infLoc,
+            nanLoc: nanLoc,
+        };
+    }
+    function validateBinaryAndProgram(shapeInfos, inputs) {
+        if (shapeInfos.length !== inputs.length) {
+            throw Error("Binary was compiled with " + shapeInfos.length + " inputs, but " +
+                ("was executed with " + inputs.length + " inputs"));
+        }
+        shapeInfos.forEach(function (s, i) {
+            var shapeA = s.logicalShape;
+            var input = inputs[i];
+            var shapeB = input.shape;
+            if (!tf.util.arraysEqual(shapeA, shapeB)) {
+                throw Error("Binary was compiled with different shapes than " +
+                    ("the current args. Shapes " + shapeA + " and " + shapeB + " must match"));
+            }
+            // The input is uploaded as uniform.
+            if (s.isUniform && input.isUniform) {
+                return;
+            }
+            var texShapeA = s.texShape;
+            var texShapeB = input.isUniform ? null : input.texData.texShape;
+            if (!tf.util.arraysEqual(texShapeA, texShapeB)) {
+                throw Error("Binary was compiled with different texture shapes than the" +
+                    (" current args. Shape " + texShapeA + " and " + texShapeB + " must match"));
+            }
+        });
+    }
+    function runProgram(gpgpu, binary, inputs, output, customSetup) {
+        validateBinaryAndProgram(binary.inShapeInfos, inputs);
+        validateBinaryAndProgram([binary.outShapeInfo], [output]);
+        var outTex = output.texData.texture;
+        var outTexShape = output.texData.texShape;
+        if (output.texData.isPacked) {
+            gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);
+        }
+        else {
+            gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);
+        }
+        gpgpu.setProgram(binary.webGLProgram);
+        // Set special uniforms (NAN, INFINITY)
+        if (tf.env().getNumber('WEBGL_VERSION') === 1) {
+            if (binary.infLoc !== null) {
+                gpgpu.gl.uniform1f(binary.infLoc, Infinity);
+            }
+        }
+        if (binary.nanLoc !== null) {
+            gpgpu.gl.uniform1f(binary.nanLoc, NaN);
+        }
+        // Set user-defined inputs
+        inputs.forEach(function (input, i) {
+            var varName = binary.program.variableNames[i];
+            var varLoc = binary.uniformLocations[varName];
+            var varOffsetLoc = binary.uniformLocations["offset" + varName];
+            if (varLoc == null) {
+                // The compiler inferred that this variable is not used in this shader.
+                return;
+            }
+            if (input.isUniform) {
+                // Upload the values of the tensor as uniform.
+                if (tf.util.sizeFromShape(input.shape) < 2) {
+                    gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);
+                }
+                else {
+                    var vals = input.uniformValues;
+                    if (!(vals instanceof Float32Array)) {
+                        vals = new Float32Array(vals);
+                    }
+                    gpgpu.gl.uniform1fv(varLoc, vals);
+                }
+                return;
+            }
+            // If the input was sliced, upload the flat offset index.
+            if (input.texData.slice != null && varOffsetLoc != null) {
+                gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);
+            }
+            gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);
+        });
+        if (customSetup != null) {
+            customSetup(gpgpu, binary.webGLProgram);
+        }
+        gpgpu.executeProgram();
+    }
+    function makeShaderKey(program, inputs, output) {
+        var keyInputs = '';
+        inputs.concat(output).forEach(function (x) {
+            var hasOffset = x.texData != null && x.texData.slice != null &&
+                x.texData.slice.flatOffset > 0;
+            var texShape = x.isUniform ? 'uniform' : x.texData.texShape;
+            keyInputs += x.shape + "_" + texShape + "_" + hasOffset;
+        });
+        var keyUserCode = program.userCode;
+        var key = program.constructor.name;
+        // Fast string concat. See https://jsperf.com/string-concatenation/14.
+        key += '_' + keyInputs + '_' + keyUserCode;
+        return key;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function simpleAbsImpl(vals) {
+        const resultValues = new Float32Array(vals.length);
+        for (let i = 0; i < vals.length; ++i) {
+            resultValues[i] = Math.abs(vals[i]);
+        }
+        return resultValues;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Template that creates implementation for binary ops. Supports broadcast.
+     */
+    function createSimpleBinaryKernelImpl(op) {
+        return (aShape, bShape, aVals, bVals, dtype) => {
+            const newShape = tf.backend_util.assertAndGetBroadcastShape(aShape, bShape);
+            const resultRank = newShape.length;
+            const resultStrides = tf.util.computeStrides(newShape);
+            const resultSize = tf.util.sizeFromShape(newShape);
+            const result = tf.util.getTypedArrayFromDType(dtype, resultSize);
+            const aRank = aShape.length;
+            const bRank = bShape.length;
+            const aStrides = tf.util.computeStrides(aShape);
+            const bStrides = tf.util.computeStrides(bShape);
+            const aBroadcastDims = tf.backend_util.getBroadcastDims(aShape, newShape);
+            const bBroadcastDims = tf.backend_util.getBroadcastDims(bShape, newShape);
+            if (aBroadcastDims.length + bBroadcastDims.length === 0) {
+                for (let i = 0; i < result.length; ++i) {
+                    result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);
+                }
+            }
+            else {
+                for (let i = 0; i < result.length; ++i) {
+                    const loc = tf.util.indexToLoc(i, resultRank, resultStrides);
+                    const aLoc = loc.slice(-aRank);
+                    aBroadcastDims.forEach(d => aLoc[d] = 0);
+                    const aIndex = tf.util.locToIndex(aLoc, aRank, aStrides);
+                    const bLoc = loc.slice(-bRank);
+                    bBroadcastDims.forEach(d => bLoc[d] = 0);
+                    const bIndex = tf.util.locToIndex(bLoc, bRank, bStrides);
+                    result[i] = op(aVals[aIndex], bVals[bIndex]);
+                }
+            }
+            return [result, newShape];
+        };
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {
+        const weightsSize = tf.util.sizeFromShape(weightsShape);
+        const outVals = tf.util.makeZerosTypedArray(size, weightsDtype);
+        for (let i = 0; i < xVals.length; i++) {
+            const value = xVals[i];
+            if (value < 0) {
+                throw new Error('Input x must be non-negative!');
+            }
+            if (value >= size) {
+                continue;
+            }
+            if (weightsSize > 0) {
+                outVals[value] += weightsVals[i];
+            }
+            else {
+                outVals[value] += 1;
+            }
+        }
+        return outVals;
+    }
+    function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {
+        const numRows = xBuf.shape[0];
+        const numCols = xBuf.shape[1];
+        const outBuf = tf.buffer([numRows, size], weightsBuf.dtype);
+        for (let i = 0; i < numRows; i++) {
+            for (let j = 0; j < numCols; j++) {
+                const value = xBuf.get(i, j);
+                if (value < 0) {
+                    throw new Error('Input x must be non-negative!');
+                }
+                if (value >= size) {
+                    continue;
+                }
+                if (binaryOutput) {
+                    outBuf.set(1, i, value);
+                }
+                else {
+                    if (weightsBuf.size > 0) {
+                        outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value);
+                    }
+                    else {
+                        outBuf.set(outBuf.get(i, value) + 1, i, value);
+                    }
+                }
+            }
+        }
+        return outBuf;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Template that creates implementation for unary op.
+     */
+    function createSimpleUnaryImpl(op) {
+        return (values, dtype, attrs) => {
+            const newValues = tf.util.getTypedArrayFromDType(dtype, values.length);
+            for (let i = 0; i < values.length; ++i) {
+                newValues[i] = op(values[i], attrs);
+            }
+            return newValues;
+        };
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function concatImpl(inputs, outShape, dtype, simplyConcat) {
+        const outVals = tf.util.getArrayFromDType(dtype, tf.util.sizeFromShape(outShape));
+        if (simplyConcat && dtype !== 'string') {
+            // Use built-in TypedArray.set() method for speed.
+            let offset = 0;
+            inputs.forEach(input => {
+                const size = tf.util.sizeFromShape(input.shape);
+                outVals.set(input.vals, offset);
+                offset += size;
+            });
+        }
+        else {
+            let colOffset = 0;
+            inputs.forEach(input => {
+                const decodedData = dtype === 'string' ?
+                    tf.backend_util.fromUint8ToStringArray(input.vals) :
+                    input.vals;
+                let tIdx = 0;
+                for (let row = 0; row < input.shape[0]; ++row) {
+                    const resIdx = row * outShape[1] + colOffset;
+                    for (let col = 0; col < input.shape[1]; ++col) {
+                        outVals[resIdx + col] = decodedData[tIdx++];
+                    }
+                }
+                colOffset += input.shape[1];
+            });
+        }
+        return outVals;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const equalImpl = createSimpleBinaryKernelImpl((a, b) => (a === b) ? 1 : 0);
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) {
+        const outBuf = tf.buffer([numSlices, sliceSize], dtype);
+        for (let i = 0; i < numSlices; i++) {
+            const index = [];
+            let flattenIndex = 0;
+            for (let j = 0; j < sliceRank; j++) {
+                const dim = indicesData[i * sliceRank + j];
+                flattenIndex += dim * strides[j];
+                index.push(dim);
+            }
+            if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {
+                throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`);
+            }
+            for (let k = 0; k < sliceSize; k++) {
+                outBuf.values[i * sliceSize + k] =
+                    paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));
+            }
+        }
+        return outBuf;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {
+        const outBuf = tf.buffer(flattenOutputShape, xBuf.dtype);
+        for (let i = 0; i < outBuf.size; ++i) {
+            const newLoc = outBuf.indexToLoc(i);
+            const originalLoc = newLoc.slice();
+            const batchIdx = originalLoc[0];
+            const indicesIdx = originalLoc[2];
+            const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);
+            originalLoc[2] = indicesBuf.values[indicesIndex];
+            const originalIndex = xBuf.locToIndex(originalLoc);
+            outBuf.values[i] = xBuf.values[originalIndex];
+        }
+        return outBuf;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const greaterImpl = createSimpleBinaryKernelImpl((a, b) => (a > b) ? 1 : 0);
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const greaterEqualImpl = createSimpleBinaryKernelImpl((a, b) => (a >= b) ? 1 : 0);
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const lessImpl = createSimpleBinaryKernelImpl((a, b) => (a < b) ? 1 : 0);
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const lessEqualImpl = createSimpleBinaryKernelImpl((a, b) => (a <= b) ? 1 : 0);
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function linSpaceImpl(start, stop, num) {
+        const step = (stop - start) / (num - 1);
+        const values = tf.util.makeZerosTypedArray(num, 'float32');
+        values[0] = start;
+        for (let i = 1; i < values.length; i++) {
+            values[i] = values[i - 1] + step;
+        }
+        return values;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function maxImpl(aVals, reduceSize, outShape, dtype) {
+        const vals = tf.util.getTypedArrayFromDType(dtype, tf.util.sizeFromShape(outShape));
+        for (let i = 0; i < vals.length; ++i) {
+            const offset = i * reduceSize;
+            let max = aVals[offset];
+            for (let j = 0; j < reduceSize; ++j) {
+                const value = aVals[offset + j];
+                if (Number.isNaN(value) ||
+                    value > max) { // comparison with NaN always return false
+                    max = value;
+                }
+            }
+            vals[i] = max;
+        }
+        return vals;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const maximumImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => Math.max(aValue, bValue)));
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const minimumImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => Math.min(aValue, bValue)));
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const multiplyImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function negImpl(xVals, xShape, xDtype) {
+        const minusOne = tf.util.createScalarValue(-1, xDtype);
+        return multiplyImpl([], xShape, minusOne, xVals, xDtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const notEqualImpl = createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function transposeImpl(xVals, xShape, dtype, perm, newShape) {
+        const xRank = xShape.length;
+        const xSize = tf.util.sizeFromShape(xShape);
+        const xStrides = tf.util.computeStrides(xShape);
+        const newStrides = tf.util.computeStrides(newShape);
+        const result = tf.util.getTypedArrayFromDType(dtype, tf.util.sizeFromShape(newShape));
+        for (let i = 0; i < xSize; ++i) {
+            const loc = tf.util.indexToLoc(i, xRank, xStrides);
+            // Permute location.
+            const newLoc = new Array(loc.length);
+            for (let i = 0; i < newLoc.length; i++) {
+                newLoc[i] = loc[perm[i]];
+            }
+            const newIndex = tf.util.locToIndex(newLoc, xRank, newStrides);
+            result[newIndex] = xVals[i];
+        }
+        return result;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function prodImpl(xShape, xDtype, xVals, reductionAxes) {
+        const [outShape, reduceShape] = tf.backend_util.computeOutAndReduceShapes(xShape, reductionAxes);
+        const outDtype = tf.upcastType(xDtype, 'int32');
+        const outVals = tf.util.makeZerosTypedArray(tf.util.sizeFromShape(outShape), outDtype);
+        const reduceSize = tf.util.sizeFromShape(reduceShape);
+        for (let i = 0; i < outVals.length; ++i) {
+            const offset = i * reduceSize;
+            let prod = 1;
+            for (let j = 0; j < reduceSize; ++j) {
+                prod *= xVals[offset + j];
+            }
+            outVals[i] = prod;
+        }
+        return { outVals, outShape, outDtype };
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function rangeImpl(start, stop, step, dtype) {
+        const sameStartStop = start === stop;
+        const increasingRangeNegativeStep = start < stop && step < 0;
+        const decreasingRangePositiveStep = stop < start && step > 1;
+        if (sameStartStop || increasingRangeNegativeStep ||
+            decreasingRangePositiveStep) {
+            return tf.util.makeZerosTypedArray(0, dtype);
+        }
+        const numElements = Math.abs(Math.ceil((stop - start) / step));
+        const values = tf.util.makeZerosTypedArray(numElements, dtype);
+        if (stop < start && step === 1) {
+            // Auto adjust the step's sign if it hasn't been set
+            // (or was set to 1)
+            step = -1;
+        }
+        values[0] = start;
+        for (let i = 1; i < values.length; i++) {
+            values[i] = values[i - 1] + step;
+        }
+        return values;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function sliceImpl(vals, begin, size, shape, dtype) {
+        const isContinous = tf.slice_util.isSliceContinous(shape, begin, size);
+        const length = tf.util.sizeFromShape(size);
+        const xStrides = tf.util.computeStrides(shape);
+        if (isContinous) {
+            const flatOffset = tf.slice_util.computeFlatOffset(begin, xStrides);
+            if (dtype === 'string') {
+                return vals.slice(flatOffset, flatOffset + length);
+            }
+            return vals.subarray(flatOffset, flatOffset + length);
+        }
+        const decodedData = dtype === 'string' ?
+            tf.backend_util.fromUint8ToStringArray(vals) :
+            vals;
+        const inBuf = tf.buffer(shape, dtype, decodedData);
+        const outBuf = tf.buffer(size, dtype);
+        for (let i = 0; i < outBuf.size; ++i) {
+            const outLoc = outBuf.indexToLoc(i);
+            const inLoc = outLoc.map((idx, j) => idx + begin[j]);
+            outBuf.set(inBuf.get(...inLoc), ...outLoc);
+        }
+        if (dtype === 'string') {
+            return tf.backend_util.fromStringArrayToUint8(outBuf.values);
+        }
+        return outBuf.values;
+    }
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) {
+        const indicesCount = indicesShape[0];
+        const denseRows = denseShape[0];
+        const emptyRowIndicator = new Array(denseRows);
+        const reverseIndexMap = new Array(indicesCount);
+        const rank = indicesShape[1];
+        if (denseRows === 0) {
+            if (indicesCount !== 0) {
+                throw new Error(`Received SparseTensor with denseShape[0] = 0 but
+         indices.shape[0] = ${indicesCount}`);
+            }
+            const outputIndices = tf.util.getArrayFromDType(indicesDType, 0);
+            const outputValues = tf.util.getArrayFromDType(valuesDType, 0);
+            return [
+                outputIndices, [0, rank], outputValues, emptyRowIndicator, reverseIndexMap
+            ];
+        }
+        let rowsAreOrdered = true;
+        let lastIndicesRow = 0;
+        const csrOffset = new Array(denseRows).fill(0);
+        for (let i = 0; i < indicesCount; ++i) {
+            // indices is a 2d tensor with shape of [N, rank]
+            const row = indices[i * rank];
+            if (row < 0) {
+                throw new Error(`indices(${i}, 0) is invalid: ${row} < 0`);
+            }
+            if (row >= denseRows) {
+                throw new Error(`indices(${i}, 0) is invalid: ${row} >= ${denseRows}`);
+            }
+            ++csrOffset[row];
+            rowsAreOrdered = rowsAreOrdered && (row >= lastIndicesRow);
+            lastIndicesRow = row;
+        }
+        let allRowsFull = true;
+        for (let row = 0; row < denseRows; ++row) {
+            // csrOffset here describes the number of elements in this dense row
+            const rowEmpty = (csrOffset[row] === 0);
+            emptyRowIndicator[row] = rowEmpty;
+            allRowsFull = allRowsFull && !rowEmpty;
+            // In filled version, each row has at least one element.
+            csrOffset[row] = Math.max(csrOffset[row], 1);
+            // Update csrOffset to represent the number of elements up to and
+            // including denseRows + 1:
+            //  csrOffset[0] == #{elements of row 0}
+            //  csrOffset[1] == #{elements of row 1} + #{elements of row 0}
+            //  ..
+            //  csrOffset[i] == starting index for elements in row i + 1.
+            if (row > 0) {
+                csrOffset[row] += csrOffset[row - 1];
+            }
+        }
+        if (allRowsFull && rowsAreOrdered) {
+            const outputIndices = indices;
+            const outputValues = values;
+            for (let i = 0; i < indicesCount; ++i) {
+                reverseIndexMap[i] = i;
+            }
+            return [
+                outputIndices, [indicesCount, rank], outputValues, emptyRowIndicator,
+                reverseIndexMap
+            ];
+        }
+        else {
+            const fullIndicesCount = csrOffset[denseRows - 1];
+            const outputIndices = tf.util.getArrayFromDType(indicesDType, fullIndicesCount * rank);
+            const outputValues = tf.util.getArrayFromDType(valuesDType, fullIndicesCount);
+            const filledCount = new Array(denseRows).fill(0);
+            // Fill in values for rows that are not missing
+            for (let i = 0; i < indicesCount; ++i) {
+                // indices is a 2d tensor with shape of [N, rank]
+                const row = indices[i * rank];
+                const offset = filledCount[row];
+                const outputI = ((row === 0) ? 0 : csrOffset[row - 1]) + offset;
+                filledCount[row]++; // Increment the filled count for this row.
+                for (let j = 0; j < rank; ++j) {
+                    // indices and outputIndices are 2d tensors with shape of [N, rank]
+                    outputIndices[outputI * rank + j] = indices[i * rank + j];
+                }
+                outputValues[outputI] = values[i];
+                // We'll need this reverse index map to backprop correctly.
+                reverseIndexMap[i] = outputI;
+            }
+            // Fill in values for rows that are missing
+            for (let row = 0; row < denseRows; ++row) {
+                const rowCount = filledCount[row];
+                if (rowCount === 0) { // We haven't filled this row
+                    const startingIndex = (row === 0) ? 0 : csrOffset[row - 1];
+                    // Remaining index values were set to zero already.
+                    // Just need to set the row index in the right location.
+                    // outputIndices is a 2d tensor with shape of [N, rank]
+                    outputIndices[startingIndex * rank + 0] = row;
+                    for (let col = 1; col < rank; ++col) {
+                        outputIndices[startingIndex * rank + col] = 0;
+                    }
+                    outputValues[startingIndex] = defaultValue;
+                }
+            }
+            return [
+                outputIndices, [fullIndicesCount, rank], outputValues, emptyRowIndicator,
+                reverseIndexMap
+            ];
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) {
+        const denseSize = tf.util.sizeFromShape(inputShape);
+        const nnz = inputIndicesShape[0];
+        const outputRank = targetShape.length;
+        // Compute the output shape. Determine product of specified dimensions, and
+        // find the index of the unspecified one.
+        const outputShape = [];
+        let product = 1;
+        let unknownIndex = -1;
+        for (let d = 0; d < outputRank; ++d) {
+            const size = targetShape[d];
+            if (size === -1) {
+                if (unknownIndex !== -1) {
+                    throw new Error(`only one output dimension may be -1, not both ${unknownIndex} and ${d}`);
+                }
+                unknownIndex = d;
+                outputShape.push(1);
+            }
+            else {
+                if (size < 0) {
+                    throw new Error(`size ${d} must be non-negative, not ${size}`);
+                }
+                product *= size;
+                outputShape.push(size);
+            }
+        }
+        if (unknownIndex !== -1) {
+            if (product <= 0) {
+                throw new Error('reshape cannot infer the missing ' +
+                    'input size for an empty tensor unless all ' +
+                    'specified input sizes are non-zero');
+            }
+            const missing = Math.trunc(denseSize / product);
+            if (product * missing !== denseSize) {
+                throw new Error(`Input to reshape is a SparseTensor with ${denseSize}
+          dense values, but the requested shape requires a multiple of ${product}. inputShape=${inputShape} outputShape= ${outputShape}`);
+            }
+            outputShape[unknownIndex] = missing;
+        }
+        const outputSize = tf.util.sizeFromShape(outputShape);
+        if (outputSize !== denseSize) {
+            throw new Error(`Input to reshape is a tensor with ${denseSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`);
+        }
+        const inputRank = inputShape.length;
+        const inputStrides = [];
+        if (inputRank > 0) {
+            inputStrides[inputRank - 1] = 1;
+            for (let d = inputRank - 2; d >= 0; --d) {
+                inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];
+            }
+        }
+        const outputStrides = [];
+        if (outputRank > 0) {
+            outputStrides[outputRank - 1] = 1;
+            for (let d = outputRank - 2; d >= 0; --d) {
+                outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];
+            }
+        }
+        const newIndices = tf.util.getArrayFromDType(inputDType, nnz * outputRank);
+        for (let i = 0; i < nnz; ++i) {
+            let id = 0;
+            for (let j = 0; j < inputRank; ++j) {
+                // inputIndices is a 2d tensor with shape of [nnz, inputRank]
+                id += inputIndices[i * inputRank + j] * inputStrides[j];
+            }
+            for (let j = 0; j < outputRank; ++j) {
+                // newIndices is a 2d tensor with shape of [nnz, outputRank]
+                newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);
+                id %= outputStrides[j];
+            }
+        }
+        return [newIndices, [nnz, outputRank], outputShape];
+    }
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function sparseSegmentReductionImpl(input, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) {
+        const numIndices = indices.length;
+        if (numIndices !== segmentIds.length) {
+            throw new Error(`segmentIds and indices should have same size.`);
+        }
+        // Flatten the array to two dimensions
+        const inputFlat = [inputShape[0], input.length / inputShape[0]];
+        const numCol = inputFlat[1];
+        // Note that the current implementation assumes that segmentIds values are
+        // sorted.
+        const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;
+        const outputRows = lastSegmentIdPlusOne;
+        if (outputRows < 0) {
+            throw new Error(`segment ids must be >= 0`);
+        }
+        const outputShape = inputShape.slice();
+        outputShape[0] = outputRows;
+        const outputLength = outputShape.reduce((product, value) => product * value, 1);
+        // Output array is initialized with the value 0 by default.
+        const output = tf.util.getArrayFromDType(inputDType, outputLength);
+        // Note that we do not initialize the output buffer with a default value, so
+        // we need to explicitly set missing indices to the default value.
+        if (numIndices === 0) {
+            if (outputRows > 0) {
+                output.fill(defaultValue);
+            }
+            return [output, outputShape];
+        }
+        if (outputRows <= 0) {
+            throw new Error(`segment ids must be >= 0`);
+        }
+        let start = 0, end = 1;
+        // Index from which the output is not initialized.
+        let uninitializedIndex = 0;
+        let outIndex = segmentIds[start];
+        while (true) {
+            // We initialize nextIndex to 0 to avoid may be uninitialized warning
+            let nextIndex = 0;
+            if (end < numIndices) {
+                nextIndex = segmentIds[end];
+                if (outIndex === nextIndex) {
+                    ++end;
+                    continue;
+                }
+                // We have a new segment here.  Verify that the segment ids are growing.
+                if (outIndex >= nextIndex) {
+                    throw new Error(`segment ids are not increasing`);
+                }
+            }
+            if (outIndex < 0 || outIndex >= outputRows) {
+                throw new Error(`Segment id ${outIndex} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`);
+            }
+            // If there is a gap between two indices, we need to set that gap to the
+            // default value.
+            if (outIndex > uninitializedIndex) {
+                output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);
+            }
+            for (let i = start; i < end; ++i) {
+                const index = indices[i];
+                if (index < 0 || index >= inputFlat[0]) {
+                    throw new Error(`Bad: indices[${i}] == ${indices[i]} out of range [0, ${inputFlat[0]})`);
+                }
+                for (let j = 0; j < numCol; j++) {
+                    output[outIndex * numCol + j] += input[index * numCol + j];
+                }
+            }
+            if (isMean) {
+                for (let j = 0; j < numCol; j++) {
+                    output[outIndex * numCol + j] /= end - start;
+                }
+            }
+            start = end;
+            ++end;
+            uninitializedIndex = outIndex + 1;
+            outIndex = nextIndex;
+            if (end > numIndices) {
+                break;
+            }
+        }
+        // Fill the gap at the end with the default value.
+        if (uninitializedIndex < outputRows) {
+            output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);
+        }
+        return [output, outputShape];
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function stridedSliceImpl(outShape, xBuf, strides, begin) {
+        const outBuf = tf.buffer(outShape, xBuf.dtype);
+        for (let i = 0; i < outBuf.size; i++) {
+            const loc = outBuf.indexToLoc(i);
+            const newLoc = new Array(loc.length);
+            for (let j = 0; j < newLoc.length; j++) {
+                newLoc[j] = loc[j] * strides[j] + begin[j];
+            }
+            outBuf.set(xBuf.get(...newLoc), ...loc);
+        }
+        return outBuf;
+    }
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * The StringNGramsOp class creates ngrams from ragged string data.
+     * The constructor contains all attributes related to the operation such as
+     * padding widths and strings, and the compute function can be used to
+     * compute the ngrams for different ragged tensor inputs.
+     */
+    class StringNGramsOp {
+        constructor(separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) {
+            this.separator = tf.util.encodeString(separator);
+            this.nGramWidths = nGramWidths;
+            this.leftPad = tf.util.encodeString(leftPad);
+            this.rightPad = tf.util.encodeString(rightPad);
+            this.padWidth = padWidth;
+            this.preserveShort = preserveShortSequences;
+        }
+        getPadWidth(nGramWidth) {
+            // Ngrams can be padded with either a fixed pad width or a dynamic pad
+            // width depending on the 'padWidth' arg, but in no case should the padding
+            // ever be wider than 'nGramWidth' - 1.
+            return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);
+        }
+        getNumNGrams(length, nGramWidth) {
+            const padWidth = this.getPadWidth(nGramWidth);
+            return Math.max(0, ((length + 2 * padWidth) - nGramWidth) + 1);
+        }
+        createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) {
+            for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {
+                const padWidth = this.getPadWidth(nGramWidth);
+                const leftPadding = Math.max(0, padWidth - nGramIndex);
+                const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));
+                const numTokens = nGramWidth - (leftPadding + rightPadding);
+                const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);
+                // Calculate the total expected size of the nGram so we can reserve the
+                // correct amount of space in the string.
+                let nGramSize = 0;
+                // Size of the left padding.
+                nGramSize += leftPadding * this.leftPad.length;
+                // Size of the tokens.
+                for (let n = 0; n < numTokens; ++n) {
+                    nGramSize += data[dataStartIndex + n].length;
+                }
+                // Size of the right padding.
+                nGramSize += rightPadding * this.rightPad.length;
+                // Size of the separators.
+                const numSeparators = leftPadding + rightPadding + numTokens - 1;
+                nGramSize += numSeparators * this.separator.length;
+                // Build the nGram.
+                output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);
+                const nGram = output[outputStartIndex + nGramIndex];
+                let nextNGramIndex = 0;
+                const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value);
+                for (let n = 0; n < leftPadding; ++n) {
+                    appendToNGram(this.leftPad);
+                    appendToNGram(this.separator);
+                }
+                // Only output first numTokens - 1 pairs of data and separator
+                for (let n = 0; n < numTokens - 1; ++n) {
+                    appendToNGram(data[dataStartIndex + n]);
+                    appendToNGram(this.separator);
+                }
+                // Handle case when there are no tokens or no right padding as these
+                // can result in consecutive separators.
+                if (numTokens > 0) {
+                    // If we have tokens, then output last and then pair each separator
+                    // with the right padding that follows, to ensure nGram ends either with
+                    // the token or with the right pad.
+                    appendToNGram(data[dataStartIndex + numTokens - 1]);
+                    for (let n = 0; n < rightPadding; ++n) {
+                        appendToNGram(this.separator);
+                        appendToNGram(this.rightPad);
+                    }
+                }
+                else {
+                    // If we don't have tokens, then the last item inserted into the nGram
+                    // has been the separator from the left padding loop above. Hence,
+                    // output right pad and separator and make sure to finish with a
+                    // padding, not a separator.
+                    for (let n = 0; n < rightPadding - 1; ++n) {
+                        appendToNGram(this.rightPad);
+                        appendToNGram(this.separator);
+                    }
+                    appendToNGram(this.rightPad);
+                }
+            }
+        }
+        // Data and splits together form the definition of the ragged tensor,
+        // where data is 1 dimensional and contains the values of the tensor
+        // and splits denotes the indices at which each row starts.
+        compute(data, splits) {
+            // Validate that the splits are valid indices into data, only if there are
+            // splits specified.
+            const inputDataSize = data.length;
+            const splitsSize = splits.length;
+            if (splitsSize > 0) {
+                let prevSplit = splits[0];
+                if (prevSplit !== 0) {
+                    throw new Error(`First split value must be 0, got ${prevSplit}`);
+                }
+                for (let i = 1; i < splitsSize; ++i) {
+                    let validSplits = splits[i] >= prevSplit;
+                    validSplits = validSplits && (splits[i] <= inputDataSize);
+                    if (!validSplits) {
+                        throw new Error(`Invalid split value ${splits[i]}, must be in [${prevSplit}, ${inputDataSize}]`);
+                    }
+                    prevSplit = splits[i];
+                }
+                if (prevSplit !== inputDataSize) {
+                    throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);
+                }
+            }
+            const numBatchItems = splitsSize - 1;
+            const nGramsSplits = tf.util.getArrayFromDType('int32', splitsSize);
+            // If there is no data or size, return an empty ragged tensor.
+            if (inputDataSize === 0 || splitsSize === 0) {
+                const empty = new Array(inputDataSize);
+                for (let i = 0; i <= numBatchItems; ++i) {
+                    nGramsSplits[i] = 0;
+                }
+                return [empty, nGramsSplits];
+            }
+            nGramsSplits[0] = 0;
+            for (let i = 1; i <= numBatchItems; ++i) {
+                const length = splits[i] - splits[i - 1];
+                let numNGrams = 0;
+                this.nGramWidths.forEach((nGramWidth) => {
+                    numNGrams += this.getNumNGrams(length, nGramWidth);
+                });
+                if (this.preserveShort && length > 0 && numNGrams === 0) {
+                    numNGrams = 1;
+                }
+                nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams;
+            }
+            const nGrams = new Array(nGramsSplits[numBatchItems]);
+            for (let i = 0; i < numBatchItems; ++i) {
+                const splitIndex = splits[i];
+                let outputStartIdx = nGramsSplits[i];
+                this.nGramWidths.forEach((nGramWidth) => {
+                    const length = splits[i + 1] - splits[i];
+                    const numNGrams = this.getNumNGrams(length, nGramWidth);
+                    this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
+                    outputStartIdx += numNGrams;
+                });
+                // If we're preserving short sequences, check to see if no sequence was
+                // generated by comparing the current output start idx to the original
+                // one (nGramSplitsdata). If no ngrams were generated, then they will
+                // be equal (since we increment outputStartIdx by numNGrams every
+                // time we create a set of ngrams.)
+                if (this.preserveShort && outputStartIdx === nGramsSplits[i]) {
+                    const dataLength = splits[i + 1] - splits[i];
+                    // One legitimate reason to not have any ngrams when this.preserveShort
+                    // is true is if the sequence itself is empty. In that case, move on.
+                    if (dataLength === 0) {
+                        continue;
+                    }
+                    // We don't have to worry about dynamic padding sizes here: if padding
+                    // was dynamic, every sequence would have had sufficient padding to
+                    // generate at least one nGram.
+                    const nGramWidth = dataLength + 2 * this.padWidth;
+                    const numNGrams = 1;
+                    this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
+                }
+            }
+            return [nGrams, nGramsSplits];
+        }
+    }
+    function stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) {
+        return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences)
+            .compute(data, dataSplits);
+    }
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function split(str, delimiters, skipEmpty) {
+        if (!str.length) {
+            return [];
+        }
+        // When the delimiter is empty, the input is split into individual characters.
+        if (delimiters.length === 0) {
+            const result = new Array(str.length);
+            for (let i = 0; i < str.length; ++i) {
+                result[i] = str.subarray(i, i + 1);
+            }
+            return result;
+        }
+        // When there is one delimiter, the input is split only at that delimiter.
+        if (delimiters.length === 1) {
+            const delimiter = delimiters[0];
+            const result = [];
+            let f = str.indexOf(delimiter);
+            while (f !== -1) {
+                const token = str.subarray(0, f);
+                if (!skipEmpty || token.length !== 0) {
+                    result.push(token);
+                }
+                str = str.subarray(f + 1);
+                f = str.indexOf(delimiter);
+            }
+            if (!skipEmpty || str.length !== 0) {
+                result.push(str);
+            }
+            return result;
+        }
+        // When there are multiple delimiters, the input is split at every instance
+        // one of the delimiters appears.
+        const result = [];
+        let tokenStart = 0;
+        for (let i = 0; i < str.length + 1; i++) {
+            if ((i === str.length) || (delimiters.indexOf(str[i]) !== -1)) {
+                const token = str.subarray(tokenStart, i);
+                if (!skipEmpty || token.length !== 0) {
+                    result.push(token);
+                }
+                tokenStart = i + 1;
+            }
+        }
+        return result;
+    }
+    function stringSplitImpl(input, delimiter, skipEmpty) {
+        const batchSize = input.length;
+        // Empty delimiter means split the input character by character.
+        const tokens = [];
+        let outputSize = 0;
+        let maxNumEntries = 0;
+        const numIndices = new Array(batchSize);
+        for (let i = 0; i < batchSize; ++i) {
+            const parts = split(input[i], delimiter, skipEmpty);
+            const nEntries = parts.length;
+            numIndices[i] = nEntries;
+            outputSize += nEntries;
+            maxNumEntries = Math.max(maxNumEntries, nEntries);
+            tokens.push(...parts);
+        }
+        const indices = tf.util.getArrayFromDType('int32', outputSize * 2);
+        const values = new Array(outputSize);
+        const shape = [batchSize, maxNumEntries];
+        let c = 0;
+        for (let i = 0; i < batchSize; ++i) {
+            for (let j = 0; j < numIndices[i]; ++j) {
+                // indices is a 2d tensor with shape of [outputSize, 2]
+                indices[c * 2] = i;
+                indices[c * 2 + 1] = j;
+                values[c] = tokens[c];
+                ++c;
+            }
+        }
+        return [indices, values, shape];
+    }
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function stringToHashBucketFastImpl(input, numBuckets) {
+        const output = tf.util.getArrayFromDType('int32', input.length);
+        for (let i = 0; i < input.length; ++i) {
+            output[i] =
+                tf.util.fingerPrint64(input[i]).modulo(numBuckets).getLowBitsUnsigned();
+        }
+        return output;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    const subImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * An implementation of the tile kernel shared between webgl and cpu for string
+     * tensors only.
+     */
+    function tileImpl(xBuf, reps) {
+        const newShape = new Array(xBuf.rank);
+        for (let i = 0; i < newShape.length; i++) {
+            newShape[i] = xBuf.shape[i] * reps[i];
+        }
+        const result = tf.buffer(newShape, xBuf.dtype);
+        for (let i = 0; i < result.values.length; ++i) {
+            const newLoc = result.indexToLoc(i);
+            const originalLoc = new Array(xBuf.rank);
+            for (let j = 0; j < originalLoc.length; j++) {
+                originalLoc[j] = newLoc[j] % xBuf.shape[j];
+            }
+            const originalIndex = xBuf.locToIndex(originalLoc);
+            result.values[i] = xBuf.values[originalIndex];
+        }
+        return result;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function topKImpl(x, xShape, xDtype, k, sorted) {
+        // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.
+        const lastDim = xShape[xShape.length - 1];
+        const [batch, size] = [x.length / lastDim, lastDim];
+        const allTopKVals = tf.util.getTypedArrayFromDType(xDtype, batch * k);
+        const allTopKIndices = tf.util.getTypedArrayFromDType('int32', batch * k);
+        for (let b = 0; b < batch; b++) {
+            const offset = b * size;
+            const vals = x.subarray(offset, offset + size);
+            const valAndInd = [];
+            for (let i = 0; i < vals.length; i++) {
+                valAndInd.push({ value: vals[i], index: i });
+            }
+            valAndInd.sort((a, b) => b.value - a.value);
+            const outOffset = b * k;
+            const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
+            const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
+            for (let i = 0; i < k; i++) {
+                topKVals[i] = valAndInd[i].value;
+                topKIndices[i] = valAndInd[i].index;
+            }
+        }
+        // Reshape back to the original input shape, except that the last
+        // dimension is k.
+        const outputShape = xShape.slice();
+        outputShape[outputShape.length - 1] = k;
+        return [
+            tf.buffer(outputShape, xDtype, allTopKVals),
+            tf.buffer(outputShape, 'int32', allTopKIndices)
+        ];
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function uniqueImpl(values, axis, shape, dtype) {
+        // Normalize and validate axis.
+        const $axis = tf.util.parseAxisParam(axis, shape)[0];
+        // Calculate the new shape that is suitable for extracting data along the
+        // given axis.
+        //
+        // The rank is 3.
+        // The size of the 1st dimension is the size of all the axes < the given axis.
+        // The size of the 2nd dimension is the same as the size of the given axis.
+        // The size of the 3rd dimension is the size of all the axes > the given axis.
+        //
+        // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the
+        // newShape would be: [2*3, 5, 4].
+        //
+        // Note that this is not the final output shape. This will be the shape for an
+        // intermediate TensorBuffer (see inputBuffer below) to allow us to extract
+        // values along the given axis. To demonstrate how it works, consider the
+        // following example:
+        //
+        // Input: a 3D tensor, with shape [1, 2, 3]
+        // [
+        //   [
+        //      [1,2,3],
+        //      [4,5,6]
+        //   ]
+        // ]
+        // Axis: 2 (the last axis).
+        // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].
+        //
+        // For this example, newShape would be: [2, 3, 1], where 2 is calculated from
+        // 1*2. The re-shaped data would look like:
+        //
+        // [
+        //   [
+        //     [1], [2], [3]
+        //   ],
+        //   [
+        //     [4], [5], [6]
+        //   ]
+        // ]
+        //
+        // Then, we can construct a 3-level nested loop by the following dimension
+        // order to extract the values along the axis (dimension1):
+        // i: dimension1       // 0,1,2 (newShape[1])
+        //   m: dimension0     // 0,1   (newShape[0])
+        //     n: dimension2   // 0     (newShape[2])
+        //
+        //                       m, i, n
+        //                      ---------
+        // Iteration 0: data at [0, 0, 0] => "1"
+        // Iteration 1: data at [1, 0, 0] => "4"
+        // We got [1,4].
+        // Iteration 2: data at [0, 1, 0] => "2"
+        // Iteration 3: data at [1, 1, 0] => "5"
+        // We got [2,5].
+        // Iteration 4: data at [0, 2, 0] => "3"
+        // Iteration 5: data at [1, 2, 0] => "6"
+        // We got [3,6].
+        const newShape = [1, shape[0], 1];
+        for (let i = 0; i < $axis; i++) {
+            newShape[0] *= shape[i];
+        }
+        newShape[1] = shape[$axis];
+        for (let i = $axis + 1; i < shape.length; i++) {
+            newShape[2] *= shape[i];
+        }
+        // A map from unique elements (their string representations) to their values
+        // in "indices" (below).
+        const uniqueElements = {};
+        // The indices of each unique element in the original tensor along the given
+        // axis. It is 1D and has the same size as the given axis.
+        const indices = new Int32Array(shape[$axis]);
+        // Create a buffer so we can easily extract value at a given location.
+        const inputBuffer = new tf.TensorBuffer(newShape, dtype, values);
+        // The indices along the given axis that have unique elements. This is a
+        // de-duped version of "indices" above.
+        const uniqueIndices = [];
+        const is1DTensor = newShape[0] === 1 && newShape[2] === 1;
+        for (let i = 0; i < shape[$axis]; i++) {
+            // Extract values along the axis.
+            let element;
+            if (is1DTensor) {
+                // Fast path for 1D tensor input.
+                element = values[i].toString();
+            }
+            else {
+                const axisValues = [];
+                for (let m = 0; m < newShape[0]; m++) {
+                    for (let n = 0; n < newShape[2]; n++) {
+                        axisValues.push(inputBuffer.get(m, i, n));
+                    }
+                }
+                element = axisValues.join(',');
+            }
+            // Dedup and update various indices.
+            if (uniqueElements[element] !== undefined) {
+                indices[i] = uniqueElements[element];
+            }
+            else {
+                const uniqueIndex = Object.keys(uniqueElements).length;
+                uniqueElements[element] = uniqueIndex;
+                indices[i] = uniqueIndex;
+                uniqueIndices.push(i);
+            }
+        }
+        // Now we know where each of the unique elements are located along the axis
+        // (uniqueIndices). Extract them from input buffer and store them in the
+        // output buffer.
+        const outputTmpShape = newShape.slice();
+        outputTmpShape[1] = Object.keys(uniqueElements).length;
+        const outputBuffer = new tf.TensorBuffer(outputTmpShape, dtype);
+        uniqueIndices.forEach((uniqueElementIndex, i) => {
+            for (let m = 0; m < newShape[0]; m++) {
+                for (let n = 0; n < newShape[2]; n++) {
+                    outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);
+                }
+            }
+        });
+        // The output shape can be calculated from the input shape with the size of
+        // the given axis replaced by the number of unique elements along that axis.
+        const outputShape = shape.slice();
+        outputShape[$axis] = outputTmpShape[1];
+        return {
+            outputValues: outputBuffer.values,
+            outputShape,
+            indices,
+        };
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var addImplCPU = addImpl, bincountImplCPU = bincountImpl, bincountReduceImplCPU = bincountReduceImpl, ceilImplCPU = ceilImpl, concatImplCPU = concatImpl, equalImplCPU = equalImpl, expImplCPU = expImpl, expm1ImplCPU = expm1Impl, floorImplCPU = floorImpl, gatherNdImplCPU = gatherNdImpl, gatherV2ImplCPU = gatherV2Impl, greaterImplCPU = greaterImpl, greaterEqualImplCPU = greaterEqualImpl, lessImplCPU = lessImpl, lessEqualImplCPU = lessEqualImpl, linSpaceImplCPU = linSpaceImpl, logImplCPU = logImpl, maxImplCPU = maxImpl, maximumImplCPU = maximumImpl, minimumImplCPU = minimumImpl, multiplyImplCPU = multiplyImpl, negImplCPU = negImpl, notEqualImplCPU = notEqualImpl, prodImplCPU = prodImpl, rangeImplCPU = rangeImpl, rsqrtImplCPU = rsqrtImpl, simpleAbsImplCPU = simpleAbsImpl, sliceImplCPU = sliceImpl, sparseFillEmptyRowsImplCPU = sparseFillEmptyRowsImpl, sparseReshapeImplCPU = sparseReshapeImpl, sparseSegmentReductionImplCPU = sparseSegmentReductionImpl, stridedSliceImplCPU = stridedSliceImpl, stringNGramsImplCPU = stringNGramsImpl, stringSplitImplCPU = stringSplitImpl, stringToHashBucketFastImplCPU = stringToHashBucketFastImpl, subImplCPU = subImpl, tileImplCPU = tileImpl, topKImplCPU = topKImpl, transposeImplCPU = transposeImpl, uniqueImplCPU = uniqueImpl;
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function getVecChannels(name, rank) {
+        return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(function (d) { return name + "." + d; });
+    }
+    function getChannels(name, rank) {
+        if (rank === 1) {
+            return [name];
+        }
+        return getVecChannels(name, rank);
+    }
+    function getSourceCoords(rank, dims) {
+        if (rank === 1) {
+            return 'rc';
+        }
+        var coords = '';
+        for (var i = 0; i < rank; i++) {
+            coords += dims[i];
+            if (i < rank - 1) {
+                coords += ',';
+            }
+        }
+        return coords;
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var PackProgram = /** @class */ (function () {
+        function PackProgram(outputShape) {
+            this.variableNames = ['A'];
+            this.packedInputs = false;
+            this.packedOutput = true;
+            // Only input / output 3D tensors.
+            this.outputShape = outputShape;
+            var rank = outputShape.length;
+            if (rank === 0) {
+                this.userCode = "\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";
+            }
+            else {
+                var channels = getChannels('rc', rank);
+                var dtype = getCoordsDataType(rank);
+                var outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);
+                var setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);
+                var output = getOutput(outputShape, channels);
+                this.userCode = "\n        void main() {\n          " + dtype + " rc = getOutputCoords();\n\n          if(" + outOfBoundsCondition + ") {\n            setOutput(vec4(0));\n          } else {\n            " + setup + "\n\n            setOutput(vec4(" + output + "));\n          }\n        }\n      ";
+            }
+        }
+        return PackProgram;
+    }());
+    function getSourceCoordsArr(rank, dims) {
+        var coords = [];
+        for (var row = 0; row <= 1; row++) {
+            for (var col = 0; col <= 1; col++) {
+                var coord = (row === 0 ? 'r' : 'rp1') + ", " + (col === 0 ? 'c' : 'cp1');
+                for (var d = 2; d < rank; d++) {
+                    coord = dims[dims.length - 1 - d] + "," + coord;
+                }
+                coords.push(coord);
+            }
+        }
+        return coords;
+    }
+    function getOutOfBoundsCondition(rank, shape, dims) {
+        if (rank === 1) {
+            return "rc > " + shape[0];
+        }
+        var cond = '';
+        for (var i = rank - 2; i < rank; i++) {
+            cond += dims[i] + " >= " + shape[i];
+            if (i < rank - 1) {
+                cond += '||';
+            }
+        }
+        return cond;
+    }
+    function getSetup(rank, cols, rows, dims) {
+        if (rank === 1) {
+            return '';
+        }
+        var innerDims = dims.slice(-2);
+        return "\n    int r = " + innerDims[0] + ";\n    int c = " + innerDims[1] + ";\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= " + cols + ";\n    bool rEdge = rp1 >= " + rows + ";\n  ";
+    }
+    function getOutput(shape, dims) {
+        var rank = shape.length;
+        var sourceCoords = getSourceCoordsArr(rank, dims);
+        if (rank === 1) {
+            return "getA(rc),\n            rc + 1 >= " + shape[0] + " ? 0. : getA(rc + 1),\n            0, 0";
+        }
+        return "getA(" + sourceCoords[0] + "),\n          cEdge ? 0. : getA(" + sourceCoords[1] + "),\n          rEdge ? 0. : getA(" + sourceCoords[2] + "),\n          rEdge || cEdge ? 0. : getA(" + sourceCoords[3] + ")";
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ReshapePackedProgram = /** @class */ (function () {
+        function ReshapePackedProgram(outputShape, inputShape) {
+            this.variableNames = ['A'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = outputShape;
+            var mainLoop = "";
+            for (var i = 0; i < 4; i++) {
+                var thisRC = "thisRC = rc;";
+                if (i % 2 === 1) {
+                    thisRC += "thisRC.z += 1;";
+                }
+                if (i > 1) {
+                    thisRC += "thisRC.y += 1;";
+                }
+                mainLoop += "\n        " + thisRC + "\n        " + (i > 0 ? "if(thisRC.y < rows && thisRC.z < cols){" : '') + "\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[" + i + "] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        " + (i > 0 ? '}' : '') + "\n      ";
+            }
+            this.userCode = "\n      " + getReshapedInputCoords(inputShape) + "\n      " + getFlatIndexFrom3D(outputShape) + "\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = " + outputShape[1] + ";\n        int cols = " + outputShape[2] + ";\n\n        " + mainLoop + "\n\n        setOutput(result);\n      }\n    ";
+        }
+        return ReshapePackedProgram;
+    }());
+    function getReshapedInputCoords(shape) {
+        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);
+        return "\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      " + coordsFromIndexSnippet + "\n      return ivec3(r, c, d);\n    }\n  ";
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var TextureManager = /** @class */ (function () {
+        function TextureManager(gpgpu) {
+            this.gpgpu = gpgpu;
+            this.numUsedTextures = 0;
+            this.numFreeTextures = 0;
+            this._numBytesAllocated = 0;
+            this._numBytesFree = 0; // How many bytes that have been allocated
+            // are available for reuse.
+            this.freeTextures = {};
+            this.logEnabled = false;
+            this.usedTextures = {};
+        }
+        TextureManager.prototype.acquireTexture = function (shapeRC, usage, isPacked) {
+            var physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);
+            var shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);
+            if (!(shapeKey in this.freeTextures)) {
+                this.freeTextures[shapeKey] = [];
+            }
+            if (!(shapeKey in this.usedTextures)) {
+                this.usedTextures[shapeKey] = [];
+            }
+            var texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);
+            if (this.freeTextures[shapeKey].length > 0) {
+                this.numFreeTextures--;
+                this.numUsedTextures++;
+                this._numBytesFree -= texBytes;
+                this.log();
+                var newTexture_1 = this.freeTextures[shapeKey].shift();
+                this.usedTextures[shapeKey].push(newTexture_1);
+                return newTexture_1;
+            }
+            var newTexture;
+            if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {
+                newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);
+            }
+            else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {
+                newTexture =
+                    this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);
+            }
+            else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {
+                newTexture =
+                    this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);
+            }
+            else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {
+                newTexture =
+                    this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);
+            }
+            else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {
+                newTexture =
+                    this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);
+            }
+            this.usedTextures[shapeKey].push(newTexture);
+            this.numUsedTextures++;
+            this._numBytesAllocated += texBytes;
+            this.log();
+            return newTexture;
+        };
+        TextureManager.prototype.releaseTexture = function (texture, shape, logicalTexType, isPacked) {
+            if (this.freeTextures == null) {
+                // Already disposed.
+                return;
+            }
+            var physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);
+            var shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);
+            if (!(shapeKey in this.freeTextures)) {
+                this.freeTextures[shapeKey] = [];
+            }
+            var texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);
+            var deleteTexThreshold = tf.env().get('WEBGL_DELETE_TEXTURE_THRESHOLD');
+            if (deleteTexThreshold !== -1 &&
+                this._numBytesAllocated > deleteTexThreshold) {
+                this.gpgpu.deleteMatrixTexture(texture);
+                this._numBytesAllocated -= texBytes;
+            }
+            else {
+                this.freeTextures[shapeKey].push(texture);
+                this.numFreeTextures++;
+                this._numBytesFree += texBytes;
+            }
+            this.numUsedTextures--;
+            var texList = this.usedTextures[shapeKey];
+            var texIndex = texList.indexOf(texture);
+            if (texIndex < 0) {
+                throw new Error('Cannot release a texture that was never provided by this ' +
+                    'texture manager');
+            }
+            texList.splice(texIndex, 1);
+            this.log();
+        };
+        TextureManager.prototype.log = function () {
+            if (!this.logEnabled) {
+                return;
+            }
+            var total = this.numFreeTextures + this.numUsedTextures;
+            console.log('Free/Used', this.numFreeTextures + " / " + this.numUsedTextures, "(" + total + ")");
+            var freeRatio = this._numBytesFree / this._numBytesAllocated;
+            console.log("Bytes allocated: " + this._numBytesAllocated);
+            console.log("Bytes unused: " + this._numBytesFree + " (" + Math.round(100 * freeRatio) + "%)");
+        };
+        Object.defineProperty(TextureManager.prototype, "numBytesAllocated", {
+            get: function () {
+                return this._numBytesAllocated;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TextureManager.prototype, "numBytesFree", {
+            get: function () {
+                return this._numBytesFree;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        TextureManager.prototype.getNumUsedTextures = function () {
+            return this.numUsedTextures;
+        };
+        TextureManager.prototype.getNumFreeTextures = function () {
+            return this.numFreeTextures;
+        };
+        TextureManager.prototype.dispose = function () {
+            var _this = this;
+            if (this.freeTextures == null) {
+                // Already disposed.
+                return;
+            }
+            for (var texShape in this.freeTextures) {
+                this.freeTextures[texShape].forEach(function (tex) {
+                    _this.gpgpu.deleteMatrixTexture(tex);
+                });
+            }
+            for (var texShape in this.usedTextures) {
+                this.usedTextures[texShape].forEach(function (tex) {
+                    _this.gpgpu.deleteMatrixTexture(tex);
+                });
+            }
+            this.freeTextures = null;
+            this.usedTextures = null;
+            this.numUsedTextures = 0;
+            this.numFreeTextures = 0;
+            this._numBytesAllocated = 0;
+            this._numBytesFree = 0;
+        };
+        return TextureManager;
+    }());
+    function numBytesForInternalFormat(gl, internalFormat) {
+        // tslint:disable-next-line:no-any
+        var glany = gl;
+        if (internalFormat === glany.R32F) {
+            return 4;
+        }
+        else if (internalFormat === glany.R16F) {
+            return 2;
+        }
+        else if (internalFormat === glany.RGBA32F) {
+            return 16;
+        }
+        else if (internalFormat === gl.RGBA) {
+            return 16;
+        }
+        else if (internalFormat === glany.RGBA16F) {
+            return 8;
+        }
+        throw new Error("Unknown internal format " + internalFormat);
+    }
+    function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {
+        // It is not possible to infer packed status from the texture type because
+        // depending on the textureConfig, different  texture types may resolve to the
+        // same internal format (e.g. in WebGL1, the internal format for
+        // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`
+        // explicitly.
+        var internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);
+        var numElements;
+        if (isPacked) {
+            var _a = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]), packedWidth = _a[0], packedHeight = _a[1];
+            numElements = packedWidth * packedHeight;
+        }
+        else {
+            var _b = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]), width = _b[0], height = _b[1];
+            numElements = width * height;
+        }
+        var bytesPerElement = numBytesForInternalFormat(gl, internalFormat);
+        return numElements * bytesPerElement;
+    }
+    function internalFormatForPhysicalTexType(physicalTexType, textureConfig) {
+        switch (physicalTexType) {
+            case PhysicalTextureType.PACKED_2X2_FLOAT32:
+                return getInternalFormatForPackedMatrixTexture(textureConfig);
+            case PhysicalTextureType.PACKED_2X2_FLOAT16:
+                return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);
+            case PhysicalTextureType.UNPACKED_FLOAT32:
+                return getInternalFormatForFloat32MatrixTexture(textureConfig);
+            case PhysicalTextureType.UNPACKED_FLOAT16:
+                return getInternalFormatForFloat16MatrixTexture(textureConfig);
+            case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:
+                return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);
+            default:
+                throw new Error("Unknown physical texture type " + physicalTexType);
+        }
+    }
+    function getPhysicalTextureForRendering(isPacked) {
+        if (tf.env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {
+            if (isPacked) {
+                return PhysicalTextureType.PACKED_2X2_FLOAT32;
+            }
+            return PhysicalTextureType.UNPACKED_FLOAT32;
+        }
+        if (isPacked) {
+            return PhysicalTextureType.PACKED_2X2_FLOAT16;
+        }
+        return PhysicalTextureType.UNPACKED_FLOAT16;
+    }
+    function getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {
+        if (logicalTexType === TextureUsage.UPLOAD) {
+            return PhysicalTextureType.PACKED_2X2_FLOAT32;
+        }
+        else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {
+            return getPhysicalTextureForRendering(isPacked);
+        }
+        else if (logicalTexType === TextureUsage.DOWNLOAD ||
+            logicalTexType === TextureUsage.PIXELS) {
+            return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;
+        }
+        throw new Error("Unknown logical texture type " + logicalTexType);
+    }
+    function getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {
+        return shapeRowsCol[0] + "_" + shapeRowsCol[1] + "_" + physicalTexType + "_" + isPacked;
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var UnaryOpProgram = /** @class */ (function () {
+        function UnaryOpProgram(aShape, opSnippet) {
+            this.variableNames = ['A'];
+            this.outputShape = aShape;
+            this.userCode = "\n      float unaryOperation(float x) {\n        " + opSnippet + "\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    ";
+        }
+        return UnaryOpProgram;
+    }());
+    var CHECK_NAN_SNIPPET = "if (isnan(x)) return x;";
+    var LINEAR = "return x;";
+    var ABS = "return abs(x);";
+    var ELU = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
+    var RELU = CHECK_NAN_SNIPPET + "\n  return (x < 0.0) ? 0.0 : x;\n";
+    var RELU6 = CHECK_NAN_SNIPPET + "\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n";
+    var CLONE = 'return x;';
+    var SIGMOID = "return 1.0 / (1.0 + exp(-1.0 * x));";
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LINEAR$1 = "return x;";
+    var ELU$1 = "\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n";
+    var RELU$1 = "\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n";
+    var RELU6$1 = "\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n";
+    var SIGMOID$1 = "return 1.0 / (1.0 + exp(-1.0 * x));";
+    var UnaryOpPackedProgram = /** @class */ (function () {
+        function UnaryOpPackedProgram(aShape, opSnippet) {
+            this.variableNames = ['A'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = aShape;
+            this.userCode = "\n      vec4 unaryOperation(vec4 x) {\n        " + opSnippet + "\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    ";
+        }
+        return UnaryOpPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var UnpackProgram = /** @class */ (function () {
+        function UnpackProgram(outputShape) {
+            this.variableNames = ['A'];
+            this.packedInputs = true;
+            this.packedOutput = false;
+            this.outputShape = outputShape;
+            var rank = outputShape.length;
+            var channels = getChannels('rc', rank);
+            var dtype = getCoordsDataType(rank);
+            var sourceCoords = getSourceCoords(rank, channels);
+            var innerDims = channels.slice(-2);
+            var coords = rank <= 1 ? 'rc' : "vec2(" + innerDims.join(',') + ")";
+            this.userCode = "\n      void main() {\n        " + dtype + " rc = getOutputCoords();\n        vec4 packedInput = getA(" + sourceCoords + ");\n\n        setOutput(getChannel(packedInput, " + coords + "));\n      }\n    ";
+        }
+        return UnpackProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var whereImpl = tf.kernel_impls.whereImpl;
+    var EPSILON_FLOAT32 = 1e-7;
+    var EPSILON_FLOAT16 = 1e-4;
+    var binaryCaches = {};
+    function getBinaryCache(webGLVersion) {
+        if (webGLVersion in binaryCaches) {
+            return binaryCaches[webGLVersion];
+        }
+        binaryCaches[webGLVersion] = {};
+        return binaryCaches[webGLVersion];
+    }
+    // Empirically determined constant used to determine size threshold for handing
+    // off execution to the CPU.
+    var CPU_HANDOFF_SIZE_THRESHOLD = tf.env().getNumber('CPU_HANDOFF_SIZE_THRESHOLD');
+    // Empirically determined constant used to decide the number of MB on GPU
+    // before we warn about high memory use. The MB are this constant * screen area
+    // * dpi / 1024 / 1024.
+    var BEFORE_PAGING_CONSTANT = 600;
+    function numMBBeforeWarning() {
+        if (tf.env().global.screen == null) {
+            return 1024; // 1 GB.
+        }
+        return (tf.env().global.screen.height * tf.env().global.screen.width *
+            window.devicePixelRatio) *
+            BEFORE_PAGING_CONSTANT / 1024 / 1024;
+    }
+    var MathBackendWebGL = /** @class */ (function (_super) {
+        __extends(MathBackendWebGL, _super);
+        function MathBackendWebGL(gpgpu) {
+            var _this = _super.call(this) || this;
+            // Maps data ids that have a pending read operation, to list of subscribers.
+            _this.pendingRead = new WeakMap();
+            // List of data ids that are scheduled for disposal, but are waiting on a
+            // pending read operation.
+            _this.pendingDisposal = new WeakSet();
+            // Used to count the number of 'shallow' sliced tensors that point to the
+            // same data id.
+            _this.dataRefCount = new WeakMap();
+            _this.numBytesInGPU = 0;
+            // Accumulated time spent (including blocking) in uploading data to webgl.
+            _this.uploadWaitMs = 0;
+            // Accumulated time spent (including blocking in downloading data from webgl.
+            _this.downloadWaitMs = 0;
+            // record the last manual GL Flush time.
+            _this.lastGlFlushTime = 0;
+            _this.warnedAboutMemory = false;
+            _this.pendingDeletes = 0;
+            _this.disposed = false;
+            if (!tf.env().getBool('HAS_WEBGL')) {
+                throw new Error('WebGL is not supported on this device');
+            }
+            if (gpgpu == null) {
+                var gl = getWebGLContext(tf.env().getNumber('WEBGL_VERSION'));
+                _this.binaryCache = getBinaryCache(tf.env().getNumber('WEBGL_VERSION'));
+                _this.gpgpu = new GPGPUContext(gl);
+                _this.canvas = gl.canvas;
+                _this.gpgpuCreatedLocally = true;
+            }
+            else {
+                _this.gpgpu = gpgpu;
+                _this.binaryCache = {};
+                _this.gpgpuCreatedLocally = false;
+                _this.canvas = gpgpu.gl.canvas;
+            }
+            _this.textureManager = new TextureManager(_this.gpgpu);
+            _this.numMBBeforeWarning = numMBBeforeWarning();
+            _this.texData = new tf.DataStorage(_this, tf.engine());
+            return _this;
+        }
+        MathBackendWebGL.prototype.nextDataId = function () {
+            return MathBackendWebGL.nextDataId++;
+        };
+        MathBackendWebGL.prototype.numDataIds = function () {
+            return this.texData.numDataIds() - this.pendingDeletes;
+        };
+        MathBackendWebGL.prototype.write = function (values, shape, dtype) {
+            if (tf.env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||
+                tf.env().getBool('DEBUG')) {
+                this.checkNumericalProblems(values);
+            }
+            if (dtype === 'complex64' && values != null) {
+                throw new Error("Cannot write to a complex64 dtype. " +
+                    "Please use tf.complex(real, imag).");
+            }
+            var dataId = { id: this.nextDataId() };
+            this.texData.set(dataId, { shape: shape, dtype: dtype, values: values, usage: TextureUsage.UPLOAD, refCount: 1 });
+            return dataId;
+        };
+        /** Return refCount of a `TensorData`. */
+        MathBackendWebGL.prototype.refCount = function (dataId) {
+            if (this.texData.has(dataId)) {
+                var tensorData = this.texData.get(dataId);
+                return tensorData.refCount;
+            }
+            return 0;
+        };
+        /** Increase refCount of a `TextureData`. */
+        MathBackendWebGL.prototype.incRef = function (dataId) {
+            var texData = this.texData.get(dataId);
+            texData.refCount++;
+        };
+        /** Decrease refCount of a `TextureData`. */
+        MathBackendWebGL.prototype.decRef = function (dataId) {
+            if (this.texData.has(dataId)) {
+                var texData = this.texData.get(dataId);
+                texData.refCount--;
+            }
+        };
+        MathBackendWebGL.prototype.move = function (dataId, values, shape, dtype, refCount) {
+            if (tf.env().getBool('DEBUG')) {
+                this.checkNumericalProblems(values);
+            }
+            if (dtype === 'complex64') {
+                throw new Error("Cannot write to a complex64 dtype. " +
+                    "Please use tf.complex(real, imag).");
+            }
+            this.texData.set(dataId, { shape: shape, dtype: dtype, values: values, usage: TextureUsage.UPLOAD, refCount: refCount });
+        };
+        MathBackendWebGL.prototype.disposeIntermediateTensorInfo = function (tensorInfo) {
+            this.disposeData(tensorInfo.dataId);
+        };
+        MathBackendWebGL.prototype.readSync = function (dataId) {
+            var texData = this.texData.get(dataId);
+            var values = texData.values, dtype = texData.dtype, complexTensorInfos = texData.complexTensorInfos, slice = texData.slice, shape = texData.shape, isPacked = texData.isPacked;
+            // The presence of `slice` indicates this tensor is a shallow slice of a
+            // different tensor, and is using that original tensor's texture. Run
+            // `clone` in order to copy that texture and read from it.
+            if (slice != null) {
+                var program = void 0;
+                if (isPacked) {
+                    program = new UnaryOpPackedProgram(shape, CLONE);
+                }
+                else {
+                    program = new UnaryOpProgram(shape, CLONE);
+                }
+                var res = this.runWebGLProgram(program, [{ dataId: dataId, shape: shape, dtype: dtype }], dtype);
+                var data = this.readSync(res.dataId);
+                this.disposeIntermediateTensorInfo(res);
+                return data;
+            }
+            if (values != null) {
+                return this.convertAndCacheOnCPU(dataId);
+            }
+            if (dtype === 'string') {
+                return values;
+            }
+            var shouldTimeProgram = this.activeTimers != null;
+            var start;
+            if (shouldTimeProgram) {
+                start = tf.util.now();
+            }
+            var result;
+            if (dtype === 'complex64') {
+                var realValues = this.readSync(complexTensorInfos.real.dataId);
+                var imagValues = this.readSync(complexTensorInfos.imag.dataId);
+                result = tf.backend_util.mergeRealAndImagArrays(realValues, imagValues);
+            }
+            else {
+                result = this.getValuesFromTexture(dataId);
+            }
+            if (shouldTimeProgram) {
+                this.downloadWaitMs += tf.util.now() - start;
+            }
+            return this.convertAndCacheOnCPU(dataId, result);
+        };
+        MathBackendWebGL.prototype.read = function (dataId) {
+            return __awaiter(this, void 0, void 0, function () {
+                var subscribers_1, texData, values, shape, slice, dtype, complexTensorInfos, isPacked, program, res, data, buffer, tmpDownloadTarget, tmpData, vals, ps, realValues, imagValues, size, dTypeVals, subscribers;
+                var _a;
+                return __generator(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            if (this.pendingRead.has(dataId)) {
+                                subscribers_1 = this.pendingRead.get(dataId);
+                                return [2 /*return*/, new Promise(function (resolve) { return subscribers_1.push(resolve); })];
+                            }
+                            texData = this.texData.get(dataId);
+                            values = texData.values, shape = texData.shape, slice = texData.slice, dtype = texData.dtype, complexTensorInfos = texData.complexTensorInfos, isPacked = texData.isPacked;
+                            // The presence of `slice` indicates this tensor is a shallow slice of a
+                            // different tensor, and is using that original tensor's texture. Run
+                            // `clone` in order to copy that texture and read from it.
+                            if (slice != null) {
+                                program = void 0;
+                                if (isPacked) {
+                                    program = new UnaryOpPackedProgram(shape, CLONE);
+                                }
+                                else {
+                                    program = new UnaryOpProgram(shape, CLONE);
+                                }
+                                res = this.runWebGLProgram(program, [{ dataId: dataId, shape: shape, dtype: dtype }], dtype);
+                                data = this.read(res.dataId);
+                                this.disposeIntermediateTensorInfo(res);
+                                return [2 /*return*/, data];
+                            }
+                            if (values != null) {
+                                return [2 /*return*/, this.convertAndCacheOnCPU(dataId)];
+                            }
+                            if (!tf.env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&
+                                tf.env().getNumber('WEBGL_VERSION') === 2) {
+                                throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and " +
+                                    "WEBGL_VERSION=2 not yet supported.");
+                            }
+                            buffer = null;
+                            if (dtype !== 'complex64' && tf.env().get('WEBGL_BUFFER_SUPPORTED')) {
+                                // Possibly copy the texture into a buffer before inserting a fence.
+                                tmpDownloadTarget = this.decode(dataId);
+                                tmpData = this.texData.get(tmpDownloadTarget.dataId);
+                                buffer = (_a = this.gpgpu).createBufferFromTexture.apply(_a, [tmpData.texture].concat(getDenseTexShape(shape)));
+                            }
+                            this.pendingRead.set(dataId, []);
+                            if (!(dtype !== 'complex64')) return [3 /*break*/, 2];
+                            // Create a fence and wait for it to resolve.
+                            return [4 /*yield*/, this.gpgpu.createAndWaitForFence()];
+                        case 1:
+                            // Create a fence and wait for it to resolve.
+                            _b.sent();
+                            _b.label = 2;
+                        case 2:
+                            if (!(dtype === 'complex64')) return [3 /*break*/, 4];
+                            return [4 /*yield*/, Promise.all([
+                                    this.read(complexTensorInfos.real.dataId),
+                                    this.read(complexTensorInfos.imag.dataId)
+                                ])];
+                        case 3:
+                            ps = _b.sent();
+                            realValues = ps[0];
+                            imagValues = ps[1];
+                            vals = tf.backend_util.mergeRealAndImagArrays(realValues, imagValues);
+                            return [3 /*break*/, 5];
+                        case 4:
+                            if (buffer == null) {
+                                vals = this.getValuesFromTexture(dataId);
+                            }
+                            else {
+                                size = tf.util.sizeFromShape(shape);
+                                vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);
+                            }
+                            _b.label = 5;
+                        case 5:
+                            if (tmpDownloadTarget != null) {
+                                this.disposeIntermediateTensorInfo(tmpDownloadTarget);
+                            }
+                            dTypeVals = this.convertAndCacheOnCPU(dataId, vals);
+                            subscribers = this.pendingRead.get(dataId);
+                            this.pendingRead.delete(dataId);
+                            // Notify all pending reads.
+                            subscribers.forEach(function (resolve) { return resolve(dTypeVals); });
+                            if (this.pendingDisposal.has(dataId)) {
+                                this.pendingDisposal.delete(dataId);
+                                if (this.disposeData(dataId)) {
+                                    tf.engine().removeDataId(dataId, this);
+                                }
+                                this.pendingDeletes--;
+                            }
+                            return [2 /*return*/, dTypeVals];
+                    }
+                });
+            });
+        };
+        MathBackendWebGL.prototype.bufferSync = function (t) {
+            var data = this.readSync(t.dataId);
+            var decodedData = data;
+            if (t.dtype === 'string') {
+                try {
+                    // Decode the bytes into string.
+                    decodedData = data.map(function (d) { return tf.util.decodeString(d); });
+                }
+                catch (_a) {
+                    throw new Error('Failed to decode encoded string bytes into utf-8');
+                }
+            }
+            return tf.buffer(t.shape, t.dtype, decodedData);
+        };
+        MathBackendWebGL.prototype.checkNumericalProblems = function (values) {
+            if (values == null) {
+                return;
+            }
+            for (var i = 0; i < values.length; i++) {
+                var num = values[i];
+                if (!canBeRepresented(num)) {
+                    if (tf.env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {
+                        throw Error("The value " + num + " cannot be represented with your " +
+                            "current settings. Consider enabling float32 rendering: " +
+                            "'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'");
+                    }
+                    throw Error("The value " + num + " cannot be represented on this device.");
+                }
+            }
+        };
+        MathBackendWebGL.prototype.getValuesFromTexture = function (dataId) {
+            var _a;
+            var _b = this.texData.get(dataId), shape = _b.shape, dtype = _b.dtype, isPacked = _b.isPacked;
+            var size = tf.util.sizeFromShape(shape);
+            if (tf.env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {
+                var tmpTarget = this.decode(dataId);
+                var tmpData_1 = this.texData.get(tmpTarget.dataId);
+                var vals_1 = (_a = this.gpgpu).downloadMatrixFromPackedTexture.apply(_a, [tmpData_1.texture].concat(getDenseTexShape(shape))).subarray(0, size);
+                this.disposeIntermediateTensorInfo(tmpTarget);
+                return vals_1;
+            }
+            var shouldUsePackedProgram = tf.env().getBool('WEBGL_PACK') && isPacked === true;
+            var outputShape = shouldUsePackedProgram ? getShapeAs3D(shape) : shape;
+            var program = shouldUsePackedProgram ?
+                new EncodeFloatPackedProgram(outputShape) :
+                new EncodeFloatProgram(outputShape);
+            var output = this.runWebGLProgram(program, [{ shape: outputShape, dtype: dtype, dataId: dataId }], 'float32');
+            var tmpData = this.texData.get(output.dataId);
+            var vals = this.gpgpu
+                .downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])
+                .subarray(0, size);
+            this.disposeIntermediateTensorInfo(output);
+            return vals;
+        };
+        MathBackendWebGL.prototype.timerAvailable = function () {
+            return tf.env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0;
+        };
+        MathBackendWebGL.prototype.time = function (f) {
+            return __awaiter(this, void 0, void 0, function () {
+                var oldActiveTimers, newActiveTimers, outerMostTime, flattenedActiveTimerQueries, flattenedActiveTimerNames, res, kernelMs_1;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            oldActiveTimers = this.activeTimers;
+                            newActiveTimers = [];
+                            outerMostTime = false;
+                            if (this.programTimersStack == null) {
+                                this.programTimersStack = newActiveTimers;
+                                outerMostTime = true;
+                            }
+                            else {
+                                this.activeTimers.push(newActiveTimers);
+                            }
+                            this.activeTimers = newActiveTimers;
+                            f();
+                            flattenedActiveTimerQueries = tf.util.flatten(this.activeTimers.map(function (d) { return d.query; }))
+                                .filter(function (d) { return d != null; });
+                            flattenedActiveTimerNames = tf.util.flatten(this.activeTimers.map(function (d) { return d.name; }))
+                                .filter(function (d) { return d != null; });
+                            this.activeTimers = oldActiveTimers;
+                            if (outerMostTime) {
+                                this.programTimersStack = null;
+                            }
+                            res = {
+                                uploadWaitMs: this.uploadWaitMs,
+                                downloadWaitMs: this.downloadWaitMs,
+                                kernelMs: null,
+                                wallMs: null // will be filled by the engine
+                            };
+                            if (!(tf.env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0)) return [3 /*break*/, 2];
+                            return [4 /*yield*/, Promise.all(flattenedActiveTimerQueries)];
+                        case 1:
+                            kernelMs_1 = _a.sent();
+                            res['kernelMs'] = tf.util.sum(kernelMs_1);
+                            res['getExtraProfileInfo'] = function () {
+                                return kernelMs_1.map(function (d, i) { return ({ name: flattenedActiveTimerNames[i], ms: d }); })
+                                    .map(function (d) { return d.name + ": " + d.ms; })
+                                    .join(', ');
+                            };
+                            return [3 /*break*/, 3];
+                        case 2:
+                            res['kernelMs'] = {
+                                error: 'WebGL query timers are not supported in this environment.'
+                            };
+                            _a.label = 3;
+                        case 3:
+                            this.uploadWaitMs = 0;
+                            this.downloadWaitMs = 0;
+                            return [2 /*return*/, res];
+                    }
+                });
+            });
+        };
+        MathBackendWebGL.prototype.memory = function () {
+            return {
+                unreliable: false,
+                numBytesInGPU: this.numBytesInGPU,
+                numBytesInGPUAllocated: this.textureManager.numBytesAllocated,
+                numBytesInGPUFree: this.textureManager.numBytesFree
+            };
+        };
+        MathBackendWebGL.prototype.startTimer = function () {
+            if (tf.env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {
+                return this.gpgpu.beginQuery();
+            }
+            return { startMs: tf.util.now(), endMs: null };
+        };
+        MathBackendWebGL.prototype.endTimer = function (query) {
+            if (tf.env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {
+                this.gpgpu.endQuery();
+                return query;
+            }
+            query.endMs = tf.util.now();
+            return query;
+        };
+        MathBackendWebGL.prototype.getQueryTime = function (query) {
+            return __awaiter(this, void 0, void 0, function () {
+                var timerQuery;
+                return __generator(this, function (_a) {
+                    if (tf.env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {
+                        return [2 /*return*/, this.gpgpu.waitForQueryAndGetTime(query)];
+                    }
+                    timerQuery = query;
+                    return [2 /*return*/, timerQuery.endMs - timerQuery.startMs];
+                });
+            });
+        };
+        /**
+         * Decrease the RefCount on the dataId and dispose the memory if the dataId
+         * has 0 refCount. If there are pending read on the data, the disposal would
+         * added to the pending delete queue. Return true if the dataId is removed
+         * from backend or the backend does not contain the dataId, false if the
+         * dataId is not removed. Memory may or may not be released even when dataId
+         * is removed, which also depends on dataRefCount, see `releaseGPU`.
+         * @param dataId
+         * @oaram force Optional, remove the data regardless of refCount
+         */
+        MathBackendWebGL.prototype.disposeData = function (dataId, force) {
+            if (force === void 0) { force = false; }
+            if (this.pendingDisposal.has(dataId)) {
+                return false;
+            }
+            // No-op if already disposed.
+            if (!this.texData.has(dataId)) {
+                return true;
+            }
+            // if force flag is set, change refCount to 0, this would ensure disposal
+            // when added to the pendingDisposal queue. Memory may or may not be
+            // released, which also depends on dataRefCount, see `releaseGPU`.
+            if (force) {
+                this.texData.get(dataId).refCount = 0;
+            }
+            else {
+                this.texData.get(dataId).refCount--;
+            }
+            if (!force && this.texData.get(dataId).refCount > 0) {
+                return false;
+            }
+            if (this.pendingRead.has(dataId)) {
+                this.pendingDisposal.add(dataId);
+                this.pendingDeletes++;
+                return false;
+            }
+            this.releaseGPUData(dataId);
+            var complexTensorInfos = this.texData.get(dataId).complexTensorInfos;
+            if (complexTensorInfos != null) {
+                this.disposeData(complexTensorInfos.real.dataId, force);
+                this.disposeData(complexTensorInfos.imag.dataId, force);
+            }
+            this.texData.delete(dataId);
+            return true;
+        };
+        MathBackendWebGL.prototype.releaseGPUData = function (dataId) {
+            var _a = this.texData.get(dataId), texture = _a.texture, dtype = _a.dtype, texShape = _a.texShape, usage = _a.usage, isPacked = _a.isPacked, slice = _a.slice;
+            var key = slice && slice.origDataId || dataId;
+            var refCount = this.dataRefCount.get(key);
+            if (refCount > 1) {
+                this.dataRefCount.set(key, refCount - 1);
+            }
+            else {
+                this.dataRefCount.delete(key);
+                if (texture != null) {
+                    this.numBytesInGPU -= this.computeBytes(texShape, dtype);
+                    this.textureManager.releaseTexture(texture, texShape, usage, isPacked);
+                }
+            }
+            var texData = this.texData.get(dataId);
+            texData.texture = null;
+            texData.texShape = null;
+            texData.isPacked = false;
+            texData.slice = null;
+        };
+        MathBackendWebGL.prototype.getTexture = function (dataId) {
+            this.uploadToGPU(dataId);
+            return this.texData.get(dataId).texture;
+        };
+        /**
+         * Returns internal information for the specific data bucket. Used in unit
+         * tests.
+         */
+        MathBackendWebGL.prototype.getDataInfo = function (dataId) {
+            return this.texData.get(dataId);
+        };
+        /*
+        Tests whether all the inputs to an op are small and on the CPU. This heuristic
+        determines when it would be faster to execute a kernel on the CPU. WebGL
+        kernels opt into running this check and forwarding when appropriate.
+        TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more
+        sustainable strategy for optimizing backend execution of ops.
+         */
+        MathBackendWebGL.prototype.shouldExecuteOnCPU = function (inputs, sizeThreshold) {
+            var _this = this;
+            if (sizeThreshold === void 0) { sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD; }
+            return tf.env().getBool('WEBGL_CPU_FORWARD') &&
+                inputs.every(function (input) { return _this.texData.get(input.dataId).texture == null &&
+                    tf.util.sizeFromShape(input.shape) < sizeThreshold; });
+        };
+        MathBackendWebGL.prototype.getGPGPUContext = function () {
+            return this.gpgpu;
+        };
+        MathBackendWebGL.prototype.where = function (condition) {
+            tf.backend_util.warn('tf.where() in webgl locks the UI thread. ' +
+                'Call tf.whereAsync() instead');
+            var condVals = condition.dataSync();
+            return whereImpl(condition.shape, condVals);
+        };
+        MathBackendWebGL.prototype.packedUnaryOp = function (x, op, dtype) {
+            var program = new UnaryOpPackedProgram(x.shape, op);
+            var outInfo = this.compileAndRun(program, [x], dtype);
+            return tf.engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);
+        };
+        // TODO(msoulanille) remove this once the backend has been modularized
+        // a copy is needed here to break a circular dependency.
+        // Also remove the op from unary_op.
+        MathBackendWebGL.prototype.abs = function (x) {
+            // TODO: handle cases when x is complex.
+            if (this.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {
+                var outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);
+                return this.makeOutput(x.shape, x.dtype, outValues);
+            }
+            if (tf.env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
+                return this.packedUnaryOp(x, ABS, x.dtype);
+            }
+            var program = new UnaryOpProgram(x.shape, ABS);
+            var outInfo = this.compileAndRun(program, [x]);
+            return tf.engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);
+        };
+        MathBackendWebGL.prototype.makeTensorInfo = function (shape, dtype, values) {
+            var dataId;
+            if (dtype === 'string' && values != null && values.length > 0 &&
+                tf.util.isString(values[0])) {
+                var encodedValues = values.map(function (d) { return tf.util.encodeString(d); });
+                dataId = this.write(encodedValues, shape, dtype);
+            }
+            else {
+                dataId = this.write(values, shape, dtype);
+            }
+            this.texData.get(dataId).usage = null;
+            return { dataId: dataId, shape: shape, dtype: dtype };
+        };
+        MathBackendWebGL.prototype.makeOutput = function (shape, dtype, values) {
+            var dataId = this.makeTensorInfo(shape, dtype, values).dataId;
+            return tf.engine().makeTensorFromDataId(dataId, shape, dtype, this);
+        };
+        MathBackendWebGL.prototype.unpackTensor = function (input) {
+            var program = new UnpackProgram(input.shape);
+            return this.runWebGLProgram(program, [input], input.dtype);
+        };
+        MathBackendWebGL.prototype.packTensor = function (input) {
+            var program = new PackProgram(input.shape);
+            var preventEagerUnpackingOutput = true;
+            return this.runWebGLProgram(program, [input], input.dtype, null /* customSetup */, preventEagerUnpackingOutput);
+        };
+        MathBackendWebGL.prototype.packedReshape = function (input, afterShape) {
+            var input3DShape = [
+                getBatchDim(input.shape)
+            ].concat(getRowsCols(input.shape));
+            var input3D = {
+                dtype: input.dtype,
+                shape: input3DShape,
+                dataId: input.dataId
+            };
+            var afterShapeAs3D = [
+                getBatchDim(afterShape)
+            ].concat(getRowsCols(afterShape));
+            var program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
+            var preventEagerUnpackingOfOutput = true;
+            var output = this.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);
+            return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };
+        };
+        MathBackendWebGL.prototype.decode = function (dataId) {
+            var texData = this.texData.get(dataId);
+            var isPacked = texData.isPacked, shape = texData.shape, dtype = texData.dtype;
+            var shapeAs3D = getShapeAs3D(shape);
+            var program;
+            if (isPacked) {
+                program = new DecodeMatrixPackedProgram(shapeAs3D);
+            }
+            else {
+                program = new DecodeMatrixProgram(shapeAs3D);
+            }
+            var preventEagerUnpackingOfOutput = true;
+            var out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype: dtype, dataId: dataId }], dtype, null /* customSetup */, preventEagerUnpackingOfOutput);
+            return { dtype: dtype, shape: shape, dataId: out.dataId };
+        };
+        MathBackendWebGL.prototype.runWebGLProgram = function (program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput) {
+            var _this = this;
+            if (preventEagerUnpackingOfOutput === void 0) { preventEagerUnpackingOfOutput = false; }
+            var output = this.makeTensorInfo(program.outputShape, outputDtype);
+            var outData = this.texData.get(output.dataId);
+            if (program.packedOutput) {
+                outData.isPacked = true;
+            }
+            if (program.outPackingScheme === PackingScheme.DENSE) {
+                var texelShape = getDenseTexShape(program.outputShape);
+                // For a densely packed output, we explicitly set texShape
+                // so it doesn't get assigned later according to our typical packing
+                // scheme wherein a single texel can only contain values from adjacent
+                // rows/cols.
+                outData.texShape = texelShape.map(function (d) { return d * 2; });
+            }
+            if (program.outTexUsage != null) {
+                outData.usage = program.outTexUsage;
+            }
+            if (tf.util.sizeFromShape(output.shape) === 0) {
+                // Short-circuit the computation since the result is empty (has 0 in its
+                // shape).
+                outData.values =
+                    tf.util.getTypedArrayFromDType(output.dtype, 0);
+                return output;
+            }
+            var dataToDispose = [];
+            var inputsData = inputs.map(function (input) {
+                if (input.dtype === 'complex64') {
+                    throw new Error("GPGPUProgram does not support complex64 input. For complex64 " +
+                        "dtypes, please separate the program into real and imaginary " +
+                        "parts.");
+                }
+                var texData = _this.texData.get(input.dataId);
+                if (texData.texture == null) {
+                    if (!program.packedInputs &&
+                        tf.util.sizeFromShape(input.shape) <=
+                            tf.env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {
+                        // Upload small tensors that live on the CPU as uniforms, not as
+                        // textures. Do this only when the environment supports 32bit floats
+                        // due to problems when comparing 16bit floats with 32bit floats.
+                        // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it
+                        // possible for packed shaders to sample from uniforms.
+                        return {
+                            shape: input.shape,
+                            texData: null,
+                            isUniform: true,
+                            uniformValues: texData.values
+                        };
+                    }
+                    // This ensures that if a packed program's inputs have not yet been
+                    // uploaded to the GPU, they get uploaded as packed right off the bat.
+                    if (program.packedInputs) {
+                        texData.isPacked = true;
+                        texData.shape = input.shape;
+                    }
+                }
+                else if (!!texData.isPacked !== !!program.packedInputs) {
+                    input = texData.isPacked ? _this.unpackTensor(input) :
+                        _this.packTensor(input);
+                    dataToDispose.push(input);
+                    texData = _this.texData.get(input.dataId);
+                }
+                else if (texData.isPacked &&
+                    !isReshapeFree(texData.shape, input.shape)) {
+                    // This is a special case where a texture exists for a tensor
+                    // but the shapes are incompatible (due to packing constraints) because
+                    // the tensor did not have a chance to go through the packed reshape
+                    // shader. This only happens when we reshape the *same* tensor to form
+                    // *distinct* inputs to an op, e.g. dotting a vector with itself. This
+                    // case will disappear once packed uploading is the default.
+                    var savedInput = input;
+                    var targetShape = input.shape;
+                    input.shape = texData.shape;
+                    input = _this.packedReshape(input, targetShape);
+                    dataToDispose.push(input);
+                    texData = _this.texData.get(input.dataId);
+                    savedInput.shape = targetShape;
+                }
+                _this.uploadToGPU(input.dataId);
+                return { shape: input.shape, texData: texData, isUniform: false };
+            });
+            this.uploadToGPU(output.dataId);
+            var outputData = { shape: output.shape, texData: outData, isUniform: false };
+            var key = makeShaderKey(program, inputsData, outputData);
+            var binary = this.getAndSaveBinary(key, function () {
+                return compileProgram(_this.gpgpu, program, inputsData, outputData);
+            });
+            var shouldTimeProgram = this.activeTimers != null;
+            var query;
+            if (shouldTimeProgram) {
+                query = this.startTimer();
+            }
+            runProgram(this.gpgpu, binary, inputsData, outputData, customSetup);
+            dataToDispose.forEach(function (info) { return _this.disposeIntermediateTensorInfo(info); });
+            if (shouldTimeProgram) {
+                query = this.endTimer(query);
+                this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });
+            }
+            var glFlushThreshold = tf.env().get('WEBGL_FLUSH_THRESHOLD');
+            // Manually GL flush requested
+            if (glFlushThreshold > 0) {
+                var time = tf.util.now();
+                if ((time - this.lastGlFlushTime) > glFlushThreshold) {
+                    this.gpgpu.gl.flush();
+                    this.lastGlFlushTime = time;
+                }
+            }
+            if (!tf.env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&
+                preventEagerUnpackingOfOutput === false) {
+                var unpacked = this.unpackTensor(output);
+                this.disposeIntermediateTensorInfo(output);
+                return unpacked;
+            }
+            return output;
+        };
+        MathBackendWebGL.prototype.compileAndRun = function (program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput) {
+            if (preventEagerUnpackingOfOutput === void 0) { preventEagerUnpackingOfOutput = false; }
+            outputDtype = outputDtype || inputs[0].dtype;
+            var outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);
+            return outInfo;
+        };
+        MathBackendWebGL.prototype.getAndSaveBinary = function (key, getBinary) {
+            if (!(key in this.binaryCache)) {
+                this.binaryCache[key] = getBinary();
+            }
+            return this.binaryCache[key];
+        };
+        MathBackendWebGL.prototype.getTextureManager = function () {
+            return this.textureManager;
+        };
+        MathBackendWebGL.prototype.dispose = function () {
+            var _this = this;
+            if (this.disposed) {
+                return;
+            }
+            // Avoid disposing the compiled webgl programs during unit testing because
+            // it slows down test execution.
+            if (!tf.env().getBool('IS_TEST')) {
+                var allKeys = Object.keys(this.binaryCache);
+                allKeys.forEach(function (key) {
+                    _this.gpgpu.deleteProgram(_this.binaryCache[key].webGLProgram);
+                    delete _this.binaryCache[key];
+                });
+            }
+            this.textureManager.dispose();
+            if (this.canvas != null &&
+                (typeof (HTMLCanvasElement) !== 'undefined' &&
+                    this.canvas instanceof HTMLCanvasElement)) {
+                this.canvas.remove();
+            }
+            else {
+                this.canvas = null;
+            }
+            if (this.gpgpuCreatedLocally) {
+                this.gpgpu.program = null;
+                this.gpgpu.dispose();
+            }
+            this.disposed = true;
+        };
+        MathBackendWebGL.prototype.floatPrecision = function () {
+            var _this = this;
+            if (this.floatPrecisionValue == null) {
+                this.floatPrecisionValue = tf.tidy(function () {
+                    if (!tf.env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {
+                        // Momentarily switching DEBUG flag to false so we don't throw an
+                        // error trying to upload a small value.
+                        var debugFlag = tf.env().getBool('DEBUG');
+                        tf.env().set('DEBUG', false);
+                        var underflowCheckValue = _this.abs(tf.scalar(1e-8)).dataSync()[0];
+                        tf.env().set('DEBUG', debugFlag);
+                        if (underflowCheckValue > 0) {
+                            return 32;
+                        }
+                    }
+                    return 16;
+                });
+            }
+            return this.floatPrecisionValue;
+        };
+        /** Returns the smallest representable number.  */
+        MathBackendWebGL.prototype.epsilon = function () {
+            return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
+        };
+        MathBackendWebGL.prototype.uploadToGPU = function (dataId) {
+            var _a;
+            var texData = this.texData.get(dataId);
+            var shape = texData.shape, dtype = texData.dtype, values = texData.values, texture = texData.texture, usage = texData.usage, isPacked = texData.isPacked;
+            if (texture != null) {
+                // Array is already on GPU. No-op.
+                return;
+            }
+            var shouldTimeProgram = this.activeTimers != null;
+            var start;
+            if (shouldTimeProgram) {
+                start = tf.util.now();
+            }
+            var texShape = texData.texShape;
+            if (texShape == null) {
+                texShape = getTextureShapeFromLogicalShape(shape, isPacked);
+                texData.texShape = texShape;
+            }
+            if (values != null) {
+                var shapeAs3D = getShapeAs3D(shape);
+                var program = void 0;
+                var width = texShape[1], height = texShape[0];
+                var isByteArray = values instanceof Uint8Array;
+                if (isPacked) {
+                    _a = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]), width = _a[0], height = _a[1];
+                    program = new EncodeMatrixPackedProgram(shapeAs3D, [height, width], isByteArray);
+                }
+                else {
+                    program =
+                        new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);
+                }
+                var tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);
+                if (isByteArray) {
+                    this.texData.get(tempDenseInputHandle.dataId).usage =
+                        TextureUsage.PIXELS;
+                }
+                else {
+                    this.texData.get(tempDenseInputHandle.dataId).usage =
+                        TextureUsage.UPLOAD;
+                }
+                this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);
+                // We want the output to remain packed regardless of the value of
+                // WEBGL_PACK.
+                var preventEagerUnpacking = true;
+                var encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);
+                // Have the original texture assume the identity of the encoded output.
+                var outputTexData = this.texData.get(encodedOutputTarget.dataId);
+                texData.texture = outputTexData.texture;
+                texData.texShape = outputTexData.texShape;
+                texData.isPacked = outputTexData.isPacked;
+                texData.usage = outputTexData.usage;
+                this.disposeIntermediateTensorInfo(tempDenseInputHandle);
+                this.texData.delete(encodedOutputTarget.dataId);
+                // Once uploaded, don't store the values on cpu.
+                texData.values = null;
+                if (shouldTimeProgram) {
+                    this.uploadWaitMs += tf.util.now() - start;
+                }
+            }
+            else {
+                var newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);
+                texData.texture = newTexture;
+            }
+        };
+        MathBackendWebGL.prototype.convertAndCacheOnCPU = function (dataId, float32Values) {
+            var texData = this.texData.get(dataId);
+            var dtype = texData.dtype;
+            this.releaseGPUData(dataId);
+            if (float32Values != null) {
+                texData.values = float32ToTypedArray(float32Values, dtype);
+            }
+            return texData.values;
+        };
+        MathBackendWebGL.prototype.acquireTexture = function (texShape, texType, dtype, isPacked) {
+            this.numBytesInGPU += this.computeBytes(texShape, dtype);
+            if (!this.warnedAboutMemory &&
+                this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {
+                var mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);
+                this.warnedAboutMemory = true;
+                console.warn("High memory usage in GPU: " + mb + " MB, " +
+                    "most likely due to a memory leak");
+            }
+            return this.textureManager.acquireTexture(texShape, texType, isPacked);
+        };
+        MathBackendWebGL.prototype.computeBytes = function (shape, dtype) {
+            return shape[0] * shape[1] * tf.util.bytesPerElement(dtype);
+        };
+        MathBackendWebGL.nextDataId = 0;
+        return MathBackendWebGL;
+    }(tf.KernelBackend));
+    function float32ToTypedArray(a, dtype) {
+        if (dtype === 'float32' || dtype === 'complex64') {
+            return a;
+        }
+        else if (dtype === 'int32' || dtype === 'bool') {
+            var result = (dtype === 'int32') ? new Int32Array(a.length) :
+                new Uint8Array(a.length);
+            for (var i = 0; i < result.length; ++i) {
+                result[i] = Math.round(a[i]);
+            }
+            return result;
+        }
+        else {
+            throw new Error("Unknown dtype " + dtype);
+        }
+    }
+
+    /** @license See the LICENSE file. */
+    // This code is auto-generated, do not modify this file!
+    var version = '3.7.0';
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Enforce use of half precision textures if available on the platform.
+     *
+     * @doc {heading: 'Environment', namespace: 'webgl'}
+     */
+    function forceHalfFloat() {
+        tf.env().set('WEBGL_FORCE_F16_TEXTURES', true);
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google Inc. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    if (tf.device_util.isBrowser()) {
+        tf.registerBackend('webgl', function () { return new MathBackendWebGL(); }, 2 /* priority */);
+    }
+    var webgl = { forceHalfFloat: forceHalfFloat };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var CHECK_NAN_SNIPPET$1 = "\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n";
+    var BinaryOpProgram = /** @class */ (function () {
+        function BinaryOpProgram(op, aShape, bShape) {
+            this.variableNames = ['A', 'B'];
+            this.outputShape = tf.backend_util.assertAndGetBroadcastShape(aShape, bShape);
+            this.userCode = "\n      float binaryOperation(float a, float b) {\n        " + op + "\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    ";
+        }
+        return BinaryOpProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var CHECK_NAN_SNIPPET$2 = "\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n";
+    var BinaryOpPackedProgram = /** @class */ (function () {
+        function BinaryOpPackedProgram(op, aShape, bShape, checkOutOfBounds) {
+            if (checkOutOfBounds === void 0) { checkOutOfBounds = false; }
+            this.variableNames = ['A', 'B'];
+            this.supportsBroadcasting = true;
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = tf.backend_util.assertAndGetBroadcastShape(aShape, bShape);
+            var rank = this.outputShape.length;
+            var checkOutOfBoundsString = '';
+            if (checkOutOfBounds) {
+                if (rank === 0 || tf.util.sizeFromShape(this.outputShape) === 1) {
+                    checkOutOfBoundsString = "\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        ";
+                }
+                else {
+                    var dtype = getCoordsDataType(rank);
+                    checkOutOfBoundsString = "\n          " + dtype + " coords = getOutputCoords();\n        ";
+                    if (rank === 1) {
+                        checkOutOfBoundsString += "\n            result.y = (coords + 1) >= " + this.outputShape[0] + " ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          ";
+                    }
+                    else {
+                        var channels = getChannels('coords', rank);
+                        checkOutOfBoundsString += "\n            bool nextRowOutOfBounds =\n              (" + channels[rank - 2] + " + 1) >= " + this.outputShape[rank - 2] + ";\n            bool nextColOutOfBounds =\n              (" + channels[rank - 1] + " + 1) >= " + this.outputShape[rank - 1] + ";\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          ";
+                    }
+                }
+            }
+            this.userCode = "\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        " + op + "\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        " + checkOutOfBoundsString + "\n\n        setOutput(result);\n      }\n    ";
+        }
+        return BinaryOpPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function identity(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var x = inputs.x;
+        backend.incRef(x.dataId);
+        return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };
+    }
+    var identityConfig = {
+        kernelName: tf.Identity,
+        backendName: 'webgl',
+        kernelFunc: identity
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * In WebGL data is stored in GPU textures which can't be efficiently copied, so
+     * complex tensors share data with their real and imaginary components. Complex
+     * tensors' reference to the components is tracked by refCount on the individual
+     * component. The refCounts are increased by the identity call.
+     *
+     * When a complex tensor is disposed, it will reduce the refCount on the
+     * components by calling disposeData on each.
+     */
+    function complex(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var real = inputs.real, imag = inputs.imag;
+        var complexInfo = backend.makeTensorInfo(real.shape, 'complex64');
+        var complex = backend.texData.get(complexInfo.dataId);
+        var realTensorInfo = identity({ inputs: { x: real }, backend: backend });
+        var imagTensorInfo = identity({ inputs: { x: imag }, backend: backend });
+        complex.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };
+        return complexInfo;
+    }
+    var complexConfig = {
+        kernelName: tf.Complex,
+        backendName: 'webgl',
+        kernelFunc: complex
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LEAKYRELU = "return (a < 0.) ? b * a : a;";
+    var LEAKYRELU_PACKED = "\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n";
+    function leakyRelu(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var alpha = attrs.alpha;
+        var $alpha = backend.makeTensorInfo([], 'float32', tf.util.createScalarValue(alpha, 'float32'));
+        var program = tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
+            new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) :
+            new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape);
+        var result = backend.runWebGLProgram(program, [x, $alpha], x.dtype);
+        backend.disposeIntermediateTensorInfo($alpha);
+        return result;
+    }
+    var leakyReluConfig = {
+        kernelName: tf.LeakyRelu,
+        backendName: 'webgl',
+        kernelFunc: leakyRelu
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var PRELU = "return (a < 0.) ? b * a : a;";
+    var PRELU_PACKED = "\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n";
+    function prelu(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var x = inputs.x, alpha = inputs.alpha;
+        var program = tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
+            new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha.shape) :
+            new BinaryOpProgram(PRELU, x.shape, alpha.shape);
+        return backend.runWebGLProgram(program, [x, alpha], x.dtype);
+    }
+    var preluConfig = {
+        kernelName: tf.Prelu,
+        backendName: 'webgl',
+        kernelFunc: prelu
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var CHECK_NAN_SNIPPET_UNARY = "if (isnan(x)) return x;";
+    var CHECK_NAN_SNIPPET_BINARY = "\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n";
+    var CHECK_NAN_SNIPPET_BINARY_PACKED = "\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n";
+    /**
+     * Template that creates a `KernelFunc` for unary ops.
+     * @param opSnippet Op snippet to create `UnaryOpProgram`.
+     * @param packedOpSnippet Op snippet to create `UnaryOpPackedProgram`.
+     * @param dtype Optional. If set, the result has this dtype. Otherwise, the
+     *     result has the same dtype as the first input. This is mainly used in
+     *     comparison kernels, such as Equal, Less, Greater, etc.
+     */
+    function unaryKernelFunc(_a) {
+        var opSnippet = _a.opSnippet, packedOpSnippet = _a.packedOpSnippet, cpuKernelImpl = _a.cpuKernelImpl, dtype = _a.dtype;
+        return function (_a) {
+            var inputs = _a.inputs, backend = _a.backend;
+            var x = inputs.x;
+            var webglBackend = backend;
+            var $dtype = dtype || x.dtype;
+            if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {
+                var xData = webglBackend.texData.get(x.dataId);
+                var outValues = cpuKernelImpl(xData.values, $dtype);
+                return webglBackend.makeTensorInfo(x.shape, $dtype, outValues);
+            }
+            var shouldUsePackedProgram = tf.env().getBool('WEBGL_PACK_UNARY_OPERATIONS') && packedOpSnippet != null;
+            var program;
+            if (shouldUsePackedProgram) {
+                program = new UnaryOpPackedProgram(x.shape, packedOpSnippet);
+            }
+            else {
+                program = new UnaryOpProgram(x.shape, opSnippet);
+            }
+            return webglBackend.runWebGLProgram(program, [x], $dtype);
+        };
+    }
+    /**
+     * Template that creates a `KernelFunc` for binary ops.
+     * @param opSnippet Op snippet to create `BinaryOpProgram`.
+     * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.
+     * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true
+     *     when creating BinaryOpPackedProgram.
+     * @param dtype Optional. If set, the result has this dtype. Otherwise, the
+     *     result has the same dtype as the first input. This is mainly used in
+     *     comparison kernels, such as Equal, Less, Greater, etc.
+     */
+    function binaryKernelFunc(_a) {
+        var opSnippet = _a.opSnippet, packedOpSnippet = _a.packedOpSnippet, _b = _a.checkOutOfBounds, checkOutOfBounds = _b === void 0 ? false : _b, _c = _a.supportsComplex, supportsComplex = _c === void 0 ? false : _c, cpuKernelImpl = _a.cpuKernelImpl, dtype = _a.dtype;
+        return function (_a) {
+            var inputs = _a.inputs, backend = _a.backend;
+            var _b = inputs, a = _b.a, b = _b.b;
+            var webglBackend = backend;
+            if (supportsComplex && a.dtype === 'complex64') {
+                var aData = webglBackend.texData.get(a.dataId);
+                var bData = webglBackend.texData.get(b.dataId);
+                var _c = [
+                    [aData.complexTensorInfos.real, bData.complexTensorInfos.real],
+                    [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]
+                ].map(function (complexParts) {
+                    var aPart = complexParts[0], bPart = complexParts[1];
+                    var aHandle = {
+                        dataId: aPart.dataId,
+                        dtype: aPart.dtype,
+                        shape: a.shape
+                    };
+                    var bHandle = {
+                        dataId: bPart.dataId,
+                        dtype: bPart.dtype,
+                        shape: b.shape
+                    };
+                    var program = new BinaryOpProgram(opSnippet, a.shape, b.shape);
+                    return webglBackend.runWebGLProgram(program, [aHandle, bHandle], tf.upcastType(aPart.dtype, bPart.dtype));
+                }), real = _c[0], imag = _c[1];
+                var complexOutput = complex({ inputs: { real: real, imag: imag }, backend: webglBackend });
+                webglBackend.disposeIntermediateTensorInfo(real);
+                webglBackend.disposeIntermediateTensorInfo(imag);
+                // TODO(annxingyuan): Implement CPU forwarding for complex inputs.
+                return complexOutput;
+            }
+            var $dtype = dtype || tf.upcastType(a.dtype, b.dtype);
+            if ((a.dtype === 'string' || b.dtype === 'string' ||
+                webglBackend.shouldExecuteOnCPU([a, b])) &&
+                cpuKernelImpl != null) {
+                var aVals = webglBackend.texData.get(a.dataId).values;
+                var bVals = webglBackend.texData.get(b.dataId).values;
+                var decodedAVals = a.dtype === 'string' ?
+                    // tslint:disable-next-line: no-any
+                    tf.backend_util.fromUint8ToStringArray(aVals) :
+                    aVals;
+                var decodedBVals = a.dtype === 'string' ?
+                    // tslint:disable-next-line: no-any
+                    tf.backend_util.fromUint8ToStringArray(bVals) :
+                    bVals;
+                var _d = cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype), outValues = _d[0], outShape = _d[1];
+                var out = webglBackend.makeTensorInfo(outShape, $dtype);
+                var outData = webglBackend.texData.get(out.dataId);
+                outData.values = outValues;
+                return out;
+            }
+            var shouldUsePackedProgram = tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS') &&
+                packedOpSnippet != null;
+            var program;
+            if (shouldUsePackedProgram) {
+                program = new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, checkOutOfBounds);
+            }
+            else {
+                program = new BinaryOpProgram(opSnippet, a.shape, b.shape);
+            }
+            return webglBackend.runWebGLProgram(program, [a, b], $dtype);
+        };
+    }
+    function mapActivationToShaderProgram(activation, packed) {
+        if (packed === void 0) { packed = false; }
+        if (activation === 'linear') {
+            if (packed) {
+                return LINEAR$1;
+            }
+            return LINEAR;
+        }
+        else if (activation === 'relu') {
+            if (packed) {
+                return RELU$1;
+            }
+            return RELU;
+        }
+        else if (activation === 'elu') {
+            if (packed) {
+                return ELU$1;
+            }
+            return ELU;
+        }
+        else if (activation === 'relu6') {
+            if (packed) {
+                return RELU6$1;
+            }
+            return RELU6;
+        }
+        else if (activation === 'prelu') {
+            if (packed) {
+                return PRELU_PACKED;
+            }
+            return PRELU;
+        }
+        else if (activation === 'leakyrelu') {
+            if (packed) {
+                return LEAKYRELU_PACKED;
+            }
+            return LEAKYRELU;
+        }
+        else if (activation === 'sigmoid') {
+            if (packed) {
+                return SIGMOID$1;
+            }
+            return SIGMOID;
+        }
+        throw new Error("Activation " + activation + " has not been implemented for the WebGL backend.");
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var MatMulPackedProgram = /** @class */ (function () {
+        function MatMulPackedProgram(aShape, bShape, outputShape, transposeA, transposeB, addBias, activation, hasPreluActivation, hasLeakyreluActivation) {
+            if (transposeA === void 0) { transposeA = false; }
+            if (transposeB === void 0) { transposeB = false; }
+            if (addBias === void 0) { addBias = false; }
+            if (activation === void 0) { activation = null; }
+            if (hasPreluActivation === void 0) { hasPreluActivation = false; }
+            if (hasLeakyreluActivation === void 0) { hasLeakyreluActivation = false; }
+            this.variableNames = ['matrixA', 'matrixB'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = outputShape;
+            var sharedDim = transposeA ? aShape[1] : aShape[2];
+            var sharedDimensionPacked = Math.ceil(sharedDim / 2);
+            var aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';
+            var bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';
+            var aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];
+            var bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];
+            var activationSnippet = '', applyActivationSnippet = '';
+            if (activation) {
+                if (hasPreluActivation) {
+                    activationSnippet = "vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          " + activation + "\n        }";
+                }
+                else if (hasLeakyreluActivation) {
+                    activationSnippet = "vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          " + activation + "\n        }";
+                }
+                else {
+                    activationSnippet = "vec4 activation(vec4 x) {\n          " + activation + "\n        }";
+                }
+                applyActivationSnippet = "result = activation(result);";
+            }
+            var addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
+            if (addBias) {
+                this.variableNames.push('bias');
+            }
+            if (hasPreluActivation) {
+                this.variableNames.push('preluActivationWeights');
+            }
+            if (hasLeakyreluActivation) {
+                this.variableNames.push('leakyreluAlpha');
+            }
+            var batchASnippet = 'rc.x';
+            var batchBSnippet = 'rc.x';
+            if (aShape[0] < bShape[0]) {
+                batchASnippet = "int(min(float(rc.x), " + (aShape[0] - 1) + ".))";
+            }
+            else if (bShape[0] < aShape[0]) {
+                batchBSnippet = "int(min(float(rc.x), " + (bShape[0] - 1) + ".))";
+            }
+            this.userCode = "\n      " + activationSnippet + "\n\n      const float sharedDimension = " + sharedDimensionPacked + ".0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < " + sharedDimensionPacked + "; i++) {\n          int batchA = " + batchASnippet + ";\n          int batchB = " + batchBSnippet + ";\n          vec4 a = getMatrixA(batchA, " + aSample + ");\n          vec4 b = getMatrixB(batchB, " + bSample + ");\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (" + aSwizzle[0] + " * " + bSwizzle[0] + ");\n          result += (" + aSwizzle[1] + " * " + bSwizzle[1] + ");\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        " + addBiasSnippet + "\n\n        " + applyActivationSnippet + "\n\n        setOutput(result);\n      }\n    ";
+        }
+        return MatMulPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // (Ar + Ai)(Br + Bi) =
+    // ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr
+    // Yr = ArBr - AB
+    // Yi = ArBi + AiBr
+    var COMPLEX_MULTIPLY = {
+        REAL: 'return areal * breal - aimag * bimag;',
+        IMAG: 'return areal * bimag + aimag * breal;'
+    };
+    var BinaryOpComplexProgram = /** @class */ (function () {
+        function BinaryOpComplexProgram(op, aShape, bShape) {
+            this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];
+            this.outputShape = tf.backend_util.assertAndGetBroadcastShape(aShape, bShape);
+            this.userCode = "\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        " + op + "\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    ";
+        }
+        return BinaryOpComplexProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var MUL = 'return a * b;';
+    function multiply(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var a = inputs.a, b = inputs.b;
+        var dtype = tf.backend_util.upcastType(a.dtype, b.dtype);
+        if (a.dtype === 'complex64') {
+            var aData = backend.texData.get(a.dataId);
+            var bData = backend.texData.get(b.dataId);
+            var realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a.shape, b.shape);
+            var imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);
+            var inputs_1 = [
+                {
+                    dataId: aData.complexTensorInfos.real.dataId,
+                    dtype: aData.complexTensorInfos.real.dtype,
+                    shape: a.shape
+                },
+                {
+                    dataId: aData.complexTensorInfos.imag.dataId,
+                    dtype: aData.complexTensorInfos.imag.dtype,
+                    shape: a.shape
+                },
+                {
+                    dataId: bData.complexTensorInfos.real.dataId,
+                    dtype: bData.complexTensorInfos.real.dtype,
+                    shape: b.shape
+                },
+                {
+                    dataId: bData.complexTensorInfos.imag.dataId,
+                    dtype: bData.complexTensorInfos.imag.dtype,
+                    shape: b.shape
+                }
+            ];
+            var realPart = backend.runWebGLProgram(realProgram, inputs_1, 'float32');
+            var imagPart = backend.runWebGLProgram(imagProgram, inputs_1, 'float32');
+            var complexOutput = complex({ inputs: { real: realPart, imag: imagPart }, backend: backend });
+            backend.disposeIntermediateTensorInfo(realPart);
+            backend.disposeIntermediateTensorInfo(imagPart);
+            // TODO(annxingyuan): CPU forwarding for complex inputs.
+            return complexOutput;
+        }
+        if (backend.shouldExecuteOnCPU([a, b])) {
+            var aData = backend.texData.get(a.dataId);
+            var bData = backend.texData.get(b.dataId);
+            var _a = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype), outValues = _a[0], outShape = _a[1];
+            var out = backend.makeTensorInfo(outShape, dtype);
+            var outData = backend.texData.get(out.dataId);
+            outData.values = outValues;
+            return out;
+        }
+        var program;
+        if (tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
+            program = new BinaryOpPackedProgram(MUL, a.shape, b.shape);
+        }
+        else {
+            program = new BinaryOpProgram(MUL, a.shape, b.shape);
+        }
+        return backend.runWebGLProgram(program, [a, b], dtype);
+    }
+    var multiplyConfig = {
+        kernelName: tf.Multiply,
+        backendName: 'webgl',
+        kernelFunc: multiply
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function packedReshape(input, afterShape, backend) {
+        var input3DShape = [getBatchDim(input.shape)].concat(getRowsCols(input.shape));
+        var input3D = {
+            dtype: input.dtype,
+            shape: input3DShape,
+            dataId: input.dataId
+        };
+        var afterShapeAs3D = [getBatchDim(afterShape)].concat(getRowsCols(afterShape));
+        var program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
+        var preventEagerUnpackingOfOutput = true;
+        var output = backend.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);
+        return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function reshape(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var shape = attrs.shape;
+        var webglBackend = backend;
+        var xSize = tf.util.sizeFromShape(x.shape);
+        var $shape = tf.util.inferFromImplicitShape(shape, xSize);
+        var $xSize = tf.util.sizeFromShape($shape);
+        tf.util.assert(xSize === $xSize, function () { return "The new shape (" + $shape + ") has " + $xSize + " elements and the old " +
+            ("shape (" + x.shape + ") has " + xSize + " elements. The new shape and old ") +
+            "shape must have the same number of elements."; });
+        var xTexData = webglBackend.texData.get(x.dataId);
+        if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) &&
+            !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {
+            return packedReshape(x, $shape, webglBackend);
+        }
+        webglBackend.incRef(x.dataId);
+        return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
+    }
+    var reshapeConfig = {
+        kernelName: tf.Reshape,
+        backendName: 'webgl',
+        kernelFunc: reshape
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var MeanProgram = /** @class */ (function () {
+        function MeanProgram(reduceInfo, divisor) {
+            this.variableNames = ['x'];
+            var windowSize = reduceInfo.windowSize, batchSize = reduceInfo.batchSize, inSize = reduceInfo.inSize, outSize = reduceInfo.outSize;
+            this.outputShape = [batchSize, outSize];
+            var windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+            var windowSizeVec4Remainder = windowSize % 4;
+            var updateSnippet = "sumValue += dot(values, ones);";
+            if (divisor != null) {
+                var denominator = 1 / divisor;
+                updateSnippet = "sumValue += dot(values * " + (tf.util.isInt(denominator) ? denominator.toPrecision(2) :
+                    denominator) + ", ones);";
+            }
+            var checkOutOfBounds = '';
+            if (inSize % windowSize > 0) {
+                checkOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return 0.0;\n        }\n      ";
+            }
+            this.userCode = "\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        " + checkOutOfBounds + "\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * " + windowSize + ";\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < " + windowSizeNearestVec4 + "; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          " + updateSnippet + "\n        }\n\n        int inIdx = inOffset + " + windowSizeNearestVec4 + ";\n        if (" + (windowSizeVec4Remainder === 1) + ") {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 2) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 3) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          " + updateSnippet + "\n        }\n        setOutput(sumValue);\n      }\n    ";
+        }
+        return MeanProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ReduceProgram = /** @class */ (function () {
+        function ReduceProgram(reduceInfo, reduceType) {
+            this.variableNames = ['x'];
+            var windowSize = reduceInfo.windowSize, batchSize = reduceInfo.batchSize, inSize = reduceInfo.inSize, outSize = reduceInfo.outSize;
+            this.outputShape = [batchSize, outSize];
+            var initializationValue = '0.0';
+            var compareOp = "";
+            if (reduceType === 'prod') {
+                initializationValue = '1.0';
+            }
+            else if (reduceType === 'min') {
+                // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
+                initializationValue = '1.0 / 1e-20';
+                compareOp = "min";
+            }
+            else if (reduceType === 'max') {
+                // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
+                initializationValue = '-1.0 / 1e-20';
+                compareOp = "max";
+            }
+            var returnValue = reduceType + "(" + reduceType + "(" + reduceType + "(" +
+                'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
+            if (reduceType === 'sum') {
+                returnValue = "sumValue";
+            }
+            else if (reduceType === 'prod') {
+                returnValue = "prodValue";
+            }
+            else if (reduceType === 'all') {
+                returnValue = "allValue";
+            }
+            else if (reduceType === 'any') {
+                returnValue = "anyValue";
+            }
+            var windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+            var windowSizeVec4Remainder = windowSize % 4;
+            var updateSnippet = "\n      if (" + (reduceType === 'sum') + ") {\n        sumValue += dot(values, ones);\n      } else if (" + (reduceType === 'prod') + ") {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = " + compareOp + "(values, minMaxValue);\n        if (" + (reduceType === 'min') + " || " + (reduceType === 'max') + ") {\n          minMaxValue = " + compareOp + "(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    ";
+            var vecType = "vec4";
+            if (reduceType === 'all') {
+                initializationValue = '1.0';
+                updateSnippet = "\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ";
+                vecType = "bvec4";
+            }
+            else if (reduceType === 'any') {
+                initializationValue = '0.0';
+                updateSnippet = "\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ";
+                vecType = "bvec4";
+            }
+            var checkOutOfBounds = '';
+            if (inSize % windowSize > 0) {
+                checkOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return initializationValue;\n        }\n      ";
+            }
+            this.userCode = "\n      const float initializationValue = " + initializationValue + ";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        " + checkOutOfBounds + "\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * " + windowSize + ";\n\n        vec4 minMaxValue = vec4(" + initializationValue + ");\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < " + windowSizeNearestVec4 + "; i += 4) {\n          int inIdx = inOffset + i;\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          " + updateSnippet + "\n        }\n\n        int inIdx = inOffset + " + windowSizeNearestVec4 + ";\n        if (" + (windowSizeVec4Remainder === 1) + ") {\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 2) + ") {\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 3) + ") {\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          " + updateSnippet + "\n        }\n        setOutput(" + returnValue + ");\n      }\n    ";
+        }
+        return ReduceProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // Returns an array of configuration objects that describe each stage of the
+    // reduction.
+    function getReductionStages(inShape) {
+        var stages = [];
+        while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {
+            var outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];
+            var windowSize = tf.backend_util.computeOptimalWindowSize(outSize);
+            stages.push({
+                inSize: outSize,
+                windowSize: windowSize,
+                outSize: Math.ceil(outSize / windowSize)
+            });
+        }
+        return stages;
+    }
+    function reduce(x, dtype, reductionType, backend) {
+        var reductionStages = getReductionStages(x.shape);
+        var result = x;
+        for (var i = 0; i < reductionStages.length; i++) {
+            var _a = reductionStages[i], inSize = _a.inSize, windowSize = _a.windowSize, outSize = _a.outSize;
+            var program = void 0;
+            var previousResult = void 0;
+            if (reductionType === 'mean') {
+                program = i === 0 ?
+                    new MeanProgram({ windowSize: windowSize, inSize: inSize, batchSize: x.shape[0], outSize: outSize }, inSize) :
+                    new MeanProgram({ windowSize: windowSize, inSize: inSize, batchSize: x.shape[0], outSize: outSize });
+            }
+            else {
+                program = new ReduceProgram({ windowSize: windowSize, inSize: inSize, batchSize: x.shape[0], outSize: outSize }, reductionType);
+            }
+            previousResult = result;
+            result = backend.runWebGLProgram(program, [result], dtype);
+            if (previousResult.dataId !== x.dataId) {
+                backend.disposeIntermediateTensorInfo(previousResult);
+            }
+        }
+        return result;
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var TransposeProgram = /** @class */ (function () {
+        function TransposeProgram(aShape, newDim) {
+            this.variableNames = ['A'];
+            var outputShape = new Array(aShape.length);
+            for (var i = 0; i < outputShape.length; i++) {
+                outputShape[i] = aShape[newDim[i]];
+            }
+            this.outputShape = outputShape;
+            this.rank = outputShape.length;
+            var dtype = getCoordsDataType(this.rank);
+            var switched = getSwitchedCoords(newDim);
+            this.userCode = "\n    void main() {\n      " + dtype + " resRC = getOutputCoords();\n      setOutput(getA(" + switched + "));\n    }\n    ";
+        }
+        return TransposeProgram;
+    }());
+    function getSwitchedCoords(newDim) {
+        var rank = newDim.length;
+        if (rank > 6) {
+            throw Error("Transpose for rank " + rank + " is not yet supported");
+        }
+        var originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];
+        var switchedCoords = new Array(rank);
+        for (var i = 0; i < newDim.length; i++) {
+            switchedCoords[newDim[i]] = originalOrder[i];
+        }
+        return switchedCoords.join();
+    }
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var TransposePackedProgram = /** @class */ (function () {
+        function TransposePackedProgram(aShape, newDim) {
+            this.variableNames = ['A'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            var outputShape = new Array(aShape.length);
+            for (var i = 0; i < outputShape.length; i++) {
+                outputShape[i] = aShape[newDim[i]];
+            }
+            this.outputShape = outputShape;
+            this.rank = outputShape.length;
+            if (this.rank > 6) {
+                throw Error("Packed transpose for rank " + this.rank + " is not yet supported.");
+            }
+            var dtype = getCoordsDataType(this.rank);
+            var outputOrder = getVecChannels('rc', this.rank);
+            var switchedOrder = new Array(this.rank);
+            for (var i = 0; i < newDim.length; i++) {
+                switchedOrder[newDim[i]] = outputOrder[i];
+            }
+            var innerDims = "vec2(" + switchedOrder.slice(-2).join() + ")";
+            var nextColumn = "++" + outputOrder[this.rank - 1] + " < " + outputShape[this.rank - 1];
+            var getc = "getChannel(getA(" + switchedOrder.join() + "), " + innerDims + ")";
+            this.userCode = "\n    void main() {\n      " + dtype + " rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = " + getc + ";\n      if(" + nextColumn + ") {\n        result[1] = " + getc + ";\n      }\n      --" + outputOrder[this.rank - 1] + ";\n      if(++" + outputOrder[this.rank - 2] + " < " + outputShape[this.rank - 2] + ") {\n        result[2] = " + getc + ";\n        if(" + nextColumn + ") {\n          result[3] = " + getc + ";\n        }\n      }\n      setOutput(result);\n    }\n    ";
+        }
+        return TransposePackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function transposeImpl$1(x, perm, backend) {
+        var program = tf.env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
+            new TransposePackedProgram(x.shape, perm) :
+            new TransposeProgram(x.shape, perm);
+        return backend.runWebGLProgram(program, [x], x.dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function sumImpl(x, axis, keepDims, backend) {
+        var reductionIndices = axis;
+        var xRank = x.shape.length;
+        var origAxes = tf.util.parseAxisParam(reductionIndices, x.shape);
+        var axes = origAxes;
+        var permutedAxes = tf.backend_util.getAxesPermutation(axes, xRank);
+        var sumInputIsTransposed = permutedAxes != null;
+        var sumInput = x;
+        if (sumInputIsTransposed) {
+            sumInput = transposeImpl$1(x, permutedAxes, backend);
+            axes = tf.backend_util.getInnerMostAxes(axes.length, xRank);
+        }
+        tf.backend_util.assertAxesAreInnerMostDims('sum', axes, xRank);
+        var _a = tf.backend_util.computeOutAndReduceShapes(sumInput.shape, axes), sumOutShape = _a[0], reduceShape = _a[1];
+        var outShape = sumOutShape;
+        if (keepDims) {
+            // rather than reshape at the end, set the target shape here.
+            outShape = tf.backend_util.expandShapeToKeepDim(sumOutShape, origAxes);
+        }
+        var inSize = tf.util.sizeFromShape(reduceShape);
+        var xSize = tf.util.sizeFromShape(x.shape);
+        var batchSize = xSize / inSize;
+        var reshapedInput = reshape({ inputs: { x: sumInput }, attrs: { shape: [batchSize, inSize] }, backend: backend });
+        var outType = tf.sumOutType(x.dtype);
+        var reduced = reduce(reshapedInput, outType, 'sum', backend);
+        var out = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend });
+        backend.disposeIntermediateTensorInfo(reshapedInput);
+        backend.disposeIntermediateTensorInfo(reduced);
+        if (sumInputIsTransposed) {
+            backend.disposeIntermediateTensorInfo(sumInput);
+        }
+        return out;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function sum(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis, keepDims = attrs.keepDims;
+        return sumImpl(x, axis, keepDims, backend);
+    }
+    var sumConfig = {
+        kernelName: tf.Sum,
+        backendName: 'webgl',
+        kernelFunc: sum
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function transpose(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var perm = attrs.perm;
+        var webglBackend = backend;
+        var xRank = x.shape.length;
+        var newShape = new Array(xRank);
+        for (var i = 0; i < newShape.length; i++) {
+            newShape[i] = x.shape[perm[i]];
+        }
+        var out;
+        if (webglBackend.shouldExecuteOnCPU([x])) {
+            var xTexData = webglBackend.texData.get(x.dataId);
+            var values = xTexData.values;
+            var outValues = transposeImplCPU(values, x.shape, x.dtype, perm, newShape);
+            out = webglBackend.makeTensorInfo(newShape, x.dtype);
+            var outData = webglBackend.texData.get(out.dataId);
+            outData.values = outValues;
+        }
+        else {
+            out = transposeImpl$1(x, perm, webglBackend);
+        }
+        return out;
+    }
+    var transposeConfig = {
+        kernelName: tf.Transpose,
+        backendName: 'webgl',
+        kernelFunc: transpose
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // Empirically determined minimal shared dimension in matmul before we forward
+    // to a.mul(b).sum() in order to take advantage of GPU parallelism. See
+    // https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.
+    var MATMUL_SHARED_DIM_THRESHOLD = 1000;
+    function batchMatMulImpl(_a) {
+        var a = _a.a, b = _a.b, transposeA = _a.transposeA, transposeB = _a.transposeB, backend = _a.backend, _b = _a.bias, bias = _b === void 0 ? null : _b, _c = _a.preluActivationWeights, preluActivationWeights = _c === void 0 ? null : _c, _d = _a.leakyreluAlpha, leakyreluAlpha = _d === void 0 ? 0 : _d, _e = _a.activation, activation = _e === void 0 ? null : _e;
+        var aRank = a.shape.length;
+        var bRank = b.shape.length;
+        var innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
+        var innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
+        var outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
+        var outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
+        var outerDimsA = a.shape.slice(0, -2);
+        var outerDimsB = b.shape.slice(0, -2);
+        var batchDimA = tf.util.sizeFromShape(outerDimsA);
+        var batchDimB = tf.util.sizeFromShape(outerDimsB);
+        var batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;
+        tf.util.assert(aRank >= 2 && bRank >= 2 && batchDimsCompatible, function () { return "Error in matMul: the input batch dimensions must either be the " +
+            "same or at least one input batch dimension must be 1. Got input " +
+            ("batch dimensions of (" + outerDimsA + ") and (" + outerDimsB + ")."); });
+        var outShapeOuterDims = batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2);
+        var outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+        tf.util.assert(innerShapeA === innerShapeB, function () { return "Error in matMul: inner shapes (" + innerShapeA + ") and (" +
+            (innerShapeB + ") of Tensors with shapes " + a.shape + " and ") +
+            (b.shape + " and transposeA=" + transposeA) +
+            (" and transposeB=" + transposeB + " must match."); });
+        var a3dShape = transposeA ?
+            [batchDimA, innerShapeA, outerShapeA] :
+            [batchDimA, outerShapeA, innerShapeA];
+        var b3dShape = transposeB ?
+            [batchDimB, outerShapeB, innerShapeB] :
+            [batchDimB, innerShapeB, outerShapeB];
+        // The rest of the implementation is designed to operate on rank-3 tensors
+        var a3d = reshape({ inputs: { x: a }, backend: backend, attrs: { shape: a3dShape } });
+        var b3d = reshape({ inputs: { x: b }, backend: backend, attrs: { shape: b3dShape } });
+        var intermediates = [a3d, b3d];
+        var batchDim = Math.max(batchDimA, batchDimB);
+        var sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
+        var hasBias = bias != null;
+        var hasPreluActivationWeights = preluActivationWeights != null;
+        var hasLeakyreluAlpha = activation === 'leakyrelu';
+        var fusedActivation = activation != null ?
+            mapActivationToShaderProgram(activation, true) :
+            null;
+        var containsFusedOps = hasBias || hasPreluActivationWeights ||
+            hasLeakyreluAlpha || fusedActivation != null;
+        var out;
+        // Since the matrices are vectors, it is faster to call mul().sum()
+        // because sum() is O(sqrt(N)) due to divide-and-conquer.
+        if ((outerShapeA === 1 || outerShapeB === 1) &&
+            sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) {
+            var aVec = a3d;
+            var bVec = b3d;
+            if (transposeA) {
+                aVec = transpose({ inputs: { x: a3d }, backend: backend, attrs: { perm: [0, 2, 1] } });
+                intermediates.push(aVec);
+            }
+            if (transposeB) {
+                bVec = transpose({ inputs: { x: b3d }, backend: backend, attrs: { perm: [0, 2, 1] } });
+                intermediates.push(bVec);
+            }
+            var shouldReshapeA = outerShapeB !== 1;
+            var shouldReshapeB = outerShapeB === 1;
+            var aVec3d = aVec;
+            if (shouldReshapeA) {
+                aVec3d = reshape({
+                    inputs: { x: aVec },
+                    backend: backend,
+                    attrs: { shape: [batchDim, sharedDim, 1] }
+                });
+                intermediates.push(aVec3d);
+            }
+            var axis = outerShapeB === 1 ? 2 : 1;
+            var bVec3d = bVec;
+            if (shouldReshapeB) {
+                bVec3d = reshape({
+                    inputs: { x: bVec },
+                    backend: backend,
+                    attrs: { shape: [batchDim, 1, sharedDim] }
+                });
+                intermediates.push(bVec3d);
+            }
+            var product = multiply({ inputs: { a: aVec3d, b: bVec3d }, backend: backend });
+            out = sum({ inputs: { x: product }, backend: backend, attrs: { axis: axis, keepDims: true } });
+            intermediates.push(product);
+        }
+        else {
+            var dtype = tf.upcastType(a.dtype, b.dtype);
+            var program = new MatMulPackedProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+            var inputs = [a3d, b3d];
+            if (bias != null) {
+                inputs.push(bias);
+            }
+            if (hasPreluActivationWeights) {
+                inputs.push(preluActivationWeights);
+            }
+            if (hasLeakyreluAlpha) {
+                var $leakyreluAlpha = backend.makeTensorInfo([], 'float32', tf.util.createScalarValue(leakyreluAlpha, 'float32'));
+                inputs.push($leakyreluAlpha);
+                intermediates.push($leakyreluAlpha);
+            }
+            out = backend.runWebGLProgram(program, inputs, dtype);
+        }
+        var outReshaped = reshape({ inputs: { x: out }, backend: backend, attrs: { shape: outShape } });
+        intermediates.push(out);
+        for (var _i = 0, intermediates_1 = intermediates; _i < intermediates_1.length; _i++) {
+            var i = intermediates_1[_i];
+            backend.disposeIntermediateTensorInfo(i);
+        }
+        return outReshaped;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function _fusedMatMul(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var a = inputs.a, b = inputs.b, bias = inputs.bias, preluActivationWeights = inputs.preluActivationWeights;
+        var transposeA = attrs.transposeA, transposeB = attrs.transposeB, activation = attrs.activation, leakyreluAlpha = attrs.leakyreluAlpha;
+        return batchMatMulImpl({
+            a: a,
+            b: b,
+            transposeA: transposeA,
+            transposeB: transposeB,
+            backend: backend,
+            bias: bias,
+            preluActivationWeights: preluActivationWeights,
+            leakyreluAlpha: leakyreluAlpha,
+            activation: activation
+        });
+    }
+    var _fusedMatMulConfig = {
+        kernelName: tf._FusedMatMul,
+        backendName: 'webgl',
+        kernelFunc: _fusedMatMul,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ABS$1 = "return abs(x);";
+    function abs(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var x = inputs.x;
+        // TODO: handle cases when x is complex. Once the cpu implementation
+        // can handle complex values, refactor to use unaryKernelFunc.
+        if (backend.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {
+            var xData = backend.texData.get(x.dataId);
+            var outValues = simpleAbsImplCPU(xData.values);
+            return backend.makeTensorInfo(x.shape, x.dtype, outValues);
+        }
+        var program;
+        if (tf.env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
+            program = new UnaryOpPackedProgram(x.shape, ABS$1);
+        }
+        else {
+            program = new UnaryOpProgram(x.shape, ABS$1);
+        }
+        return backend.runWebGLProgram(program, [x], x.dtype);
+    }
+    var absConfig = {
+        kernelName: tf.Abs,
+        backendName: 'webgl',
+        kernelFunc: abs
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ACOS = CHECK_NAN_SNIPPET + "\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n";
+    var acos = unaryKernelFunc({ opSnippet: ACOS });
+    var acosConfig = {
+        kernelName: tf.Acos,
+        backendName: 'webgl',
+        kernelFunc: acos,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ACOSH = CHECK_NAN_SNIPPET + "\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));";
+    var acosh = unaryKernelFunc({ opSnippet: ACOSH });
+    var acoshConfig = {
+        kernelName: tf.Acosh,
+        backendName: 'webgl',
+        kernelFunc: acosh,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ADD = 'return a + b;';
+    var addKernelFunc = binaryKernelFunc({
+        opSnippet: ADD,
+        packedOpSnippet: ADD,
+        supportsComplex: true,
+        cpuKernelImpl: addImplCPU
+    });
+    var addConfig = {
+        kernelName: tf.Add,
+        backendName: 'webgl',
+        kernelFunc: addKernelFunc
+    };
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var AddNProgram = /** @class */ (function () {
+        function AddNProgram(outputShape, shapes) {
+            this.outputShape = [];
+            this.outputShape = outputShape;
+            this.variableNames = shapes.map(function (_, i) { return "T" + i; });
+            var snippets = [];
+            // Get target elements from every input tensor.
+            this.variableNames.forEach(function (variable) {
+                snippets.push("float v" + variable + " = get" + variable + "AtOutCoords();");
+            });
+            // Calculate the sum of all elements.
+            var operation = this.variableNames
+                .map(function (variable) {
+                return "v" + variable;
+            })
+                .join(' + ');
+            this.userCode = "\n      void main() {\n        " + snippets.join('\n        ') + "\n\n        float result = " + operation + ";\n        setOutput(result);\n      }\n    ";
+        }
+        return AddNProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var AddNPackedProgram = /** @class */ (function () {
+        function AddNPackedProgram(outputShape, shapes) {
+            this.outputShape = [];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = outputShape;
+            this.variableNames = shapes.map(function (_, i) { return "T" + i; });
+            var snippets = [];
+            // Get target elements from every input tensor.
+            this.variableNames.forEach(function (variable) {
+                snippets.push("vec4 v" + variable + " = get" + variable + "AtOutCoords();");
+            });
+            // Calculate the sum of all elements.
+            var operation = this.variableNames
+                .map(function (variable) {
+                return "v" + variable;
+            })
+                .join(' + ');
+            this.userCode = "\n      void main() {\n        " + snippets.join('\n        ') + "\n\n        vec4 result = " + operation + ";\n        setOutput(result);\n      }\n    ";
+        }
+        return AddNPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function addN(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var tensors = inputs;
+        if (tensors.length === 1) {
+            return identity({ inputs: { x: tensors[0] }, backend: backend });
+        }
+        // Limit the number of uploaded textures for optimization.
+        if (tensors.length > tf.env().get('WEBGL_MAX_TEXTURES_IN_SHADER')) {
+            var midIndex = Math.floor(tensors.length / 2);
+            var leftSide = addN({ inputs: tensors.slice(0, midIndex), backend: backend });
+            var rightSide = addN({ inputs: tensors.slice(midIndex), backend: backend });
+            return addN({ inputs: [leftSide, rightSide], backend: backend });
+        }
+        var dtype = tensors.map(function (t) { return t.dtype; }).reduce(function (d1, d2) { return tf.upcastType(d1, d2); });
+        var shapes = tensors.map(function (t) { return t.shape; });
+        // We can make sure shapes are identical in op level.
+        var usePackedOp = tf.env().getBool('WEBGL_PACK');
+        var program = usePackedOp ?
+            new AddNPackedProgram(tensors[0].shape, shapes) :
+            new AddNProgram(tensors[0].shape, shapes);
+        return backend.runWebGLProgram(program, tensors, dtype);
+    }
+    var addNConfig = {
+        kernelName: tf.AddN,
+        backendName: 'webgl',
+        kernelFunc: addN
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function all(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis, keepDims = attrs.keepDims;
+        var xRank = x.shape.length;
+        var origAxes = tf.util.parseAxisParam(axis, x.shape);
+        var axes = origAxes;
+        var permutedAxes = tf.backend_util.getAxesPermutation(axes, xRank);
+        var permutedX = x;
+        if (permutedAxes != null) {
+            permutedX = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } });
+            axes = tf.backend_util.getInnerMostAxes(axes.length, xRank);
+        }
+        tf.backend_util.assertAxesAreInnerMostDims('all', axes, xRank);
+        var _a = tf.backend_util.computeOutAndReduceShapes(permutedX.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var inSize = tf.util.sizeFromShape(reduceShape);
+        var a2D = reshape({ inputs: { x: permutedX }, backend: backend, attrs: { shape: [-1, inSize] } });
+        var reduced = reduce(a2D, a2D.dtype, 'all', backend);
+        var res;
+        if (keepDims) {
+            var newShape = tf.backend_util.expandShapeToKeepDim(outShape, origAxes);
+            res = reshape({ inputs: { x: reduced }, backend: backend, attrs: { shape: newShape } });
+        }
+        else {
+            res = reshape({ inputs: { x: reduced }, backend: backend, attrs: { shape: outShape } });
+        }
+        backend.disposeIntermediateTensorInfo(a2D);
+        backend.disposeIntermediateTensorInfo(reduced);
+        if (permutedAxes != null) {
+            backend.disposeIntermediateTensorInfo(permutedX);
+        }
+        return res;
+    }
+    var allConfig = {
+        kernelName: tf.All,
+        backendName: 'webgl',
+        kernelFunc: all
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function any(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis, keepDims = attrs.keepDims;
+        var xRank = x.shape.length;
+        var origAxes = tf.util.parseAxisParam(axis, x.shape);
+        var axes = origAxes;
+        var permutedAxes = tf.backend_util.getAxesPermutation(axes, xRank);
+        var permutedX = x;
+        if (permutedAxes != null) {
+            permutedX = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } });
+            axes = tf.backend_util.getInnerMostAxes(axes.length, xRank);
+        }
+        tf.backend_util.assertAxesAreInnerMostDims('any', axes, xRank);
+        var _a = tf.backend_util.computeOutAndReduceShapes(permutedX.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var inSize = tf.util.sizeFromShape(reduceShape);
+        var a2D = reshape({ inputs: { x: permutedX }, backend: backend, attrs: { shape: [-1, inSize] } });
+        var reduced = reduce(a2D, a2D.dtype, 'any', backend);
+        var res;
+        if (keepDims) {
+            var newShape = tf.backend_util.expandShapeToKeepDim(outShape, origAxes);
+            res = reshape({ inputs: { x: reduced }, backend: backend, attrs: { shape: newShape } });
+        }
+        else {
+            res = reshape({ inputs: { x: reduced }, backend: backend, attrs: { shape: outShape } });
+        }
+        backend.disposeIntermediateTensorInfo(a2D);
+        backend.disposeIntermediateTensorInfo(reduced);
+        if (permutedAxes != null) {
+            backend.disposeIntermediateTensorInfo(permutedX);
+        }
+        return res;
+    }
+    var anyConfig = {
+        kernelName: tf.Any,
+        backendName: 'webgl',
+        kernelFunc: any
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ArgMinMaxProgram = /** @class */ (function () {
+        function ArgMinMaxProgram(reduceInfo, op, firstPass) {
+            this.variableNames = ['A'];
+            var windowSize = reduceInfo.windowSize, batchSize = reduceInfo.batchSize, outSize = reduceInfo.outSize;
+            if (!firstPass) {
+                this.variableNames.push('bestIndicesA');
+            }
+            this.outputShape = [batchSize, outSize];
+            var compOp = (op === 'max') ? '>' : '<';
+            var indexSnippet = firstPass ?
+                'inOffset + i;' :
+                'round(getBestIndicesA(batch, inOffset + i));';
+            this.userCode = "\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * " + windowSize + ";\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < " + windowSize + "; i++) {\n          int inIdx = " + indexSnippet + ";\n          float candidate = getA(batch, inIdx);\n          if (candidate " + compOp + " bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    ";
+        }
+        return ArgMinMaxProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ArgMinMaxPackedProgram = /** @class */ (function () {
+        function ArgMinMaxPackedProgram(shape, windowSize, op, firstPass) {
+            this.variableNames = ['A'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            tf.util.assert(shape.length > 2, function () { return "Packed arg" + (op.charAt(0).toUpperCase() +
+                op.slice(1)) + " supports only inputs with rank above 2."; });
+            var inSize = shape[shape.length - 1];
+            var outSize = Math.ceil(inSize / windowSize);
+            this.outputShape = shape.slice(0, -1);
+            if (outSize > 1) {
+                this.outputShape.push(outSize);
+            }
+            if (!firstPass) {
+                this.variableNames.push('bestIndicesA');
+            }
+            var outShape = this.outputShape;
+            var rank = outShape.length;
+            var dtype = getCoordsDataType(rank);
+            var coords = getChannels('coords', rank);
+            var sourceLocSetup;
+            var sourceRank;
+            if (outSize === 1) {
+                sourceRank = rank + 1;
+                var sourceLocDType = getCoordsDataType(sourceRank);
+                sourceLocSetup = "\n        " + sourceLocDType + " sourceLocR = " + sourceLocDType + "(" + coords.join() + ", 0);\n        ++" + coords[rank - 1] + ";\n        " + sourceLocDType + " sourceLocG = " + sourceLocDType + "(" + coords.join() + ", 0);\n        ++" + coords[rank - 2] + ";\n        " + sourceLocDType + " sourceLocA = " + sourceLocDType + "(" + coords.join() + ", 0);\n        --" + coords[rank - 1] + ";\n        " + sourceLocDType + " sourceLocB = " + sourceLocDType + "(" + coords.join() + ", 0);\n        --" + coords[rank - 2] + ";";
+            }
+            else {
+                sourceRank = rank;
+                sourceLocSetup = "\n        " + dtype + " sourceLocR = coords;\n        ++" + coords[rank - 1] + ";\n        " + dtype + " sourceLocG = coords;\n        ++" + coords[rank - 2] + ";\n        " + dtype + " sourceLocA = coords;\n        --" + coords[rank - 1] + ";\n        " + dtype + " sourceLocB = coords;\n        --" + coords[rank - 2] + ";";
+            }
+            var channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);
+            var inChannel = '.' + channels[sourceRank - 1]; // e.g. ".b" for rank 3.
+            var intChannels = channels.map(function (x) { return 'int ' + x; });
+            var srcRCoords = getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');
+            var srcGCoords = getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');
+            var srcBCoords = getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');
+            var srcACoords = getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');
+            var compOp = (op === 'max') ? 'greaterThan' : 'lessThan';
+            var fetchCandidateIdx = firstPass ? '' : "\n          inIdx = round(vec4(getBestIndicesAChannel(" + srcRCoords.join() + "),\n                             getBestIndicesAChannel(" + srcGCoords.join() + "),\n                             getBestIndicesAChannel(" + srcBCoords.join() + "),\n                             getBestIndicesAChannel(" + srcACoords.join() + ")));";
+            var fetchValue = "vec4(\n            getAChannel(" + srcRCoords.join() + "),\n            hasNextCol ? getAChannel(" + srcGCoords.join() + ") : 0.,\n            hasNextRow ? getAChannel(" + srcBCoords.join() + ") : 0.,\n            hasNextRow && hasNextCol ? getAChannel(" + srcACoords.join() + ") : 0.)";
+            var getBestIndicesAChannelSnippet = firstPass ? '' : "\n      float getBestIndicesAChannel(" + intChannels.join() + ") {\n        return getChannel(getBestIndicesA(" + channels.join() + "),\n                                          vec2(" + channels.slice(-2).join() + "));\n      }";
+            this.userCode = "\n      float getAChannel(" + intChannels.join() + ") {\n        return getChannel(getA(" + channels.join() + "),\n                               vec2(" + channels.slice(-2).join() + "));\n      }\n      " + getBestIndicesAChannelSnippet + "\n      void main() {\n        " + dtype + " coords = getOutputCoords();\n        bool hasNextCol = " + coords[rank - 1] + " < " + (outShape[rank - 1] - 1) + ";\n        bool hasNextRow = " + coords[rank - 2] + " < " + (outShape[rank - 2] - 1) + ";\n        " + sourceLocSetup + "\n        ivec4 srcIdx = ivec4(sourceLocR" + inChannel + ", sourceLocG" + inChannel + ",\n          sourceLocB" + inChannel + ", sourceLocA" + inChannel + ") * " + windowSize + ";\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = " + fetchValue + ";\n\n        for (int i = 0; i < " + windowSize + "; i++) {\n          inIdx = srcIdx;\n          " + fetchCandidateIdx + "\n          vec4 candidate = " + fetchValue + ";\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(" + compOp + "(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    ";
+        }
+        return ArgMinMaxPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function argReduce(backend, x, reduceType, bestIndicesA) {
+        if (bestIndicesA === void 0) { bestIndicesA = null; }
+        var batchSize = x.shape[0];
+        var inSize = x.shape[1];
+        if (bestIndicesA != null) {
+            batchSize = bestIndicesA.shape[0];
+            inSize = bestIndicesA.shape[1];
+        }
+        var windowSize = tf.backend_util.computeOptimalWindowSize(inSize);
+        var reduceInfo = { windowSize: windowSize, inSize: inSize, batchSize: batchSize, outSize: Math.ceil(inSize / windowSize) };
+        var program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);
+        var inputs = [x];
+        if (bestIndicesA != null) {
+            inputs.push(bestIndicesA);
+        }
+        var output = backend.runWebGLProgram(program, inputs, 'int32');
+        // No need to run another GPGPU program.
+        if (output.shape[1] === 1) {
+            return output;
+        }
+        var result = argReduce(backend, x, reduceType, output);
+        backend.disposeIntermediateTensorInfo(output);
+        return result;
+    }
+    function argReducePacked(backend, x, reduceType, bestIndicesA) {
+        if (bestIndicesA === void 0) { bestIndicesA = null; }
+        var inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;
+        var inSize = inShape[inShape.length - 1];
+        var windowSize = tf.backend_util.computeOptimalWindowSize(inSize);
+        var program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);
+        var inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];
+        var output = backend.runWebGLProgram(program, inputs, 'int32');
+        if (output.shape.length === x.shape.length) {
+            var result = argReducePacked(backend, x, reduceType, output);
+            backend.disposeIntermediateTensorInfo(output);
+            return result;
+        }
+        return output;
+    }
+    function argMinMaxReduce(backend, x, axis, reduceType) {
+        var axes = [axis];
+        tf.backend_util.assertAxesAreInnerMostDims('arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.shape.length);
+        if (!tf.env().getBool('WEBGL_PACK_REDUCE') || x.shape.length <= 2) {
+            var intermediateTensorInfos = [];
+            var _a = tf.backend_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var inSize = tf.util.sizeFromShape(reduceShape);
+            var a2D = reshape({ inputs: { x: x }, backend: backend, attrs: { shape: [-1, inSize] } });
+            intermediateTensorInfos.push(a2D);
+            var reduced = argReduce(backend, a2D, reduceType);
+            intermediateTensorInfos.push(reduced);
+            var reshaped = reshape({ inputs: { x: reduced }, backend: backend, attrs: { shape: outShape } });
+            intermediateTensorInfos.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+            return reshaped;
+        }
+        return argReducePacked(backend, x, reduceType);
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function argMax(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis;
+        var axes = tf.util.parseAxisParam(axis, x.shape);
+        var permutedAxes = tf.backend_util.getAxesPermutation(axes, x.shape.length);
+        var $x = x;
+        var intermediateTensorInfos = [];
+        if (permutedAxes != null) {
+            $x = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } });
+            intermediateTensorInfos.push($x);
+            axes = tf.backend_util.getInnerMostAxes(axes.length, $x.shape.length);
+        }
+        tf.backend_util.assertAxesAreInnerMostDims('argMax', [axes[0]], $x.shape.length);
+        var out = argMinMaxReduce(backend, $x, axes[0], 'max');
+        intermediateTensorInfos.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return out;
+    }
+    var argMaxConfig = {
+        kernelName: tf.ArgMax,
+        backendName: 'webgl',
+        kernelFunc: argMax
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function argMin(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis;
+        var axes = tf.util.parseAxisParam(axis, x.shape);
+        var permutedAxes = tf.backend_util.getAxesPermutation(axes, x.shape.length);
+        var $x = x;
+        var intermediateTensorInfos = [];
+        if (permutedAxes != null) {
+            $x = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } });
+            intermediateTensorInfos.push($x);
+            axes = tf.backend_util.getInnerMostAxes(axes.length, $x.shape.length);
+        }
+        tf.backend_util.assertAxesAreInnerMostDims('argMin', [axes[0]], $x.shape.length);
+        var out = argMinMaxReduce(backend, $x, axes[0], 'min');
+        intermediateTensorInfos.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return out;
+    }
+    var argMinConfig = {
+        kernelName: tf.ArgMin,
+        backendName: 'webgl',
+        kernelFunc: argMin
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ASIN = CHECK_NAN_SNIPPET + "\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n";
+    var asin = unaryKernelFunc({ opSnippet: ASIN });
+    var asinConfig = {
+        kernelName: tf.Asin,
+        backendName: 'webgl',
+        kernelFunc: asin,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ASINH = CHECK_NAN_SNIPPET + "return log(x + sqrt(x * x + 1.0));";
+    var asinh = unaryKernelFunc({ opSnippet: ASINH });
+    var asinhConfig = {
+        kernelName: tf.Asinh,
+        backendName: 'webgl',
+        kernelFunc: asinh,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ATAN = CHECK_NAN_SNIPPET + "\n  return atan(x);\n";
+    var atan = unaryKernelFunc({ opSnippet: ATAN });
+    var atanConfig = {
+        kernelName: tf.Atan,
+        backendName: 'webgl',
+        kernelFunc: atan,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ATAN2 = CHECK_NAN_SNIPPET_BINARY + "\n  return atan(a, b);\n";
+    var ATAN2_PACKED = "\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  " +
+        CHECK_NAN_SNIPPET_BINARY_PACKED + "\n  return result;\n";
+    var atan2 = binaryKernelFunc({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED });
+    var atan2Config = {
+        kernelName: tf.Atan2,
+        backendName: 'webgl',
+        kernelFunc: atan2,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ATANH = CHECK_NAN_SNIPPET + "\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;";
+    var atanh = unaryKernelFunc({ opSnippet: ATANH });
+    var atanhConfig = {
+        kernelName: tf.Atanh,
+        backendName: 'webgl',
+        kernelFunc: atanh,
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var Pool2DProgram = /** @class */ (function () {
+        function Pool2DProgram(convInfo, poolType, computePositions, flattenPositions, includeBatchInIndex) {
+            if (flattenPositions === void 0) { flattenPositions = false; }
+            if (includeBatchInIndex === void 0) { includeBatchInIndex = false; }
+            this.variableNames = ['x'];
+            if (poolType === 'avg' && computePositions) {
+                throw new Error('Cannot compute positions for average pool.');
+            }
+            var filterWidth = convInfo.filterWidth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            this.outputShape = convInfo.outShape;
+            var isAvgPool = poolType === 'avg';
+            var batchFlattenPositionStr = "((batch  * " + convInfo.inHeight + " + xR) * " + convInfo.inWidth + " + xC) * " + convInfo.inChannels + " + d";
+            var flattenPositionStr = "(xR * " + convInfo.inWidth + " + xC) * " + convInfo.inChannels + " + d";
+            var initializationValue = '0.0';
+            if (!isAvgPool) {
+                // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
+                initializationValue = '-1.0 / 1e-20';
+            }
+            if (computePositions) {
+                var compareOp_1 = '>=';
+                this.userCode = "\n        const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n        const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < " + effectiveFilterHeight + ";\n              wR += " + dilationHeight + ") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int wC = 0; wC < " + effectiveFilterWidth + ";\n                wC += " + dilationWidth + ") {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value " + compareOp_1 + " currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = " + (flattenPositions ? (includeBatchInIndex ? batchFlattenPositionStr :
+                    flattenPositionStr) :
+                    "wR * " + effectiveFilterWidth + " + wC") + ";\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";
+                return;
+            }
+            var compareOp = 'max';
+            var returnValue = poolType + "(" + poolType + "(" + poolType + "(" +
+                'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
+            if (poolType === 'avg') {
+                returnValue = "avgValue / count";
+            }
+            var filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
+            var filterWidthVec4Remainder = filterWidth % 4;
+            var updateSnippet = "\n      if (" + isAvgPool + ") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = " + compareOp + "(values, minMaxValue);\n      }\n    ";
+            this.userCode = "\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n      const float initializationValue = " + initializationValue + ";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(" + initializationValue + ");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < " + effectiveFilterHeight + ";\n            wR += " + dilationHeight + ") {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + filterWidthNearestVec4 + "; wC += 4) {\n            int xC = xCCorner + wC * " + dilationWidth + ";\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + " + dilationWidth + ", d),\n              getValue(batch, xR, xC + 2 * " + dilationWidth + ", d),\n              getValue(batch, xR, xC + 3 * " + dilationWidth + ", d)\n            );\n\n            " + updateSnippet + "\n          }\n\n          int xC = xCCorner + " + filterWidthNearestVec4 + ";\n          if (" + (filterWidthVec4Remainder === 1) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            " + updateSnippet + "\n          } else if (" + (filterWidthVec4Remainder === 2) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + " + dilationWidth + ", d),\n              initializationValue,\n              initializationValue\n            );\n\n            " + updateSnippet + "\n          } else if (" + (filterWidthVec4Remainder === 3) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + " + dilationWidth + ", d),\n              getValue(batch, xR, xC + 2 * " + dilationWidth + ", d),\n              initializationValue\n            );\n\n            " + updateSnippet + "\n          }\n        }\n        setOutput(" + returnValue + ");\n      }\n    ";
+        }
+        return Pool2DProgram;
+    }());
+    var Pool3DProgram = /** @class */ (function () {
+        function Pool3DProgram(convInfo, poolType, computePositions, flattenPositions, includeBatchInIndex) {
+            if (flattenPositions === void 0) { flattenPositions = false; }
+            if (includeBatchInIndex === void 0) { includeBatchInIndex = false; }
+            this.variableNames = ['x'];
+            if (poolType === 'avg' && computePositions) {
+                throw new Error('Cannot compute positions for average pool.');
+            }
+            var filterWidth = convInfo.filterWidth;
+            var strideDepth = convInfo.strideDepth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationDepth = convInfo.dilationDepth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var effectiveFilterDepth = convInfo.effectiveFilterDepth;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padFront = convInfo.padInfo.front;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            this.outputShape = convInfo.outShape;
+            var isAvgPool = poolType === 'avg';
+            var initializationValue = '0.0';
+            if (!isAvgPool) {
+                // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
+                initializationValue = '-1.0 / 1e-20';
+            }
+            if (computePositions) {
+                var compareOp_2 = '>=';
+                this.userCode = "\n        const ivec3 strides =\n            ivec3(" + strideDepth + ", " + strideHeight + ", " + strideWidth + ");\n        const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < " + effectiveFilterDepth + ";\n              wD += " + dilationDepth + ") {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= " + convInfo.inDepth + ") {\n              continue;\n            }\n\n            for (int wR = 0; wR < " + effectiveFilterHeight + ";\n                wR += " + dilationHeight + ") {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n                continue;\n              }\n\n              for (int wC = 0; wC < " + effectiveFilterWidth + ";\n                  wC += " + dilationWidth + ") {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value " + compareOp_2 + " currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = " + (flattenPositions ?
+                    (includeBatchInIndex ?
+                        "(((batch * " + convInfo.inDepth + " + xD) * " + convInfo.inHeight + " + xR) * " + convInfo.inWidth + " + xC) * " + convInfo.inChannels + " + ch" :
+                        "((xD * " + convInfo.inHeight + " + xR) * " + convInfo.inWidth + " + xC) * " + convInfo.inChannels + " + ch") :
+                    "wD * " + effectiveFilterHeight + " * " + effectiveFilterWidth + " +\n                      wR * " + effectiveFilterWidth + " + wC") + ";\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";
+                return;
+            }
+            var compareOp = 'max';
+            var returnValue = poolType + "(" + poolType + "(" + poolType + "(" +
+                'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
+            if (poolType === 'avg') {
+                returnValue = "avgValue / count";
+            }
+            var filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
+            var filterWidthVec4Remainder = filterWidth % 4;
+            var updateSnippet = "\n      if (" + isAvgPool + ") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = " + compareOp + "(values, minMaxValue);\n      }\n    ";
+            this.userCode = "\n      const ivec3 strides =\n        ivec3(" + strideDepth + ", " + strideHeight + ", " + strideWidth + ");\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n      const float initializationValue = " + initializationValue + ";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(" + initializationValue + ");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < " + effectiveFilterDepth + ";\n            wD += " + dilationDepth + ") {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= " + convInfo.inDepth + ") {\n            continue;\n          }\n\n          for (int wR = 0; wR < " + effectiveFilterHeight + ";\n            wR += " + dilationHeight + ") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int wC = 0; wC < " + filterWidthNearestVec4 + "; wC += 4) {\n              int xC = xCCorner + wC * " + dilationWidth + ";\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + " + dilationWidth + ", ch),\n                getValue(batch, xD, xR, xC + 2 * " + dilationWidth + ", ch),\n                getValue(batch, xD, xR, xC + 3 * " + dilationWidth + ", ch)\n              );\n\n              " + updateSnippet + "\n            }\n\n            int xC = xCCorner + " + filterWidthNearestVec4 + ";\n            if (" + (filterWidthVec4Remainder === 1) + ") {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              " + updateSnippet + "\n            } else if (" + (filterWidthVec4Remainder === 2) + ") {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + " + dilationWidth + ", ch),\n                initializationValue,\n                initializationValue\n              );\n\n              " + updateSnippet + "\n            } else if (" + (filterWidthVec4Remainder === 3) + ") {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + " + dilationWidth + ", ch),\n                getValue(batch, xD, xR, xC + 2 * " + dilationWidth + ", ch),\n                initializationValue\n              );\n\n              " + updateSnippet + "\n            }\n          }\n          setOutput(" + returnValue + ");\n        }\n      }\n    ";
+        }
+        return Pool3DProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function avgPool(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        assertNotComplex(x, 'avgPool');
+        var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode;
+        var dilations = 1;
+        tf.util.assert(tf.backend_util.eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in avgPool: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
+        var convInfo = tf.backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);
+        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&
+            tf.util.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+            return identity({ inputs: { x: x }, backend: backend });
+        }
+        var avgPoolProgram = new Pool2DProgram(convInfo, 'avg', false);
+        return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');
+    }
+    var avgPoolConfig = {
+        kernelName: tf.AvgPool,
+        backendName: 'webgl',
+        kernelFunc: avgPool
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function avgPool3D(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode, dataFormat = attrs.dataFormat;
+        var dilations = [1, 1, 1];
+        var convInfo = tf.backend_util.computePool3DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);
+        var avgPoolProgram = new Pool3DProgram(convInfo, 'avg', false);
+        return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');
+    }
+    var avgPool3DConfig = {
+        kernelName: tf.AvgPool3D,
+        backendName: 'webgl',
+        kernelFunc: avgPool3D
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var AvgPool2DBackpropProgram = /** @class */ (function () {
+        function AvgPool2DBackpropProgram(convInfo) {
+            this.variableNames = ['dy'];
+            this.outputShape = convInfo.inShape;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+            var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+            var avgMultiplier = 1 / (filterHeight * filterWidth);
+            this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n      const float avgMultiplier = float(" + avgMultiplier + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + effectiveFilterHeight + ";\n            wR += " + dilationHeight + ") {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < " + effectiveFilterWidth + ";\n            wC+= " + dilationWidth + ") {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return AvgPool2DBackpropProgram;
+    }());
+    var AvgPool3DBackpropProgram = /** @class */ (function () {
+        function AvgPool3DBackpropProgram(convInfo) {
+            this.variableNames = ['dy'];
+            this.outputShape = convInfo.inShape;
+            var filterDepth = convInfo.filterDepth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var strideDepth = convInfo.strideDepth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationDepth = convInfo.dilationDepth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var effectiveFilterDepth = convInfo.effectiveFilterDepth;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
+            var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+            var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+            var avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
+            this.userCode = "\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n      const float avgMultiplier = float(" + avgMultiplier + ");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < " + effectiveFilterDepth + ";\n            wD += " + dilationDepth + ") {\n          float dyD = float(dyDCorner + wD) / " + strideDepth + ".0;\n\n          if (dyD < 0.0 || dyD >= " + convInfo.outDepth + ".0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < " + effectiveFilterHeight + ";\n              wR += " + dilationHeight + ") {\n            float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n            if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < " + effectiveFilterWidth + ";\n                wC += " + dilationWidth + ") {\n              float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n              if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return AvgPool3DBackpropProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function avgPool3DGrad(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var dy = inputs.dy, input = inputs.input;
+        var x = input;
+        var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode;
+        var dilations = [1, 1, 1];
+        var convInfo = tf.backend_util.computePool3DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);
+        var avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo);
+        return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);
+    }
+    var avgPoolGrad3DConfig = {
+        kernelName: tf.AvgPool3DGrad,
+        backendName: 'webgl',
+        kernelFunc: avgPool3DGrad
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function avgPoolGrad(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var dy = inputs.dy, input = inputs.input;
+        var x = input;
+        assertNotComplex([dy, input], 'avgPoolGrad');
+        var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad;
+        var convInfo = tf.backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);
+        var avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);
+        return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);
+    }
+    var avgPoolGradConfig = {
+        kernelName: tf.AvgPoolGrad,
+        backendName: 'webgl',
+        kernelFunc: avgPoolGrad
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function batchMatMul(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var a = inputs.a, b = inputs.b;
+        var transposeA = attrs.transposeA, transposeB = attrs.transposeB;
+        return batchMatMulImpl({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, backend: backend });
+    }
+    var batchMatMulConfig = {
+        kernelName: tf.BatchMatMul,
+        backendName: 'webgl',
+        kernelFunc: batchMatMul,
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var BatchNormProgram = /** @class */ (function () {
+        function BatchNormProgram(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
+            this.outputShape = [];
+            this.variableNames = ['x', 'mean', 'variance'];
+            tf.backend_util.assertAndGetBroadcastShape(xShape, meanShape);
+            tf.backend_util.assertAndGetBroadcastShape(xShape, varianceShape);
+            var offsetSnippet = '0.0';
+            if (offsetShape != null) {
+                tf.backend_util.assertAndGetBroadcastShape(xShape, offsetShape);
+                this.variableNames.push('offset');
+                offsetSnippet = 'getOffsetAtOutCoords()';
+            }
+            var scaleSnippet = '1.0';
+            if (scaleShape != null) {
+                tf.backend_util.assertAndGetBroadcastShape(xShape, scaleShape);
+                this.variableNames.push('scale');
+                scaleSnippet = 'getScaleAtOutCoords()';
+            }
+            this.outputShape = xShape;
+            this.userCode = "\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = " + offsetSnippet + ";\n        float scale = " + scaleSnippet + ";\n        float inv = scale * inversesqrt(variance + float(" + varianceEpsilon + "));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    ";
+        }
+        return BatchNormProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var BatchNormPackedProgram = /** @class */ (function () {
+        function BatchNormPackedProgram(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.variableNames = ['x', 'mean', 'variance'];
+            tf.backend_util.assertAndGetBroadcastShape(xShape, meanShape);
+            tf.backend_util.assertAndGetBroadcastShape(xShape, varianceShape);
+            var offsetSnippet = 'vec4(0.0)';
+            if (offsetShape != null) {
+                tf.backend_util.assertAndGetBroadcastShape(xShape, offsetShape);
+                this.variableNames.push('offset');
+                offsetSnippet = 'getOffsetAtOutCoords()';
+            }
+            var scaleSnippet = 'vec4(1.0)';
+            if (scaleShape != null) {
+                tf.backend_util.assertAndGetBroadcastShape(xShape, scaleShape);
+                this.variableNames.push('scale');
+                scaleSnippet = 'getScaleAtOutCoords()';
+            }
+            this.outputShape = xShape;
+            this.userCode = "\n      void main() {\n        vec4 offset = " + offsetSnippet + ";\n        vec4 scale = " + scaleSnippet + ";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(" + varianceEpsilon + "));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    ";
+        }
+        return BatchNormPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var batchNorm = function (_a) {
+        var inputs = _a.inputs, backend = _a.backend, attrs = _a.attrs;
+        var x = inputs.x, mean = inputs.mean, variance = inputs.variance, offset = inputs.offset, scale = inputs.scale;
+        tf.util.assert(mean.shape.length === variance.shape.length, function () { return 'Batch normalization gradient requires mean and variance to have ' +
+            'equal ranks.'; });
+        tf.util.assert(offset == null || mean.shape.length === offset.shape.length, function () { return 'Batch normalization gradient requires mean and offset to have ' +
+            'equal ranks.'; });
+        tf.util.assert(scale == null || mean.shape.length === scale.shape.length, function () { return 'Batch normalization gradient requires mean and scale to have ' +
+            'equal ranks.'; });
+        var varianceEpsilon = attrs.varianceEpsilon;
+        if (varianceEpsilon == null) {
+            varianceEpsilon = 0.001;
+        }
+        var finalInputs = [x, mean, variance];
+        var offsetShape = null;
+        if (offset != null) {
+            offsetShape = offset.shape;
+            finalInputs.push(offset);
+        }
+        var scaleShape = null;
+        if (scale != null) {
+            scaleShape = scale.shape;
+            finalInputs.push(scale);
+        }
+        var program = tf.env().getBool('WEBGL_PACK_NORMALIZATION') ?
+            new BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) :
+            new BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);
+        var output = backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);
+        return output;
+    };
+    var batchNormConfig = {
+        kernelName: tf.FusedBatchNorm,
+        backendName: 'webgl',
+        kernelFunc: batchNorm,
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SliceProgram = /** @class */ (function () {
+        function SliceProgram(destSize) {
+            this.variableNames = ['source'];
+            this.outputShape = destSize;
+            this.rank = destSize.length;
+            var dtype = getCoordsDataType(this.rank);
+            var uniformPart = "uniform int start[" + this.rank + "];";
+            var sourceCoords = getCoords(this.rank);
+            var body;
+            var coordSum = destSize.map(function (_, i) {
+                return "sourceLoc." + coords[i] + " = start[" + i + "] + coords." + coords[i] + ";";
+            });
+            body = "\n        " + dtype + " sourceLoc;\n        " + dtype + " coords = getOutputCoords();\n        " + coordSum.join('\n') + "\n      ";
+            this.userCode = "\n      " + uniformPart + "\n      void main() {\n        " + body + "\n        setOutput(getSource(" + sourceCoords + "));\n      }\n    ";
+        }
+        SliceProgram.prototype.getCustomSetupFunc = function (start) {
+            var _this = this;
+            if (start.length !== this.rank) {
+                throw Error("The rank (" + this.rank + ") of the program must match the " +
+                    ("length of start (" + start.length + ")"));
+            }
+            return function (gpgpu, webGLProgram) {
+                if (_this.startLoc == null) {
+                    _this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');
+                    if (_this.startLoc == null) {
+                        // This means the compiler has optimized and realized it doesn't need
+                        // the uniform.
+                        return;
+                    }
+                }
+                gpgpu.gl.uniform1iv(_this.startLoc, start);
+            };
+        };
+        return SliceProgram;
+    }());
+    var coords = ['x', 'y', 'z', 'w', 'u', 'v'];
+    function getCoords(rank) {
+        if (rank === 1) {
+            return 'sourceLoc';
+        }
+        else if (rank <= 6) {
+            return coords.slice(0, rank).map(function (x) { return 'sourceLoc.' + x; }).join(',');
+        }
+        else {
+            throw Error("Slicing for rank " + rank + " is not yet supported");
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SlicePackedProgram = /** @class */ (function () {
+        function SlicePackedProgram(destSize) {
+            this.variableNames = ['source'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = destSize;
+            this.rank = destSize.length;
+            var dtype = getCoordsDataType(this.rank);
+            var coords = getChannels('coords', this.rank);
+            var sourceLoc = getChannels('sourceLoc', this.rank);
+            var innerDims = this.rank === 1 ? 'sourceLoc' : "vec2(" + sourceLoc.slice(-2).join() + ")";
+            var getChannel = "getChannel(getSource(" + sourceLoc.join() + "), " + innerDims + ")";
+            var upperRow = "\n      result.x = " + getChannel + ";\n      if (++" + coords[this.rank - 1] + " < " + destSize[this.rank - 1] + ") {\n        ++" + sourceLoc[this.rank - 1] + ";\n        result.y = " + getChannel + ";\n        --" + sourceLoc[this.rank - 1] + ";\n      }\n    ";
+            var lowerRow = this.rank === 1 ? '' : "\n      --" + coords[this.rank - 1] + ";\n      if (++" + coords[this.rank - 2] + " < " + destSize[this.rank - 2] + ") {\n        ++" + sourceLoc[this.rank - 2] + ";\n        result.z = " + getChannel + ";\n        if (++" + coords[this.rank - 1] + " < " + destSize[this.rank - 1] + ") {\n          ++" + sourceLoc[this.rank - 1] + ";\n          result.w = " + getChannel + ";\n        }\n      }\n    ";
+            var sourceLocSetup = this.rank <= 4 ?
+                "sourceLoc = coords +\n            " + dtype + "(" + destSize.map(function (_, i) { return "start[" + i + "]"; }).join() + ");" :
+                destSize.map(function (_, i) { return sourceLoc[i] + " = " + coords[i] + " + start[" + i + "];"; })
+                    .join('\n');
+            this.userCode = "\n      uniform int start[" + this.rank + "];\n      void main() {\n        " + dtype + " coords = getOutputCoords();\n        " + dtype + " sourceLoc;\n        " + sourceLocSetup + "\n        vec4 result = vec4(0.);\n        " + upperRow + "\n        " + lowerRow + "\n        setOutput(result);\n      }\n    ";
+        }
+        SlicePackedProgram.prototype.getCustomSetupFunc = function (start) {
+            var _this = this;
+            if (start.length !== this.rank) {
+                throw Error("The rank (" + this.rank + ") of the program must match the " +
+                    ("length of start (" + start.length + ")"));
+            }
+            return function (gpgpu, webGLProgram) {
+                if (_this.startLoc == null) {
+                    _this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');
+                    if (_this.startLoc == null) {
+                        // This means the compiler has optimized and realized it doesn't need
+                        // the uniform.
+                        return;
+                    }
+                }
+                gpgpu.gl.uniform1iv(_this.startLoc, start);
+            };
+        };
+        return SlicePackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function shallowSlice(x, begin, size, backend) {
+        var xTexData = backend.texData.get(x.dataId);
+        var t = backend.makeTensorInfo(size, x.dtype);
+        var newTexData = backend.texData.get(t.dataId);
+        // Copy texture data from the original tensor.
+        Object.assign(newTexData, xTexData);
+        newTexData.refCount = 1;
+        newTexData.shape = size;
+        newTexData.dtype = x.dtype;
+        var flatOffset = tf.slice_util.computeFlatOffset(begin, tf.util.computeStrides(x.shape));
+        if (xTexData.slice) {
+            // We are slicing an already sliced tensor, so we have to accumulate
+            // the offset.
+            flatOffset += xTexData.slice.flatOffset;
+        }
+        newTexData.slice = {
+            flatOffset: flatOffset,
+            // Point to the original dataId, which is used to do ref counting.
+            origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId
+        };
+        // Increase the ref count for that data bucket.
+        var refCount = backend.dataRefCount.get(newTexData.slice.origDataId) || 1;
+        backend.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);
+        return t;
+    }
+    function slice(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var begin = attrs.begin, size = attrs.size;
+        var _a = tf.slice_util.parseSliceParams(x, begin, size), $begin = _a[0], $size = _a[1];
+        tf.slice_util.assertParamsValid(x, $begin, $size);
+        if (tf.util.sizeFromShape($size) === 0) {
+            return backend.makeTensorInfo($size, x.dtype, []);
+        }
+        // Run on cpu if dtype is string. For string, the backend represents it
+        // as Uint8Array[], where each Uint8Array is a character. Given that the
+        // computation is only on the outer array, uploading the whole data onto
+        // gpu is wasteful. Also, currently webgl doesn't have a design to
+        // upload and retrieve Uint8Array[] between cpu and gpu. Therefore, we
+        // just run the kernel on cpu if dtype is string.
+        if (backend.shouldExecuteOnCPU([x]) || x.dtype === 'string') {
+            var xTexData = backend.texData.get(x.dataId);
+            var outValues = sliceImplCPU(xTexData.values, $begin, $size, x.shape, x.dtype);
+            return backend.makeTensorInfo($size, x.dtype, outValues);
+        }
+        var isPacked = backend.texData.get(x.dataId).isPacked;
+        var isContinous = tf.slice_util.isSliceContinous(x.shape, $begin, $size);
+        if (isPacked || !isContinous) {
+            var program = tf.env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
+                new SlicePackedProgram($size) :
+                new SliceProgram($size);
+            var customSetup = program.getCustomSetupFunc($begin);
+            return backend.runWebGLProgram(program, [x], x.dtype, customSetup);
+        }
+        backend.uploadToGPU(x.dataId);
+        return shallowSlice(x, $begin, $size, backend);
+    }
+    var sliceConfig = {
+        kernelName: tf.Slice,
+        backendName: 'webgl',
+        kernelFunc: slice
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var batchToSpaceND = function (args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var blockShape = attrs.blockShape, crops = attrs.crops;
+        tf.util.assert(x.shape.length <= 4, function () { return 'batchToSpaceND for rank > 4 with a WebGL backend not ' +
+            'implemented yet'; });
+        var prod = blockShape.reduce(function (a, b) { return a * b; });
+        var reshaped = tf.backend_util.getReshaped(x.shape, blockShape, prod);
+        var permuted = tf.backend_util.getPermuted(reshaped.length, blockShape.length);
+        var reshapedPermuted = tf.backend_util.getReshapedPermuted(x.shape, blockShape, prod);
+        var sliceBeginCoords = tf.backend_util.getSliceBeginCoords(crops, blockShape.length);
+        var sliceSize = tf.backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);
+        var toDispose = [];
+        var reshapedIntermediate = reshape({ inputs: { x: x }, backend: backend, attrs: { shape: reshaped } });
+        var transposedIntermediate = transpose({ inputs: { x: reshapedIntermediate }, backend: backend, attrs: { perm: permuted } });
+        var reshapedIntermediate2 = reshape({
+            inputs: { x: transposedIntermediate },
+            backend: backend,
+            attrs: { shape: reshapedPermuted }
+        });
+        var sliced = slice({
+            inputs: { x: reshapedIntermediate2 },
+            backend: backend,
+            attrs: { begin: sliceBeginCoords, size: sliceSize }
+        });
+        toDispose.push(reshapedIntermediate);
+        toDispose.push(transposedIntermediate);
+        toDispose.push(reshapedIntermediate2);
+        toDispose.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return sliced;
+    };
+    var batchToSpaceNDConfig = {
+        kernelName: tf.BatchToSpaceND,
+        backendName: 'webgl',
+        kernelFunc: batchToSpaceND
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function bincount(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, weights = inputs.weights;
+        var size = attrs.size;
+        var xVals = backend.readSync(x.dataId);
+        var weightsVals = backend.readSync(weights.dataId);
+        var outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);
+        return backend.makeTensorInfo([size], weights.dtype, outVals);
+    }
+    var bincountConfig = {
+        kernelName: tf.Bincount,
+        backendName: 'webgl',
+        kernelFunc: bincount
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var NOT_EQUAL = "return float(a != b);";
+    var notEqual = binaryKernelFunc({ opSnippet: NOT_EQUAL, cpuKernelImpl: notEqualImplCPU, dtype: 'bool' });
+    var notEqualConfig = {
+        kernelName: tf.NotEqual,
+        backendName: 'webgl',
+        kernelFunc: notEqual,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function real(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var input = inputs.input;
+        var inputData = backend.texData.get(input.dataId);
+        return identity({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend });
+    }
+    var realConfig = {
+        kernelName: tf.Real,
+        backendName: 'webgl',
+        kernelFunc: real
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var TO_INT = "return float(int(x));";
+    function int(input, backend) {
+        var program = new UnaryOpProgram(input.shape, TO_INT);
+        var output = backend.runWebGLProgram(program, [input], 'int32');
+        return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function cast(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var dtype = attrs.dtype;
+        // Casting to complex64.
+        if (dtype === 'complex64') {
+            if (x.dtype === 'complex64') {
+                return identity({ inputs: { x: x }, backend: backend });
+            }
+            // TODO(annxingyuan): Import kernel function once zeros is modularized.
+            var zerosTensor = tf.zeros(x.shape);
+            var floatX = cast({ inputs: { x: x }, backend: backend, attrs: { dtype: 'float32' } });
+            var result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend: backend });
+            zerosTensor.dispose();
+            backend.disposeIntermediateTensorInfo(floatX);
+            return result;
+        }
+        // Casting from complex64
+        if (x.dtype === 'complex64') {
+            var realPart = real({ inputs: { input: x }, backend: backend });
+            var result = cast({ inputs: { x: realPart }, backend: backend, attrs: { dtype: dtype } });
+            backend.disposeIntermediateTensorInfo(realPart);
+            return result;
+        }
+        if (!tf.util.hasEncodingLoss(x.dtype, dtype)) {
+            // We don't change the underlying data, since we cast to higher
+            // precision.
+            var result = identity({ inputs: { x: x }, backend: backend });
+            return { dataId: result.dataId, shape: result.shape, dtype: dtype };
+        }
+        if (dtype === 'int32') {
+            return int(x, backend);
+        }
+        if (dtype === 'bool') {
+            var zerosTensorInfo = backend.makeTensorInfo([], 'bool', tf.util.getTypedArrayFromDType('bool', 1));
+            var binaryInputs = { a: x, b: zerosTensorInfo };
+            var result = notEqual({ inputs: binaryInputs, backend: backend });
+            backend.disposeIntermediateTensorInfo(zerosTensorInfo);
+            return result;
+        }
+        throw new Error("Error in Cast: failed to cast " + x.dtype + " to " + dtype);
+    }
+    var castConfig = {
+        kernelName: tf.Cast,
+        backendName: 'webgl',
+        kernelFunc: cast
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var CEIL = "return ceil(x);";
+    var ceil = unaryKernelFunc({ opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU });
+    var ceilConfig = {
+        kernelName: tf.Ceil,
+        backendName: 'webgl',
+        kernelFunc: ceil
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ClipProgram = /** @class */ (function () {
+        function ClipProgram(aShape) {
+            this.variableNames = ['A'];
+            this.outputShape = aShape;
+            this.userCode = "\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    ";
+        }
+        ClipProgram.prototype.getCustomSetupFunc = function (min, max) {
+            var _this = this;
+            return function (gpgpu, webGLProgram) {
+                if (_this.minLoc == null) {
+                    _this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');
+                    _this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');
+                }
+                gpgpu.gl.uniform1f(_this.minLoc, min);
+                gpgpu.gl.uniform1f(_this.maxLoc, max);
+            };
+        };
+        return ClipProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ClipPackedProgram = /** @class */ (function () {
+        function ClipPackedProgram(aShape) {
+            this.variableNames = ['A'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = aShape;
+            this.userCode = "\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    ";
+        }
+        ClipPackedProgram.prototype.getCustomSetupFunc = function (min, max) {
+            var _this = this;
+            return function (gpgpu, webGLProgram) {
+                if (_this.minLoc == null) {
+                    _this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');
+                    _this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');
+                }
+                gpgpu.gl.uniform1f(_this.minLoc, min);
+                gpgpu.gl.uniform1f(_this.maxLoc, max);
+            };
+        };
+        return ClipPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function clipByValue(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var clipValueMin = attrs.clipValueMin, clipValueMax = attrs.clipValueMax;
+        var program;
+        if (tf.env().getBool('WEBGL_PACK_CLIP')) {
+            program = new ClipPackedProgram(x.shape);
+        }
+        else {
+            program = new ClipProgram(x.shape);
+        }
+        var customSetup = program.getCustomSetupFunc(clipValueMin, clipValueMax);
+        return backend.runWebGLProgram(program, [x], x.dtype, customSetup);
+    }
+    var clipByValueConfig = {
+        kernelName: tf.ClipByValue,
+        backendName: 'webgl',
+        kernelFunc: clipByValue
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ComplexAbsProgram = /** @class */ (function () {
+        function ComplexAbsProgram(shape) {
+            this.variableNames = ['real', 'imag'];
+            this.outputShape = shape;
+            this.userCode = "\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    ";
+        }
+        return ComplexAbsProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // Returns a TensorInfo with the complex shape and the dataId of the
+    // underlying part. We need to do this because a reshaped complex tensor is
+    // not reflected in its parts.
+    function makeComplexComponentTensorInfo(complexTensor, complexPart) {
+        return {
+            dataId: complexPart.dataId,
+            dtype: complexPart.dtype,
+            shape: complexTensor.shape
+        };
+    }
+    function complexAbs(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var x = inputs.x;
+        var xData = backend.texData.get(x.dataId);
+        var program = new ComplexAbsProgram(x.shape);
+        var programInputs = [
+            makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),
+            makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag),
+        ];
+        return backend.runWebGLProgram(program, programInputs, programInputs[0].dtype);
+    }
+    var complexAbsConfig = {
+        kernelName: tf.ComplexAbs,
+        backendName: 'webgl',
+        kernelFunc: complexAbs
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ConcatProgram = /** @class */ (function () {
+        // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().
+        function ConcatProgram(shapes) {
+            this.outputShape = [];
+            this.outputShape = tf.backend_util.computeOutShape(shapes, 1 /* axis */);
+            this.variableNames = shapes.map(function (_, i) { return "T" + i; });
+            var offsets = new Array(shapes.length - 1);
+            offsets[0] = shapes[0][1];
+            for (var i = 1; i < offsets.length; i++) {
+                offsets[i] = offsets[i - 1] + shapes[i][1];
+            }
+            var snippets = ["if (yC < " + offsets[0] + ") setOutput(getT0(yR, yC));"];
+            for (var i = 1; i < offsets.length; i++) {
+                var shift = offsets[i - 1];
+                snippets.push("else if (yC < " + offsets[i] + ") " +
+                    ("setOutput(getT" + i + "(yR, yC-" + shift + "));"));
+            }
+            var lastIndex = offsets.length;
+            var lastShift = offsets[offsets.length - 1];
+            snippets.push("else setOutput(getT" + lastIndex + "(yR, yC-" + lastShift + "));");
+            this.userCode = "\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        " + snippets.join('\n        ') + "\n      }\n    ";
+        }
+        return ConcatProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ConcatPackedProgram = /** @class */ (function () {
+        function ConcatPackedProgram(shapes, axis) {
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = [];
+            this.outputShape = tf.backend_util.computeOutShape(shapes, axis);
+            var shape = this.outputShape;
+            var rank = shape.length;
+            var dtype = getCoordsDataType(rank);
+            var coords = getChannels('coords', rank);
+            var channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);
+            this.variableNames = shapes.map(function (_, i) { return "T" + i; });
+            var offsets = new Array(shapes.length - 1);
+            offsets[0] = shapes[0][axis];
+            for (var i = 1; i < offsets.length; i++) {
+                offsets[i] = offsets[i - 1] + shapes[i][axis];
+            }
+            var channel = channels[axis];
+            var lastChannels = channels.slice(-2);
+            var allChannels = channels.join();
+            var getValueSnippet = "if (" + channel + " < " + offsets[0] + ") {\n        return getChannel(\n            getT0(" + allChannels + "), vec2(" + lastChannels.join() + "));\n        }";
+            for (var i = 1; i < offsets.length; i++) {
+                var shift_1 = offsets[i - 1];
+                // Note: the >= comparison below may seem unnecessary given the check
+                // above but is needed to workaround branch execution issues on some
+                // devices. It makes all the conditions exclusive without relying on
+                // execution order.
+                getValueSnippet += "\n        if (" + channel + " < " + offsets[i] + "  && " + channel + " >= " + offsets[i - 1] + ") {\n          return getChannel(\n            getT" + i + "(" + shiftedChannels(channels, channel, shift_1) + "),\n            vec2(" + shiftedChannels(lastChannels, channel, shift_1) + "));\n        }";
+            }
+            var lastIndex = offsets.length;
+            var shift = offsets[offsets.length - 1];
+            getValueSnippet += "\n        return getChannel(\n          getT" + lastIndex + "(" + shiftedChannels(channels, channel, shift) + "),\n          vec2(" + shiftedChannels(lastChannels, channel, shift) + "));";
+            this.userCode = "\n      float getValue(" + channels.map(function (x) { return 'int ' + x; }) + ") {\n        " + getValueSnippet + "\n      }\n\n      void main() {\n        " + dtype + " coords = getOutputCoords();\n        vec4 result = vec4(getValue(" + coords + "), 0., 0., 0.);\n\n        " + coords[rank - 1] + " = " + coords[rank - 1] + " + 1;\n        if (" + coords[rank - 1] + " < " + shape[rank - 1] + ") {\n          result.g = getValue(" + coords + ");\n        }\n\n        " + coords[rank - 2] + " = " + coords[rank - 2] + " + 1;\n        if (" + coords[rank - 2] + " < " + shape[rank - 2] + ") {\n          result.a = getValue(" + coords + ");\n        }\n\n        " + coords[rank - 1] + " = " + coords[rank - 1] + " - 1;\n        if (" + coords[rank - 2] + " < " + shape[rank - 2] + " &&\n            " + coords[rank - 1] + " < " + shape[rank - 1] + ") {\n          result.b = getValue(" + coords + ");\n        }\n        setOutput(result);\n      }\n    ";
+        }
+        return ConcatPackedProgram;
+    }());
+    /**
+     * Return an expression for coordinates into a vector where a given channel
+     * will be offset by [shift].
+     *
+     * @param channels the channels to consider
+     * @param channel the channel we want shifted
+     * @param shift  the amount to subtract from the channel.
+     *
+     * @returns a string of the form 'x, y-[shift], z' where any one channel can
+     * have the shift applied.
+     */
+    function shiftedChannels(channels, channel, shift) {
+        var channelIdx = channels.indexOf(channel);
+        var res = channels.map(function (c, idx) {
+            if (idx === channelIdx) {
+                return c + " - " + shift;
+            }
+            else {
+                return c;
+            }
+        });
+        return res.join();
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function imag(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var input = inputs.input;
+        var inputData = backend.texData.get(input.dataId);
+        return identity({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend });
+    }
+    var imagConfig = {
+        kernelName: tf.Imag,
+        backendName: 'webgl',
+        kernelFunc: imag
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function concatImpl$1(inputs, axis, backend) {
+        var dtype = inputs[0].dtype;
+        if (dtype === 'complex64') {
+            var reals = inputs.map(function (t) { return real({ inputs: { input: t }, backend: backend }); });
+            var imags = inputs.map(function (t) { return imag({ inputs: { input: t }, backend: backend }); });
+            var realConcated = concatImpl$1(reals, axis, backend);
+            var imagConcated = concatImpl$1(imags, axis, backend);
+            var result_1 = complex({ inputs: { real: realConcated, imag: imagConcated }, backend: backend });
+            reals.forEach(function (r) { return backend.disposeIntermediateTensorInfo(r); });
+            imags.forEach(function (i) { return backend.disposeIntermediateTensorInfo(i); });
+            backend.disposeIntermediateTensorInfo(realConcated);
+            backend.disposeIntermediateTensorInfo(imagConcated);
+            return result_1;
+        }
+        var runOnCpu = backend.shouldExecuteOnCPU(inputs);
+        // Run on cpu if dtype is string. For string, the backend represents it
+        // as Uint8Array[], where each Uint8Array is a character. Given that the
+        // computation is only on the outer array, uploading the whole data onto
+        // gpu is wasteful. Also, currently webgl doesn't have a design to
+        // upload and retrieve Uint8Array[] between cpu and gpu. Therefore, we
+        // just run the kernel on cpu if dtype is string.
+        if (dtype === 'string') {
+            runOnCpu = true;
+        }
+        if (runOnCpu) {
+            // Any concat of n-dimensional tensors across any axis can be reduced to
+            // a concatenation of two-dimensional tensors across the axis 1 by first
+            // partitioning the axes of the original tensors into those less than the
+            // axis to be concatenated and the rest. Then reshape the tensors
+            // into a two-dimensional tensor by collapsing these two sets of axes and
+            // concatenate the resulting matrices across the axis 1, finally reshaping
+            // the result to have the proper shape.
+            var tensors2D_1 = inputs.map(function (t) {
+                var innerSize = tf.util.sizeFromShape(t.shape.slice(axis));
+                var shape = [-1, innerSize];
+                return reshape({ inputs: { x: t }, backend: backend, attrs: { shape: shape } });
+            });
+            var inputsValShapes = tensors2D_1.map(function (t) {
+                return { vals: backend.readSync(t.dataId), shape: t.shape };
+            });
+            // Concats 2d tensors along axis=1.
+            var outShape_1 = tf.backend_util.computeOutShape(tensors2D_1.map(function (t) { return t.shape; }), 1 /* axis */);
+            var simplyConcat = tensors2D_1[0].shape[0] === 1;
+            var outVals = concatImplCPU(inputsValShapes, outShape_1, dtype, simplyConcat);
+            var finalOutShape = tf.backend_util.computeOutShape(inputs.map(function (t) { return t.shape; }), axis);
+            var outInfo = backend.makeTensorInfo(finalOutShape, dtype, outVals);
+            tensors2D_1.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+            return outInfo;
+        }
+        if (inputs.length > tf.env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {
+            var midIndex = Math.floor(inputs.length / 2);
+            var leftSide = concatImpl$1(inputs.slice(0, midIndex), axis, backend);
+            var rightSide = concatImpl$1(inputs.slice(midIndex), axis, backend);
+            var result_2 = concatImpl$1([leftSide, rightSide], axis, backend);
+            backend.disposeIntermediateTensorInfo(leftSide);
+            backend.disposeIntermediateTensorInfo(rightSide);
+            return result_2;
+        }
+        if (tf.env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') &&
+            inputs[0].shape.length > 1) {
+            var program_1 = new ConcatPackedProgram(inputs.map(function (t) { return t.shape; }), axis);
+            return backend.runWebGLProgram(program_1, inputs, dtype);
+        }
+        var _a = computeTensors2D(inputs, axis, backend), tensors2D = _a.tensors2D, outShape = _a.outShape;
+        var program = new ConcatProgram(tensors2D.map(function (t) { return t.shape; }));
+        var result = backend.runWebGLProgram(program, tensors2D, dtype);
+        tensors2D.forEach(function (r) { return backend.disposeIntermediateTensorInfo(r); });
+        var reshapedResult = reshape({ inputs: { x: result }, attrs: { shape: outShape }, backend: backend });
+        backend.disposeIntermediateTensorInfo(result);
+        return reshapedResult;
+    }
+    function computeTensors2D(inputs, axis, backend) {
+        // Any concat of n-dimensional tensors across any axis can be reduced to
+        // a concatenation of two-dimensional tensors across the axis 1 by first
+        // partitioning the axes of the original tensors into those less than the
+        // axis to be concatenated and the rest. Then reshape the tensors
+        // into a two-dimensional tensor by collapsing these two sets of axes and
+        // concatenate the resulting matrices across the axis 1, finally reshaping
+        // the result to have the proper shape.
+        var outShape = tf.backend_util.computeOutShape(inputs.map(function (t) { return t.shape; }), axis);
+        var tensors2D = inputs.map(function (x) { return reshape({
+            inputs: { x: x },
+            attrs: { shape: [-1, tf.util.sizeFromShape(x.shape.slice(axis))] },
+            backend: backend
+        }); });
+        return { tensors2D: tensors2D, outShape: outShape };
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function concat(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var axis = attrs.axis;
+        var $axis = tf.util.parseAxisParam(axis, inputs[0].shape)[0];
+        var outShape = tf.backend_util.computeOutShape(inputs.map(function (t) { return t.shape; }), $axis);
+        if (tf.util.sizeFromShape(outShape) === 0) {
+            return backend.makeTensorInfo(outShape, inputs[0].dtype, []);
+        }
+        // Keep only non-empty tensors (ignore tensors with 0 in their shape).
+        var $inputs = inputs.filter(function (t) { return tf.util.sizeFromShape(t.shape) > 0; });
+        if ($inputs.length === 1) {
+            return identity({ inputs: { x: $inputs[0] }, backend: backend });
+        }
+        var shapes = $inputs.map(function (t) { return t.shape; });
+        tf.backend_util.assertParamsConsistent(shapes, $axis);
+        return concatImpl$1($inputs, $axis, backend);
+    }
+    var concatConfig = {
+        kernelName: tf.Concat,
+        backendName: 'webgl',
+        kernelFunc: concat
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var Conv2DProgram = /** @class */ (function () {
+        function Conv2DProgram(convInfo, addBias, activation, hasPreluActivationWeights, hasLeakyreluAlpha) {
+            if (addBias === void 0) { addBias = false; }
+            if (activation === void 0) { activation = null; }
+            if (hasPreluActivationWeights === void 0) { hasPreluActivationWeights = false; }
+            if (hasLeakyreluAlpha === void 0) { hasLeakyreluAlpha = false; }
+            this.variableNames = ['x', 'W'];
+            this.outputShape = convInfo.outShape;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
+            var inputDepthVec4Remainder = convInfo.inChannels % 4;
+            var isChannelsLast = convInfo.dataFormat === 'channelsLast';
+            var rowDim = isChannelsLast ? 1 : 2;
+            var colDim = isChannelsLast ? 2 : 3;
+            var channelDim = isChannelsLast ? 3 : 1;
+            var activationSnippet = '', applyActivationSnippet = '';
+            if (activation) {
+                if (hasPreluActivationWeights) {
+                    activationSnippet = "float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          " + activation + "\n        }";
+                }
+                else if (hasLeakyreluAlpha) {
+                    activationSnippet = "float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          " + activation + "\n        }";
+                }
+                else {
+                    activationSnippet = "\n          float activation(float x) {\n            " + activation + "\n          }\n        ";
+                }
+                applyActivationSnippet = "result = activation(result);";
+            }
+            var addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
+            if (addBias) {
+                this.variableNames.push('bias');
+            }
+            if (hasPreluActivationWeights) {
+                this.variableNames.push('preluActivationWeights');
+            }
+            if (hasLeakyreluAlpha) {
+                this.variableNames.push('leakyreluAlpha');
+            }
+            this.userCode = "\n      " + activationSnippet + "\n\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[" + channelDim + "];\n\n        ivec2 xRCCorner =\n            ivec2(coords[" + rowDim + "], coords[" + colDim + "]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          int xR = xRCorner + wR * " + dilationHeight + ";\n\n          if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            int xC = xCCorner + wC * " + dilationWidth + ";\n\n            if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < " + inputDepthNearestVec4 + "; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (" + isChannelsLast + ") {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (" + (inputDepthVec4Remainder === 1) + ") {\n\n              if (" + isChannelsLast + ") {\n                dotProd +=\n                    getX(batch, xR, xC, " + inputDepthNearestVec4 + ") *\n                    getW(wR, wC, " + inputDepthNearestVec4 + ", d2);\n              } else {\n                dotProd +=\n                    getX(batch, " + inputDepthNearestVec4 + ", xR, xC) *\n                    getW(wR, wC, " + inputDepthNearestVec4 + ", d2);\n              }\n\n            } else if (" + (inputDepthVec4Remainder === 2) + ") {\n              vec2 wValues = vec2(\n                getW(wR, wC, " + inputDepthNearestVec4 + ", d2),\n                getW(wR, wC, " + inputDepthNearestVec4 + " + 1, d2)\n              );\n\n              if (" + isChannelsLast + ") {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, " + inputDepthNearestVec4 + "),\n                  getX(batch, xR, xC, " + inputDepthNearestVec4 + " + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, " + inputDepthNearestVec4 + ", xR, xC),\n                  getX(batch, " + inputDepthNearestVec4 + " + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (" + (inputDepthVec4Remainder === 3) + ") {\n              vec3 wValues = vec3(\n                getW(wR, wC, " + inputDepthNearestVec4 + ", d2),\n                getW(wR, wC, " + inputDepthNearestVec4 + " + 1, d2),\n                getW(wR, wC, " + inputDepthNearestVec4 + " + 2, d2)\n              );\n\n              if (" + isChannelsLast + ") {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, " + inputDepthNearestVec4 + "),\n                  getX(batch, xR, xC, " + inputDepthNearestVec4 + " + 1),\n                  getX(batch, xR, xC, " + inputDepthNearestVec4 + " + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, " + inputDepthNearestVec4 + ", xR, xC),\n                  getX(batch, " + inputDepthNearestVec4 + " + 1, xR, xC),\n                  getX(batch, " + inputDepthNearestVec4 + " + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        " + addBiasSnippet + "\n        " + applyActivationSnippet + "\n        setOutput(result);\n      }\n    ";
+        }
+        return Conv2DProgram;
+    }());
+    var Conv3DProgram = /** @class */ (function () {
+        function Conv3DProgram(convInfo) {
+            this.variableNames = ['x', 'W'];
+            this.outputShape = convInfo.outShape;
+            var padFront = convInfo.padInfo.front;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            var strideDepth = convInfo.strideDepth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationDepth = convInfo.dilationDepth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var filterDepth = convInfo.filterDepth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
+            var inputDepthVec4Remainder = convInfo.inChannels % 4;
+            this.userCode = "\n      const ivec3 strides = ivec3(" + strideDepth + ", " + strideHeight + ", " + strideWidth + ");\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < " + filterDepth + "; wF++) {\n          int xF = xFCorner + wF * " + dilationDepth + ";\n\n          if (xF < 0 || xF >= " + convInfo.inDepth + ") {\n            continue;\n          }\n\n          for (int wR = 0; wR < " + filterHeight + "; wR++) {\n            int xR = xRCorner + wR * " + dilationHeight + ";\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int wC = 0; wC < " + filterWidth + "; wC++) {\n              int xC = xCCorner + wC * " + dilationWidth + ";\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < " + inputDepthNearestVec4 + "; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (" + (inputDepthVec4Remainder === 1) + ") {\n                dotProd +=\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + ") *\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + ", d2);\n              } else if (" + (inputDepthVec4Remainder === 2) + ") {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + "),\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + " + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + ", d2),\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + " + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (" + (inputDepthVec4Remainder === 3) + ") {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + "),\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + " + 1),\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + " + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + ", d2),\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + " + 1, d2),\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + " + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return Conv3DProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var Im2ColPackedProgram = /** @class */ (function () {
+        function Im2ColPackedProgram(outputShape, inputShape, convInfo) {
+            this.variableNames = ['A'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = outputShape;
+            var filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, strideWidth = convInfo.strideWidth, strideHeight = convInfo.strideHeight, padInfo = convInfo.padInfo, outWidth = convInfo.outWidth, dilationWidth = convInfo.dilationWidth, dilationHeight = convInfo.dilationHeight, dataFormat = convInfo.dataFormat;
+            var left = padInfo.left, top = padInfo.top;
+            var itemsPerBlockRow = inChannels * filterWidth;
+            var glsl = getGlslDifferences();
+            var isChannelsLast = dataFormat === 'channelsLast';
+            var rowDim = isChannelsLast ? 0 : 1;
+            var colDim = isChannelsLast ? 1 : 2;
+            var unrolled = "";
+            for (var row = 0; row <= 1; row++) {
+                for (var col = 0; col <= 1; col++) {
+                    unrolled += "\n          blockIndex = rc.y + " + col + ";\n          pos = rc.x + " + row + ";\n\n          if(blockIndex < " + outputShape[1] + " && pos < " + outputShape[0] + ") {\n            offsetY = int(blockIndex / (" + outWidth + ")) * " + strideHeight + " - " + top + ";\n            d0 = offsetY + " + dilationHeight + " * (pos / " + itemsPerBlockRow + ");\n\n            if(d0 < " + inputShape[rowDim] + " && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), " + outWidth + ".) * " + strideWidth + ". - " + left + ".);\n              d1 = offsetX + " + dilationWidth + " * (int(mod(float(pos), " + itemsPerBlockRow + ".) / " + inChannels + ".));\n\n              if(d1 < " + inputShape[colDim] + " && d1 >= 0) {\n\n                ch = int(mod(float(pos), " + inChannels + ".));\n\n                if (" + isChannelsLast + ") {\n                  innerDims = vec2(d1, ch);\n                  result[" + (row * 2 + col) + "] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[" + (row * 2 + col) + "] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        ";
+                }
+            }
+            this.userCode = "\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        " + unrolled + "\n\n        " + glsl.output + " = result;\n      }\n    ";
+        }
+        return Im2ColPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // For 1x1 kernels that iterate through every point in the input, convolution
+    // can be expressed as matrix multiplication (without need for memory
+    // remapping).
+    function conv2dByMatMul(_a) {
+        var x = _a.x, filter = _a.filter, convInfo = _a.convInfo, backend = _a.backend, _b = _a.bias, bias = _b === void 0 ? null : _b, _c = _a.preluActivationWeights, preluActivationWeights = _c === void 0 ? null : _c, _d = _a.leakyreluAlpha, leakyreluAlpha = _d === void 0 ? 0 : _d, _e = _a.activation, activation = _e === void 0 ? null : _e;
+        // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the
+        // result from 2D to 4D.
+        var xShape = x.shape;
+        var xTexData = backend.texData.get(x.dataId);
+        var sharedMatMulDim = convInfo.inChannels;
+        var outerShapeX = xShape[0] * xShape[1] * xShape[2];
+        var outerShapeFilter = convInfo.outChannels;
+        var isChannelsLast = convInfo.dataFormat === 'channelsLast';
+        var transposeA = false;
+        var transposeB = false;
+        var out;
+        var intermediates = [];
+        // TODO: Once reduction ops are packed, batchMatMul will always be packed
+        // and we can remove this condition.
+        var batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) &&
+            sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;
+        var reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;
+        if (batchMatMulWillBeUnpacked || !tf.env().getBool('WEBGL_LAZILY_UNPACK') ||
+            !tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ||
+            !reshapeWillBeExpensive) {
+            var targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] :
+                xShape[0] * xShape[2] * xShape[3];
+            var xReshaped = reshape({
+                inputs: { x: x },
+                backend: backend,
+                attrs: { shape: [1, targetShape, convInfo.inChannels] }
+            });
+            var filterReshaped = reshape({
+                inputs: { x: filter },
+                backend: backend,
+                attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }
+            });
+            var result = batchMatMulImpl({
+                a: xReshaped,
+                b: filterReshaped,
+                transposeA: transposeA,
+                transposeB: transposeB,
+                backend: backend,
+                bias: bias,
+                activation: activation,
+                preluActivationWeights: preluActivationWeights,
+                leakyreluAlpha: leakyreluAlpha
+            });
+            out = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: convInfo.outShape } });
+            intermediates.push(xReshaped);
+            intermediates.push(filterReshaped);
+            intermediates.push(result);
+        }
+        else {
+            // Following optimization is specific to packed |x| with odd row count
+            // (For example, in channelLast mode, 'row count' refers to x.shape[2]):
+            // we avoid expensive packed 2x2 reshape by padding row count to next,
+            // even number. When x.shape[2] is odd, the result of packed batchMatMul is
+            // the same (has the same texture layout and and values in the texture) as
+            // it is for even x.shape[2] + 1. We make the odd-rows tensor to look like
+            // even-rows tensor before the operation and, after the batchMatMul,
+            // fix the even-rows result to have odd number of rows.
+            var targetShape = isChannelsLast ?
+                xShape[0] * xShape[1] * (xShape[2] + 1) :
+                xShape[0] * xShape[2] * (xShape[3] + 1);
+            var xReshaped_1 = {
+                dataId: x.dataId,
+                shape: [1, targetShape, convInfo.inChannels],
+                dtype: x.dtype
+            };
+            // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.
+            // Decrementing row count, after batchMatMul->...->compileProgram leads to
+            // invalid row count within the reference in GPGPUBinary.inShapeInfos.
+            // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos
+            // in compileProgram method, but that would affect compilation of all
+            // programs - instead, provide a copy here, with even row count, before
+            // calling batchMatMul->...->compileProgram and after that, the original
+            // xTexData.shape is restored.
+            var originalXTexDataShape = xTexData.shape;
+            xTexData.shape = xTexData.shape.slice();
+            xTexData.shape[xTexData.shape.length - 2]++;
+            tf.util.assert(isReshapeFree(xTexData.shape, xReshaped_1.shape), function () { return "packed reshape " + xTexData.shape + " to " + xReshaped_1.shape + " isn't free"; });
+            var filterReshaped = reshape({
+                inputs: { x: filter },
+                backend: backend,
+                attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }
+            });
+            intermediates.push(filterReshaped);
+            var pointwiseConv = batchMatMulImpl({
+                a: xReshaped_1,
+                b: filterReshaped,
+                backend: backend,
+                transposeA: transposeA,
+                transposeB: transposeB,
+                bias: bias,
+                activation: activation,
+                preluActivationWeights: preluActivationWeights,
+                leakyreluAlpha: leakyreluAlpha
+            });
+            var pointwiseConvTexData = backend.texData.get(pointwiseConv.dataId);
+            tf.util.assert(pointwiseConvTexData.isPacked, function () { return 'batchMatMul result is expected to be packed'; });
+            // Restore the input shape to original.
+            xTexData.shape = originalXTexDataShape;
+            // Set the output shape - there is no need for expensive reshape as data
+            // layout is already correct.
+            pointwiseConvTexData.shape = convInfo.outShape;
+            out = identity({ inputs: { x: pointwiseConv }, backend: backend });
+            out.shape = convInfo.outShape;
+            intermediates.push(pointwiseConv);
+        }
+        for (var _i = 0, intermediates_1 = intermediates; _i < intermediates_1.length; _i++) {
+            var i = intermediates_1[_i];
+            backend.disposeIntermediateTensorInfo(i);
+        }
+        return out;
+    }
+    // Implements the im2row algorithm as outlined in "High Performance
+    // Convolutional Neural Networks for Document Processing" (Suvisoft, 2006)
+    function conv2dWithIm2Row(_a) {
+        var x = _a.x, filter = _a.filter, convInfo = _a.convInfo, backend = _a.backend, _b = _a.bias, bias = _b === void 0 ? null : _b, _c = _a.preluActivationWeights, preluActivationWeights = _c === void 0 ? null : _c, _d = _a.leakyreluAlpha, leakyreluAlpha = _d === void 0 ? 0 : _d, _e = _a.activation, activation = _e === void 0 ? null : _e;
+        // Rearranges conv2d input so each block to be convolved over forms the
+        // column of a new matrix with shape [filterWidth * filterHeight *
+        // inChannels, outHeight * outWidth]. The filter is also rearranged so each
+        // output channel forms a row of a new matrix with shape [outChannels,
+        // filterWidth * filterHeight * inChannels]. The convolution is then
+        // computed by multiplying these matrices and reshaping the result.
+        var filterWidth = convInfo.filterWidth, filterHeight = convInfo.filterHeight, inChannels = convInfo.inChannels, outWidth = convInfo.outWidth, outHeight = convInfo.outHeight, dataFormat = convInfo.dataFormat;
+        var isChannelsLast = dataFormat === 'channelsLast';
+        var sharedDim = filterWidth * filterHeight * inChannels;
+        var numCols = outHeight * outWidth;
+        var x2ColShape = [sharedDim, numCols];
+        var transposeA = true;
+        var transposeB = false;
+        var intermediates = [];
+        var xSqueezed = reshape({ inputs: { x: x }, backend: backend, attrs: { shape: x.shape.slice(1) } });
+        var w2Row = reshape({
+            inputs: { x: filter },
+            backend: backend,
+            attrs: { shape: [1, sharedDim, tf.util.sizeFromShape(filter.shape) / sharedDim] }
+        });
+        intermediates.push(xSqueezed);
+        intermediates.push(w2Row);
+        var im2ColProgram = new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);
+        var im2Col = backend.runWebGLProgram(im2ColProgram, [xSqueezed], 'float32');
+        var im2ColReshaped = reshape({
+            inputs: { x: im2Col },
+            backend: backend,
+            attrs: { shape: [1, x2ColShape[0], x2ColShape[1]] }
+        });
+        intermediates.push(im2Col);
+        intermediates.push(im2ColReshaped);
+        var hasBias = bias != null;
+        var hasPreluActivationWeights = preluActivationWeights != null;
+        var hasLeakyreluAlpha = activation === 'leakyrelu';
+        var fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;
+        var matmulProgram = new MatMulPackedProgram(im2ColReshaped.shape, w2Row.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+        var inputs = [im2ColReshaped, w2Row];
+        if (bias) {
+            inputs.push(bias);
+        }
+        if (hasPreluActivationWeights) {
+            inputs.push(preluActivationWeights);
+        }
+        if (hasLeakyreluAlpha) {
+            var $leakyreluAlpha = backend.makeTensorInfo([], 'float32', tf.util.createScalarValue(leakyreluAlpha, 'float32'));
+            inputs.push($leakyreluAlpha);
+            intermediates.push($leakyreluAlpha);
+        }
+        var product = backend.runWebGLProgram(matmulProgram, inputs, 'float32');
+        var outShape = isChannelsLast ?
+            [1, outHeight, outWidth, convInfo.outChannels] :
+            [1, convInfo.outChannels, outHeight, outWidth];
+        var out = reshape({ inputs: { x: product }, backend: backend, attrs: { shape: outShape } });
+        intermediates.push(product);
+        for (var _i = 0, intermediates_2 = intermediates; _i < intermediates_2.length; _i++) {
+            var i = intermediates_2[_i];
+            backend.disposeIntermediateTensorInfo(i);
+        }
+        return out;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function conv2d(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, filter = inputs.filter;
+        var strides = attrs.strides, pad = attrs.pad, dataFormat = attrs.dataFormat, dilations = attrs.dilations, dimRoundingMode = attrs.dimRoundingMode;
+        var $dataFormat = tf.backend_util.convertConv2DDataFormat(dataFormat);
+        var convInfo = tf.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, false /* depthwise */, $dataFormat);
+        var out;
+        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&
+            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&
+            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&
+            (convInfo.padInfo.type === 'SAME' || convInfo.padInfo.type === 'VALID')) {
+            out = conv2dByMatMul({ x: x, filter: filter, convInfo: convInfo, backend: backend });
+        }
+        else if (tf.env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {
+            out = conv2dWithIm2Row({ x: x, filter: filter, convInfo: convInfo, backend: backend });
+        }
+        else {
+            var program = new Conv2DProgram(convInfo);
+            out = backend.runWebGLProgram(program, [x, filter], 'float32');
+        }
+        var outReshaped = reshape({ inputs: { x: out }, backend: backend, attrs: { shape: convInfo.outShape } });
+        backend.disposeIntermediateTensorInfo(out);
+        return outReshaped;
+    }
+    var conv2DConfig = {
+        kernelName: tf.Conv2D,
+        backendName: 'webgl',
+        kernelFunc: conv2d,
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var Conv2DDerFilterProgram = /** @class */ (function () {
+        function Conv2DDerFilterProgram(convInfo) {
+            this.variableNames = ['x', 'dy'];
+            this.outputShape = convInfo.filterShape;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            var isChannelsLast = convInfo.dataFormat === 'channelsLast';
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n          for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n            int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n              int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              if (" + isChannelsLast + ") {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return Conv2DDerFilterProgram;
+    }());
+    var Conv2DDerInputProgram = /** @class */ (function () {
+        function Conv2DDerInputProgram(convInfo) {
+            this.variableNames = ['dy', 'W'];
+            this.outputShape = convInfo.inShape;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var isChannelsLast = convInfo.dataFormat === 'channelsLast';
+            var padTop = filterHeight - 1 - convInfo.padInfo.top;
+            var padLeft = filterWidth - 1 - convInfo.padInfo.left;
+            var rowDim = isChannelsLast ? 1 : 2;
+            var colDim = isChannelsLast ? 2 : 3;
+            var channelDim = isChannelsLast ? 3 : 1;
+            this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[" + channelDim + "];\n\n        ivec2 dyCorner = ivec2(coords[" + rowDim + "], coords[" + colDim + "]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = " + filterHeight + " - 1 - wR;\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = " + filterWidth + " - 1 - wC;\n\n            for (int d2 = 0; d2 < " + convInfo.outChannels + "; d2++) {\n\n              if (" + isChannelsLast + ") {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return Conv2DDerInputProgram;
+    }());
+    var Conv3DDerFilterProgram = /** @class */ (function () {
+        function Conv3DDerFilterProgram(convInfo) {
+            this.variableNames = ['x', 'dy'];
+            this.outputShape = convInfo.filterShape;
+            var strideDepth = convInfo.strideDepth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var padFront = convInfo.padInfo.front;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            this.userCode = "\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n          for (int yF = 0; yF < " + convInfo.outDepth + "; yF++) {\n            int xF = wF + yF * " + strideDepth + " - " + padFront + ";\n\n            if (xF < 0 || xF >= " + convInfo.inDepth + ") {\n              continue;\n            }\n\n            for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n              int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n              if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n                continue;\n              }\n\n              for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n                int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n                if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return Conv3DDerFilterProgram;
+    }());
+    var Conv3DDerInputProgram = /** @class */ (function () {
+        function Conv3DDerInputProgram(convInfo) {
+            this.variableNames = ['dy', 'W'];
+            this.outputShape = convInfo.inShape;
+            var filterDepth = convInfo.filterDepth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var strideDepth = convInfo.strideDepth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var padFront = filterDepth - 1 - convInfo.padInfo.front;
+            var padTop = filterHeight - 1 - convInfo.padInfo.top;
+            var padLeft = filterWidth - 1 - convInfo.padInfo.left;
+            this.userCode = "\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < " + filterDepth + "; wF++) {\n          float dyF = float(dyFCorner + wF) / " + strideDepth + ".0;\n\n          if (dyF < 0.0 || dyF >= " + convInfo.outDepth + ".0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = " + filterDepth + " - 1 - wF;\n\n          for (int wR = 0; wR < " + filterHeight + "; wR++) {\n            float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n            if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = " + filterHeight + " - 1 - wR;\n\n            for (int wC = 0; wC < " + filterWidth + "; wC++) {\n              float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n              if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = " + filterWidth + " - 1 - wC;\n\n              for (int d2 = 0; d2 < " + convInfo.outChannels + "; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return Conv3DDerInputProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function conv2DBackpropFilter(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, dy = inputs.dy;
+        var strides = attrs.strides, pad = attrs.pad, dataFormat = attrs.dataFormat, dimRoundingMode = attrs.dimRoundingMode, filterShape = attrs.filterShape;
+        var $dataFormat = tf.backend_util.convertConv2DDataFormat(dataFormat);
+        var convInfo = tf.backend_util.computeConv2DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad, dimRoundingMode, false /* depthwise */, $dataFormat);
+        var program = new Conv2DDerFilterProgram(convInfo);
+        return backend.runWebGLProgram(program, [x, dy], 'float32');
+    }
+    var conv2DBackpropFilterConfig = {
+        kernelName: tf.Conv2DBackpropFilter,
+        backendName: 'webgl',
+        kernelFunc: conv2DBackpropFilter,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function conv2DBackpropInput(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var dy = inputs.dy, filter = inputs.filter;
+        var inputShape = attrs.inputShape, strides = attrs.strides, pad = attrs.pad, dataFormat = attrs.dataFormat, dimRoundingMode = attrs.dimRoundingMode;
+        var $dataFormat = tf.backend_util.convertConv2DDataFormat(dataFormat);
+        var convInfo = tf.backend_util.computeConv2DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);
+        var program = new Conv2DDerInputProgram(convInfo);
+        return backend.runWebGLProgram(program, [dy, filter], 'float32');
+    }
+    var conv2DBackpropInputConfig = {
+        kernelName: tf.Conv2DBackpropInput,
+        backendName: 'webgl',
+        kernelFunc: conv2DBackpropInput,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function conv3D(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, filter = inputs.filter;
+        var strides = attrs.strides, pad = attrs.pad, dilations = attrs.dilations;
+        var convInfo = tf.backend_util.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad);
+        var program = new Conv3DProgram(convInfo);
+        return backend.runWebGLProgram(program, [x, filter], 'float32');
+    }
+    var conv3DConfig = {
+        kernelName: tf.Conv3D,
+        backendName: 'webgl',
+        kernelFunc: conv3D,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function conv3DBackpropFilterV2(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, dy = inputs.dy;
+        var strides = attrs.strides, pad = attrs.pad, filterShape = attrs.filterShape;
+        var convInfo = tf.backend_util.computeConv3DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad);
+        var program = new Conv3DDerFilterProgram(convInfo);
+        return backend.runWebGLProgram(program, [x, dy], 'float32');
+    }
+    var conv3DBackpropFilterV2Config = {
+        kernelName: tf.Conv3DBackpropFilterV2,
+        backendName: 'webgl',
+        kernelFunc: conv3DBackpropFilterV2
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function conv3DBackpropInput(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var dy = inputs.dy, filter = inputs.filter;
+        var pad = attrs.pad, strides = attrs.strides, inputShape = attrs.inputShape;
+        var convInfo = tf.backend_util.computeConv3DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad);
+        var program = new Conv3DDerInputProgram(convInfo);
+        return backend.runWebGLProgram(program, [dy, filter], 'float32');
+    }
+    var conv3DBackpropInputConfig = {
+        kernelName: tf.Conv3DBackpropInputV2,
+        backendName: 'webgl',
+        kernelFunc: conv3DBackpropInput,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var COS = CHECK_NAN_SNIPPET_UNARY + "\n  return cos(x);\n";
+    var cos = unaryKernelFunc({ opSnippet: COS });
+    var cosConfig = {
+        kernelName: tf.Cos,
+        backendName: 'webgl',
+        kernelFunc: cos,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var COSH = "\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n";
+    var cosh = unaryKernelFunc({ opSnippet: COSH });
+    var coshConfig = {
+        kernelName: tf.Cosh,
+        backendName: 'webgl',
+        kernelFunc: cosh,
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var CropAndResizeProgram = /** @class */ (function () {
+        function CropAndResizeProgram(imageShape, boxShape, cropSize, method, extrapolationValue) {
+            this.variableNames = ['Image', 'Boxes', 'BoxInd'];
+            this.outputShape = [];
+            var batch = imageShape[0], imageHeight = imageShape[1], imageWidth = imageShape[2], depth = imageShape[3];
+            var numBoxes = boxShape[0];
+            var cropHeight = cropSize[0], cropWidth = cropSize[1];
+            this.outputShape = [numBoxes, cropHeight, cropWidth, depth];
+            var methodId = method === 'bilinear' ? 1 : 0;
+            var _a = [imageHeight - 1 + ".0", imageWidth - 1 + ".0"], inputHeightFloat = _a[0], inputWidthFloat = _a[1];
+            var _b = cropHeight > 1 ?
+                [
+                    "" + (imageHeight - 1) / (cropHeight - 1),
+                    '(y2-y1) * height_ratio',
+                    "y1*" + inputHeightFloat + " + float(y)*(height_scale)",
+                ] :
+                [
+                    '0.0',
+                    '0.0',
+                    "0.5 * (y1+y2) * " + inputHeightFloat,
+                ], heightRatio = _b[0], heightScale = _b[1], inY = _b[2];
+            var _c = cropWidth > 1 ?
+                [
+                    "" + (imageWidth - 1) / (cropWidth - 1),
+                    '(x2-x1) * width_ratio',
+                    "x1*" + inputWidthFloat + " + float(x)*(width_scale)",
+                ] :
+                [
+                    '0.0',
+                    '0.0',
+                    "0.5 * (x1+x2) * " + inputWidthFloat,
+                ], widthRatio = _c[0], widthScale = _c[1], inX = _c[2];
+            // Reference implementation
+            // tslint:disable-next-line:max-line-length
+            // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc
+            this.userCode = "\n      const float height_ratio = float(" + heightRatio + ");\n      const float width_ratio = float(" + widthRatio + ");\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= " + batch + ") {\n          return;\n        }\n\n        float height_scale = " + heightScale + ";\n        float width_scale = " + widthScale + ";\n\n        float in_y = " + inY + ";\n        if( in_y < 0.0 || in_y > " + inputHeightFloat + " ) {\n          setOutput(float(" + extrapolationValue + "));\n          return;\n        }\n        float in_x = " + inX + ";\n        if( in_x < 0.0 || in_x > " + inputWidthFloat + " ) {\n          setOutput(float(" + extrapolationValue + "));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(" + methodId + " == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    ";
+        }
+        return CropAndResizeProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var cropAndResize = function (args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var image = inputs.image, boxes = inputs.boxes, boxInd = inputs.boxInd;
+        var cropSize = attrs.cropSize, method = attrs.method, extrapolationValue = attrs.extrapolationValue;
+        var program = new CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);
+        return backend.runWebGLProgram(program, [image, boxes, boxInd], 'float32');
+    };
+    var cropAndResizeConfig = {
+        kernelName: tf.CropAndResize,
+        backendName: 'webgl',
+        kernelFunc: cropAndResize
+    };
+
+    var CumSumProgram = /** @class */ (function () {
+        function CumSumProgram(shape, exclusive, reverse) {
+            this.variableNames = ['x'];
+            this.outputShape = shape;
+            var rank = shape.length;
+            var val = exclusive ? '0.0' : "getX(" + getCoords$1(rank, 'coords') + ")";
+            var length = shape[shape.length - 1];
+            var condition = '';
+            var idxString = '';
+            // When exclusive is set, the cumsum op becomes roll op that copies the
+            // value from the previous index based on the direction specified by the
+            // reverse flag.
+            if (exclusive) {
+                condition = reverse ? "end != " + (length - 1) : 'end != 0';
+                idxString = reverse ? 'end + 1' : 'end - 1';
+            }
+            else {
+                condition = reverse ? "end + pow2 < " + length : 'end >= pow2';
+                idxString = (reverse ? 'end + pow2' : 'end - pow2');
+            }
+            this.userCode = "\n      uniform float index;\n      void main() {\n        " + getCoordsDataType(rank) + " coords = getOutputCoords();\n        int end = " + getFinalCoord(rank, 'coords') + ";\n        float val = " + val + ";\n        int pow2 = int(pow(2.0, index));\n        if (" + condition + ") {\n          int idx = " + idxString + ";\n          " + getFinalCoord(rank, 'coords') + " = idx;\n          val += getX(" + getCoords$1(rank, 'coords') + ");\n        }\n        setOutput(val);\n      }\n    ";
+        }
+        CumSumProgram.prototype.getCustomSetupFunc = function (index) {
+            var _this = this;
+            return function (gpgpu, webGLProgram) {
+                if (_this.index == null) {
+                    _this.index = gpgpu.getUniformLocation(webGLProgram, 'index');
+                }
+                gpgpu.gl.uniform1f(_this.index, index);
+            };
+        };
+        return CumSumProgram;
+    }());
+    function getCoords$1(rank, name) {
+        if (rank === 1) {
+            return "" + name;
+        }
+        else if (rank === 2) {
+            return name + ".x, " + name + ".y";
+        }
+        else if (rank === 3) {
+            return name + ".x, " + name + ".y, " + name + ".z";
+        }
+        else if (rank === 4) {
+            return name + ".x, " + name + ".y, " + name + ".z, " + name + ".w";
+        }
+        else {
+            throw Error("Cumulative sum for rank " + rank + " is not yet supported");
+        }
+    }
+    function getFinalCoord(rank, name) {
+        if (rank === 1) {
+            return "" + name;
+        }
+        else if (rank === 2) {
+            return name + ".y";
+        }
+        else if (rank === 3) {
+            return name + ".z";
+        }
+        else if (rank === 4) {
+            return name + ".w";
+        }
+        else {
+            throw Error("Cumulative sum for rank " + rank + " is not yet supported");
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function cumsum(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis, exclusive = attrs.exclusive, reverse = attrs.reverse;
+        var xRank = x.shape.length;
+        var permutation = tf.backend_util.getAxesPermutation([axis], xRank);
+        var permutedX = x;
+        if (permutation != null) {
+            permutedX = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutation } });
+        }
+        var permutedAxis = tf.backend_util.getInnerMostAxes(1, xRank)[0];
+        if (permutedAxis !== xRank - 1) {
+            throw new Error("WebGL cumsum shader expects an inner-most axis=" + (x.shape.length - 1) + " " +
+                ("but got axis=" + axis));
+        }
+        var size = permutedX.shape[permutedAxis];
+        var result = identity({ inputs: { x: permutedX }, backend: backend });
+        // Use cumsum parallel algorithm, ref:
+        // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda
+        for (var i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {
+            var program = new CumSumProgram(permutedX.shape, false, reverse);
+            var customSetup = program.getCustomSetupFunc(i);
+            var prevResult = result;
+            result =
+                backend.runWebGLProgram(program, [result], result.dtype, customSetup);
+            backend.disposeIntermediateTensorInfo(prevResult);
+        }
+        // For exclusive cumsum, shift the end result in the direction of sum
+        // and add 0 to the front index.
+        if (exclusive) {
+            var program = new CumSumProgram(permutedX.shape, exclusive, reverse);
+            var prevResult = result;
+            result = backend.runWebGLProgram(program, [result], result.dtype);
+            backend.disposeIntermediateTensorInfo(prevResult);
+        }
+        if (permutation != null) {
+            var reversePermutation = tf.backend_util.getUndoAxesPermutation(permutation);
+            var reverseTransposedResult = transpose({ inputs: { x: result }, backend: backend, attrs: { perm: reversePermutation } });
+            backend.disposeIntermediateTensorInfo(result);
+            backend.disposeIntermediateTensorInfo(permutedX);
+            return reverseTransposedResult;
+        }
+        return result;
+    }
+    var cumsumConfig = {
+        kernelName: tf.Cumsum,
+        backendName: 'webgl',
+        kernelFunc: cumsum
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function denseBincount(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, weights = inputs.weights;
+        var size = attrs.size, binaryOutput = attrs.binaryOutput;
+        if (x.shape.length === 1) {
+            var xVals = backend.readSync(x.dataId);
+            var weightsVals = backend.readSync(weights.dataId);
+            var outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);
+            return backend.makeTensorInfo([size], weights.dtype, outVals);
+        }
+        else if (x.shape.length === 2) {
+            var xBuf = backend.bufferSync(x);
+            var weightsBuf = backend.bufferSync(weights);
+            var outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size, binaryOutput);
+            return backend.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
+        }
+        throw new Error("Error in denseBincount: input must be at most rank 2, but got rank" +
+            (x.shape.length + "."));
+    }
+    var denseBincountConfig = {
+        kernelName: tf.DenseBincount,
+        backendName: 'webgl',
+        kernelFunc: denseBincount
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var DepthToSpaceProgram = /** @class */ (function () {
+        function DepthToSpaceProgram(outputShape, blockSize, dataFormat) {
+            this.variableNames = ['x'];
+            this.outputShape = [];
+            this.outputShape = outputShape;
+            this.blockSize = blockSize;
+            this.dataFormat = dataFormat;
+            this.userCode = "\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = " + this.getHeightCoordString() + ";\n      int w = " + this.getWidthCoordString() + ";\n      int d = " + this.getDepthCoordString() + ";\n\n      int in_h = h / " + blockSize + ";\n      int offset_h = imod(h, " + blockSize + ");\n      int in_w = w / " + blockSize + ";\n      int offset_w = imod(w, " + blockSize + ");\n      int offset_d = (offset_h * " + blockSize + " + offset_w) *\n        " + this.getOutputDepthSize() + ";\n      int in_d = d + offset_d;\n\n      float result = " + this.getInputSamplingString() + ";\n      setOutput(result);\n    }\n  ";
+        }
+        DepthToSpaceProgram.prototype.getHeightCoordString = function () {
+            if (this.dataFormat === 'NHWC') {
+                return "coords[1]";
+            }
+            else {
+                return "coords[2]";
+            }
+        };
+        DepthToSpaceProgram.prototype.getWidthCoordString = function () {
+            if (this.dataFormat === 'NHWC') {
+                return "coords[2]";
+            }
+            else {
+                return "coords[3]";
+            }
+        };
+        DepthToSpaceProgram.prototype.getDepthCoordString = function () {
+            if (this.dataFormat === 'NHWC') {
+                return "coords[3]";
+            }
+            else {
+                return "coords[1]";
+            }
+        };
+        DepthToSpaceProgram.prototype.getOutputDepthSize = function () {
+            if (this.dataFormat === 'NHWC') {
+                return this.outputShape[3];
+            }
+            else {
+                return this.outputShape[1];
+            }
+        };
+        DepthToSpaceProgram.prototype.getInputSamplingString = function () {
+            if (this.dataFormat === 'NHWC') {
+                return "getX(b, in_h, in_w, in_d)";
+            }
+            else {
+                return "getX(b, in_d, in_h, in_w)";
+            }
+        };
+        return DepthToSpaceProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function depthToSpace(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var blockSize = attrs.blockSize, dataFormat = attrs.dataFormat;
+        tf.util.assert(blockSize > 1, function () { return "blockSize should be > 1 for depthToSpace, but was: " + blockSize; });
+        var batchSize = x.shape[0];
+        var inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];
+        var inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];
+        var inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];
+        var outputHeight = inputHeight * blockSize;
+        var outputWidth = inputWidth * blockSize;
+        var outputDepth = inputDepth / (blockSize * blockSize);
+        var outputShape = (dataFormat === 'NHWC') ?
+            [batchSize, outputHeight, outputWidth, outputDepth] :
+            [batchSize, outputDepth, outputHeight, outputWidth];
+        var program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);
+        return backend.runWebGLProgram(program, [x], x.dtype);
+    }
+    var depthToSpaceConfig = {
+        kernelName: tf.DepthToSpace,
+        backendName: 'webgl',
+        kernelFunc: depthToSpace
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var DepthwiseConv2DProgram = /** @class */ (function () {
+        function DepthwiseConv2DProgram(convInfo, addBias, activation, hasPreluActivation, hasLeakyReluAlpha) {
+            if (addBias === void 0) { addBias = false; }
+            if (activation === void 0) { activation = null; }
+            if (hasPreluActivation === void 0) { hasPreluActivation = false; }
+            if (hasLeakyReluAlpha === void 0) { hasLeakyReluAlpha = false; }
+            this.variableNames = ['x', 'W'];
+            this.outputShape = convInfo.outShape;
+            var xNumRows = convInfo.inHeight;
+            var xNumCols = convInfo.inWidth;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var channelMul = convInfo.outChannels / convInfo.inChannels;
+            var activationSnippet = '', applyActivationSnippet = '';
+            if (activation) {
+                if (hasPreluActivation) {
+                    activationSnippet = "float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          " + activation + "\n        }";
+                }
+                else if (hasLeakyReluAlpha) {
+                    activationSnippet = "float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          " + activation + "\n        }";
+                }
+                else {
+                    activationSnippet = "\n          float activation(float x) {\n            " + activation + "\n          }\n        ";
+                }
+                applyActivationSnippet = "result = activation(result);";
+            }
+            var addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
+            if (addBias) {
+                this.variableNames.push('bias');
+            }
+            if (hasPreluActivation) {
+                this.variableNames.push('preluActivationWeights');
+            }
+            if (hasLeakyReluAlpha) {
+                this.variableNames.push('leakyreluAlpha');
+            }
+            this.userCode = "\n      " + activationSnippet + "\n\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / " + channelMul + ";\n        int q = d2 - d1 * " + channelMul + ";\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          int xR = xRCorner + wR * " + dilationHeight + ";\n\n          if (xR < 0 || xR >= " + xNumRows + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            int xC = xCCorner + wC * " + dilationWidth + ";\n\n            if (xC < 0 || xC >= " + xNumCols + ") {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        " + addBiasSnippet + "\n        " + applyActivationSnippet + "\n        setOutput(result);\n      }\n    ";
+        }
+        return DepthwiseConv2DProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var DepthwiseConvPacked2DProgram = /** @class */ (function () {
+        function DepthwiseConvPacked2DProgram(convInfo, addBias, activation, hasPreluActivation, hasLeakyReluAlpha) {
+            if (addBias === void 0) { addBias = false; }
+            if (activation === void 0) { activation = null; }
+            if (hasPreluActivation === void 0) { hasPreluActivation = false; }
+            if (hasLeakyReluAlpha === void 0) { hasLeakyReluAlpha = false; }
+            this.variableNames = ['x', 'W'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = convInfo.outShape;
+            var channelMul = convInfo.outChannels / convInfo.inChannels;
+            var xNumRows = convInfo.inHeight;
+            var xNumCols = convInfo.inWidth;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var texelsAcross = filterWidth;
+            var mainLoop = "\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;";
+            for (var c = 0; c < filterWidth; c++) {
+                mainLoop += "\n          vec4 xTexelC" + c * 2 + ";\n          int xTexelC" + c * 2 + "Ready;\n          vec4 xC" + c + ";";
+            }
+            /**
+             * This vectorized implementation works by gathering the values needed for
+             * each output channel's dot product into vec4's and then multiplying them
+             * all together (this happens in the final double for-loop below). Most of
+             * the main loop consists of constructing these vec4's with the minimum
+             * number of texture2D calls, which means making use of all four returned
+             * values from a texture2D call at once.
+             */
+            for (var r = 0; r < filterHeight; r++) {
+                for (var c = 0; c < filterWidth; c++) {
+                    mainLoop += "\n          xTexelC" + c * 2 + " = vec4(0.0);\n          xTexelC" + c * 2 + "Ready = 0;\n          xC" + c + " = vec4(0.0);";
+                }
+                mainLoop += "\n        xR = xRCorner + " + r * dilationHeight + ";\n        if (xR >=0 && xR < " + xNumRows + ") {\n      ";
+                for (var texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {
+                    var colIndex = texelC * 2;
+                    var c = colIndex * dilationWidth;
+                    mainLoop += "\n          xC = xCCorner + " + c + ";\n          ";
+                    if (strideWidth === 1) {
+                        if (colIndex < filterWidth) {
+                            // If padding is odd, the outer texels have to be composed.
+                            if (padLeft % 2 === 1) {
+                                // TODO: Ensure vec4 previous does not result in redundant sample,
+                                // and avoid setting xTexelRC's that exceed the boundary in the
+                                // first place rather than resetting them to vec4(0)).
+                                // To compute xCOffset:
+                                // - If padding is odd, we must add 1 to ensure we ask for an
+                                // even-numbered row.
+                                // - We subtract 2 to access the previous texel.
+                                mainLoop += "\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < " + xNumCols + " && xTexelC" + c + "Ready == 0) {\n                  xTexelC" + c + " = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= " + xNumCols + ") {\n                    xTexelC" + c + ".zw = vec2(0.0);\n                  }\n                  xTexelC" + c + "Ready = 1;\n                }\n              ";
+                                // This texel has been read in previous iteration if the dilation
+                                // is 1.
+                                if (dilationWidth === 1 && c > 0) {
+                                    mainLoop += "\n                xC" + colIndex + " = vec4(xTexelC" + (c - 2) + ".zw, xTexelC" + c + ".xy);\n                ";
+                                }
+                                else {
+                                    mainLoop += "\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < " + xNumCols + ") {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= " + xNumCols + ") {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC" + colIndex + " = vec4(previous.zw, xTexelC" + c + ".xy);\n                  } else {\n                    xC" + colIndex + " = vec4(0.0, 0.0, xTexelC" + c + ".xy);\n                  }\n                  ";
+                                }
+                            }
+                            else {
+                                // Padding is even, so xRC corresponds to a single texel.
+                                mainLoop += "\n                if (xC >= 0 && xC < " + xNumCols + " && xTexelC" + c + "Ready == 0) {\n                  xTexelC" + c + " = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= " + xNumCols + ") {\n                    xTexelC" + c + ".zw = vec2(0.0);\n                  }\n                  xTexelC" + c + "Ready = 1;\n                }\n\n                xC" + colIndex + " = xTexelC" + c + ";\n                ";
+                            }
+                            if (c + 1 < filterWidth) {
+                                // If dilation is even, the second entry should match the first
+                                // (either both are composed or both are single samples). But if
+                                // dilation is odd, then the second entry should be the opposite
+                                // of the first (if the first is composed, the second is a single
+                                // sample, and vice versa.)
+                                var nextTexelOffset = padLeft % 2 === 0 ?
+                                    tf.util.nearestLargerEven(dilationWidth) :
+                                    dilationWidth;
+                                if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||
+                                    (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {
+                                    mainLoop += "\n                  xCOffset = xC + " + padLeft % 2 + " + " + nextTexelOffset + ";\n\n                  if (xCOffset >= 0 && xCOffset < " + xNumCols + " && xTexelC" + (c + 2) + "Ready == 0) {\n                    xTexelC" + (c + 2) + " = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= " + xNumCols + ") {\n                      xTexelC" + (c + 2) + ".zw = vec2(0.0);\n                    }\n                    xTexelC" + (c + 2) + "Ready = 1;\n                  }\n                  ";
+                                    // If dilation > 1 then the xRC's will not be able to share any
+                                    // values, so each xRC will require two unique calls to getX.
+                                    if (dilationWidth > 1) {
+                                        mainLoop += "\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < " + xNumCols + " && xTexelC" + c + "Ready == 0) {\n                      xTexelC" + c + " = getX(batch, xR, xCOffset, d1);\n                      xTexelC" + c + "Ready = 1;\n                    }\n                    ";
+                                    }
+                                    mainLoop += "\n                  xC" + (colIndex + 1) + " = vec4(xTexelC" + c + ".zw, xTexelC" + (c + 2) + ".xy);\n                  ";
+                                }
+                                else {
+                                    // If dilation is 1 and padding is odd, we have already read the
+                                    // texel when constructing the previous x value. Here we can
+                                    // simply skip the texture read.
+                                    if (nextTexelOffset === 1) {
+                                        mainLoop += "\n                    xC" + (colIndex + 1) + " = xTexelC" + c + ";\n                    ";
+                                    }
+                                    else {
+                                        mainLoop += "\n                    xCOffset = xC + " + nextTexelOffset + ";\n\n                    if (xCOffset >= 0 && xCOffset < " + xNumCols + " && xTexelC" + (c + 2) + "Ready == 0) {\n                      xTexelC" + (c + 2) + " = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= " + xNumCols + ") {\n                        xTexelC" + (c + 2) + ".zw = vec2(0.0);\n                      }\n                      xTexelC" + (c + 2) + "Ready = 1;\n                    }\n\n                    xC" + (colIndex + 1) + " = xTexelC" + (c + 2) + ";\n                    ";
+                                    }
+                                }
+                            }
+                        }
+                    }
+                    else { // stride === 2
+                        if (c < filterWidth) {
+                            // Depending on whether padLeft is even or odd, we want either the
+                            // xy or zw channels from X texels for xC${colIndex}. If padLeft is
+                            // even, xC${colIndex +1} is simply the zw channels of texels we've
+                            // already sampled. But if padLeft is odd, xC{$c + 1}.zw will
+                            // need to come from the xy channels of a new texel, hence the `
+                            // vec4
+                            // final` initialized below.
+                            if (padLeft % 2 === 1) {
+                                mainLoop += "\n                xCOffset = xC + 1 - " + strideWidth + ";\n                if(xCOffset >= 0 && xCOffset < " + xNumCols + " && xTexelC" + c + "Ready == 0) {\n                  xTexelC" + c + " = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= " + xNumCols + ") {\n                    xTexelC" + c + ".zw = vec2(0.0);\n                  }\n                  xTexelC" + c + "Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < " + xNumCols + " && xTexelC" + (c + 2) + "Ready == 0) {\n                  xTexelC" + (c + 2) + " = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= " + xNumCols + ") {\n                    xTexelC" + (c + 2) + ".zw = vec2(0.0);\n                  }\n                  xTexelC" + (c + 2) + "Ready = 1;\n                }\n\n                xC" + colIndex + " = vec4(xTexelC" + c + ".zw, xTexelC" + (c + 2) + ".zw);\n              ";
+                                if (c + 1 < filterWidth) {
+                                    mainLoop += "\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + " + strideWidth + ";\n                  if(xCOffset >= 0 && xCOffset < " + xNumCols + ") {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC" + (colIndex + 1) + " = vec4(xTexelC" + (c + 2) + ".xy, final.xy);\n                ";
+                                }
+                            }
+                            else {
+                                mainLoop += "\n                if(xC >= 0 && xC < " + xNumCols + " && xTexelC" + c + "Ready == 0) {\n                  xTexelC" + c + " = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= " + xNumCols + ") {\n                    xTexelC" + c + ".zw = vec2(0.0);\n                  }\n                  xTexelC" + c + "Ready = 1;\n                }\n\n                xCOffset = xC + " + strideWidth + ";\n                if(xCOffset >= 0 && xCOffset < " + xNumCols + " && xTexelC" + (c + 2) + "Ready == 0) {\n                  xTexelC" + (c + 2) + " = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= " + xNumCols + ") {\n                    xTexelC" + (c + 2) + ".zw = vec2(0.);\n                  }\n                  xTexelC" + (c + 2) + "Ready = 1;\n                }\n\n                xC" + colIndex + " = vec4(\n                  xTexelC" + c + ".xy, xTexelC" + (c + 2) + ".xy);\n              ";
+                                if (c + 1 < filterWidth) {
+                                    mainLoop += "\n                  xC" + (colIndex + 1) + " = vec4(xTexelC" + c + ".zw, xTexelC" + (c + 2) + ".zw);\n                ";
+                                }
+                            }
+                        }
+                    }
+                    // localize the dotProd accumulation within the loop, the theory is for
+                    // GPU with limited cache, accumulate sum across large amount of
+                    // veriables will cause lots of cache misses. (i.e. 5x5 filter will have
+                    // 50 variables)
+                    if (colIndex < filterWidth) {
+                        mainLoop += "\n            wTexel = getW(" + r + ", " + c + ", d1, q);\n            dotProd += xC" + colIndex + " * vec4(wTexel.xz, wTexel.xz);\n          ";
+                        if (c + 1 < filterWidth) {
+                            mainLoop += "\n              wTexel = getW(" + r + ", " + (c + 1) + ", d1, q);\n              dotProd += xC" + (colIndex + 1) + " * vec4(wTexel.xz, wTexel.xz);\n            ";
+                        }
+                    }
+                }
+                mainLoop += "\n        }\n      ";
+            }
+            var activationSnippet = '', applyActivationSnippet = '';
+            if (activation) {
+                if (hasPreluActivation) {
+                    activationSnippet = "vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          " + activation + "\n        }";
+                }
+                else if (hasLeakyReluAlpha) {
+                    activationSnippet = "vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          " + activation + "\n        }";
+                }
+                else {
+                    activationSnippet = "vec4 activation(vec4 x) {\n          " + activation + "\n        }";
+                }
+                applyActivationSnippet = "result = activation(result);";
+            }
+            var addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
+            if (addBias) {
+                this.variableNames.push('bias');
+            }
+            if (hasPreluActivation) {
+                this.variableNames.push('preluActivationWeights');
+            }
+            if (hasLeakyReluAlpha) {
+                this.variableNames.push('leakyreluAlpha');
+            }
+            this.userCode = "\n      " + activationSnippet + "\n\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / " + channelMul + ";\n        int q = d2 - d1 * " + channelMul + ";\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        " + mainLoop + "\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        " + addBiasSnippet + "\n        " + applyActivationSnippet + "\n        setOutput(result);\n      }\n    ";
+        }
+        return DepthwiseConvPacked2DProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function depthwiseConv2dNative(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, filter = inputs.filter;
+        var strides = attrs.strides, pad = attrs.pad, dilations = attrs.dilations, dimRoundingMode = attrs.dimRoundingMode;
+        var $dilations = dilations;
+        if ($dilations == null) {
+            $dilations = [1, 1];
+        }
+        tf.util.assert(tf.backend_util.eitherStridesOrDilationsAreOne(strides, $dilations), function () { return 'Error in depthwiseConv2d: Either strides or dilations must be ' +
+            ("1. Got strides " + strides + " and dilations '" + $dilations + "'"); });
+        var convInfo = tf.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);
+        var program;
+        if (tf.env().getBool('WEBGL_PACK_DEPTHWISECONV') && convInfo.strideWidth <= 2 &&
+            convInfo.outChannels / convInfo.inChannels === 1) {
+            program = new DepthwiseConvPacked2DProgram(convInfo);
+        }
+        else {
+            program = new DepthwiseConv2DProgram(convInfo);
+        }
+        return backend.runWebGLProgram(program, [x, filter], 'float32');
+    }
+    var depthwiseConv2dNativeConfig = {
+        kernelName: tf.DepthwiseConv2dNative,
+        backendName: 'webgl',
+        kernelFunc: depthwiseConv2dNative,
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var DepthwiseConv2DDerFilterProgram = /** @class */ (function () {
+        function DepthwiseConv2DDerFilterProgram(convInfo) {
+            this.variableNames = ['x', 'dy'];
+            this.outputShape = convInfo.filterShape;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            var channelMul = convInfo.outChannels / convInfo.inChannels;
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * " + channelMul + " + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n          for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n            int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n              int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return DepthwiseConv2DDerFilterProgram;
+    }());
+    var DepthwiseConv2DDerInputProgram = /** @class */ (function () {
+        function DepthwiseConv2DDerInputProgram(convInfo) {
+            this.variableNames = ['dy', 'W'];
+            this.outputShape = convInfo.inShape;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var padTop = filterHeight - 1 - convInfo.padInfo.top;
+            var padLeft = filterWidth - 1 - convInfo.padInfo.left;
+            var channelMul = convInfo.outChannels / convInfo.inChannels;
+            this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = " + filterHeight + " - 1 - wR;\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = " + filterWidth + " - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < " + channelMul + "; dm++) {\n              int d2 = d1 * " + channelMul + " + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return DepthwiseConv2DDerInputProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function depthwiseConv2dNativeBackpropFilter(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, dy = inputs.dy;
+        var strides = attrs.strides, dilations = attrs.dilations, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode, filterShape = attrs.filterShape;
+        var convInfo = tf.backend_util.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
+        var program = new DepthwiseConv2DDerFilterProgram(convInfo);
+        return backend.runWebGLProgram(program, [x, dy], 'float32');
+    }
+    var depthwiseConv2dNativeBackpropFilterConfig = {
+        kernelName: tf.DepthwiseConv2dNativeBackpropFilter,
+        backendName: 'webgl',
+        kernelFunc: depthwiseConv2dNativeBackpropFilter
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function depthwiseConv2dNativeBackpropInput(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var dy = inputs.dy, filter = inputs.filter;
+        var strides = attrs.strides, dilations = attrs.dilations, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode, inputShape = attrs.inputShape;
+        var convInfo = tf.backend_util.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
+        var program = new DepthwiseConv2DDerInputProgram(convInfo);
+        return backend.runWebGLProgram(program, [dy, filter], 'float32');
+    }
+    var depthwiseConv2dNativeBackpropInputConfig = {
+        kernelName: tf.DepthwiseConv2dNativeBackpropInput,
+        backendName: 'webgl',
+        kernelFunc: depthwiseConv2dNativeBackpropInput
+    };
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var DiagProgram = /** @class */ (function () {
+        function DiagProgram(size) {
+            this.variableNames = ['X'];
+            this.outputShape = [size, size];
+            this.userCode = "\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    ";
+        }
+        return DiagProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function diag(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var x = inputs.x;
+        var outShape = x.shape.concat(x.shape);
+        var xSize = tf.util.sizeFromShape(x.shape);
+        var flat = reshape({ inputs: { x: x }, backend: backend, attrs: { shape: [xSize] } });
+        var program = new DiagProgram(xSize);
+        var res = backend.runWebGLProgram(program, [flat], flat.dtype);
+        var out = reshape({ inputs: { x: res }, backend: backend, attrs: { shape: outShape } });
+        backend.disposeIntermediateTensorInfo(flat);
+        backend.disposeIntermediateTensorInfo(res);
+        return out;
+    }
+    var diagConfig = {
+        kernelName: tf.Diag,
+        backendName: 'webgl',
+        kernelFunc: diag
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var Dilation2DProgram = /** @class */ (function () {
+        function Dilation2DProgram(convInfo) {
+            this.variableNames = ['x', 'W'];
+            this.outputShape = convInfo.outShape;
+            var inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, padInfo = convInfo.padInfo, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, dilationHeight = convInfo.dilationHeight, dilationWidth = convInfo.dilationWidth;
+            var padTop = padInfo.top, padLeft = padInfo.left;
+            this.userCode = "\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < " + filterHeight + "; h++) {\n          int hIn = hBeg + h * " + dilationHeight + ";\n\n          if (hIn >= 0 && hIn < " + inHeight + ") {\n            for (int w = 0; w < " + filterWidth + "; w++) {\n              int wIn = wBeg + w * " + dilationWidth + ";\n\n              if (wIn >= 0 && wIn < " + inWidth + ") {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    ";
+        }
+        return Dilation2DProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function dilation2D(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, filter = inputs.filter;
+        var strides = attrs.strides, pad = attrs.pad, dilations = attrs.dilations;
+        var convInfo = tf.backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);
+        var out;
+        var program = new Dilation2DProgram(convInfo);
+        out = backend.runWebGLProgram(program, [x, filter], 'float32');
+        var outReshaped = reshape({ inputs: { x: out }, backend: backend, attrs: { shape: convInfo.outShape } });
+        backend.disposeIntermediateTensorInfo(out);
+        return outReshaped;
+    }
+    var dilation2DConfig = {
+        kernelName: tf.Dilation2D,
+        backendName: 'webgl',
+        kernelFunc: dilation2D,
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function einsum(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var equation = attrs.equation;
+        var tensors = inputs;
+        var _a = tf.backend_util.decodeEinsumEquation(equation, tensors.length), allDims = _a.allDims, summedDims = _a.summedDims, idDims = _a.idDims;
+        tf.backend_util.checkEinsumDimSizes(allDims.length, idDims, tensors);
+        var _b = tf.backend_util.getEinsumComputePath(summedDims, idDims), path = _b.path, steps = _b.steps;
+        var nSteps = steps.length;
+        var out = null;
+        var numDimsRemaining = allDims.length;
+        var tensorsToDispose = [];
+        for (var i = 0; i < nSteps; ++i) {
+            for (var _i = 0, _c = steps[i]; _i < _c.length; _i++) {
+                var idTerm = _c[_i];
+                var _d = tf.backend_util.getEinsumPermutation(numDimsRemaining, idDims[idTerm]), perm = _d.permutationIndices, dimsToExpand = _d.expandDims;
+                var x = void 0;
+                if (tf.backend_util.isIdentityPermutation(perm)) {
+                    x = tensors[idTerm];
+                }
+                else {
+                    x = transpose({ inputs: { x: tensors[idTerm] }, backend: backend, attrs: { perm: perm } });
+                    tensorsToDispose.push(x);
+                }
+                var targetShape = x.shape.slice();
+                for (var k = 0; k < dimsToExpand.length; ++k) {
+                    targetShape.splice(dimsToExpand[k], 0, 1);
+                }
+                if (!tf.util.arraysEqual(x.shape, targetShape)) {
+                    x = reshape({ inputs: { x: x }, backend: backend, attrs: { shape: targetShape } });
+                    tensorsToDispose.push(x);
+                }
+                if (out === null) {
+                    out = x;
+                }
+                else {
+                    // tslint:disable-next-line: no-unnecessary-type-assertion
+                    out = multiply({ inputs: { a: x, b: out }, backend: backend });
+                    tensorsToDispose.push(out);
+                }
+            }
+            if (i < nSteps - 1) {
+                if (path[i] >= 0) {
+                    out = sum({
+                        inputs: { x: out },
+                        backend: backend,
+                        attrs: {
+                            axis: path[i] - (allDims.length - numDimsRemaining),
+                            keepDims: false
+                        }
+                    });
+                    tensorsToDispose.push(out);
+                }
+                numDimsRemaining--;
+            }
+        }
+        // Clean up intermediate tensors.
+        for (var _e = 0, tensorsToDispose_1 = tensorsToDispose; _e < tensorsToDispose_1.length; _e++) {
+            var tensorInfo = tensorsToDispose_1[_e];
+            if (tensorInfo === out) {
+                continue;
+            }
+            backend.disposeIntermediateTensorInfo(tensorInfo);
+        }
+        return out;
+    }
+    var einsumConfig = {
+        kernelName: tf.Einsum,
+        backendName: 'webgl',
+        kernelFunc: einsum
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ELU$2 = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
+    var ELU_PACKED = "\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n";
+    var elu = unaryKernelFunc({ opSnippet: ELU$2, packedOpSnippet: ELU_PACKED });
+    var eluConfig = {
+        kernelName: tf.Elu,
+        backendName: 'webgl',
+        kernelFunc: elu
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ELU_DER = "return (b >= 1.0) ? a : a * (b + 1.0);";
+    var ELU_DER_PACKED = "\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n";
+    var eluGrad = function (args) {
+        var inputs = args.inputs, backend = args.backend;
+        var dy = inputs.dy, y = inputs.y;
+        var program = tf.env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
+            new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) :
+            new BinaryOpProgram(ELU_DER, dy.shape, y.shape);
+        return backend.runWebGLProgram(program, [dy, y], dy.dtype);
+    };
+    var eluGradConfig = {
+        kernelName: tf.EluGrad,
+        backendName: 'webgl',
+        kernelFunc: eluGrad
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var PACKED_EQUAL = "\n  return vec4(equal(a, b));\n";
+    var EQUAL = "return float(a == b);";
+    var equal = binaryKernelFunc({
+        opSnippet: EQUAL,
+        packedOpSnippet: PACKED_EQUAL,
+        dtype: 'bool',
+        cpuKernelImpl: equalImplCPU,
+    });
+    var equalConfig = {
+        kernelName: tf.Equal,
+        backendName: 'webgl',
+        kernelFunc: equal
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ERF = "\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = " + tf.backend_util.ERF_P + ";\n  float a1 = " + tf.backend_util.ERF_A1 + ";\n  float a2 = " + tf.backend_util.ERF_A2 + ";\n  float a3 = " + tf.backend_util.ERF_A3 + ";\n  float a4 = " + tf.backend_util.ERF_A4 + ";\n  float a5 = " + tf.backend_util.ERF_A5 + ";\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n";
+    var erf = unaryKernelFunc({ opSnippet: ERF });
+    var erfConfig = {
+        kernelName: tf.Erf,
+        backendName: 'webgl',
+        kernelFunc: erf,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var EXP = "return exp(x);";
+    var exp = unaryKernelFunc({ opSnippet: EXP, packedOpSnippet: EXP, cpuKernelImpl: expImplCPU });
+    var expConfig = {
+        kernelName: tf.Exp,
+        backendName: 'webgl',
+        kernelFunc: exp
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function expandDims(args) {
+        var inputs = args.inputs, attrs = args.attrs, backend = args.backend;
+        var dim = attrs.dim;
+        var input = inputs.input;
+        var inputRank = input.shape.length;
+        var newShape = input.shape.slice();
+        var $dim = dim;
+        if (dim < 0) {
+            // Negative value is counted from the tail of rank.
+            tf.util.assert(-(inputRank + 1) <= dim, function () { return "Axis must be in the interval [" + -(inputRank + 1) + ", " + inputRank + "]"; });
+            $dim = inputRank + dim + 1;
+        }
+        newShape.splice($dim, 0, 1);
+        return reshape({ inputs: { x: input }, backend: backend, attrs: { shape: newShape } });
+    }
+    var expandDimsConfig = {
+        kernelName: tf.ExpandDims,
+        backendName: 'webgl',
+        kernelFunc: expandDims,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var EXPM1 = "return exp(x) - 1.0;";
+    var expm1 = unaryKernelFunc({ opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU });
+    var expm1Config = {
+        kernelName: tf.Expm1,
+        backendName: 'webgl',
+        kernelFunc: expm1
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var FFTProgram = /** @class */ (function () {
+        function FFTProgram(component, inputShape, inverse) {
+            this.variableNames = ['real', 'imag'];
+            var innerDim = inputShape[1];
+            this.outputShape = inputShape;
+            var exponentMultiplierSnippet = inverse ? "2.0 * " + Math.PI : "-2.0 * " + Math.PI;
+            var resultDenominator = inverse ? innerDim + ".0" : '1.0';
+            var opString;
+            if (component === 'real') {
+                opString = 'return real * expR - imag * expI;';
+            }
+            else if (component === 'imag') {
+                opString = 'return real * expI + imag * expR;';
+            }
+            else {
+                throw new Error("FFT component must be either \"real\" or \"imag\", got " + component + ".");
+            }
+            this.userCode = "\n      const float exponentMultiplier = " + exponentMultiplierSnippet + ";\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        " + opString + "\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(" + innerDim + ");\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < " + innerDim + "; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / " + resultDenominator + ";\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    ";
+        }
+        return FFTProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function fftImpl(x, inverse, backend) {
+        var xData = backend.texData.get(x.dataId);
+        var inputSize = tf.util.sizeFromShape(x.shape);
+        // Collapse all outer dimensions to a single batch dimension.
+        var innerDimensionSize = x.shape[x.shape.length - 1];
+        var batch = inputSize / innerDimensionSize;
+        var input2D = reshape({ inputs: { x: x }, backend: backend, attrs: { shape: [batch, innerDimensionSize] } });
+        var xShape = input2D.shape;
+        var realProgram = new FFTProgram('real', xShape, inverse);
+        var imagProgram = new FFTProgram('imag', xShape, inverse);
+        var inputs = [
+            {
+                dataId: xData.complexTensorInfos.real.dataId,
+                dtype: xData.complexTensorInfos.real.dtype,
+                shape: xShape
+            },
+            {
+                dataId: xData.complexTensorInfos.imag.dataId,
+                dtype: xData.complexTensorInfos.imag.dtype,
+                shape: xShape
+            }
+        ];
+        var realPart = backend.runWebGLProgram(realProgram, inputs, 'float32');
+        var imagPart = backend.runWebGLProgram(imagProgram, inputs, 'float32');
+        var complexOutput = complex({ inputs: { real: realPart, imag: imagPart }, backend: backend });
+        backend.disposeIntermediateTensorInfo(realPart);
+        backend.disposeIntermediateTensorInfo(imagPart);
+        var complexOutputReshaped = reshape({ inputs: { x: complexOutput }, backend: backend, attrs: { shape: x.shape } });
+        backend.disposeIntermediateTensorInfo(input2D);
+        backend.disposeIntermediateTensorInfo(complexOutput);
+        return complexOutputReshaped;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function fft(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var input = inputs.input;
+        return fftImpl(input, false /* inverse */, backend);
+    }
+    var fftConfig = {
+        kernelName: tf.FFT,
+        backendName: 'webgl',
+        kernelFunc: fft
+    };
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var FillProgram = /** @class */ (function () {
+        function FillProgram(shape, value) {
+            this.outputShape = [];
+            this.variableNames = ['x'];
+            this.outputShape = shape;
+            this.userCode = "\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    ";
+        }
+        FillProgram.prototype.getCustomSetupFunc = function (value) {
+            var _this = this;
+            return function (gpgpu, webGLProgram) {
+                if (_this.valueLoc == null) {
+                    _this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');
+                }
+                gpgpu.gl.uniform1f(_this.valueLoc, value);
+            };
+        };
+        return FillProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function fill(args) {
+        var backend = args.backend, attrs = args.attrs;
+        var shape = attrs.shape, value = attrs.value;
+        var dtype = attrs.dtype;
+        dtype = dtype || tf.util.inferDtype(value);
+        if (dtype === 'string') {
+            // String type should be handled in CPU memory.
+            var values = tf.util.getArrayFromDType(dtype, tf.util.sizeFromShape(shape));
+            values.fill(value);
+            return backend.makeTensorInfo(shape, dtype, values);
+        }
+        else {
+            var program = new FillProgram(shape, value);
+            var customSetup = program.getCustomSetupFunc(value);
+            return backend.runWebGLProgram(program, [], dtype, customSetup);
+        }
+    }
+    var fillConfig = {
+        kernelName: tf.Fill,
+        backendName: 'webgl',
+        kernelFunc: fill
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var FlipLeftRightProgram = /** @class */ (function () {
+        function FlipLeftRightProgram(imageShape) {
+            this.variableNames = ['Image'];
+            this.outputShape = [];
+            var imageWidth = imageShape[2];
+            this.outputShape = imageShape;
+            this.userCode = "\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = " + imageWidth + " - x;\n          float outputValue;\n          if(coordX >= 0 && coordX < " + imageWidth + ") {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    ";
+        }
+        return FlipLeftRightProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var flipLeftRightConfig = {
+        kernelName: tf.FlipLeftRight,
+        backendName: 'webgl',
+        kernelFunc: function (_a) {
+            var inputs = _a.inputs, backend = _a.backend;
+            var image = inputs.image;
+            var webglBackend = backend;
+            var program = new FlipLeftRightProgram(image.shape);
+            var output = webglBackend.runWebGLProgram(program, [image], image.dtype);
+            return output;
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var FLOOR = "return floor(x);";
+    var floor = unaryKernelFunc({ opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU });
+    var floorConfig = {
+        kernelName: tf.Floor,
+        backendName: 'webgl',
+        kernelFunc: floor,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // We use native integer division to deal with floating point imprecision. Since
+    // we implement floor division and glsl implements truncated division, we
+    // correct for this by subtracting 1 from result when the result is negative and
+    // there is a remainder.
+    var INT_DIV = "\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n";
+    var INT_DIV_PACKED = "\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n";
+    var floorDiv = binaryKernelFunc({ opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: 'int32' });
+    var floorDivConfig = {
+        kernelName: tf.FloorDiv,
+        backendName: 'webgl',
+        kernelFunc: floorDiv
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var FromPixelsProgram = /** @class */ (function () {
+        function FromPixelsProgram(outputShape) {
+            this.variableNames = ['A'];
+            var glsl = getGlslDifferences();
+            var height = outputShape[0], width = outputShape[1];
+            this.outputShape = outputShape;
+            this.userCode = "\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + width + ".0, " + height + ".0);\n\n        vec4 values = " + glsl.texture2D + "(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    ";
+        }
+        return FromPixelsProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var FromPixelsPackedProgram = /** @class */ (function () {
+        function FromPixelsPackedProgram(outputShape) {
+            this.variableNames = ['A'];
+            this.packedInputs = false;
+            this.packedOutput = true;
+            var glsl = getGlslDifferences();
+            var height = outputShape[0], width = outputShape[1];
+            this.outputShape = outputShape;
+            this.userCode = "\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(" + width + ".0, " + height + ".0);\n            vec4 values = " + glsl.texture2D + "(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        " + glsl.output + " = result;\n      }\n    ";
+        }
+        return FromPixelsPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var fromPixelsConfig = {
+        kernelName: tf.FromPixels,
+        backendName: 'webgl',
+        kernelFunc: fromPixels,
+    };
+    var fromPixels2DContext;
+    function fromPixels(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var pixels = inputs.pixels;
+        var numChannels = attrs.numChannels;
+        var isVideo = typeof (HTMLVideoElement) !== 'undefined' &&
+            pixels instanceof HTMLVideoElement;
+        var isImage = typeof (HTMLImageElement) !== 'undefined' &&
+            pixels instanceof HTMLImageElement;
+        var _a = isVideo ?
+            [
+                pixels.videoWidth,
+                pixels.videoHeight
+            ] :
+            [pixels.width, pixels.height], width = _a[0], height = _a[1];
+        var texShape = [height, width];
+        var outShape = [height, width, numChannels];
+        if (isImage || isVideo) {
+            if (fromPixels2DContext == null) {
+                fromPixels2DContext = document.createElement('canvas').getContext('2d');
+            }
+            fromPixels2DContext.canvas.width = width;
+            fromPixels2DContext.canvas.height = height;
+            fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
+            pixels = fromPixels2DContext.canvas;
+        }
+        var tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');
+        // This is a byte texture with pixels.
+        backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;
+        backend.gpgpu.uploadPixelDataToTexture(backend.getTexture(tempPixelHandle.dataId), pixels);
+        var program = tf.env().getBool('WEBGL_PACK') ?
+            new FromPixelsPackedProgram(outShape) :
+            new FromPixelsProgram(outShape);
+        var res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');
+        backend.disposeData(tempPixelHandle.dataId);
+        return res;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function fusedConv2d(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, filter = inputs.filter, bias = inputs.bias, preluActivationWeights = inputs.preluActivationWeights;
+        var strides = attrs.strides, pad = attrs.pad, dataFormat = attrs.dataFormat, dilations = attrs.dilations, dimRoundingMode = attrs.dimRoundingMode, activation = attrs.activation, leakyreluAlpha = attrs.leakyreluAlpha;
+        var $dataFormat = tf.backend_util.convertConv2DDataFormat(dataFormat);
+        var convInfo = tf.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, false /* depthwise */, $dataFormat);
+        var out;
+        var intermediates = [];
+        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&
+            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&
+            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&
+            (convInfo.padInfo.type === 'SAME' || convInfo.padInfo.type === 'VALID')) {
+            out = conv2dByMatMul({
+                x: x,
+                filter: filter,
+                convInfo: convInfo,
+                backend: backend,
+                bias: bias,
+                activation: activation,
+                preluActivationWeights: preluActivationWeights,
+                leakyreluAlpha: leakyreluAlpha
+            });
+        }
+        else if (tf.env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {
+            out = conv2dWithIm2Row({
+                x: x,
+                filter: filter,
+                convInfo: convInfo,
+                backend: backend,
+                bias: bias,
+                activation: activation,
+                preluActivationWeights: preluActivationWeights,
+                leakyreluAlpha: leakyreluAlpha
+            });
+        }
+        else {
+            var hasBias = bias != null;
+            var hasPreluActivationWeights = preluActivationWeights != null;
+            var hasLeakyreluAlpha = activation === 'leakyrelu';
+            var fusedActivation = activation ? mapActivationToShaderProgram(activation, false) : null;
+            var program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+            var inputs_1 = [x, filter];
+            if (bias) {
+                inputs_1.push(bias);
+            }
+            if (preluActivationWeights) {
+                inputs_1.push(preluActivationWeights);
+            }
+            if (hasLeakyreluAlpha) {
+                var $leakyreluAlpha = backend.makeTensorInfo([], 'float32', tf.util.createScalarValue(leakyreluAlpha, 'float32'));
+                inputs_1.push($leakyreluAlpha);
+                intermediates.push($leakyreluAlpha);
+            }
+            out = backend.runWebGLProgram(program, inputs_1, 'float32');
+        }
+        var outReshaped = reshape({ inputs: { x: out }, backend: backend, attrs: { shape: convInfo.outShape } });
+        intermediates.push(out);
+        intermediates.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return outReshaped;
+    }
+    var fusedConv2DConfig = {
+        kernelName: tf.FusedConv2D,
+        backendName: 'webgl',
+        kernelFunc: fusedConv2d,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function fusedDepthwiseConv2D(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, filter = inputs.filter, bias = inputs.bias, preluActivationWeights = inputs.preluActivationWeights;
+        var strides = attrs.strides, pad = attrs.pad, dilations = attrs.dilations, dimRoundingMode = attrs.dimRoundingMode, activation = attrs.activation, leakyreluAlpha = attrs.leakyreluAlpha;
+        var intermediates = [];
+        var $dilations = dilations;
+        if ($dilations == null) {
+            $dilations = [1, 1];
+        }
+        tf.util.assert(tf.backend_util.eitherStridesOrDilationsAreOne(strides, $dilations), function () { return 'Error in depthwiseConv2d: Either strides or dilations must be ' +
+            ("1. Got strides " + strides + " and dilations '" + $dilations + "'"); });
+        var convInfo = tf.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);
+        var shouldPackDepthwiseConv = tf.env().getBool('WEBGL_PACK_DEPTHWISECONV') &&
+            convInfo.strideWidth <= 2 &&
+            convInfo.outChannels / convInfo.inChannels === 1;
+        var fusedActivation = activation ?
+            mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :
+            null;
+        var programInputs = [x, filter];
+        var hasBias = bias != null;
+        var hasPreluActivationWeights = preluActivationWeights != null;
+        var hasLeakyreluAlpha = activation === 'leakyrelu';
+        if (hasBias) {
+            programInputs.push(bias);
+        }
+        if (hasPreluActivationWeights) {
+            programInputs.push(preluActivationWeights);
+        }
+        if (hasLeakyreluAlpha) {
+            var $leakyreluAlpha = backend.makeTensorInfo([], 'float32', tf.util.createScalarValue(leakyreluAlpha, 'float32'));
+            programInputs.push($leakyreluAlpha);
+            intermediates.push($leakyreluAlpha);
+        }
+        var program;
+        if (shouldPackDepthwiseConv) {
+            program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+        }
+        else {
+            program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+        }
+        var result = backend.runWebGLProgram(program, programInputs, 'float32');
+        intermediates.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return result;
+    }
+    var fusedDepthwiseConv2DConfig = {
+        kernelName: tf.FusedDepthwiseConv2D,
+        backendName: 'webgl',
+        kernelFunc: fusedDepthwiseConv2D,
+    };
+
+    var GatherNDProgram = /** @class */ (function () {
+        function GatherNDProgram(sliceDim, strides, shape) {
+            this.sliceDim = sliceDim;
+            this.strides = strides;
+            this.variableNames = ['x', 'indices'];
+            this.outputShape = shape;
+            var stridesType = getCoordsDataType(strides.length);
+            var dtype = getCoordsDataType(shape.length);
+            var strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';
+            this.userCode = "\n        " + stridesType + " strides = " + stridesType + "(" + this.strides + ");\n         void main() {\n          " + dtype + " coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < " + this.sliceDim + "; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * " + strideString + ";\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      ";
+        }
+        return GatherNDProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function gatherNd(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var params = inputs.params, indices = inputs.indices;
+        var indicesShape = indices.shape;
+        var sliceRank = indicesShape[indicesShape.length - 1];
+        var paramsSize = tf.util.sizeFromShape(params.shape);
+        var _a = tf.backend_util.prepareAndValidate(params, indices), resultShape = _a[0], numSlices = _a[1], sliceSize = _a[2], strides = _a[3];
+        var flattenIndices = reshape({ inputs: { x: indices }, backend: backend, attrs: { shape: [numSlices, sliceRank] } });
+        var flattenX = reshape({
+            inputs: { x: params },
+            backend: backend,
+            attrs: { shape: [(tf.util.sizeFromShape(params.shape) / sliceSize), sliceSize] }
+        });
+        if (backend.shouldExecuteOnCPU([params, indices]) ||
+            params.dtype === 'string') {
+            var indicesData = backend.readSync(indices.dataId);
+            var paramsBuf = backend.bufferSync(params);
+            var outValue = gatherNdImplCPU(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);
+            return backend.makeTensorInfo(resultShape, params.dtype, outValue.values);
+        }
+        var program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);
+        var res = backend.runWebGLProgram(program, [flattenX, flattenIndices], flattenX.dtype);
+        var reshaped = reshape({ inputs: { x: res }, backend: backend, attrs: { shape: resultShape } });
+        backend.disposeIntermediateTensorInfo(flattenIndices);
+        backend.disposeIntermediateTensorInfo(flattenX);
+        backend.disposeIntermediateTensorInfo(res);
+        return reshaped;
+    }
+    var gatherNdConfig = {
+        kernelName: tf.GatherNd,
+        backendName: 'webgl',
+        kernelFunc: gatherNd
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var GatherProgram = /** @class */ (function () {
+        function GatherProgram(aShape, outputShape) {
+            this.variableNames = ['A', 'indices'];
+            this.outputShape = outputShape;
+            this.rank = outputShape.length;
+            var dtype = getCoordsDataType(this.rank);
+            var sourceCoords = getSourceCoords$1(aShape);
+            this.userCode = "\n      void main() {\n        " + dtype + " resRC = getOutputCoords();\n        setOutput(getA(" + sourceCoords + "));\n      }\n    ";
+        }
+        return GatherProgram;
+    }());
+    // The input and output are always flattened into rank 4 tensors.
+    function getSourceCoords$1(aShape, axis) {
+        var currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
+        var sourceCoords = [];
+        for (var i = 0; i < aShape.length; i++) {
+            if (i === 2) {
+                sourceCoords.push('int(getIndices(resRC.x, resRC.z))');
+            }
+            else {
+                sourceCoords.push("" + currentCoords[i]);
+            }
+        }
+        return sourceCoords.join();
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function gatherV2(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, indices = inputs.indices;
+        var axis = attrs.axis, batchDims = attrs.batchDims;
+        var parsedAxis = tf.util.parseAxisParam(axis, x.shape)[0];
+        var shapeInfo = tf.backend_util.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
+        var indicesSize = tf.util.sizeFromShape(indices.shape);
+        var toDispose = [];
+        var flattenX = reshape({
+            inputs: { x: x },
+            backend: backend,
+            attrs: {
+                shape: [
+                    shapeInfo.batchSize, shapeInfo.outerSize, shapeInfo.dimSize,
+                    shapeInfo.sliceSize
+                ]
+            }
+        });
+        var flattenIndex = reshape({
+            inputs: { x: indices },
+            backend: backend,
+            attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }
+        });
+        toDispose.push(flattenX);
+        toDispose.push(flattenIndex);
+        var flattenOutputShape = [
+            shapeInfo.batchSize, shapeInfo.outerSize, indicesSize / shapeInfo.batchSize,
+            shapeInfo.sliceSize
+        ];
+        if (backend.shouldExecuteOnCPU([x, indices]) || x.dtype === 'string') {
+            var indicesBuf = backend.bufferSync(flattenIndex);
+            var xBuf = backend.bufferSync(flattenX);
+            var outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape);
+            toDispose.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+            return backend.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
+        }
+        var program = new GatherProgram(flattenX.shape, flattenOutputShape);
+        var res = backend.runWebGLProgram(program, [flattenX, flattenIndex], flattenX.dtype);
+        toDispose.push(res);
+        var reshaped = reshape({ inputs: { x: res }, backend: backend, attrs: { shape: shapeInfo.outputShape } });
+        toDispose.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return reshaped;
+    }
+    var gatherV2Config = {
+        kernelName: tf.GatherV2,
+        backendName: 'webgl',
+        kernelFunc: gatherV2
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var GREATER = "return float(a > b);";
+    var GREATER_PACKED = "\n  return vec4(greaterThan(a, b));\n";
+    var greater = binaryKernelFunc({
+        opSnippet: GREATER,
+        packedOpSnippet: GREATER_PACKED,
+        cpuKernelImpl: greaterImplCPU,
+        dtype: 'bool'
+    });
+    var greaterConfig = {
+        kernelName: tf.Greater,
+        backendName: 'webgl',
+        kernelFunc: greater
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var GREATER_EQUAL = "return float(a >= b);";
+    var GREATER_EQUAL_PACKED = "\n  return vec4(greaterThanEqual(a, b));\n";
+    var greaterEqual = binaryKernelFunc({
+        opSnippet: GREATER_EQUAL,
+        packedOpSnippet: GREATER_EQUAL_PACKED,
+        dtype: 'bool',
+        cpuKernelImpl: greaterEqualImplCPU
+    });
+    var greaterEqualConfig = {
+        kernelName: tf.GreaterEqual,
+        backendName: 'webgl',
+        kernelFunc: greaterEqual
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function ifft(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var input = inputs.input;
+        return fftImpl(input, true /* inverse */, backend);
+    }
+    var ifftConfig = {
+        kernelName: tf.IFFT,
+        backendName: 'webgl',
+        kernelFunc: ifft
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var IS_FINITE = "return float(!isnan(x) && !isinf(x));";
+    var isFinite = unaryKernelFunc({ opSnippet: IS_FINITE, dtype: 'bool' });
+    var isFiniteConfig = {
+        kernelName: tf.IsFinite,
+        backendName: 'webgl',
+        kernelFunc: isFinite,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var IS_INF = "return float(isinf(x));";
+    var isInf = unaryKernelFunc({ opSnippet: IS_INF, dtype: 'bool' });
+    var isInfConfig = {
+        kernelName: tf.IsInf,
+        backendName: 'webgl',
+        kernelFunc: isInf,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var IS_NAN = "return float(isnan(x));";
+    var isNaN = unaryKernelFunc({ opSnippet: IS_NAN, dtype: 'bool' });
+    var isNaNConfig = {
+        kernelName: tf.IsNan,
+        backendName: 'webgl',
+        kernelFunc: isNaN,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LESS = "return float(a < b);";
+    var LESS_PACKED = "\n  return vec4(lessThan(a, b));\n";
+    var less = binaryKernelFunc({
+        opSnippet: LESS,
+        packedOpSnippet: LESS_PACKED,
+        cpuKernelImpl: lessImplCPU,
+        dtype: 'bool'
+    });
+    var lessConfig = {
+        kernelName: tf.Less,
+        backendName: 'webgl',
+        kernelFunc: less
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LESS_EQUAL = "return float(a <= b);";
+    var LESS_EQUAL_PACKED = "\n  return vec4(lessThanEqual(a, b));\n";
+    var lessEqual = binaryKernelFunc({
+        opSnippet: LESS_EQUAL,
+        packedOpSnippet: LESS_EQUAL_PACKED,
+        cpuKernelImpl: lessEqualImplCPU,
+        dtype: 'bool'
+    });
+    var lessEqualConfig = {
+        kernelName: tf.LessEqual,
+        backendName: 'webgl',
+        kernelFunc: lessEqual
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function linSpace(args) {
+        var backend = args.backend, attrs = args.attrs;
+        var start = attrs.start, stop = attrs.stop, num = attrs.num;
+        // TODO: Use CPU implementation due to the precision problem in Safari.
+        var outVals = linSpaceImplCPU(start, stop, num);
+        return backend.makeTensorInfo([outVals.length], 'float32', outVals);
+    }
+    var linSpaceConfig = {
+        kernelName: tf.LinSpace,
+        backendName: 'webgl',
+        kernelFunc: linSpace
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LOG = "if (x < 0.0) return NAN;\n  return log(x);";
+    var LOG_PACKED = "\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n";
+    var log = unaryKernelFunc({ opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU });
+    var logConfig = {
+        kernelName: tf.Log,
+        backendName: 'webgl',
+        kernelFunc: log
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LOG1P = "return log(1.0 + x);";
+    var log1p = unaryKernelFunc({ opSnippet: LOG1P });
+    var log1pConfig = {
+        kernelName: tf.Log1p,
+        backendName: 'webgl',
+        kernelFunc: log1p,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LOGICAL_AND = "return float(a >= 1.0 && b >= 1.0);";
+    var LOGICAL_AND_PACKED = "\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n";
+    var logicalAnd = binaryKernelFunc({
+        opSnippet: LOGICAL_AND,
+        packedOpSnippet: LOGICAL_AND_PACKED,
+        dtype: 'bool'
+    });
+    var logicalAndConfig = {
+        kernelName: tf.LogicalAnd,
+        backendName: 'webgl',
+        kernelFunc: logicalAnd
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LOGICAL_NOT = "return float(!(x >= 1.0));";
+    var logicalNot = unaryKernelFunc({ opSnippet: LOGICAL_NOT });
+    var logicalNotConfig = {
+        kernelName: tf.LogicalNot,
+        backendName: 'webgl',
+        kernelFunc: logicalNot,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LOGICAL_OR = "return float(a >= 1.0 || b >= 1.0);";
+    var LOGICAL_OR_PACKED = "\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n";
+    var logicalOr = binaryKernelFunc({ opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: 'bool' });
+    var logicalOrConfig = {
+        kernelName: tf.LogicalOr,
+        backendName: 'webgl',
+        kernelFunc: logicalOr
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LRNProgram = /** @class */ (function () {
+        function LRNProgram(xShape, radius, bias, alpha, beta) {
+            this.variableNames = ['x'];
+            this.outputShape = [];
+            var rad = radius;
+            var maxD = xShape[3] - 1;
+            this.outputShape = xShape;
+            // optimize pow(bias + alpha * sum, -beta)
+            // src: https://github.com/tensorflow/tensorflow/..
+            // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..
+            // tensorflow/core/kernels/mkl_lrn_op.cc#L320
+            var powOperator;
+            var basis = "float(" + bias + ") + float(" + alpha + ") * sum";
+            if (beta === 0.5) {
+                powOperator = "inversesqrt(" + basis + ")";
+            }
+            else if (beta === 1.0) {
+                powOperator = "1.0/(" + basis + ")";
+            }
+            else {
+                powOperator = "exp(log(" + basis + ") * float(-" + beta + "));";
+            }
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -" + rad + "; j <= " + rad + "; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  " + maxD + ") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * " + powOperator + ";\n        setOutput(val);\n      }\n    ";
+        }
+        return LRNProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LRNPackedProgram = /** @class */ (function () {
+        function LRNPackedProgram(xShape, radius, bias, alpha, beta) {
+            this.variableNames = ['x'];
+            this.outputShape = [];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            var rad = radius;
+            var maxD = xShape[3] - 1;
+            this.outputShape = xShape;
+            // optimize pow(bias + alpha * sum, -beta)
+            // src: https://github.com/tensorflow/tensorflow/..
+            // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..
+            // tensorflow/core/kernels/mkl_lrn_op.cc#L320
+            var powOperator;
+            var basis = "float(" + bias + ") + float(" + alpha + ") * sum";
+            if (beta === 0.5) {
+                powOperator = "inversesqrt(" + basis + ")";
+            }
+            else if (beta === 1.0) {
+                powOperator = "1.0/(" + basis + ")";
+            }
+            else {
+                powOperator = "exp(log(" + basis + ") * float(-" + beta + "));";
+            }
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < " + this.outputShape[3] + ";\n        bool hasNextRow = c < " + this.outputShape[2] + ";\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - " + rad + ";\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - " + rad + "; j <= " + rad + "; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(" + maxD + "));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * " + powOperator + ";\n        setOutput(result);\n      }\n    ";
+        }
+        return LRNPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var lrn = function (args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var depthRadius = attrs.depthRadius, bias = attrs.bias, alpha = attrs.alpha, beta = attrs.beta;
+        var program = tf.env().getBool('WEBGL_PACK_NORMALIZATION') ?
+            new LRNPackedProgram(x.shape, depthRadius, bias, alpha, beta) :
+            new LRNProgram(x.shape, depthRadius, bias, alpha, beta);
+        return backend.runWebGLProgram(program, [x], x.dtype);
+    };
+    // tslint:disable-next-line: variable-name
+    var LRNConfig = {
+        kernelName: tf.LRN,
+        backendName: 'webgl',
+        kernelFunc: lrn
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var LRNGradProgram = /** @class */ (function () {
+        function LRNGradProgram(inputShape, depthRadius, bias, alpha, beta) {
+            this.variableNames = ['inputImage', 'outputImage', 'dy'];
+            this.outputShape = [];
+            this.outputShape = inputShape;
+            this.depth = inputShape[3];
+            this.depthRadius = depthRadius;
+            this.bias = bias;
+            this.alpha = alpha;
+            this.beta = beta;
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < " + this.depth + "; ++d) {\n          int depthBegin = int(max(0.0, float(d - " + depthRadius + ")));\n          int depthEnd = int(min(float(" + this.depth + "),\n              float(d + " + depthRadius + " + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = " + this.depth + ";\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(" + alpha + ") * norm + float(" + bias + ");\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(" + alpha + ")\n                * float(" + beta + ")\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * " + beta + ");\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    ";
+        }
+        return LRNGradProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var lrnGrad = function (args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, y = inputs.y, dy = inputs.dy;
+        var depthRadius = attrs.depthRadius, bias = attrs.bias, alpha = attrs.alpha, beta = attrs.beta;
+        var program = new LRNGradProgram(x.shape, depthRadius, bias, alpha, beta);
+        return backend.runWebGLProgram(program, [x, y, dy], x.dtype);
+    };
+    // tslint:disable-next-line: variable-name
+    var LRNGradConfig = {
+        kernelName: tf.LRNGrad,
+        backendName: 'webgl',
+        kernelFunc: lrnGrad
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function maxImpl$1(x, reduceShape, outShape, backend) {
+        var inSize = tf.util.sizeFromShape(reduceShape);
+        var xSize = tf.util.sizeFromShape(x.shape);
+        var batchSize = xSize / inSize;
+        var reshapedInput = reshape({ inputs: { x: x }, attrs: { shape: [batchSize, inSize] }, backend: backend });
+        var reduced = reduce(reshapedInput, x.dtype, 'max', backend);
+        var reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend });
+        backend.disposeIntermediateTensorInfo(reshapedInput);
+        backend.disposeIntermediateTensorInfo(reduced);
+        return reshapedOutput;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function max(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var reductionIndices = attrs.reductionIndices, keepDims = attrs.keepDims;
+        var xRank = x.shape.length;
+        var origAxes = tf.util.parseAxisParam(reductionIndices, x.shape);
+        var axes = origAxes;
+        var permutedAxes = tf.backend_util.getAxesPermutation(axes, xRank);
+        var maxInputIsTransposed = permutedAxes != null;
+        var shouldExecuteOnCPU = backend.shouldExecuteOnCPU([x]);
+        var maxInput = x;
+        if (maxInputIsTransposed) {
+            if (shouldExecuteOnCPU) {
+                var xTexData = backend.texData.get(maxInput.dataId);
+                var values = xTexData.values;
+                var newShape = new Array(xRank);
+                for (var i = 0; i < newShape.length; i++) {
+                    newShape[i] = x.shape[permutedAxes[i]];
+                }
+                var maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
+                maxInput = backend.makeTensorInfo(newShape, x.dtype);
+                var maxInputData = backend.texData.get(maxInput.dataId);
+                maxInputData.values = maxInputValues;
+            }
+            else {
+                maxInput = transposeImpl$1(x, permutedAxes, backend);
+            }
+            axes = tf.backend_util.getInnerMostAxes(axes.length, xRank);
+        }
+        tf.backend_util.assertAxesAreInnerMostDims('max', axes, xRank);
+        var _a = tf.backend_util.computeOutAndReduceShapes(maxInput.shape, axes), maxOutShape = _a[0], reduceShape = _a[1];
+        var outShape = maxOutShape;
+        if (keepDims) {
+            // rather than reshape at the end, set the target shape here.
+            outShape = tf.backend_util.expandShapeToKeepDim(maxOutShape, origAxes);
+        }
+        var out;
+        if (shouldExecuteOnCPU) {
+            var xTexData = backend.texData.get(maxInput.dataId);
+            var values = xTexData.values;
+            var outValues = maxImplCPU(values, tf.util.sizeFromShape(reduceShape), outShape, x.dtype);
+            out = backend.makeTensorInfo(outShape, x.dtype);
+            var outData = backend.texData.get(out.dataId);
+            outData.values = outValues;
+        }
+        else {
+            out = maxImpl$1(maxInput, reduceShape, outShape, backend);
+        }
+        if (maxInputIsTransposed) {
+            backend.disposeIntermediateTensorInfo(maxInput);
+        }
+        return out;
+    }
+    var maxConfig = {
+        kernelName: tf.Max,
+        backendName: 'webgl',
+        kernelFunc: max
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var MAXIMUM = CHECK_NAN_SNIPPET$1 + "\n  return max(a, b);\n";
+    var MAXIMUM_PACKED = "\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  " +
+        CHECK_NAN_SNIPPET$2 + "\n  return result;\n";
+    var maximum = binaryKernelFunc({
+        opSnippet: MAXIMUM,
+        packedOpSnippet: MAXIMUM_PACKED,
+        cpuKernelImpl: maximumImplCPU
+    });
+    var maximumConfig = {
+        kernelName: tf.Maximum,
+        backendName: 'webgl',
+        kernelFunc: maximum
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function maxPool(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        assertNotComplex(x, 'maxPool');
+        var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode;
+        var dilations = 1;
+        tf.util.assert(tf.backend_util.eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in maxPool: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
+        var convInfo = tf.backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);
+        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&
+            tf.util.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+            return identity({ inputs: { x: x }, backend: backend });
+        }
+        var maxPoolProgram = new Pool2DProgram(convInfo, 'max', false);
+        return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);
+    }
+    var maxPoolConfig = {
+        kernelName: tf.MaxPool,
+        backendName: 'webgl',
+        kernelFunc: maxPool
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function maxPool3d(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dataFormat = attrs.dataFormat, dimRoundingMode = attrs.dimRoundingMode;
+        var dilations = [1, 1, 1];
+        var convInfo = tf.backend_util.computePool3DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);
+        var maxPoolProgram = new Pool3DProgram(convInfo, 'max', false);
+        return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);
+    }
+    var maxPool3DConfig = {
+        kernelName: tf.MaxPool3D,
+        backendName: 'webgl',
+        kernelFunc: maxPool3d
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var MaxPool2DBackpropProgram = /** @class */ (function () {
+        function MaxPool2DBackpropProgram(convInfo) {
+            this.variableNames = ['dy', 'maxPos'];
+            this.outputShape = convInfo.inShape;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+            var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+            var lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;
+            this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + effectiveFilterHeight + ";\n          wR += " + dilationHeight + ") {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < " + effectiveFilterWidth + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = " + lastIndex + " - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * " + effectiveFilterWidth + " + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return MaxPool2DBackpropProgram;
+    }());
+    var MaxPool3DBackpropProgram = /** @class */ (function () {
+        function MaxPool3DBackpropProgram(convInfo) {
+            this.variableNames = ['dy', 'maxPos'];
+            this.outputShape = convInfo.inShape;
+            var strideDepth = convInfo.strideDepth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationDepth = convInfo.dilationDepth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var effectiveFilterDepth = convInfo.effectiveFilterDepth;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
+            var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+            var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+            var lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;
+            this.userCode = "\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < " + effectiveFilterDepth + ";\n           wD += " + dilationDepth + ") {\n          float dyD = float(dyDCorner + wD) / " + strideDepth + ".0;\n\n          if (dyD < 0.0 || dyD >= " + convInfo.outDepth + ".0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < " + effectiveFilterHeight + ";\n              wR += " + dilationHeight + ") {\n            float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n            if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < " + effectiveFilterWidth + ";\n                wC += " + dilationWidth + ") {\n              float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n              if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = " + lastIndex + " -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * " + effectiveFilterHeight + " * " + effectiveFilterWidth + " +\n                  wR * " + effectiveFilterWidth + " + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return MaxPool3DBackpropProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function maxPool3DGrad(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var dy = inputs.dy, input = inputs.input;
+        var x = input;
+        var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode;
+        var dilations = [1, 1, 1];
+        var convInfo = tf.backend_util.computePool3DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);
+        var maxPool3dPositionsProgram = new Pool3DProgram(convInfo, 'max', true /* get positions */);
+        var maxPool3dPositions = backend.runWebGLProgram(maxPool3dPositionsProgram, [x], x.dtype);
+        var maxPoolBackpropProgram = new MaxPool3DBackpropProgram(convInfo);
+        var result = backend.runWebGLProgram(maxPoolBackpropProgram, [dy, maxPool3dPositions], x.dtype);
+        backend.disposeIntermediateTensorInfo(maxPool3dPositions);
+        return result;
+    }
+    var maxPoolGrad3DConfig = {
+        kernelName: tf.MaxPool3DGrad,
+        backendName: 'webgl',
+        kernelFunc: maxPool3DGrad
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function maxPoolGrad(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var dy = inputs.dy, input = inputs.input, output = inputs.output;
+        var x = input;
+        assertNotComplex([input, output], 'maxPoolGrad');
+        var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode;
+        var convInfo = tf.backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);
+        var getPositions = true;
+        var maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', getPositions);
+        var maxPoolPositions = backend.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);
+        var maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);
+        var result = backend.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);
+        backend.disposeIntermediateTensorInfo(maxPoolPositions);
+        return result;
+    }
+    var maxPoolGradConfig = {
+        kernelName: tf.MaxPoolGrad,
+        backendName: 'webgl',
+        kernelFunc: maxPoolGrad
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, backend) {
+        var program = new Pool2DProgram(convInfo, 'max', false);
+        var poolOutput = backend.runWebGLProgram(program, [x], 'float32');
+        program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);
+        var indexOutput = backend.runWebGLProgram(program, [x], 'float32');
+        return [poolOutput, indexOutput];
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var maxPoolWithArgmaxConfig = {
+        kernelName: tf.MaxPoolWithArgmax,
+        backendName: 'webgl',
+        kernelFunc: function (_a) {
+            var inputs = _a.inputs, attrs = _a.attrs, backend = _a.backend;
+            var x = inputs.x;
+            var _b = attrs, filterSize = _b.filterSize, strides = _b.strides, pad = _b.pad, includeBatchInIndex = _b.includeBatchInIndex;
+            var webglBackend = backend;
+            tf.util.assert(x.shape.length === 4, function () { return "Error in maxPool: input must be rank 4 but got rank " + x.shape.length + "."; });
+            var dilations = [1, 1];
+            tf.util.assert(tf.backend_util.eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in maxPool: Either strides or dilations must be 1. ' +
+                ("Got strides " + strides + " and dilations '" + dilations + "'"); });
+            var convInfo = tf.backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad);
+            var _c = maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, webglBackend), result = _c[0], indexes = _c[1];
+            return [result, indexes];
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function meanImpl(x, reduceShape, outShape, backend) {
+        var inSize = tf.util.sizeFromShape(reduceShape);
+        var xSize = tf.util.sizeFromShape(x.shape);
+        var batchSize = xSize / inSize;
+        var reshapedInput = reshape({ inputs: { x: x }, attrs: { shape: [batchSize, inSize] }, backend: backend });
+        var reduced = reduce(reshapedInput, 'float32', 'mean', backend);
+        var reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend });
+        backend.disposeIntermediateTensorInfo(reshapedInput);
+        backend.disposeIntermediateTensorInfo(reduced);
+        return reshapedOutput;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var meanConfig = {
+        kernelName: tf.Mean,
+        backendName: 'webgl',
+        kernelFunc: function (_a) {
+            var inputs = _a.inputs, attrs = _a.attrs, backend = _a.backend;
+            var x = inputs.x;
+            var _b = attrs, keepDims = _b.keepDims, axis = _b.axis;
+            var webglBackend = backend;
+            var xRank = x.shape.length;
+            var origAxes = tf.util.parseAxisParam(axis, x.shape);
+            var axes = origAxes;
+            var permutedAxes = tf.backend_util.getAxesPermutation(axes, xRank);
+            var meanInputIsTransposed = permutedAxes != null;
+            var shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);
+            var intermediates = [];
+            var meanInput = x;
+            if (meanInputIsTransposed) {
+                if (shouldExecuteOnCPU) {
+                    var xTexData = webglBackend.texData.get(meanInput.dataId);
+                    var values = xTexData.values;
+                    var newShape = new Array(xRank);
+                    for (var i = 0; i < newShape.length; i++) {
+                        newShape[i] = x.shape[permutedAxes[i]];
+                    }
+                    var meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
+                    meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);
+                    var meanInputData = webglBackend.texData.get(meanInput.dataId);
+                    meanInputData.values = meanInputValues;
+                }
+                else {
+                    meanInput = transposeImpl$1(x, permutedAxes, webglBackend);
+                }
+                intermediates.push(meanInput);
+                axes = tf.backend_util.getInnerMostAxes(axes.length, xRank);
+            }
+            tf.backend_util.assertAxesAreInnerMostDims('sum', axes, xRank);
+            var _c = tf.backend_util.computeOutAndReduceShapes(meanInput.shape, axes), meanOutShape = _c[0], reduceShape = _c[1];
+            var outShape = meanOutShape;
+            if (keepDims) {
+                // rather than reshape at the end, set the target shape here.
+                outShape = tf.backend_util.expandShapeToKeepDim(meanOutShape, origAxes);
+            }
+            var out = meanImpl(meanInput, reduceShape, outShape, webglBackend);
+            for (var _i = 0, intermediates_1 = intermediates; _i < intermediates_1.length; _i++) {
+                var i = intermediates_1[_i];
+                webglBackend.disposeIntermediateTensorInfo(i);
+            }
+            return out;
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function min(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis, keepDims = attrs.keepDims;
+        var xRank = x.shape.length;
+        var origAxes = tf.util.parseAxisParam(axis, x.shape);
+        var axes = origAxes;
+        var permutedAxes = tf.backend_util.getAxesPermutation(axes, xRank);
+        var permutedX = x;
+        if (permutedAxes != null) {
+            permutedX = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } });
+            axes = tf.backend_util.getInnerMostAxes(axes.length, x.shape.length);
+        }
+        tf.backend_util.assertAxesAreInnerMostDims('min', axes, xRank);
+        var _a = tf.backend_util.computeOutAndReduceShapes(permutedX.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var inSize = tf.util.sizeFromShape(reduceShape);
+        var a2D = reshape({ inputs: { x: permutedX }, backend: backend, attrs: { shape: [-1, inSize] } });
+        var reduced = reduce(a2D, a2D.dtype, 'min', backend);
+        var res;
+        if (keepDims) {
+            var newShape = tf.backend_util.expandShapeToKeepDim(outShape, origAxes);
+            res = reshape({ inputs: { x: reduced }, backend: backend, attrs: { shape: newShape } });
+        }
+        else {
+            res = reshape({ inputs: { x: reduced }, backend: backend, attrs: { shape: outShape } });
+        }
+        backend.disposeIntermediateTensorInfo(a2D);
+        backend.disposeIntermediateTensorInfo(reduced);
+        if (permutedAxes != null) {
+            backend.disposeIntermediateTensorInfo(permutedX);
+        }
+        return res;
+    }
+    var minConfig = {
+        kernelName: tf.Min,
+        backendName: 'webgl',
+        kernelFunc: min
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var MINIMUM = CHECK_NAN_SNIPPET$1 + "\n  return min(a, b);\n";
+    var MINIMUM_PACKED = "\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  " +
+        CHECK_NAN_SNIPPET$2 + "\n  return result;\n";
+    var minimum = binaryKernelFunc({
+        opSnippet: MINIMUM,
+        packedOpSnippet: MINIMUM_PACKED,
+        cpuKernelImpl: minimumImplCPU
+    });
+    var minimumConfig = {
+        kernelName: tf.Minimum,
+        backendName: 'webgl',
+        kernelFunc: minimum
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var MirrorPadProgram = /** @class */ (function () {
+        function MirrorPadProgram(xShape, paddings, mode) {
+            this.variableNames = ['x'];
+            this.outputShape = paddings.map(function (p, i) { return p[0] /* beforePad */ + xShape[i] + p[1]; } /* afterPad */);
+            var rank = xShape.length;
+            var dtype = getCoordsDataType(rank);
+            var start = paddings.map(function (p) { return p[0]; }).join(',');
+            var end = paddings.map(function (p, i) { return p[0] + xShape[i]; }).join(',');
+            var unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);
+            var offset = mode === 'reflect' ? 0 : 1;
+            if (rank === 1) {
+                this.userCode = "\n        int start = " + start + ";\n        int end = " + end + ";\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - " + offset + ";\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + " + offset + ";\n          }\n          setOutput(getX(outC - start));\n        }\n      ";
+                return;
+            }
+            this.userCode = "\n      " + dtype + " start = " + dtype + "(" + start + ");\n      " + dtype + " end = " + dtype + "(" + end + ");\n\n      void main() {\n        " + dtype + " outC = getOutputCoords();\n        for (int i = 0; i < " + rank + "; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - " + offset + ";\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + " + offset + ";\n          }\n        }\n        " + dtype + " coords = outC - start;\n        setOutput(getX(" + unpackedCoords + "));\n      }\n    ";
+        }
+        return MirrorPadProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Example shader code for
+     * `mirrorPad(tf.tensor1d([1, 2, 3], 'int32'), [[2, 2]], 'reflect')`
+     * ```
+     *    const int start = int(2);
+     *    const int end = int(5);
+     *
+     *    void main() {
+     *       int outputLoc = getOutputCoords();
+     *       vec4 result = vec4(0.);
+     *
+     *       int rc = outputLoc;
+     *
+     *       int source = rc;
+     *       if (source < start) {
+     *         source = start * 2 - source - 0;
+     *       } else if (source >= end) {
+     *         source = (end - 1) * 2 - source + 0;
+     *       }
+     *       source -= start;
+     *
+     *       result[0] = getChannel(getX(source), source);
+     *       rc += 1;
+     *       if(rc < 6) {
+     *          int source = rc;
+     *          if (source < start) {
+     *            source = start * 2 - source - 0;
+     *          } else if (source >= end) {
+     *            source = (end - 1) * 2 - source + 0;
+     *          }
+     *          source -= start;
+     *
+     *         result[1] = getChannel(getX(source), source);
+     *       }
+     *
+     *       setOutput(result);
+     *     }
+     * ```
+     */
+    var MirrorPadPackedProgram = /** @class */ (function () {
+        function MirrorPadPackedProgram(xShape, paddings, mode) {
+            this.variableNames = ['x'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = paddings.map(function (p, i) { return p[0] /* beforePad */ + xShape[i] + p[1]; } /* afterPad */);
+            var rank = xShape.length;
+            var dtype = getCoordsDataType(rank);
+            var start = paddings.map(function (p) { return p[0]; }).join(',');
+            var end = paddings.map(function (p, i) { return p[0] + xShape[i]; }).join(',');
+            var coords = getChannels('rc', rank);
+            var source = getChannels('source', rank);
+            var cLimit = coords[rank - 1] + " < " + this.outputShape[rank - 1];
+            var innerDims = rank === 1 ? 'source' : "vec2(" + source.slice(-2).join() + ")";
+            var offset = mode === 'reflect' ? 0 : 1;
+            var mainLoop = '';
+            if (rank === 1) {
+                var padSetup = "\n        " + dtype + " source = rc;\n        if (source < start) {\n          source = start * 2 - source - " + offset + ";\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + " + offset + ";\n        }\n        source -= start;\n      ";
+                mainLoop = "\n        " + dtype + " rc = outputLoc;\n        " + padSetup + "\n        result[0] = getChannel(getX(" + source.join() + "), " + innerDims + ");\n        " + coords[rank - 1] + " += 1;\n        if(" + cLimit + ") {\n          " + padSetup + "\n          result[1] = getChannel(getX(" + source.join() + "), " + innerDims + ");\n        }\n      ";
+            }
+            else {
+                var padSetup = "\n        " + dtype + " source = rc;\n        " + dtype + " lt = " + dtype + "(lessThan(source, start));\n        " + dtype + " gte = " + dtype + "(greaterThanEqual(source, end));\n        " + dtype + " orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - " + offset + ") +\n                gte * ((end - 1) * 2 - source + " + offset + ");\n        source -= start;\n      ";
+                mainLoop = "\n        " + dtype + " rc = outputLoc;\n        " + padSetup + "\n        result[0] = getChannel(getX(" + source.join() + "), " + innerDims + ");\n        " + coords[rank - 1] + " += 1;\n        if(" + cLimit + ") {\n          " + padSetup + "\n          result[1] = getChannel(getX(" + source.join() + "), " + innerDims + ");\n        }\n        rc = outputLoc;\n        " + coords[rank - 2] + " += 1;\n        if(" + coords[rank - 2] + " < " + this.outputShape[rank - 2] + ") {\n          " + padSetup + "\n          result[2] = getChannel(getX(" + source.join() + "), " + innerDims + ");\n          " + coords[rank - 1] + " += 1;\n          if(" + cLimit + ") {\n            " + padSetup + "\n            result[3] = getChannel(getX(" + source.join() + "), " + innerDims + ");\n          }\n        }\n      ";
+            }
+            this.userCode = "\n      const " + dtype + " start = " + dtype + "(" + start + ");\n      const " + dtype + " end = " + dtype + "(" + end + ");\n\n      void main() {\n        " + dtype + " outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        " + mainLoop + "\n        setOutput(result);\n      }\n    ";
+        }
+        return MirrorPadPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var mirrorPadKernelFunc = function (_a) {
+        var inputs = _a.inputs, backend = _a.backend, attrs = _a.attrs;
+        var x = inputs.x;
+        var paddings = attrs.paddings, mode = attrs.mode;
+        var program = tf.env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
+            new MirrorPadPackedProgram(x.shape, paddings, mode) :
+            new MirrorPadProgram(x.shape, paddings, mode);
+        var output = backend.runWebGLProgram(program, [x], x.dtype);
+        return output;
+    };
+    var mirrorPadConfig = {
+        kernelName: tf.MirrorPad,
+        backendName: 'webgl',
+        kernelFunc: mirrorPadKernelFunc,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var MOD = "if (b == 0.0) return NAN;\n  return mod(a, b);";
+    var MOD_PACKED = "\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  " +
+        CHECK_NAN_SNIPPET$2 + "\n  return result;\n";
+    var mod = binaryKernelFunc({
+        opSnippet: MOD,
+        packedOpSnippet: MOD_PACKED,
+    });
+    var modConfig = {
+        kernelName: tf.Mod,
+        backendName: 'webgl',
+        kernelFunc: mod
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var MultinomialProgram = /** @class */ (function () {
+        function MultinomialProgram(batchSize, numOutcomes, numSamples) {
+            this.variableNames = ['probs'];
+            this.outputShape = [batchSize, numSamples];
+            this.userCode = "\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < " + (numOutcomes - 1) + "; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(" + (numOutcomes - 1) + "));\n      }\n    ";
+        }
+        MultinomialProgram.prototype.getCustomSetupFunc = function (seed) {
+            var _this = this;
+            return function (gpgpu, webGLProgram) {
+                if (_this.seedLoc == null) {
+                    _this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');
+                }
+                gpgpu.gl.uniform1f(_this.seedLoc, seed);
+            };
+        };
+        return MultinomialProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // Without the equality check div produces 0.9999 for a = b, which when
+    // floored can cause errors.
+    var DIV = "\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;";
+    // We do the same as in ./binaryop_gpu, with vec4 and ivec4.
+    // On Linux, the vectorized implementation produces NaNs when a and b are 0.
+    var DIV_PACKED = "\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n";
+    var realDiv = binaryKernelFunc({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true });
+    var realDivConfig = {
+        kernelName: tf.RealDiv,
+        backendName: 'webgl',
+        kernelFunc: realDiv,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SUB = 'return a - b;';
+    var sub = binaryKernelFunc({
+        opSnippet: SUB,
+        packedOpSnippet: SUB,
+        supportsComplex: true,
+        cpuKernelImpl: subImplCPU
+    });
+    var subConfig = {
+        kernelName: tf.Sub,
+        backendName: 'webgl',
+        kernelFunc: sub
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function softmax(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var logits = inputs.logits;
+        var dim = attrs.dim;
+        var axes = tf.util.parseAxisParam([dim], logits.shape);
+        var maxLogit = max({
+            inputs: { x: logits },
+            backend: backend,
+            attrs: { reductionIndices: axes, keepDims: false }
+        });
+        var expandedShape = tf.backend_util.expandShapeToKeepDim(maxLogit.shape, axes);
+        var maxLogitsReshaped = reshape({ inputs: { x: maxLogit }, backend: backend, attrs: { shape: expandedShape } });
+        var a = sub({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend });
+        var b = exp({ inputs: { x: a }, backend: backend });
+        var sumExp = sum({ inputs: { x: b }, backend: backend, attrs: { axis: axes, keepDims: false } });
+        var sumExpReshaped = reshape({ inputs: { x: sumExp }, backend: backend, attrs: { shape: expandedShape } });
+        var res = realDiv({ inputs: { a: b, b: sumExpReshaped }, backend: backend });
+        backend.disposeIntermediateTensorInfo(maxLogit);
+        backend.disposeIntermediateTensorInfo(maxLogitsReshaped);
+        backend.disposeIntermediateTensorInfo(a);
+        backend.disposeIntermediateTensorInfo(b);
+        backend.disposeIntermediateTensorInfo(sumExp);
+        backend.disposeIntermediateTensorInfo(sumExpReshaped);
+        return res;
+    }
+    var softmaxConfig = {
+        kernelName: tf.Softmax,
+        backendName: 'webgl',
+        kernelFunc: softmax
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function multinomial(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var logits = inputs.logits;
+        var numSamples = attrs.numSamples, seed = attrs.seed, normalized = attrs.normalized;
+        var probs = normalized ?
+            logits :
+            softmax({ inputs: { logits: logits }, backend: backend, attrs: { dim: logits.shape.length - 1 } });
+        var batchSize = probs.shape[0];
+        var numOutcomes = probs.shape[1];
+        var program = new MultinomialProgram(batchSize, numOutcomes, numSamples);
+        var customSetup = program.getCustomSetupFunc(seed);
+        var res = backend.runWebGLProgram(program, [probs], 'int32', customSetup);
+        if (!normalized) {
+            backend.disposeIntermediateTensorInfo(probs);
+        }
+        return res;
+    }
+    var multinomialConfig = {
+        kernelName: tf.Multinomial,
+        backendName: 'webgl',
+        kernelFunc: multinomial
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var NEG = "return -x;";
+    // This doesn't use unaryKernelFunc because negImplCPU is not of type
+    // SimpleUnaryKernelImplCPU.
+    function neg(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var x = inputs.x;
+        if (backend.shouldExecuteOnCPU([x])) {
+            var xData = backend.texData.get(x.dataId);
+            var _a = negImplCPU(xData.values, x.shape, x.dtype), outValues = _a[0], newShape = _a[1];
+            return backend.makeTensorInfo(newShape, x.dtype, outValues);
+        }
+        var program;
+        if (tf.env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
+            program = new UnaryOpPackedProgram(x.shape, NEG);
+        }
+        else {
+            program = new UnaryOpProgram(x.shape, NEG);
+        }
+        return backend.runWebGLProgram(program, [x], x.dtype);
+    }
+    var negConfig = {
+        kernelName: tf.Neg,
+        backendName: 'webgl',
+        kernelFunc: neg
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var nonMaxSuppressionV3Impl = tf.kernel_impls.nonMaxSuppressionV3Impl;
+    function nonMaxSuppressionV3(args) {
+        tf.backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +
+            'Call tf.nonMaxSuppressionAsync() instead');
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var boxes = inputs.boxes, scores = inputs.scores;
+        var maxOutputSize = attrs.maxOutputSize, iouThreshold = attrs.iouThreshold, scoreThreshold = attrs.scoreThreshold;
+        var boxesVals = backend.readSync(boxes.dataId);
+        var scoresVals = backend.readSync(scores.dataId);
+        var selectedIndices = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold).selectedIndices;
+        return backend.makeTensorInfo([selectedIndices.length], 'int32', new Int32Array(selectedIndices));
+    }
+    var nonMaxSuppressionV3Config = {
+        kernelName: tf.NonMaxSuppressionV3,
+        backendName: 'webgl',
+        kernelFunc: nonMaxSuppressionV3
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var nonMaxSuppressionV4Impl = tf.kernel_impls.nonMaxSuppressionV4Impl;
+    function nonMaxSuppressionV4(args) {
+        tf.backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +
+            'Call tf.nonMaxSuppressionAsync() instead');
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var boxes = inputs.boxes, scores = inputs.scores;
+        var maxOutputSize = attrs.maxOutputSize, iouThreshold = attrs.iouThreshold, scoreThreshold = attrs.scoreThreshold, padToMaxOutputSize = attrs.padToMaxOutputSize;
+        var boxesVals = backend.readSync(boxes.dataId);
+        var scoresVals = backend.readSync(scores.dataId);
+        var _a = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize), selectedIndices = _a.selectedIndices, validOutputs = _a.validOutputs;
+        return [
+            backend.makeTensorInfo([selectedIndices.length], 'int32', new Int32Array(selectedIndices)),
+            backend.makeTensorInfo([], 'int32', new Int32Array([validOutputs]))
+        ];
+    }
+    var nonMaxSuppressionV4Config = {
+        kernelName: tf.NonMaxSuppressionV4,
+        backendName: 'webgl',
+        kernelFunc: nonMaxSuppressionV4
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var nonMaxSuppressionV5Impl = tf.kernel_impls.nonMaxSuppressionV5Impl;
+    function nonMaxSuppressionV5(args) {
+        tf.backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +
+            'Call tf.nonMaxSuppressionAsync() instead');
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var boxes = inputs.boxes, scores = inputs.scores;
+        var maxOutputSize = attrs.maxOutputSize, iouThreshold = attrs.iouThreshold, scoreThreshold = attrs.scoreThreshold, softNmsSigma = attrs.softNmsSigma;
+        var boxesVals = backend.readSync(boxes.dataId);
+        var scoresVals = backend.readSync(scores.dataId);
+        var maxOutputSizeVal = maxOutputSize;
+        var iouThresholdVal = iouThreshold;
+        var scoreThresholdVal = scoreThreshold;
+        var softNmsSigmaVal = softNmsSigma;
+        var _a = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal), selectedIndices = _a.selectedIndices, selectedScores = _a.selectedScores;
+        return [
+            backend.makeTensorInfo([selectedIndices.length], 'int32', new Int32Array(selectedIndices)),
+            backend.makeTensorInfo([selectedScores.length], 'float32', new Float32Array(selectedScores))
+        ];
+    }
+    var nonMaxSuppressionV5Config = {
+        kernelName: tf.NonMaxSuppressionV5,
+        backendName: 'webgl',
+        kernelFunc: nonMaxSuppressionV5
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var OneHotProgram = /** @class */ (function () {
+        function OneHotProgram(numIndices, depth, onValue, offValue) {
+            this.variableNames = ['indices'];
+            this.outputShape = [numIndices, depth];
+            this.userCode = "\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(" + offValue + "), float(" + onValue + "),\n                      float(index == coords.y)));\n      }\n    ";
+        }
+        return OneHotProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var oneHot = function (args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var indices = inputs.indices;
+        var depth = attrs.depth, onValue = attrs.onValue, offValue = attrs.offValue;
+        var indicesSize = tf.util.sizeFromShape(indices.shape);
+        var program = new OneHotProgram(indicesSize, depth, onValue, offValue);
+        var reshaped = reshape({ inputs: { x: indices }, backend: backend, attrs: { shape: [indicesSize] } });
+        var result = backend.runWebGLProgram(program, [reshaped], indices.dtype);
+        backend.disposeIntermediateTensorInfo(reshaped);
+        var outShape = indices.shape.concat([depth]);
+        var out = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: outShape } });
+        backend.disposeIntermediateTensorInfo(result);
+        return out;
+    };
+    var oneHotConfig = {
+        kernelName: tf.OneHot,
+        backendName: 'webgl',
+        kernelFunc: oneHot
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function zerosLike(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var x = inputs.x;
+        if (x.dtype === 'complex64') {
+            var realPart = real({ inputs: { input: x }, backend: backend });
+            var r = zerosLike({ inputs: { x: realPart }, backend: backend });
+            var imagPart = imag({ inputs: { input: x }, backend: backend });
+            var i = zerosLike({ inputs: { x: imagPart }, backend: backend });
+            var result = complex({ inputs: { real: r, imag: i }, backend: backend });
+            backend.disposeIntermediateTensorInfo(realPart);
+            backend.disposeIntermediateTensorInfo(r);
+            backend.disposeIntermediateTensorInfo(imagPart);
+            backend.disposeIntermediateTensorInfo(i);
+            return result;
+        }
+        else {
+            return fill({
+                attrs: {
+                    shape: x.shape,
+                    dtype: x.dtype,
+                    value: x.dtype === 'string' ? '' : 0
+                },
+                backend: backend
+            });
+        }
+    }
+    var zerosLikeConfig = {
+        kernelName: tf.ZerosLike,
+        backendName: 'webgl',
+        kernelFunc: zerosLike
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function onesLike(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var x = inputs.x;
+        if (x.dtype === 'string') {
+            throw new Error('onesLike is not supported under string dtype');
+        }
+        else if (x.dtype === 'complex64') {
+            var realPart = real({ inputs: { input: x }, backend: backend });
+            var r = onesLike({ inputs: { x: realPart }, backend: backend });
+            var imagPart = imag({ inputs: { input: x }, backend: backend });
+            var i = zerosLike({ inputs: { x: imagPart }, backend: backend });
+            var result = complex({ inputs: { real: r, imag: i }, backend: backend });
+            backend.disposeIntermediateTensorInfo(realPart);
+            backend.disposeIntermediateTensorInfo(r);
+            backend.disposeIntermediateTensorInfo(imagPart);
+            backend.disposeIntermediateTensorInfo(i);
+            return result;
+        }
+        else {
+            // TODO(cais, smilkov): Add WebGL shader for onesLike:
+            //   https://github.com/tensorflow/tfjs/issues/1293
+            return fill({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend });
+        }
+    }
+    var onesLikeConfig = {
+        kernelName: tf.OnesLike,
+        backendName: 'webgl',
+        kernelFunc: onesLike
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function pack(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var axis = attrs.axis;
+        if (inputs.length === 1) {
+            return expandDims({ inputs: { input: inputs[0] }, backend: backend, attrs: { dim: axis } });
+        }
+        var shape = inputs[0].shape;
+        var dtype = inputs[0].dtype;
+        inputs.forEach(function (t) {
+            tf.util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');
+            tf.util.assert(dtype === t.dtype, function () { return 'All tensors passed to stack must have matching dtypes'; });
+        });
+        var intermediateTensorInfos = [];
+        var expandedTensors = inputs.map(function (t) {
+            var expandedT = expandDims({ inputs: { input: t }, backend: backend, attrs: { dim: axis } });
+            intermediateTensorInfos.push(expandedT);
+            return expandedT;
+        });
+        var result = concat({ inputs: expandedTensors, backend: backend, attrs: { axis: axis } });
+        intermediateTensorInfos.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return result;
+    }
+    var packConfig = {
+        kernelName: tf.Pack,
+        backendName: 'webgl',
+        kernelFunc: pack
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var PadProgram = /** @class */ (function () {
+        function PadProgram(xShape, paddings, constantValue) {
+            this.variableNames = ['x'];
+            this.outputShape = paddings.map(function (p, i) { return p[0] /* beforePad */ + xShape[i] + p[1]; } /* afterPad */);
+            var rank = xShape.length;
+            var type = getCoordsDataType(rank);
+            var start = paddings.map(function (p) { return p[0]; }).join(',');
+            var end = paddings.map(function (p, i) { return p[0] + xShape[i]; }).join(',');
+            var unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);
+            if (rank === 1) {
+                this.userCode = "\n        int start = " + start + ";\n        int end = " + end + ";\n        uniform float value;\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(value);\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      ";
+                return;
+            }
+            this.userCode = "\n      " + type + " start = " + type + "(" + start + ");\n      " + type + " end = " + type + "(" + end + ");\n      uniform float value;\n\n      void main() {\n        " + type + " outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(value);\n        } else {\n          " + type + " coords = outC - start;\n          setOutput(getX(" + unpackedCoords + "));\n        }\n      }\n    ";
+        }
+        PadProgram.prototype.getCustomSetupFunc = function (value) {
+            var _this = this;
+            return function (gpgpu, webGLProgram) {
+                if (_this.valueLoc == null) {
+                    _this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');
+                }
+                gpgpu.gl.uniform1f(_this.valueLoc, value);
+            };
+        };
+        return PadProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var PadPackedProgram = /** @class */ (function () {
+        function PadPackedProgram(xShape, paddings, constantValue) {
+            this.variableNames = ['x'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = paddings.map(function (p, i) { return p[0] /* beforePad */ + xShape[i] + p[1]; } /* afterPad */);
+            var rank = xShape.length;
+            var dtype = getCoordsDataType(rank);
+            var start = paddings.map(function (p) { return p[0]; }).join(',');
+            var end = paddings.map(function (p, i) { return p[0] + xShape[i]; }).join(',');
+            var coords = getChannels('rc', rank);
+            var source = getChannels('source', rank);
+            var cLimit = coords[rank - 1] + " < " + this.outputShape[rank - 1];
+            var innerDims = rank === 1 ? 'source' : "vec2(" + source.slice(-2).join() + ")";
+            var componentSetup = [
+                dtype + " rc = outputLoc;", coords[rank - 1] + " += 1;\n       if(" + cLimit + ") {\n      ",
+                rank === 1 ? '' : "}\n       rc = outputLoc;\n       " + coords[rank - 2] + " += 1;\n       if(" + coords[rank - 2] + " < " + this.outputShape[rank - 2] + ") {",
+                rank === 1 ? '' : "  " + coords[rank - 1] + " += 1;\n         if(" + cLimit + ") {"
+            ];
+            var paddingArea = rank === 1 ?
+                'rc < start || rc >= end' :
+                'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';
+            var mainLoop = '';
+            for (var i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {
+                mainLoop += "\n        " + componentSetup[i] + "\n        if (" + paddingArea + ") {\n          result[" + i + "] = float(value);\n        } else {\n          " + dtype + " source = rc - start;\n          result[" + i + "] = getChannel(getX(" + source.join() + "), " + innerDims + ");\n        }\n      ";
+            }
+            mainLoop += (rank === 1 ? "} " : "}}");
+            this.userCode = "\n      const " + dtype + " start = " + dtype + "(" + start + ");\n      const " + dtype + " end = " + dtype + "(" + end + ");\n      uniform float value;\n\n      void main() {\n        " + dtype + " outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        " + mainLoop + "\n        setOutput(result);\n      }\n    ";
+        }
+        PadPackedProgram.prototype.getCustomSetupFunc = function (value) {
+            var _this = this;
+            return function (gpgpu, webGLProgram) {
+                if (_this.valueLoc == null) {
+                    _this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');
+                }
+                gpgpu.gl.uniform1f(_this.valueLoc, value);
+            };
+        };
+        return PadPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var padV2 = function (args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var paddings = attrs.paddings, constantValue = attrs.constantValue;
+        var program = tf.env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
+            new PadPackedProgram(x.shape, paddings, constantValue) :
+            new PadProgram(x.shape, paddings, constantValue);
+        var customSetup = program.getCustomSetupFunc(constantValue);
+        return backend.runWebGLProgram(program, [x], x.dtype, customSetup);
+    };
+    var padV2Config = {
+        kernelName: tf.PadV2,
+        backendName: 'webgl',
+        kernelFunc: padV2
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var POW = "\n  if(a < 0.0 && floor(b) < b){\n    return NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  return (round(mod(b, 2.0)) != 1) ?\n      pow(abs(a), b) : sign(a) * pow(abs(a), b);\n";
+    var POW_PACKED = "\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  " +
+        CHECK_NAN_SNIPPET$2 + "\n  return result;\n";
+    var pow = binaryKernelFunc({ opSnippet: POW, packedOpSnippet: POW_PACKED });
+    var powConfig = {
+        kernelName: tf.Pow,
+        backendName: 'webgl',
+        kernelFunc: pow
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function prod(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis, keepDims = attrs.keepDims;
+        var xRank = x.shape.length;
+        var toDispose = [];
+        var origAxes = tf.util.parseAxisParam(axis, x.shape);
+        var axes = origAxes;
+        var permutedAxes = tf.backend_util.getAxesPermutation(axes, xRank);
+        var permutedX = x;
+        if (permutedAxes != null) {
+            permutedX = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } });
+            axes = tf.backend_util.getInnerMostAxes(axes.length, xRank);
+            toDispose.push(permutedX);
+        }
+        tf.backend_util.assertAxesAreInnerMostDims('prod', axes, xRank);
+        var res;
+        if (backend.shouldExecuteOnCPU([permutedX])) {
+            var xVals = backend.texData.get(permutedX.dataId).values;
+            var _a = prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes), outVals = _a.outVals, outShape = _a.outShape, outDtype = _a.outDtype;
+            res = backend.makeTensorInfo(outShape, outDtype, outVals);
+        }
+        else {
+            var _b = tf.backend_util.computeOutAndReduceShapes(permutedX.shape, axes), outShape = _b[0], reduceShape = _b[1];
+            var inSize = tf.util.sizeFromShape(reduceShape);
+            var a2D = reshape({ inputs: { x: permutedX }, backend: backend, attrs: { shape: [-1, inSize] } });
+            var outputDType = tf.sumOutType(x.dtype);
+            var reduced = reduce(a2D, outputDType, 'prod', backend);
+            res = reshape({ inputs: { x: reduced }, backend: backend, attrs: { shape: outShape } });
+            toDispose.push(a2D);
+            toDispose.push(reduced);
+        }
+        if (keepDims) {
+            toDispose.push(res);
+            var newShape = tf.backend_util.expandShapeToKeepDim(res.shape, origAxes);
+            res = reshape({ inputs: { x: res }, backend: backend, attrs: { shape: newShape } });
+        }
+        toDispose.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return res;
+    }
+    var prodConfig = {
+        kernelName: tf.Prod,
+        backendName: 'webgl',
+        kernelFunc: prod
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var range = function (args) {
+        var backend = args.backend, attrs = args.attrs;
+        var start = attrs.start, stop = attrs.stop, step = attrs.step, dtype = attrs.dtype;
+        var values = rangeImplCPU(start, stop, step, dtype);
+        return backend.makeTensorInfo([values.length], dtype, values);
+    };
+    var rangeConfig = {
+        kernelName: tf.Range,
+        backendName: 'webgl',
+        kernelFunc: range
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var RECIPROCAL = "return 1.0 / x;";
+    var reciprocal = unaryKernelFunc({ opSnippet: RECIPROCAL });
+    var reciprocalConfig = {
+        kernelName: tf.Reciprocal,
+        backendName: 'webgl',
+        kernelFunc: reciprocal,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var RELU$2 = CHECK_NAN_SNIPPET + "\n  return (x < 0.0) ? 0.0 : x;\n";
+    var RELU_PACKED = "\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n";
+    var relu = unaryKernelFunc({ opSnippet: RELU$2, packedOpSnippet: RELU_PACKED });
+    var reluConfig = {
+        kernelName: tf.Relu,
+        backendName: 'webgl',
+        kernelFunc: relu
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var RELU6$2 = CHECK_NAN_SNIPPET + "\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n";
+    var RELU6_PACKED = "\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n";
+    var relu6 = unaryKernelFunc({ opSnippet: RELU6$2, packedOpSnippet: RELU6_PACKED });
+    var relu6Config = {
+        kernelName: tf.Relu6,
+        backendName: 'webgl',
+        kernelFunc: relu6
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ResizeBilinearProgram = /** @class */ (function () {
+        function ResizeBilinearProgram(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
+            this.variableNames = ['A'];
+            this.outputShape = [];
+            var batch = inputShape[0], oldHeight = inputShape[1], oldWidth = inputShape[2], depth = inputShape[3];
+            this.outputShape = [batch, newHeight, newWidth, depth];
+            var effectiveInSize = [
+                (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+                (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+            ];
+            var effectiveOutSize = [
+                (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+                (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+            ];
+            var sourceFracIndexRC;
+            if (halfPixelCenters) {
+                sourceFracIndexRC =
+                    "(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC" +
+                        " - vec2(0.5)";
+            }
+            else {
+                sourceFracIndexRC = "vec2(yRC) * effectiveInputOverOutputRatioRC";
+            }
+            this.userCode = "\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          " + effectiveInSize[0] / effectiveOutSize[0] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ");\n      const vec2 inputShapeRC = vec2(" + oldHeight + ".0, " + oldWidth + ".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = " + sourceFracIndexRC + ";\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    ";
+        }
+        return ResizeBilinearProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ResizeBilinearPackedProgram = /** @class */ (function () {
+        function ResizeBilinearPackedProgram(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
+            this.variableNames = ['A'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = [];
+            var batch = inputShape[0], oldHeight = inputShape[1], oldWidth = inputShape[2], depth = inputShape[3];
+            this.outputShape = [batch, newHeight, newWidth, depth];
+            var effectiveInSize = [
+                (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+                (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+            ];
+            var effectiveOutSize = [
+                (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+                (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+            ];
+            var sourceFracIndexRC;
+            if (halfPixelCenters) {
+                sourceFracIndexRC = "(vec3(yRC) + vec3(0.5)) * " +
+                    "effectiveInputOverOutputRatioRC - vec3(0.5)";
+            }
+            else {
+                sourceFracIndexRC = "vec3(yRC) * effectiveInputOverOutputRatioRC";
+            }
+            this.userCode = "\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          " + effectiveInSize[0] / effectiveOutSize[0] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ");\n      const vec3 inputShapeRC = vec3(" + oldHeight + ".0, " + oldWidth + ".0,\n                                     " + oldWidth + ".0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = " + sourceFracIndexRC + ";\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < " + (depth - 1) + ";\n        bool hasNextRow = coords.z < " + (newWidth - 1) + ";\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    ";
+        }
+        return ResizeBilinearPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function resizeBilinear(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var images = inputs.images;
+        var alignCorners = attrs.alignCorners, halfPixelCenters = attrs.halfPixelCenters, size = attrs.size;
+        var newHeight = size[0], newWidth = size[1];
+        var program = tf.env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?
+            new ResizeBilinearPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) :
+            new ResizeBilinearProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);
+        return backend.runWebGLProgram(program, [images], 'float32');
+    }
+    var resizeBilinearConfig = {
+        kernelName: tf.ResizeBilinear,
+        backendName: 'webgl',
+        kernelFunc: resizeBilinear
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ResizeBilinearBackpropProgram = /** @class */ (function () {
+        function ResizeBilinearBackpropProgram(dyShape, inputShape, alignCorners) {
+            this.variableNames = ['dy'];
+            this.outputShape = [];
+            this.outputShape = inputShape;
+            var xHeight = inputShape[1], xWidth = inputShape[2];
+            var yHeight = dyShape[1], yWidth = dyShape[2];
+            // In the backwards pass, we want to find the pixels that were generated for
+            // each pixel in the input image the forward pass and add the corresponding
+            // coefficient from dy to the gradient (with some interpolation).
+            var effectiveXSize = [
+                (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
+                (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
+            ];
+            var effectiveYSize = [
+                (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
+                (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
+            ];
+            var heightScale = effectiveXSize[0] / effectiveYSize[0];
+            var widthScale = effectiveXSize[1] / effectiveYSize[1];
+            var invHeightScale = 1 / heightScale;
+            var invWidthScale = 1 / widthScale;
+            // This defines the size of the window of values around a particular
+            // index in dy that we want to search for contributions to dx.
+            var winHeight = (Math.ceil(invHeightScale) * 2) + 2;
+            var winWidth = (Math.ceil(invWidthScale) * 2) + 2;
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(" + heightScale + ");\n        const float widthScale = float(" + widthScale + ");\n\n        const float invHeightScale = float(" + invHeightScale + ");\n        const float invWidthScale = float(" + invWidthScale + ");\n\n        const int winHeight = int(" + winHeight + ");\n        const int winWidth = int(" + winWidth + ");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= " + yHeight + ") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= " + yWidth + ") {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), " + (xHeight - 1) + ".0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), " + (xWidth - 1) + ".0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ";
+        }
+        return ResizeBilinearBackpropProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function resizeBilinearGrad(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var images = inputs.images, dy = inputs.dy;
+        var alignCorners = attrs.alignCorners;
+        var program = new ResizeBilinearBackpropProgram(dy.shape, images.shape, alignCorners);
+        return backend.runWebGLProgram(program, [dy], dy.dtype);
+    }
+    var resizeBilinearGradConfig = {
+        kernelName: tf.ResizeBilinearGrad,
+        backendName: 'webgl',
+        kernelFunc: resizeBilinearGrad
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ResizeNearestNeighborProgram = /** @class */ (function () {
+        function ResizeNearestNeighborProgram(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
+            this.variableNames = ['A'];
+            this.outputShape = [];
+            var batch = inputShape[0], oldHeight = inputShape[1], oldWidth = inputShape[2], depth = inputShape[3];
+            this.outputShape = [batch, newHeight, newWidth, depth];
+            var effectiveInSize = [
+                (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+                (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+            ];
+            var effectiveOutSize = [
+                (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+                (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+            ];
+            // When align corners is false, we rounds the value with floor.
+            var roundBase = alignCorners ? '0.5' : '0.0';
+            var sourceFracIndexRC;
+            if (halfPixelCenters) {
+                sourceFracIndexRC =
+                    "max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC" +
+                        ", vec2(0.0))";
+            }
+            else {
+                sourceFracIndexRC = "vec2(yRC) * effectiveInputOverOutputRatioRC";
+            }
+            this.userCode = "\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          " + effectiveInSize[0] / effectiveOutSize[0] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ");\n      const vec2 inputShapeRC = vec2(" + oldHeight + ".0, " + oldWidth + ".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = " + sourceFracIndexRC + ";\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + " + roundBase + ")));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    ";
+        }
+        return ResizeNearestNeighborProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ResizeNearestNeighborPackedProgram = /** @class */ (function () {
+        function ResizeNearestNeighborPackedProgram(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
+            this.variableNames = ['A'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            this.outputShape = [];
+            var batch = inputShape[0], oldHeight = inputShape[1], oldWidth = inputShape[2], depth = inputShape[3];
+            this.outputShape = [batch, newHeight, newWidth, depth];
+            var effectiveInSize = [
+                (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+                (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+            ];
+            var effectiveOutSize = [
+                (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+                (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+            ];
+            // When align corners is false, we rounds the value with floor.
+            var roundBase = alignCorners ? '0.5' : '0.0';
+            var sourceFracIndexRC;
+            if (halfPixelCenters) {
+                sourceFracIndexRC = "max((vec3(yRC) + vec3(0.5)) * " +
+                    "effectiveInputOverOutputRatioRC, vec3(0.0))";
+            }
+            else {
+                sourceFracIndexRC = "vec3(yRC) * effectiveInputOverOutputRatioRC";
+            }
+            this.userCode = "\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          " + effectiveInSize[0] / effectiveOutSize[0] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ");\n      const vec3 inputShapeRC = vec3(" + oldHeight + ".0, " + oldWidth + ".0,\n                                     " + oldWidth + ".0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = " + sourceFracIndexRC + ";\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + " + roundBase + ")));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < " + (depth - 1) + ";\n        bool hasNextRow = coords.z < " + (newWidth - 1) + ";\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    ";
+        }
+        return ResizeNearestNeighborPackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function resizeNearestNeighbor(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var images = inputs.images;
+        var alignCorners = attrs.alignCorners, halfPixelCenters = attrs.halfPixelCenters, size = attrs.size;
+        var newHeight = size[0], newWidth = size[1];
+        var program = tf.env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?
+            new ResizeNearestNeighborPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) :
+            new ResizeNearestNeighborProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);
+        return backend.runWebGLProgram(program, [images], images.dtype);
+    }
+    var resizeNearestNeighborConfig = {
+        kernelName: tf.ResizeNearestNeighbor,
+        backendName: 'webgl',
+        kernelFunc: resizeNearestNeighbor
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ResizeNearestNeigborBackpropProgram = /** @class */ (function () {
+        function ResizeNearestNeigborBackpropProgram(dyShape, inputShape, alignCorners) {
+            this.variableNames = ['dy'];
+            this.outputShape = [];
+            this.outputShape = inputShape;
+            var xHeight = inputShape[1], xWidth = inputShape[2];
+            var yHeight = dyShape[1], yWidth = dyShape[2];
+            // In the backwards pass, we want to find the pixels that were generated for
+            // each pixel in the input image the forward pass and add the corresponding
+            // coefficient from dy to the gradient (with some interpolation).
+            var effectiveXSize = [
+                (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
+                (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
+            ];
+            var effectiveYSize = [
+                (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
+                (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
+            ];
+            var heightScale = effectiveXSize[0] / effectiveYSize[0];
+            var widthScale = effectiveXSize[1] / effectiveYSize[1];
+            var invHeightScale = 1 / heightScale;
+            var invWidthScale = 1 / widthScale;
+            // This defines the size of the window of values around a particular
+            // index in dy that we want to search for contributions to dx.
+            var winHeight = (Math.ceil(invHeightScale) * 2) + 2;
+            var winWidth = (Math.ceil(invWidthScale) * 2) + 2;
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(" + heightScale + ");\n        const float widthScale = float(" + widthScale + ");\n\n        const float invHeightScale = float(" + invHeightScale + ");\n        const float invWidthScale = float(" + invWidthScale + ");\n\n        const int winHeight = int(" + winHeight + ");\n        const int winWidth = int(" + winWidth + ");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= " + yHeight + ") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= " + yWidth + ") {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(" + effectiveXSize[0] + ") *\n                (float(dyR) / float(" + effectiveYSize[0] + "));\n\n            float sourceFracCol =\n                float(" + effectiveXSize[1] + ") *\n                  (float(dyC) / float(" + effectiveYSize[1] + "));\n\n            int sourceNearestRow = int(min(\n                float(int(" + xHeight + ") - 1),\n                " + alignCorners + " ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(" + xWidth + ") - 1),\n                " + alignCorners + " ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ";
+        }
+        return ResizeNearestNeigborBackpropProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function resizeNearestNeighborGrad(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var images = inputs.images, dy = inputs.dy;
+        var alignCorners = attrs.alignCorners;
+        var program = new ResizeNearestNeigborBackpropProgram(dy.shape, images.shape, alignCorners);
+        return backend.runWebGLProgram(program, [dy], dy.dtype);
+    }
+    var resizeNearestNeighborGradConfig = {
+        kernelName: tf.ResizeNearestNeighborGrad,
+        backendName: 'webgl',
+        kernelFunc: resizeNearestNeighborGrad
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ReverseProgram = /** @class */ (function () {
+        function ReverseProgram(xShape, axis) {
+            this.variableNames = ['x'];
+            var rank = xShape.length;
+            if (rank > 4) {
+                throw new Error("WebGL backend: Reverse of rank-" + rank + " tensor is not yet supported");
+            }
+            this.outputShape = xShape;
+            if (rank === 1) {
+                this.userCode = "\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(" + xShape[0] + " - coord - 1));\n        }\n      ";
+                return;
+            }
+            var getInCoord = function (i) {
+                if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
+                    return xShape[i] + " - coords[" + i + "] - 1";
+                }
+                return "coords[" + i + "]";
+            };
+            var inCoords = xShape.map(function (_, i) { return getInCoord(i); }).join(',');
+            var type = getCoordsDataType(rank);
+            this.userCode = "\n      void main() {\n        " + type + " coords = getOutputCoords();\n        setOutput(getX(" + inCoords + "));\n      }\n    ";
+        }
+        return ReverseProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ReversePackedProgram = /** @class */ (function () {
+        function ReversePackedProgram(xShape, axis) {
+            this.variableNames = ['x'];
+            this.packedInputs = true;
+            this.packedOutput = true;
+            var rank = xShape.length;
+            if (rank > 4) {
+                throw new Error("WebGL backend: Reverse of rank-" + rank + " tensor is not yet supported");
+            }
+            this.outputShape = xShape;
+            var channels = getChannels('rc', rank);
+            var nextColumn = channels[rank - 1] + " + 1 < " + this.outputShape[rank - 1];
+            var nextRow = channels[rank - 2] + " + 1 < " + this.outputShape[rank - 2];
+            var type = getCoordsDataType(rank);
+            if (rank === 1) {
+                this.userCode = "\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(" + xShape[0] + " - rc - 1),\n            " + xShape[0] + " - rc - 1);\n          if(" + nextColumn + "){\n              result.g = getChannel(getX(" + xShape[0] + " - (rc  + 1) - 1),\n                " + xShape[0] + " - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      ";
+            }
+            else {
+                this.userCode = "\n        void main() {\n          " + type + " rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = " + getR(channels.slice()) + ";\n          if(" + nextColumn + "){\n            result.g = " + getG(channels.slice()) + ";\n          }\n          if(" + nextRow + ") {\n            result.b = " + getB(channels.slice()) + ";\n            if(" + nextColumn + ") {\n              result.a = " + getA(channels.slice()) + ";\n            }\n          }\n          setOutput(result);\n        }\n    ";
+            }
+            function getR(channels) {
+                return getChannel(channels);
+            }
+            function getG(channels) {
+                channels[rank - 1] = '(' + channels[rank - 1] + " + 1)";
+                return getChannel(channels);
+            }
+            function getB(channels) {
+                channels[rank - 2] = '(' + channels[rank - 2] + " + 1)";
+                return getChannel(channels);
+            }
+            function getA(channels) {
+                channels[rank - 1] = '(' + channels[rank - 1] + " + 1)";
+                channels[rank - 2] = '(' + channels[rank - 2] + " + 1)";
+                return getChannel(channels);
+            }
+            function getChannel(channels) {
+                var inCoordsArray = xShape.map(function (_, i) { return getInCoord(i, channels); });
+                var inCoords = inCoordsArray.join(',');
+                var innerDims = inCoordsArray.slice(-2).join(',');
+                return "getChannel(getX(" + inCoords + "), vec2(" + innerDims + "))";
+            }
+            function getInCoord(i, channels1) {
+                if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
+                    return xShape[i] + " - " + channels1[i] + " - 1";
+                }
+                else {
+                    return "" + channels1[i];
+                }
+            }
+        }
+        return ReversePackedProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function reverse(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var dims = attrs.dims;
+        var xRank = x.shape.length;
+        var $dims = tf.util.parseAxisParam(dims, x.shape);
+        if (xRank === 0) {
+            return identity({ inputs: { x: x }, backend: backend });
+        }
+        var program = tf.env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
+            new ReversePackedProgram(x.shape, $dims) :
+            new ReverseProgram(x.shape, $dims);
+        return backend.runWebGLProgram(program, [x], x.dtype);
+    }
+    var reverseConfig = {
+        kernelName: tf.Reverse,
+        backendName: 'webgl',
+        kernelFunc: reverse
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var RotateProgram = /** @class */ (function () {
+        function RotateProgram(imageShape, fillValue) {
+            this.variableNames = ['Image'];
+            this.outputShape = [];
+            var imageHeight = imageShape[1];
+            var imageWidth = imageShape[2];
+            this.outputShape = imageShape;
+            var fillSnippet = '';
+            if (typeof fillValue === 'number') {
+                fillSnippet = "float outputValue = " + fillValue.toFixed(2) + ";";
+            }
+            else {
+                fillSnippet = "\n        vec3 fill = vec3(" + fillValue.join(',') + ");\n        float outputValue = fill[coords[3]];";
+            }
+            this.userCode = "\n        uniform vec4 params;\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          " + fillSnippet + "\n          if(coordX >= 0 && coordX < " + imageWidth + " && coordY >= 0 && coordY < " + imageHeight + ") {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    ";
+        }
+        RotateProgram.prototype.getCustomSetupFunc = function (centerX, centerY, sinFactor, cosFactor) {
+            var _this = this;
+            return function (gpgpu, webGLProgram) {
+                if (_this.paramsLoc == null) {
+                    _this.paramsLoc =
+                        gpgpu.getUniformLocationNoThrow(webGLProgram, 'params');
+                }
+                gpgpu.gl.uniform4f(_this.paramsLoc, centerX, centerY, sinFactor, cosFactor);
+            };
+        };
+        return RotateProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var rotateWithOffsetConfig = {
+        kernelName: tf.RotateWithOffset,
+        backendName: 'webgl',
+        kernelFunc: function (_a) {
+            var inputs = _a.inputs, attrs = _a.attrs, backend = _a.backend;
+            var image = inputs.image;
+            var _b = attrs, radians = _b.radians, fillValue = _b.fillValue, center = _b.center;
+            var webglBackend = backend;
+            var program = new RotateProgram(image.shape, fillValue);
+            var _c = tf.backend_util.getImageCenter(center, image.shape[1], image.shape[2]), centerX = _c[0], centerY = _c[1];
+            var customSetup = program.getCustomSetupFunc(centerX, centerY, Math.sin(radians), Math.cos(radians));
+            var output = webglBackend.runWebGLProgram(program, [image], image.dtype, customSetup);
+            return output;
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ROUND = "\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n";
+    var round = unaryKernelFunc({ opSnippet: ROUND });
+    var roundConfig = {
+        kernelName: tf.Round,
+        backendName: 'webgl',
+        kernelFunc: round,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var RSQRT = "return inversesqrt(x);";
+    var rsqrt = unaryKernelFunc({ opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU });
+    var rsqrtConfig = {
+        kernelName: tf.Rsqrt,
+        backendName: 'webgl',
+        kernelFunc: rsqrt
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ScatterProgram = /** @class */ (function () {
+        function ScatterProgram(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex) {
+            this.variableNames = ['updates', 'indices', 'defaultValue'];
+            this.outputShape = shape;
+            var stridesType = getCoordsDataType(strides.length);
+            var dtype = getCoordsDataType(shape.length);
+            var indicesString = '';
+            if (indicesRank === 1) {
+                indicesString = 'i';
+            }
+            else if (indicesRank === 2) {
+                indicesString = 'i, j';
+            }
+            var indicesSnippet = "getIndices(" + indicesString + ")";
+            var updatesString = '';
+            if (updatesRank === 1) {
+                updatesString = 'i';
+            }
+            else if (updatesRank === 2) {
+                updatesString = 'i, coords[1]';
+            }
+            var updatesSnippet = "getUpdates(" + updatesString + ")";
+            var strideString = sliceDim > 1 ? 'strides[j]' : 'strides';
+            this.userCode = "\n        " + stridesType + " strides = " + stridesType + "(" + strides + ");\n\n        void main() {\n          " + dtype + " coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < " + updateSize + "; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < " + sliceDim + "; j++) {\n              int index = round(" + indicesSnippet + ");\n              flattenedIndex += index * " + strideString + ";\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += " + updatesSnippet + ";\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      ";
+        }
+        return ScatterProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function scatterNd(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var indices = inputs.indices, updates = inputs.updates;
+        var shape = attrs.shape;
+        var _a = tf.backend_util.calculateShapes(updates, indices, shape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize;
+        var flattenShape = [outputSize / sliceSize, sliceSize];
+        if (outputSize === 0) {
+            return backend.makeTensorInfo(shape, indices.dtype);
+        }
+        var flattenIndices = reshape({ inputs: { x: indices }, backend: backend, attrs: { shape: [numUpdates, sliceRank] } });
+        var flattenX = reshape({ inputs: { x: updates }, backend: backend, attrs: { shape: [numUpdates, sliceSize] } });
+        var defaultValue = backend.makeTensorInfo([], 'float32', new Float32Array([0])); // scalar(0)
+        var program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape);
+        var res = backend.runWebGLProgram(program, [flattenX, flattenIndices, defaultValue], flattenX.dtype);
+        var reshaped = reshape({ inputs: { x: res }, backend: backend, attrs: { shape: shape } });
+        backend.disposeIntermediateTensorInfo(flattenIndices);
+        backend.disposeIntermediateTensorInfo(flattenX);
+        backend.disposeIntermediateTensorInfo(res);
+        backend.disposeIntermediateTensorInfo(defaultValue);
+        return reshaped;
+    }
+    var scatterNdConfig = {
+        kernelName: tf.ScatterNd,
+        backendName: 'webgl',
+        kernelFunc: scatterNd
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SelectProgram = /** @class */ (function () {
+        function SelectProgram(cRank, shape, rank) {
+            this.variableNames = ['c', 'a', 'b'];
+            this.outputShape = shape;
+            var cCoords;
+            var abCoords;
+            if (rank > 4) {
+                throw Error("Where for rank " + rank + " is not yet supported");
+            }
+            if (rank === 1) {
+                abCoords = "resRC";
+                cCoords = "resRC";
+            }
+            else {
+                var currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
+                var cCoordVars = [];
+                var abCoordVars = [];
+                for (var i = 0; i < shape.length; i++) {
+                    abCoordVars.push("" + currentCoords[i]);
+                    if (i < cRank) {
+                        cCoordVars.push("" + currentCoords[i]);
+                    }
+                }
+                cCoords = cCoordVars.join();
+                abCoords = abCoordVars.join();
+            }
+            var dtype = getCoordsDataType(rank);
+            this.userCode = "\n      void main() {\n        " + dtype + " resRC = getOutputCoords();\n        float cVal = getC(" + cCoords + ");\n        if (cVal >= 1.0) {\n          setOutput(getA(" + abCoords + "));\n        } else {\n          setOutput(getB(" + abCoords + "));\n        }\n      }\n    ";
+        }
+        return SelectProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function select(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var condition = inputs.condition, t = inputs.t, e = inputs.e;
+        var program = new SelectProgram(condition.shape.length, t.shape, t.shape.length);
+        return backend.runWebGLProgram(program, [condition, t, e], tf.upcastType(t.dtype, e.dtype));
+    }
+    var selectConfig = {
+        kernelName: tf.Select,
+        backendName: 'webgl',
+        kernelFunc: select
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SELU = "\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = " + tf.backend_util.SELU_SCALEALPHA + ";\n  float scale = " + tf.backend_util.SELU_SCALE + ";\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n";
+    var selu = unaryKernelFunc({ opSnippet: SELU });
+    var seluConfig = {
+        kernelName: tf.Selu,
+        backendName: 'webgl',
+        kernelFunc: selu,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SIGMOID$2 = "return 1.0 / (1.0 + exp(-1.0 * x));";
+    var sigmoid = unaryKernelFunc({ opSnippet: SIGMOID$2 });
+    var sigmoidConfig = {
+        kernelName: tf.Sigmoid,
+        backendName: 'webgl',
+        kernelFunc: sigmoid,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // Sign does not propagate NANs.
+    var SIGN = "\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n";
+    var sign = unaryKernelFunc({ opSnippet: SIGN });
+    var signConfig = {
+        kernelName: tf.Sign,
+        backendName: 'webgl',
+        kernelFunc: sign,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SIN = CHECK_NAN_SNIPPET_UNARY + "\n  return sin(x);\n";
+    var sin = unaryKernelFunc({ opSnippet: SIN });
+    var sinConfig = {
+        kernelName: tf.Sin,
+        backendName: 'webgl',
+        kernelFunc: sin,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SINH = "\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n";
+    var sinh = unaryKernelFunc({ opSnippet: SINH });
+    var sinhConfig = {
+        kernelName: tf.Sinh,
+        backendName: 'webgl',
+        kernelFunc: sinh,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SOFTPLUS = "\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n";
+    var softplus = unaryKernelFunc({ opSnippet: SOFTPLUS });
+    var softplusConfig = {
+        kernelName: tf.Softplus,
+        backendName: 'webgl',
+        kernelFunc: softplus,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var spaceToBatchND = function (args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var blockShape = attrs.blockShape, paddings = attrs.paddings;
+        tf.util.assert(x.shape.length <= 4, function () { return 'spaceToBatchND for rank > 4 with a WebGL backend not ' +
+            'implemented yet'; });
+        var prod = blockShape.reduce(function (a, b) { return a * b; });
+        var completePaddings = [[0, 0]];
+        completePaddings.push.apply(completePaddings, paddings);
+        for (var i = 1 + blockShape.length; i < x.shape.length; ++i) {
+            completePaddings.push([0, 0]);
+        }
+        var toDispose = [];
+        var paddedX = padV2({
+            inputs: { x: x },
+            backend: backend,
+            attrs: { paddings: completePaddings, constantValue: 0 }
+        });
+        var reshapedPaddedShape = tf.backend_util.getReshaped(paddedX.shape, blockShape, prod, false);
+        var permutedReshapedPaddedPermutation = tf.backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+        var flattenShape = tf.backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);
+        var reshapedPaddedX = reshape({ inputs: { x: paddedX }, backend: backend, attrs: { shape: reshapedPaddedShape } });
+        var paddedXT = transpose({
+            inputs: { x: reshapedPaddedX },
+            backend: backend,
+            attrs: { perm: permutedReshapedPaddedPermutation }
+        });
+        var result = reshape({ inputs: { x: paddedXT }, backend: backend, attrs: { shape: flattenShape } });
+        toDispose.push(paddedX);
+        toDispose.push(reshapedPaddedX);
+        toDispose.push(paddedXT);
+        toDispose.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return result;
+    };
+    var spaceToBatchNDConfig = {
+        kernelName: tf.SpaceToBatchND,
+        backendName: 'webgl',
+        kernelFunc: spaceToBatchND
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function sparseFillEmptyRows(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var indices = inputs.indices, values = inputs.values, denseShape = inputs.denseShape, defaultValue = inputs.defaultValue;
+        if (denseShape.shape.length !== 1) {
+            throw new Error("Dense shape must be a vector, saw:\n         " + denseShape.shape);
+        }
+        if (indices.shape.length !== 2) {
+            throw new Error("Indices must be a matrix, saw:\n         " + indices.shape);
+        }
+        if (values.shape.length !== 1) {
+            throw new Error("Values must be a vector, saw:\n         " + values.shape);
+        }
+        if (defaultValue.shape.length !== 0) {
+            throw new Error("Default value must be a scalar, saw:\n        " + defaultValue.shape);
+        }
+        var $indices = backend.readSync(indices.dataId);
+        var $values = backend.readSync(values.dataId);
+        var $denseShape = backend.readSync(denseShape.dataId);
+        var $defaultValue = backend.readSync(defaultValue.dataId)[0];
+        var _a = sparseFillEmptyRowsImplCPU($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue), outputIndices = _a[0], outputIndicesShape = _a[1], outputValues = _a[2], emptyRowIndicator = _a[3], reverseIndexMap = _a[4];
+        return [
+            backend.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),
+            backend.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),
+            backend.makeTensorInfo([emptyRowIndicator.length], 'bool', new Uint8Array(emptyRowIndicator.map(function (value) { return Number(value); }))),
+            backend.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap)),
+        ];
+    }
+    var sparseFillEmptyRowsConfig = {
+        kernelName: tf.SparseFillEmptyRows,
+        backendName: 'webgl',
+        kernelFunc: sparseFillEmptyRows,
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function sparseReshape(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var inputIndices = inputs.inputIndices, inputShape = inputs.inputShape, newShape = inputs.newShape;
+        if (inputIndices.shape.length !== 2) {
+            throw new Error("Input indices should be a matrix but received shape " + inputIndices.shape);
+        }
+        if (inputShape.shape.length !== 1) {
+            throw new Error("Input shape should be a vector but received shape " + inputShape.shape);
+        }
+        if (newShape.shape.length !== 1) {
+            throw new Error("Target shape should be a vector but received shape " + newShape.shape);
+        }
+        var $inputShape = Array.from(backend.readSync(inputShape.dataId));
+        var $inputIndices = backend.readSync(inputIndices.dataId);
+        var targetShape = Array.from(backend.readSync(newShape.dataId));
+        var _a = sparseReshapeImplCPU($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape), newIndices = _a[0], indicesShape = _a[1], outputShape = _a[2];
+        return [
+            backend.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),
+            backend.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape)),
+        ];
+    }
+    var sparseReshapeConfig = {
+        kernelName: tf.SparseReshape,
+        backendName: 'webgl',
+        kernelFunc: sparseReshape,
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function sparseSegmentMean(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var data = inputs.data, indices = inputs.indices, segmentIds = inputs.segmentIds;
+        if (data.shape.length < 1) {
+            throw new Error("Data should be at least 1 dimensional but received scalar");
+        }
+        if (indices.shape.length !== 1) {
+            throw new Error("Indices should be a vector but received shape\n              " + indices.shape);
+        }
+        if (segmentIds.shape.length !== 1) {
+            throw new Error("Segment ids should be a vector but received shape\n              " + segmentIds.shape);
+        }
+        var $data = backend.readSync(data.dataId);
+        var $indices = backend.readSync(indices.dataId);
+        var $segmentIds = backend.readSync(segmentIds.dataId);
+        var _a = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds, true), outputData = _a[0], outputDataShape = _a[1];
+        return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);
+    }
+    var sparseSegmentMeanConfig = {
+        kernelName: tf.SparseSegmentMean,
+        backendName: 'webgl',
+        kernelFunc: sparseSegmentMean,
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function sparseSegmentSum(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var data = inputs.data, indices = inputs.indices, segmentIds = inputs.segmentIds;
+        if (data.shape.length < 1) {
+            throw new Error("Data should be at least 1 dimensional but received scalar");
+        }
+        if (indices.shape.length !== 1) {
+            throw new Error("Indices should be a vector but received shape\n             " + indices.shape);
+        }
+        if (segmentIds.shape.length !== 1) {
+            throw new Error("Segment ids should be a vector but received shape\n             " + segmentIds.shape);
+        }
+        var $data = backend.readSync(data.dataId);
+        var $indices = backend.readSync(indices.dataId);
+        var $segmentIds = backend.readSync(segmentIds.dataId);
+        var _a = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds), outputData = _a[0], outputDataShape = _a[1];
+        return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);
+    }
+    var sparseSegmentSumConfig = {
+        kernelName: tf.SparseSegmentSum,
+        backendName: 'webgl',
+        kernelFunc: sparseSegmentSum,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function sparseToDense(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var sparseIndices = inputs.sparseIndices, sparseValues = inputs.sparseValues, defaultValue = inputs.defaultValue;
+        var outputShape = attrs.outputShape;
+        var _a = tf.backend_util.calculateShapes(sparseValues, sparseIndices, outputShape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, strides = _a.strides, outputSize = _a.outputSize;
+        var sumDupeIndices = false;
+        var program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices);
+        var res = backend.runWebGLProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype);
+        var reshaped = reshape({ inputs: { x: res }, backend: backend, attrs: { shape: outputShape } });
+        backend.disposeIntermediateTensorInfo(res);
+        return reshaped;
+    }
+    var sparseToDenseConfig = {
+        kernelName: tf.SparseToDense,
+        backendName: 'webgl',
+        kernelFunc: sparseToDense
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function splitV(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var numOrSizeSplits = attrs.numOrSizeSplits, axis = attrs.axis;
+        var $axis = tf.util.parseAxisParam(axis, x.shape)[0];
+        var splitSizes = tf.backend_util.prepareSplitSize(x, numOrSizeSplits, $axis);
+        var xRank = x.shape.length;
+        var begin = new Array(xRank).fill(0);
+        var size = x.shape.slice();
+        return splitSizes.map(function (s) {
+            var sliceSize = size.slice();
+            sliceSize[$axis] = s;
+            var sliceT = slice({ inputs: { x: x }, backend: backend, attrs: { begin: begin, size: sliceSize } });
+            begin[$axis] += s;
+            return sliceT;
+        });
+    }
+    var splitVConfig = {
+        kernelName: tf.SplitV,
+        backendName: 'webgl',
+        kernelFunc: splitV
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SQRT = "return sqrt(x);";
+    var sqrt = unaryKernelFunc({ opSnippet: SQRT });
+    var sqrtConfig = {
+        kernelName: tf.Sqrt,
+        backendName: 'webgl',
+        kernelFunc: sqrt
+    };
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SQUARE = "return x * x;";
+    var square = unaryKernelFunc({ opSnippet: SQUARE });
+    var squareConfig = {
+        kernelName: tf.Square,
+        backendName: 'webgl',
+        kernelFunc: square,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';
+    var squaredDifference = binaryKernelFunc({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE });
+    var squaredDifferenceConfig = {
+        kernelName: tf.SquaredDifference,
+        backendName: 'webgl',
+        kernelFunc: squaredDifference,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function step(_a) {
+        var inputs = _a.inputs, attrs = _a.attrs, backend = _a.backend;
+        var x = inputs.x;
+        var opSnippet = CHECK_NAN_SNIPPET + ("\n    return x > 0.0 ? 1.0 : float(" + attrs.alpha + ");\n  ");
+        var program = new UnaryOpProgram(x.shape, opSnippet);
+        return backend.runWebGLProgram(program, [x], x.dtype);
+    }
+    var stepConfig = {
+        kernelName: tf.Step,
+        backendName: 'webgl',
+        kernelFunc: step,
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var StridedSliceProgram = /** @class */ (function () {
+        function StridedSliceProgram(begin, strides, size) {
+            this.variableNames = ['x'];
+            this.outputShape = size;
+            var rank = size.length;
+            var inputDtype = getCoordsDataType(size.length);
+            var dtype = getCoordsDataType(size.length);
+            var newCoords = '';
+            if (rank === 1) {
+                newCoords = 'coords * strides + begin';
+            }
+            else {
+                var outputAxis_1 = 0;
+                newCoords =
+                    size.map(function (_, i) {
+                        outputAxis_1++;
+                        return size.length === 1 ?
+                            "coords * strides[" + i + "] + begin[" + i + "]" :
+                            "coords[" + (outputAxis_1 - 1) + "] * strides[" + i + "] + begin[" + i + "]";
+                    })
+                        .join(',');
+            }
+            this.userCode = "\n      " + inputDtype + " begin = " + inputDtype + "(" + begin + ");\n      " + inputDtype + " strides = " + inputDtype + "(" + strides + ");\n\n      void main() {\n        " + dtype + " coords = getOutputCoords();\n        setOutput(getX(" + newCoords + "));\n      }\n    ";
+        }
+        return StridedSliceProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function stridedSlice(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var begin = attrs.begin, end = attrs.end, strides = attrs.strides, beginMask = attrs.beginMask, endMask = attrs.endMask, ellipsisMask = attrs.ellipsisMask, newAxisMask = attrs.newAxisMask, shrinkAxisMask = attrs.shrinkAxisMask;
+        var _a = tf.slice_util.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask), nonStrided = _a.nonStrided, $begin = _a.$begin, $strides = _a.$strides, size = _a.size, newShape = _a.newShape, outShape = _a.outShape;
+        var $x = reshape({ inputs: { x: x }, backend: backend, attrs: { shape: newShape } });
+        var result;
+        if (nonStrided) {
+            var sliced = slice({ inputs: { x: $x }, backend: backend, attrs: { begin: $begin, size: size } });
+            result = reshape({ inputs: { x: sliced }, backend: backend, attrs: { shape: outShape } });
+            backend.disposeIntermediateTensorInfo(sliced);
+        }
+        else if (outShape.some(function (axis) { return axis === 0; })) {
+            result = backend.makeTensorInfo(outShape, x.dtype, []);
+        }
+        else {
+            var shouldExecuteOnCPU = backend.shouldExecuteOnCPU([$x]);
+            if (shouldExecuteOnCPU) {
+                var xTexData = backend.texData.get($x.dataId);
+                var values = xTexData.values;
+                var xBuf = tf.buffer($x.shape, $x.dtype, values);
+                var resultValues = stridedSliceImplCPU(outShape, xBuf, $strides, $begin);
+                result = backend.makeTensorInfo(outShape, $x.dtype, resultValues.values);
+            }
+            else {
+                var program = new StridedSliceProgram($begin, $strides, outShape);
+                result = backend.runWebGLProgram(program, [$x], $x.dtype);
+            }
+        }
+        var resultReshaped = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: outShape } });
+        backend.disposeIntermediateTensorInfo($x);
+        backend.disposeIntermediateTensorInfo(result);
+        return resultReshaped;
+    }
+    var stridedSliceConfig = {
+        kernelName: tf.StridedSlice,
+        backendName: 'webgl',
+        kernelFunc: stridedSlice
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function stringNGrams(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var separator = attrs.separator, nGramWidths = attrs.nGramWidths, leftPad = attrs.leftPad, rightPad = attrs.rightPad, padWidth = attrs.padWidth, preserveShortSequences = attrs.preserveShortSequences;
+        var data = inputs.data, dataSplits = inputs.dataSplits;
+        var $data = backend.readSync(data.dataId);
+        var $dataSplits = backend.readSync(dataSplits.dataId);
+        var _a = stringNGramsImplCPU($data, $dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences), nGrams = _a[0], nGramsSplits = _a[1];
+        return [
+            backend.makeTensorInfo([nGrams.length], 'string', nGrams),
+            backend.makeTensorInfo(dataSplits.shape, 'int32', nGramsSplits),
+        ];
+    }
+    var stringNGramsConfig = {
+        kernelName: tf.StringNGrams,
+        backendName: 'webgl',
+        kernelFunc: stringNGrams,
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function stringSplit(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var skipEmpty = attrs.skipEmpty;
+        var input = inputs.input, delimiter = inputs.delimiter;
+        if (input.dtype !== 'string') {
+            throw new Error('Input must be of datatype string');
+        }
+        if (input.shape.length !== 1) {
+            throw new Error("Input must be a vector, got shape: " + input.shape);
+        }
+        if (delimiter.shape.length !== 0) {
+            throw new Error("Delimiter must be a scalar, got shape: " + delimiter.shape);
+        }
+        var $input = backend.readSync(input.dataId);
+        var $delimiter = backend.readSync(delimiter.dataId)[0];
+        var _a = stringSplitImplCPU($input, $delimiter, skipEmpty), indices = _a[0], values = _a[1], shape = _a[2];
+        var outputSize = values.length;
+        return [
+            backend.makeTensorInfo([outputSize, 2], 'int32', indices),
+            backend.makeTensorInfo([outputSize], 'string', values),
+            backend.makeTensorInfo([2], 'int32', new Int32Array(shape))
+        ];
+    }
+    var stringSplitConfig = {
+        kernelName: tf.StringSplit,
+        backendName: 'webgl',
+        kernelFunc: stringSplit,
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function stringToHashBucketFast(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var numBuckets = attrs.numBuckets;
+        var input = inputs.input;
+        if (input.dtype !== 'string') {
+            throw new Error('Input must be of datatype string');
+        }
+        if (numBuckets <= 0) {
+            throw new Error("Number of buckets must be at least 1");
+        }
+        var $input = backend.readSync(input.dataId);
+        var output = stringToHashBucketFastImplCPU($input, numBuckets);
+        return backend.makeTensorInfo(input.shape, 'int32', output);
+    }
+    var stringToHashBucketFastConfig = {
+        kernelName: tf.StringToHashBucketFast,
+        backendName: 'webgl',
+        kernelFunc: stringToHashBucketFast,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var TAN = "return tan(x);";
+    var tan = unaryKernelFunc({ opSnippet: TAN });
+    var tanConfig = {
+        kernelName: tf.Tan,
+        backendName: 'webgl',
+        kernelFunc: tan,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var TANH = "\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n";
+    var tanh = unaryKernelFunc({ opSnippet: TANH });
+    var tanhConfig = {
+        kernelName: tf.Tanh,
+        backendName: 'webgl',
+        kernelFunc: tanh,
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var TileProgram = /** @class */ (function () {
+        function TileProgram(aShape, reps) {
+            this.variableNames = ['A'];
+            var outputShape = new Array(aShape.length);
+            for (var i = 0; i < outputShape.length; i++) {
+                outputShape[i] = aShape[i] * reps[i];
+            }
+            this.outputShape = outputShape;
+            this.rank = outputShape.length;
+            var dtype = getCoordsDataType(this.rank);
+            var sourceCoords = getSourceCoords$2(aShape);
+            this.userCode = "\n      void main() {\n        " + dtype + " resRC = getOutputCoords();\n        setOutput(getA(" + sourceCoords + "));\n      }\n    ";
+        }
+        return TileProgram;
+    }());
+    function getSourceCoords$2(aShape) {
+        var rank = aShape.length;
+        if (rank > 5) {
+            throw Error("Tile for rank " + rank + " is not yet supported");
+        }
+        if (rank === 1) {
+            return "imod(resRC, " + aShape[0] + ")";
+        }
+        var currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];
+        var sourceCoords = [];
+        for (var i = 0; i < aShape.length; i++) {
+            sourceCoords.push("imod(" + currentCoords[i] + ", " + aShape[i] + ")");
+        }
+        return sourceCoords.join();
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function tile(params) {
+        var inputs = params.inputs, backend = params.backend, attrs = params.attrs;
+        var x = inputs.x;
+        var reps = attrs.reps;
+        // tile gpu program cannot handle rank > 5 case.
+        if (x.dtype === 'string' || x.shape.length > 5) {
+            // Even thought string tensor is always on CPU, just to be consistent on how
+            // to access tensor data.
+            var data = backend.readSync(x.dataId);
+            var value = x.dtype === 'string' ?
+                data.map(function (d) { return tf.util.decodeString(d); }) :
+                data;
+            var buf = tf.buffer(x.shape, x.dtype, value);
+            var outBuf = tileImplCPU(buf, reps);
+            return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+        }
+        var program = new TileProgram(x.shape, reps);
+        var output = backend.runWebGLProgram(program, [x], x.dtype);
+        return output;
+    }
+    var tileConfig = {
+        kernelName: tf.Tile,
+        backendName: 'webgl',
+        kernelFunc: tile,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function topK(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var k = attrs.k, sorted = attrs.sorted;
+        var xVals = backend.readSync(x.dataId);
+        var _a = topKImplCPU(xVals, x.shape, x.dtype, k), allTopKVals = _a[0], allTopKIndices = _a[1];
+        return [
+            backend.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
+            backend.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
+        ];
+    }
+    var topKConfig = {
+        kernelName: tf.TopK,
+        backendName: 'webgl',
+        kernelFunc: topK
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var TransformProgram = /** @class */ (function () {
+        function TransformProgram(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape) {
+            this.variableNames = ['Image', 'Transforms'];
+            this.outputShape = outShape;
+            var interpolationModeId = interpolation === 'nearest' ? 1 : 2;
+            var fillModeId;
+            switch (fillMode) {
+                case 'constant':
+                    fillModeId = 1;
+                    break;
+                case 'reflect':
+                    fillModeId = 2;
+                    break;
+                case 'wrap':
+                    fillModeId = 3;
+                    break;
+                case 'nearest':
+                    fillModeId = 4;
+                    break;
+                default:
+                    fillModeId = 1;
+                    break;
+            }
+            this.userCode = "\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(" + fillModeId + " == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (" + fillModeId + " == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (" + fillModeId + " == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < " + imageHeight + " && 0 <= coordX && coordX < " + imageWidth + ") {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(" + fillValue + ");\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(" + fillValue + ");\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(" + imageWidth + "));\n                float mapY = mapCoord(inY, float(" + imageHeight + "));\n\n                if (" + interpolationModeId + " == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        ";
+        }
+        return TransformProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function transform(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var image = inputs.image, transforms = inputs.transforms;
+        var interpolation = attrs.interpolation, fillMode = attrs.fillMode, fillValue = attrs.fillValue, outputShape = attrs.outputShape;
+        var _a = image.shape, batch = _a[0], imageHeight = _a[1], imageWidth = _a[2], numChannels = _a[3];
+        var _b = outputShape != null ? outputShape : [imageHeight, imageWidth], outHeight = _b[0], outWidth = _b[1];
+        var outShape = [batch, outHeight, outWidth,
+            numChannels];
+        var program = new TransformProgram(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape);
+        return backend.runWebGLProgram(program, [image, transforms], 'float32');
+    }
+    var transformConfig = {
+        kernelName: tf.Transform,
+        backendName: 'webgl',
+        kernelFunc: transform
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function unique(args) {
+        var inputs = args.inputs, attrs = args.attrs, backend = args.backend;
+        var axis = attrs.axis;
+        var x = inputs.x;
+        assertNotComplex(x, 'unique');
+        // For now, always forward calculation to the CPU backend.
+        console.warn('WARNING: ', 'UI might be locked temporarily as data is being downloaded');
+        var values = backend.readSync(x.dataId);
+        var _a = uniqueImplCPU(values, axis, x.shape, x.dtype), outputValues = _a.outputValues, outputShape = _a.outputShape, indices = _a.indices;
+        return [
+            backend.makeTensorInfo(outputShape, x.dtype, outputValues),
+            backend.makeTensorInfo([indices.length], 'int32', indices),
+        ];
+    }
+    var uniqueConfig = {
+        kernelName: tf.Unique,
+        backendName: 'webgl',
+        kernelFunc: unique,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function unpack(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var value = inputs.value;
+        var axis = attrs.axis;
+        if (axis < 0) {
+            axis += value.shape.length;
+        }
+        var x = value;
+        var xRank = x.shape.length;
+        var num = value.shape[axis];
+        var outShape = new Array(xRank - 1);
+        var outIndex = 0;
+        for (var i = 0; i < xRank; i++) {
+            if (i !== axis) {
+                outShape[outIndex++] = x.shape[i];
+            }
+        }
+        var toDispose = [];
+        var begin = new Array(xRank).fill(0);
+        var size = x.shape.slice();
+        size[axis] = 1;
+        var res = new Array(num);
+        for (var i = 0; i < res.length; i++) {
+            begin[axis] = i;
+            var sliced = slice({ inputs: { x: x }, backend: backend, attrs: { begin: begin, size: size } });
+            var reshaped = reshape({ inputs: { x: sliced }, backend: backend, attrs: { shape: outShape } });
+            res[i] = reshaped;
+            toDispose.push(sliced);
+        }
+        toDispose.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return res;
+    }
+    var unpackConfig = {
+        kernelName: tf.Unpack,
+        backendName: 'webgl',
+        kernelFunc: unpack
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SegmentOpProgram = /** @class */ (function () {
+        function SegmentOpProgram(segOpInfo, segOpType) {
+            this.variableNames = ['x', 'segmentIds'];
+            var windowSize = segOpInfo.windowSize;
+            var batchSize = segOpInfo.batchSize;
+            var inSize = segOpInfo.inSize;
+            var numSegments = segOpInfo.numSegments;
+            var outSize = numSegments * Math.ceil(inSize / windowSize);
+            this.outputShape = [batchSize, outSize];
+            var initializationValue = '0.0';
+            var returnValue = "sumValue";
+            var windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+            var windowSizeVec4Remainder = windowSize % 4;
+            var updateSnippet = "\n        sumValue += dot(values, segFilter);\n    ";
+            var checkValueOutOfBounds = '';
+            if (inSize % windowSize > 0) {
+                checkValueOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return initializationValue;\n        }\n      ";
+            }
+            var checkSegmentIdOutOfBounds = '';
+            if (inSize % windowSize > 0) {
+                checkSegmentIdOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return -1.0;\n        }\n      ";
+            }
+            this.userCode = "\n      const float initializationValue = " + initializationValue + ";\n\n      float getValue(int batch, int inIdx) {\n        " + checkValueOutOfBounds + "\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        " + checkSegmentIdOutOfBounds + "\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          " + numSegments + ")) * float(" + windowSize + "));\n        int currentSeg = int(mod(float(outIdx), float(" + numSegments + ")));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < " + windowSizeNearestVec4 + "; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          " + updateSnippet + "\n        }\n\n        int inIdx = inOffset + " + windowSizeNearestVec4 + ";\n        if (" + (windowSizeVec4Remainder === 1) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 2) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 3) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          " + updateSnippet + "\n        }\n        setOutput(" + returnValue + ");\n      }\n    ";
+        }
+        return SegmentOpProgram;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function unsortedSegmentSum(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, segmentIds = inputs.segmentIds;
+        var numSegments = attrs.numSegments;
+        var xRank = x.shape.length;
+        var toDispose = [];
+        var axis = 0;
+        var permutation = tf.backend_util.getAxesPermutation([axis], xRank);
+        var permutedX = x;
+        if (permutation != null) {
+            permutedX = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutation } });
+            toDispose.push(permutedX);
+            axis = tf.backend_util.getInnerMostAxes(1, xRank)[0];
+        }
+        var outShape = tf.backend_util.segment_util.computeOutShape(permutedX.shape, axis, numSegments);
+        var inSize = tf.util.sizeFromShape([permutedX.shape[axis]]);
+        var a2D = reshape({ inputs: { x: permutedX }, backend: backend, attrs: { shape: [-1, inSize] } });
+        toDispose.push(a2D);
+        var outputDType = tf.sumOutType(x.dtype);
+        var segOpCompute = function (x, segOpType, segmentIds, dtype, numSegments) {
+            var batchSize = x.shape[0];
+            var inSize = x.shape[1];
+            var windowSize = tf.backend_util.segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);
+            var segOpInfo = { windowSize: windowSize, inSize: inSize, batchSize: batchSize, numSegments: numSegments };
+            var program = new SegmentOpProgram(segOpInfo, segOpType);
+            var output = backend.compileAndRun(program, [x, segmentIds], dtype);
+            toDispose.push(output);
+            // No need to run another GPGPU program.
+            if (output.shape[1] === numSegments) {
+                return output;
+            }
+            var rangeInfo = range({
+                backend: backend,
+                attrs: { start: 0, stop: numSegments, step: 1, dtype: 'float32' }
+            });
+            var tileInfo = tile({
+                inputs: { x: rangeInfo },
+                backend: backend,
+                attrs: { reps: [inSize / windowSize] }
+            });
+            toDispose.push(rangeInfo);
+            toDispose.push(tileInfo);
+            var result = segOpCompute(output, segOpType, tileInfo, dtype, numSegments);
+            return result;
+        };
+        var segOpResult = segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments);
+        var reshaped = reshape({ inputs: { x: segOpResult }, backend: backend, attrs: { shape: outShape } });
+        var result = reshaped;
+        if (permutation != null) {
+            toDispose.push(reshaped);
+            var perm = tf.backend_util.getUndoAxesPermutation(permutation);
+            result = transpose({ inputs: { x: result }, backend: backend, attrs: { perm: perm } });
+        }
+        toDispose.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return result;
+    }
+    var unsortedSegmentSumConfig = {
+        kernelName: tf.UnsortedSegmentSum,
+        backendName: 'webgl',
+        kernelFunc: unsortedSegmentSum
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // List all kernel configs here
+    var kernelConfigs = [
+        LRNConfig,
+        LRNGradConfig,
+        _fusedMatMulConfig,
+        absConfig,
+        acosConfig,
+        acoshConfig,
+        addConfig,
+        addNConfig,
+        allConfig,
+        anyConfig,
+        argMaxConfig,
+        argMinConfig,
+        asinConfig,
+        asinhConfig,
+        atan2Config,
+        atanConfig,
+        atanhConfig,
+        avgPool3DConfig,
+        avgPoolConfig,
+        avgPoolGrad3DConfig,
+        avgPoolGradConfig,
+        batchMatMulConfig,
+        batchNormConfig,
+        batchToSpaceNDConfig,
+        bincountConfig,
+        castConfig,
+        ceilConfig,
+        clipByValueConfig,
+        complexAbsConfig,
+        complexConfig,
+        concatConfig,
+        conv2DBackpropFilterConfig,
+        conv2DBackpropInputConfig,
+        conv2DConfig,
+        conv3DBackpropFilterV2Config,
+        conv3DBackpropInputConfig,
+        conv3DConfig,
+        cosConfig,
+        coshConfig,
+        cropAndResizeConfig,
+        cumsumConfig,
+        denseBincountConfig,
+        depthToSpaceConfig,
+        depthwiseConv2dNativeBackpropFilterConfig,
+        depthwiseConv2dNativeBackpropInputConfig,
+        depthwiseConv2dNativeConfig,
+        diagConfig,
+        dilation2DConfig,
+        einsumConfig,
+        eluConfig,
+        eluGradConfig,
+        equalConfig,
+        erfConfig,
+        expConfig,
+        expandDimsConfig,
+        expm1Config,
+        fftConfig,
+        fillConfig,
+        flipLeftRightConfig,
+        floorConfig,
+        floorDivConfig,
+        fromPixelsConfig,
+        fusedConv2DConfig,
+        fusedDepthwiseConv2DConfig,
+        gatherNdConfig,
+        gatherV2Config,
+        greaterConfig,
+        greaterEqualConfig,
+        identityConfig,
+        ifftConfig,
+        imagConfig,
+        isFiniteConfig,
+        isInfConfig,
+        isNaNConfig,
+        leakyReluConfig,
+        lessConfig,
+        lessEqualConfig,
+        linSpaceConfig,
+        log1pConfig,
+        logConfig,
+        logicalAndConfig,
+        logicalNotConfig,
+        logicalOrConfig,
+        maxConfig,
+        maxPool3DConfig,
+        maxPoolConfig,
+        maxPoolGrad3DConfig,
+        maxPoolGradConfig,
+        maxPoolWithArgmaxConfig,
+        maximumConfig,
+        meanConfig,
+        minConfig,
+        minimumConfig,
+        mirrorPadConfig,
+        modConfig,
+        multinomialConfig,
+        multiplyConfig,
+        negConfig,
+        nonMaxSuppressionV3Config,
+        nonMaxSuppressionV4Config,
+        nonMaxSuppressionV5Config,
+        notEqualConfig,
+        oneHotConfig,
+        onesLikeConfig,
+        packConfig,
+        padV2Config,
+        powConfig,
+        preluConfig,
+        prodConfig,
+        rangeConfig,
+        realConfig,
+        realDivConfig,
+        reciprocalConfig,
+        relu6Config,
+        reluConfig,
+        reshapeConfig,
+        resizeBilinearConfig,
+        resizeBilinearGradConfig,
+        resizeNearestNeighborConfig,
+        resizeNearestNeighborGradConfig,
+        reverseConfig,
+        rotateWithOffsetConfig,
+        roundConfig,
+        rsqrtConfig,
+        scatterNdConfig,
+        selectConfig,
+        seluConfig,
+        sigmoidConfig,
+        signConfig,
+        sinConfig,
+        sinhConfig,
+        sliceConfig,
+        softmaxConfig,
+        softplusConfig,
+        spaceToBatchNDConfig,
+        sparseFillEmptyRowsConfig,
+        sparseReshapeConfig,
+        sparseSegmentMeanConfig,
+        sparseSegmentSumConfig,
+        sparseToDenseConfig,
+        splitVConfig,
+        sqrtConfig,
+        squareConfig,
+        squaredDifferenceConfig,
+        stepConfig,
+        stridedSliceConfig,
+        stringNGramsConfig,
+        stringSplitConfig,
+        stringToHashBucketFastConfig,
+        subConfig,
+        sumConfig,
+        tanConfig,
+        tanhConfig,
+        tileConfig,
+        topKConfig,
+        transformConfig,
+        transposeConfig,
+        uniqueConfig,
+        unpackConfig,
+        unsortedSegmentSumConfig,
+        zerosLikeConfig
+    ];
+    for (var _i = 0, kernelConfigs_1 = kernelConfigs; _i < kernelConfigs_1.length; _i++) {
+        var kernelConfig = kernelConfigs_1[_i];
+        tf.registerKernel(kernelConfig);
+    }
+
+    exports.GPGPUContext = GPGPUContext;
+    exports.MathBackendWebGL = MathBackendWebGL;
+    exports.forceHalfFloat = forceHalfFloat;
+    exports.gpgpu_util = gpgpu_util;
+    exports.setWebGLContext = setWebGLContext;
+    exports.version_webgl = version;
+    exports.webgl = webgl;
+    exports.webgl_util = webgl_util;
+
+    Object.defineProperty(exports, '__esModule', { value: true });
+
+})));
+//# sourceMappingURL=tf-backend-webgl.js.map
diff --git a/thirdparty/tfjs/tf-backend-webgl.js.map b/thirdparty/tfjs/tf-backend-webgl.js.map
new file mode 100644
index 0000000..7c77dd0
--- /dev/null
+++ b/thirdparty/tfjs/tf-backend-webgl.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tf-backend-webgl.js","sources":["../src/canvas_util.ts","../src/tex_util.ts","../src/webgl_util.ts","../src/flags_webgl.ts","../src/glsl_version.ts","../src/shader_compiler_util.ts","../src/decode_matrix_gpu.ts","../src/decode_matrix_packed_gpu.ts","../src/encode_float_gpu.ts","../src/encode_float_packed_gpu.ts","../src/encode_matrix_gpu.ts","../src/encode_matrix_packed_gpu.ts","../src/gpgpu_util.ts","../src/gpgpu_context.ts","../src/shader_compiler.ts","../src/gpgpu_math.ts","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Abs.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Add.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Ceil.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Equal.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Exp.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Expm1.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Floor.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd_Impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Greater.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GreaterEqual.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Less.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LessEqual.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Maximum.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Minimum.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multiply.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Neg.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NotEqual.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prod.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Rsqrt.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Slice.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentReduction_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sub.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK_impl.js","../node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique_impl.js","../src/kernel_utils/shared.ts","../src/packing_util.ts","../src/pack_gpu.ts","../src/reshape_packed_gpu.ts","../src/texture_manager.ts","../src/unaryop_gpu.ts","../src/unaryop_packed_gpu.ts","../src/unpack_gpu.ts","../src/backend_webgl.ts","../src/version.ts","../src/webgl.ts","../src/base.ts","../src/binaryop_gpu.ts","../src/binaryop_packed_gpu.ts","../src/kernels/Identity.ts","../src/kernels/Complex.ts","../src/kernels/LeakyRelu.ts","../src/kernels/Prelu.ts","../src/kernel_utils/kernel_funcs_utils.ts","../src/mulmat_packed_gpu.ts","../src/binaryop_complex_gpu.ts","../src/kernels/Multiply.ts","../src/kernel_utils/reshape.ts","../src/kernels/Reshape.ts","../src/mean_gpu.ts","../src/reduce_gpu.ts","../src/kernel_utils/reduce.ts","../src/transpose_gpu.ts","../src/transpose_packed_gpu.ts","../src/kernels/Transpose_impl.ts","../src/kernels/Sum_impl.ts","../src/kernels/Sum.ts","../src/kernels/Transpose.ts","../src/kernels/BatchMatMul_impl.ts","../src/kernels/_FusedMatMul.ts","../src/kernels/Abs.ts","../src/kernels/Acos.ts","../src/kernels/Acosh.ts","../src/kernels/Add.ts","../src/addn_gpu.ts","../src/addn_packed_gpu.ts","../src/kernels/AddN.ts","../src/kernels/All.ts","../src/kernels/Any.ts","../src/argminmax_gpu.ts","../src/argminmax_packed_gpu.ts","../src/kernel_utils/arg_min_max.ts","../src/kernels/ArgMax.ts","../src/kernels/ArgMin.ts","../src/kernels/Asin.ts","../src/kernels/Asinh.ts","../src/kernels/Atan.ts","../src/kernels/Atan2.ts","../src/kernels/Atanh.ts","../src/pool_gpu.ts","../src/kernels/AvgPool.ts","../src/kernels/AvgPool3D.ts","../src/avg_pool_backprop_gpu.ts","../src/kernels/AvgPool3DGrad.ts","../src/kernels/AvgPoolGrad.ts","../src/kernels/BatchMatMul.ts","../src/batchnorm_gpu.ts","../src/batchnorm_packed_gpu.ts","../src/kernels/BatchNorm.ts","../src/slice_gpu.ts","../src/slice_packed_gpu.ts","../src/kernels/Slice.ts","../src/kernels/BatchToSpaceND.ts","../src/kernels/Bincount.ts","../src/kernels/NotEqual.ts","../src/kernels/Real.ts","../src/kernel_utils/int.ts","../src/kernels/Cast.ts","../src/kernels/Ceil.ts","../src/clip_gpu.ts","../src/clip_packed_gpu.ts","../src/kernels/ClipByValue.ts","../src/complex_abs_gpu.ts","../src/kernels/ComplexAbs.ts","../src/concat_gpu.ts","../src/concat_packed_gpu.ts","../src/kernels/Imag.ts","../src/kernels/Concat_impl.ts","../src/kernels/Concat.ts","../src/conv_gpu.ts","../src/im2col_packed_gpu.ts","../src/kernels/Conv2D_impl.ts","../src/kernels/Conv2D.ts","../src/conv_backprop_gpu.ts","../src/kernels/Conv2DBackpropFilter.ts","../src/kernels/Conv2DBackpropInput.ts","../src/kernels/Conv3D.ts","../src/kernels/Conv3DBackpropFilterV2.ts","../src/kernels/Conv3DBackpropInputV2.ts","../src/kernels/Cos.ts","../src/kernels/Cosh.ts","../src/crop_and_resize_gpu.ts","../src/kernels/CropAndResize.ts","../src/cumsum_gpu.ts","../src/kernels/Cumsum.ts","../src/kernels/DenseBincount.ts","../src/depth_to_space_gpu.ts","../src/kernels/DepthToSpace.ts","../src/conv_gpu_depthwise.ts","../src/conv_packed_gpu_depthwise.ts","../src/kernels/DepthwiseConv2dNative.ts","../src/conv_backprop_gpu_depthwise.ts","../src/kernels/DepthwiseConv2dNativeBackpropFilter.ts","../src/kernels/DepthwiseConv2dNativeBackpropInput.ts","../src/diag_gpu.ts","../src/kernels/Diag.ts","../src/dilation_gpu.ts","../src/kernels/Dilation2D.ts","../src/kernels/Einsum.ts","../src/kernels/Elu.ts","../src/kernels/EluGrad.ts","../src/kernels/Equal.ts","../src/kernels/Erf.ts","../src/kernels/Exp.ts","../src/kernels/ExpandDims.ts","../src/kernels/Expm1.ts","../src/fft_gpu.ts","../src/kernels/FFT_impl.ts","../src/kernels/FFT.ts","../src/fill_gpu.ts","../src/kernels/Fill.ts","../src/flip_left_right_gpu.ts","../src/kernels/FlipLeftRight.ts","../src/kernels/Floor.ts","../src/kernels/FloorDiv.ts","../src/kernels/FromPixels_utils/from_pixels_gpu.ts","../src/kernels/FromPixels_utils/from_pixels_packed_gpu.ts","../src/kernels/FromPixels.ts","../src/kernels/FusedConv2D.ts","../src/kernels/FusedDepthwiseConv2D.ts","../src/gather_nd_gpu.ts","../src/kernels/GatherNd.ts","../src/gather_gpu.ts","../src/kernels/GatherV2.ts","../src/kernels/Greater.ts","../src/kernels/GreaterEqual.ts","../src/kernels/IFFT.ts","../src/kernels/IsFinite.ts","../src/kernels/IsInf.ts","../src/kernels/IsNaN.ts","../src/kernels/Less.ts","../src/kernels/LessEqual.ts","../src/kernels/LinSpace.ts","../src/kernels/Log.ts","../src/kernels/Log1p.ts","../src/kernels/LogicalAnd.ts","../src/kernels/LogicalNot.ts","../src/kernels/LogicalOr.ts","../src/lrn_gpu.ts","../src/lrn_packed_gpu.ts","../src/kernels/LRN.ts","../src/lrn_grad_gpu.ts","../src/kernels/LRNGrad.ts","../src/kernels/Max_impl.ts","../src/kernels/Max.ts","../src/kernels/Maximum.ts","../src/kernels/MaxPool.ts","../src/kernels/MaxPool3D.ts","../src/max_pool_backprop_gpu.ts","../src/kernels/MaxPool3DGrad.ts","../src/kernels/MaxPoolGrad.ts","../src/kernels/MaxPoolWithArgmax_impl.ts","../src/kernels/MaxPoolWithArgmax.ts","../src/kernels/Mean_impl.ts","../src/kernels/Mean.ts","../src/kernels/Min.ts","../src/kernels/Minimum.ts","../src/mirror_pad_gpu.ts","../src/mirror_pad_packed_gpu.ts","../src/kernels/MirrorPad.ts","../src/kernels/Mod.ts","../src/multinomial_gpu.ts","../src/kernels/RealDiv.ts","../src/kernels/Sub.ts","../src/kernels/Softmax.ts","../src/kernels/Multinomial.ts","../src/kernels/Neg.ts","../src/kernels/NonMaxSuppressionV3.ts","../src/kernels/NonMaxSuppressionV4.ts","../src/kernels/NonMaxSuppressionV5.ts","../src/onehot_gpu.ts","../src/kernels/OneHot.ts","../src/kernels/ZerosLike.ts","../src/kernels/OnesLike.ts","../src/kernels/Pack.ts","../src/pad_gpu.ts","../src/pad_packed_gpu.ts","../src/kernels/PadV2.ts","../src/kernels/Pow.ts","../src/kernels/Prod.ts","../src/kernels/Range.ts","../src/kernels/Reciprocal.ts","../src/kernels/Relu.ts","../src/kernels/Relu6.ts","../src/resize_bilinear_gpu.ts","../src/resize_bilinear_packed_gpu.ts","../src/kernels/ResizeBilinear.ts","../src/resize_bilinear_backprop_gpu.ts","../src/kernels/ResizeBilinearGrad.ts","../src/resize_nearest_neighbor_gpu.ts","../src/resize_nearest_neighbor_packed_gpu.ts","../src/kernels/ResizeNearestNeighbor.ts","../src/resize_nearest_neighbor_backprop_gpu.ts","../src/kernels/ResizeNearestNeighborGrad.ts","../src/reverse_gpu.ts","../src/reverse_packed_gpu.ts","../src/kernels/Reverse.ts","../src/rotate_gpu.ts","../src/kernels/RotateWithOffset.ts","../src/kernels/Round.ts","../src/kernels/Rsqrt.ts","../src/scatter_gpu.ts","../src/kernels/ScatterNd.ts","../src/select_gpu.ts","../src/kernels/Select.ts","../src/kernels/Selu.ts","../src/kernels/Sigmoid.ts","../src/kernels/Sign.ts","../src/kernels/Sin.ts","../src/kernels/Sinh.ts","../src/kernels/Softplus.ts","../src/kernels/SpaceToBatchND.ts","../src/kernels/SparseFillEmptyRows.ts","../src/kernels/SparseReshape.ts","../src/kernels/SparseSegmentMean.ts","../src/kernels/SparseSegmentSum.ts","../src/kernels/SparseToDense.ts","../src/kernels/SplitV.ts","../src/kernels/Sqrt.ts","../src/kernels/Square.ts","../src/kernels/SquaredDifference.ts","../src/kernels/Step.ts","../src/strided_slice_gpu.ts","../src/kernels/StridedSlice.ts","../src/kernels/StringNGrams.ts","../src/kernels/StringSplit.ts","../src/kernels/StringToHashBucketFast.ts","../src/kernels/Tan.ts","../src/kernels/Tanh.ts","../src/tile_gpu.ts","../src/kernels/Tile.ts","../src/kernels/TopK.ts","../src/transform_gpu.ts","../src/kernels/Transform.ts","../src/kernels/Unique.ts","../src/kernels/Unpack.ts","../src/segment_gpu.ts","../src/kernels/UnsortedSegmentSum.ts","../src/register_all_kernels.ts"],"sourcesContent":["/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nconst contexts: {[key: string]: WebGLRenderingContext} = {};\n\nconst WEBGL_ATTRIBUTES: WebGLContextAttributes = {\n  alpha: false,\n  antialias: false,\n  premultipliedAlpha: false,\n  preserveDrawingBuffer: false,\n  depth: false,\n  stencil: false,\n  failIfMajorPerformanceCaveat: true\n};\n\nexport function clearWebGLContext(webGLVersion: number) {\n  delete contexts[webGLVersion];\n}\n\nexport function setWebGLContext(\n    webGLVersion: number, gl: WebGLRenderingContext) {\n  contexts[webGLVersion] = gl;\n}\n\nexport function getWebGLContext(webGLVersion: number): WebGLRenderingContext {\n  if (!(webGLVersion in contexts)) {\n    const newCtx = getWebGLRenderingContext(webGLVersion);\n    if (newCtx !== null) {\n      contexts[webGLVersion] = newCtx;\n    } else {\n      console.log('Could not get context for WebGL version', webGLVersion);\n      return null;\n    }\n  }\n  const gl = contexts[webGLVersion];\n  if (gl.isContextLost()) {\n    delete contexts[webGLVersion];\n    return getWebGLContext(webGLVersion);\n  }\n\n  gl.disable(gl.DEPTH_TEST);\n  gl.disable(gl.STENCIL_TEST);\n  gl.disable(gl.BLEND);\n  gl.disable(gl.DITHER);\n  gl.disable(gl.POLYGON_OFFSET_FILL);\n  gl.disable(gl.SAMPLE_COVERAGE);\n  gl.enable(gl.SCISSOR_TEST);\n  gl.enable(gl.CULL_FACE);\n  gl.cullFace(gl.BACK);\n\n  return contexts[webGLVersion];\n}\n\nfunction createCanvas(webGLVersion: number) {\n  if (typeof OffscreenCanvas !== 'undefined' && webGLVersion === 2) {\n    return new OffscreenCanvas(300, 150);\n  } else if (typeof document !== 'undefined') {\n    return document.createElement('canvas');\n  } else {\n    throw new Error('Cannot create a canvas in this context');\n  }\n}\n\nfunction getWebGLRenderingContext(webGLVersion: number): WebGLRenderingContext {\n  if (webGLVersion !== 1 && webGLVersion !== 2) {\n    throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n  }\n  const canvas = createCanvas(webGLVersion);\n\n  canvas.addEventListener('webglcontextlost', (ev: Event) => {\n    ev.preventDefault();\n    delete contexts[webGLVersion];\n  }, false);\n  if (webGLVersion === 1) {\n    return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES)) as\n        WebGLRenderingContext;\n  }\n  return canvas.getContext('webgl2', WEBGL_ATTRIBUTES) as WebGLRenderingContext;\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DataId, DataType, env, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nexport enum PackingScheme {\n  /**\n   * All values in a single texel are densely packed without any constraints.\n   *\n   * This is how the shader encodes a tensor with shape = [2, 3, 4]\n   * (indices are [batch, row, col]).\n   *\n   * 000|001   010|011   020|021\n   * -------   -------   -------\n   * 002|003   012|013   022|023\n   *\n   * 100|101   110|111   120|121\n   * -------   -------   -------\n   * 102|103   112|113   122|123\n   *\n   */\n  DENSE,\n\n  /**\n   * Single texels contain only values from the same batch, and from adjacent\n   * rows and columns.\n   *\n   * This is how the shader encodes a tensor with shape = [2, 3, 5]\n   * (indices are [batch, row, col]).\n   *\n   * 000|001   002|003   004|xxx   020|021   022|023   024|xxx\n   * -------   -------   -------   -------   -------   -------\n   * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n   *\n   * 100|101   102|103   104|xxx   120|121   122|123   124|xxx\n   * -------   -------   -------   -------   -------   -------\n   * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n   *\n   */\n  SHARED_BATCH\n}\n\nexport enum TextureUsage {\n  RENDER,\n  UPLOAD,\n  PIXELS,\n  DOWNLOAD\n}\n\nexport enum PhysicalTextureType {\n  UNPACKED_FLOAT16,\n  UNPACKED_FLOAT32,\n  PACKED_4X1_UNSIGNED_BYTE,\n  PACKED_2X2_FLOAT32,\n  PACKED_2X2_FLOAT16\n}\n\nexport interface TextureData {\n  // Required.\n  shape: number[];\n  dtype: DataType;\n\n  // Optional.\n  values?: backend_util.BackendValues;\n  texture?: WebGLTexture;\n  // For complex numbers, the real and imaginary parts are stored as their own\n  // individual tensorInfos, with a parent joining the two with the\n  // complexTensors field. When this is defined, texture will be null.\n  complexTensorInfos?: {real: TensorInfo, imag: TensorInfo};\n  /** [rows, columns] shape of the texture. */\n  texShape?: [number, number];\n  usage?: TextureUsage;\n  isPacked?: boolean;\n\n  refCount: number;\n\n  // Available when the tensor has been sliced.\n  slice?: {\n    // Offset in the 'flat index' space.\n    flatOffset: number;\n    // Used for counting how many sliced tensors point to the same texture.\n    origDataId: DataId;\n  };\n}\n\nexport function getUnpackedMatrixTextureShapeWidthHeight(\n    rows: number, columns: number): [number, number] {\n  return [columns, rows];\n}\n\nexport function getUnpackedArraySizeFromMatrixSize(\n    matrixSize: number, channelsPerTexture: number): number {\n  return matrixSize * channelsPerTexture;\n}\n\nexport function getColorMatrixTextureShapeWidthHeight(\n    rows: number, columns: number): [number, number] {\n  return [columns * 4, rows];\n}\n\n/**\n * Get shape for densely packed RGBA texture.\n */\nexport function getDenseTexShape(shape: number[]): [number, number] {\n  const size = util.sizeFromShape(shape);\n  const texelsNeeded = Math.ceil(size / 4);\n  return util.sizeToSquarishShape(texelsNeeded);\n}\n\nexport function getMatrixSizeFromUnpackedArraySize(\n    unpackedSize: number, channelsPerTexture: number): number {\n  if (unpackedSize % channelsPerTexture !== 0) {\n    throw new Error(\n        `unpackedSize (${unpackedSize}) must be a multiple of ` +\n        `${channelsPerTexture}`);\n  }\n  return unpackedSize / channelsPerTexture;\n}\n\nexport function decodeMatrixFromUnpackedColorRGBAArray(\n    unpackedArray: Float32Array, matrix: Float32Array, channels: number) {\n  const requiredSize = unpackedArray.length * channels / 4;\n  if (matrix.length < requiredSize) {\n    throw new Error(\n        `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n  }\n  let dst = 0;\n  for (let src = 0; src < unpackedArray.length; src += 4) {\n    for (let c = 0; c < channels; c++) {\n      matrix[dst++] = unpackedArray[src + c];\n    }\n  }\n}\n\nexport function getPackedMatrixTextureShapeWidthHeight(\n    rows: number, columns: number): [number, number] {\n  return [\n    Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n  ];\n}\n\nexport function getPackedRGBAArraySizeFromMatrixShape(\n    rows: number, columns: number): number {\n  const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return w * h * 4;\n}\n\nexport interface TextureConfig {\n  internalFormatFloat: number;\n  textureFormatFloat: number;\n  internalFormatPackedHalfFloat: number;\n  internalFormatHalfFloat: number;\n  internalFormatPackedFloat: number;\n\n  // The format to use during a gl.readPixels call.\n  downloadTextureFormat: number;\n  // How many channels need to be unpacked after a gl.readPixels call.\n  downloadUnpackNumChannels: number;\n\n  defaultNumChannels: number;\n  textureTypeHalfFloat: number;\n  textureTypeFloat: number;\n}\n\nexport function getTextureConfig(\n    // tslint:disable-next-line:no-any\n    gl: WebGLRenderingContext, textureHalfFloatExtension?: any): TextureConfig {\n  // tslint:disable-next-line:no-any\n  const glany = gl as any;\n\n  let internalFormatFloat: number;\n  let internalFormatHalfFloat: number;\n  let internalFormatPackedHalfFloat: number;\n  let internalFormatPackedFloat: number;\n  let textureFormatFloat: number;\n\n  let downloadTextureFormat: number;\n  let downloadUnpackNumChannels: number;\n\n  let defaultNumChannels: number;\n  let textureTypeHalfFloat: number;\n  let textureTypeFloat: number;\n\n  if (env().getNumber('WEBGL_VERSION') === 2) {\n    internalFormatFloat = glany.R32F;\n    internalFormatHalfFloat = glany.R16F;\n    internalFormatPackedHalfFloat = glany.RGBA16F;\n    internalFormatPackedFloat = glany.RGBA32F;\n    textureFormatFloat = glany.RED;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 1;\n    textureTypeHalfFloat = glany.HALF_FLOAT;\n    textureTypeFloat = glany.FLOAT;\n  } else {\n    internalFormatFloat = gl.RGBA;\n    internalFormatHalfFloat = gl.RGBA;\n    internalFormatPackedHalfFloat = gl.RGBA;\n    internalFormatPackedFloat = glany.RGBA;\n    textureFormatFloat = gl.RGBA;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 4;\n    textureTypeHalfFloat = textureHalfFloatExtension != null ?\n        textureHalfFloatExtension.HALF_FLOAT_OES :\n        null;\n    textureTypeFloat = gl.FLOAT;\n  }\n  downloadTextureFormat = gl.RGBA;\n\n  return {\n    internalFormatFloat,\n    internalFormatHalfFloat,\n    internalFormatPackedHalfFloat,\n    internalFormatPackedFloat,\n    textureFormatFloat,\n    downloadTextureFormat,\n    downloadUnpackNumChannels,\n    defaultNumChannels,\n    textureTypeHalfFloat,\n    textureTypeFloat\n  };\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {getWebGLContext} from './canvas_util';\nimport {getTextureConfig} from './tex_util';\n\nexport function callAndCheck<T>(gl: WebGLRenderingContext, func: () => T): T {\n  const returnValue = func();\n  if (env().getBool('DEBUG')) {\n    checkWebGLError(gl);\n  }\n  return returnValue;\n}\n\nfunction checkWebGLError(gl: WebGLRenderingContext) {\n  const error = gl.getError();\n  if (error !== gl.NO_ERROR) {\n    throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n  }\n}\n\n// https://en.wikipedia.org/wiki/Half-precision_floating-point_format\nconst MIN_FLOAT16 = 5.96e-8;\nconst MAX_FLOAT16 = 65504;\n\nexport function canBeRepresented(num: number): boolean {\n  if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||\n      (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {\n    return true;\n  }\n  return false;\n}\n\nexport function getWebGLErrorMessage(\n    gl: WebGLRenderingContext, status: number): string {\n  switch (status) {\n    case gl.NO_ERROR:\n      return 'NO_ERROR';\n    case gl.INVALID_ENUM:\n      return 'INVALID_ENUM';\n    case gl.INVALID_VALUE:\n      return 'INVALID_VALUE';\n    case gl.INVALID_OPERATION:\n      return 'INVALID_OPERATION';\n    case gl.INVALID_FRAMEBUFFER_OPERATION:\n      return 'INVALID_FRAMEBUFFER_OPERATION';\n    case gl.OUT_OF_MEMORY:\n      return 'OUT_OF_MEMORY';\n    case gl.CONTEXT_LOST_WEBGL:\n      return 'CONTEXT_LOST_WEBGL';\n    default:\n      return `Unknown error code ${status}`;\n  }\n}\n\nexport function getExtensionOrThrow(\n    gl: WebGLRenderingContext, extensionName: string): {} {\n  return throwIfNull<{}>(\n      gl, () => gl.getExtension(extensionName),\n      'Extension \"' + extensionName + '\" not supported on this browser.');\n}\n\nexport function createVertexShader(\n    gl: WebGLRenderingContext, vertexShaderSource: string): WebGLShader {\n  const vertexShader: WebGLShader = throwIfNull<WebGLShader>(\n      gl, () => gl.createShader(gl.VERTEX_SHADER),\n      'Unable to create vertex WebGLShader.');\n  callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n  callAndCheck(gl, () => gl.compileShader(vertexShader));\n  if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n    console.log(gl.getShaderInfoLog(vertexShader));\n    throw new Error('Failed to compile vertex shader.');\n  }\n  return vertexShader;\n}\n\nexport function createFragmentShader(\n    gl: WebGLRenderingContext, fragmentShaderSource: string): WebGLShader {\n  const fragmentShader: WebGLShader = throwIfNull<WebGLShader>(\n      gl, () => gl.createShader(gl.FRAGMENT_SHADER),\n      'Unable to create fragment WebGLShader.');\n  callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n  callAndCheck(gl, () => gl.compileShader(fragmentShader));\n  if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n    logShaderSourceAndInfoLog(\n        fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n    throw new Error('Failed to compile fragment shader.');\n  }\n  return fragmentShader;\n}\n\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(\n    shaderSource: string, shaderInfoLog: string) {\n  const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n  if (lineNumberRegexResult == null) {\n    console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n    console.log(shaderSource);\n    return;\n  }\n\n  const lineNumber = +lineNumberRegexResult[1];\n\n  const shaderLines = shaderSource.split('\\n');\n  const pad = shaderLines.length.toString().length + 2;\n  const linesWithLineNumbers = shaderLines.map(\n      (line, lineNumber) =>\n          util.rightPad((lineNumber + 1).toString(), pad) + line);\n  let maxLineLength = 0;\n  for (let i = 0; i < linesWithLineNumbers.length; i++) {\n    maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n  }\n\n  const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n  const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n  const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n\n  console.log(beforeErrorLines.join('\\n'));\n  console.log(shaderInfoLog.split('\\n')[0]);\n  console.log(\n      `%c ${util.rightPad(errorLine[0], maxLineLength)}`,\n      'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n  console.log(afterErrorLines.join('\\n'));\n}\n\nexport function createProgram(gl: WebGLRenderingContext): WebGLProgram {\n  return throwIfNull<WebGLProgram>(\n      gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\n\nexport function linkProgram(gl: WebGLRenderingContext, program: WebGLProgram) {\n  callAndCheck(gl, () => gl.linkProgram(program));\n  if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error('Failed to link vertex and fragment shaders.');\n  }\n}\n\nexport function validateProgram(\n    gl: WebGLRenderingContext, program: WebGLProgram) {\n  callAndCheck(gl, () => gl.validateProgram(program));\n  if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error('Shader program validation failed.');\n  }\n}\n\nexport function createStaticVertexBuffer(\n    gl: WebGLRenderingContext, data: Float32Array): WebGLBuffer {\n  const buffer: WebGLBuffer = throwIfNull<WebGLBuffer>(\n      gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n  callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer;\n}\n\nexport function createStaticIndexBuffer(\n    gl: WebGLRenderingContext, data: Uint16Array): WebGLBuffer {\n  const buffer: WebGLBuffer = throwIfNull<WebGLBuffer>(\n      gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n  callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n  callAndCheck(\n      gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer;\n}\n\nexport function getNumChannels(): number {\n  if (env().getNumber('WEBGL_VERSION') === 2) {\n    return 1;\n  }\n  return 4;\n}\n\nexport function createTexture(gl: WebGLRenderingContext): WebGLTexture {\n  return throwIfNull<WebGLTexture>(\n      gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\n\nexport function validateTextureSize(width: number, height: number) {\n  const maxTextureSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n  if ((width <= 0) || (height <= 0)) {\n    const requested = `[${width}x${height}]`;\n    throw new Error('Requested texture size ' + requested + ' is invalid.');\n  }\n  if ((width > maxTextureSize) || (height > maxTextureSize)) {\n    const requested = `[${width}x${height}]`;\n    const max = `[${maxTextureSize}x${maxTextureSize}]`;\n    throw new Error(\n        'Requested texture size ' + requested +\n        ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n  }\n}\n\nexport function createFramebuffer(gl: WebGLRenderingContext): WebGLFramebuffer {\n  return throwIfNull<WebGLFramebuffer>(\n      gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\n\nexport function bindVertexBufferToProgramAttribute(\n    gl: WebGLRenderingContext, program: WebGLProgram, attribute: string,\n    buffer: WebGLBuffer, arrayEntriesPerItem: number, itemStrideInBytes: number,\n    itemOffsetInBytes: number): boolean {\n  const loc = gl.getAttribLocation(program, attribute);\n  if (loc === -1) {\n    // The GPU compiler decided to strip out this attribute because it's unused,\n    // thus no need to bind.\n    return false;\n  }\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n  callAndCheck(\n      gl,\n      () => gl.vertexAttribPointer(\n          loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes,\n          itemOffsetInBytes));\n  callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n  return true;\n}\n\nexport function bindTextureUnit(\n    gl: WebGLRenderingContext, texture: WebGLTexture, textureUnit: number) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\n\nexport function unbindTextureUnit(\n    gl: WebGLRenderingContext, textureUnit: number) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nexport function getProgramUniformLocationOrThrow(\n    gl: WebGLRenderingContext, program: WebGLProgram,\n    uniformName: string): WebGLUniformLocation {\n  return throwIfNull<WebGLUniformLocation>(\n      gl, () => gl.getUniformLocation(program, uniformName),\n      'uniform \"' + uniformName + '\" not present in program.');\n}\n\nexport function getProgramUniformLocation(\n    gl: WebGLRenderingContext, program: WebGLProgram,\n    uniformName: string): WebGLUniformLocation {\n  return gl.getUniformLocation(program, uniformName);\n}\n\nexport function bindTextureToProgramUniformSampler(\n    gl: WebGLRenderingContext, texture: WebGLTexture,\n    uniformSamplerLocation: WebGLUniformLocation, textureUnit: number) {\n  callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n  callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\n\nexport function bindCanvasToFramebuffer(gl: WebGLRenderingContext) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n  callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n  callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\n\nexport function bindColorTextureToFramebuffer(\n    gl: WebGLRenderingContext, texture: WebGLTexture,\n    framebuffer: WebGLFramebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(\n      gl,\n      () => gl.framebufferTexture2D(\n          gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\n\nexport function unbindColorTextureFromFramebuffer(\n    gl: WebGLRenderingContext, framebuffer: WebGLFramebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(\n      gl,\n      () => gl.framebufferTexture2D(\n          gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\n\nexport function validateFramebuffer(gl: WebGLRenderingContext) {\n  const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n  if (status !== gl.FRAMEBUFFER_COMPLETE) {\n    throw new Error(\n        'Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n  }\n}\n\nexport function getFramebufferErrorMessage(\n    gl: WebGLRenderingContext, status: number): string {\n  switch (status) {\n    case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n      return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n    case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n      return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n    case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n      return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n    case gl.FRAMEBUFFER_UNSUPPORTED:\n      return 'FRAMEBUFFER_UNSUPPORTED';\n    default:\n      return `unknown error ${status}`;\n  }\n}\n\nfunction throwIfNull<T>(\n    gl: WebGLRenderingContext, returnTOrNull: () => T | null,\n    failureMessage: string): T {\n  const tOrNull: T|null = callAndCheck(gl, () => returnTOrNull());\n  if (tOrNull == null) {\n    throw new Error(failureMessage);\n  }\n  return tOrNull;\n}\n\nfunction validateTextureUnit(gl: WebGLRenderingContext, textureUnit: number) {\n  const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n  const glTextureUnit = textureUnit + gl.TEXTURE0;\n  if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n    const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n    throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n  }\n}\n\nexport function getBatchDim(shape: number[], dimsToSkip = 2): number {\n  return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\n\nexport function getRowsCols(shape: number[]): [number, number] {\n  if (shape.length === 0) {\n    throw Error('Cannot get rows and columns of an empty shape array.');\n  }\n\n  return [\n    shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n  ];\n}\n\nexport function getShapeAs3D(shape: number[]): [number, number, number] {\n  let shapeAs3D: [number, number, number] = [1, 1, 1];\n  const isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);\n  if (!isScalar) {\n    shapeAs3D =\n        [getBatchDim(shape), ...getRowsCols(shape)] as [number, number, number];\n  }\n  return shapeAs3D;\n}\n\nexport function getTextureShapeFromLogicalShape(\n    logShape: number[], isPacked = false): [number, number] {\n  let maxTexSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n  if (isPacked) {\n    maxTexSize = maxTexSize * 2;\n\n    // This logic ensures we accurately count the number of packed texels needed\n    // to accommodate the tensor. We can only pack values in the same texel if\n    // they are from adjacent pairs of rows/cols within the same batch. So if a\n    // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n    // fact that the texels containing the third row are half empty.\n    logShape = logShape.map(\n        (d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n\n    // Packed texture height is at least 2 (the channel height of a single\n    // texel).\n    if (logShape.length === 1) {\n      logShape = [2, logShape[0]];\n    }\n  }\n\n  // If logical shape is 2, we don't squeeze, since we want to match physical.\n  if (logShape.length !== 2) {\n    const squeezeResult = util.squeezeShape(logShape);\n    logShape = squeezeResult.newShape;\n  }\n\n  let size = util.sizeFromShape(logShape);\n  if (logShape.length <= 1 && size <= maxTexSize) {\n    return [1, size];\n  } else if (\n      logShape.length === 2 && logShape[0] <= maxTexSize &&\n      logShape[1] <= maxTexSize) {\n    return logShape as [number, number];\n  } else if (\n      logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n      logShape[2] <= maxTexSize) {\n    return [logShape[0] * logShape[1], logShape[2]];\n  } else if (\n      logShape.length === 3 && logShape[0] <= maxTexSize &&\n      logShape[1] * logShape[2] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2]];\n  } else if (\n      logShape.length === 4 &&\n      logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n      logShape[3] <= maxTexSize) {\n    return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n  } else if (\n      logShape.length === 4 && logShape[0] <= maxTexSize &&\n      logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n  } else {\n    if (isPacked) {\n      // For packed textures size equals the number of channels required to\n      // accommodate the texture data. However in order to squarify such that\n      // inner dimensions stay even, we rewrite size to equal the number of\n      // texels. Then in the return statement we rehydrate the squarified\n      // dimensions to channel units.\n\n      const batchDim = getBatchDim(logShape);\n      let rows = 2, cols = 2;\n      if (logShape.length) {\n        [rows, cols] = getRowsCols(logShape);\n      }\n      size = batchDim * (rows / 2) * (cols / 2);\n      return util.sizeToSquarishShape(size).map(d => d * 2) as [number, number];\n    }\n    return util.sizeToSquarishShape(size);\n  }\n}\n\nfunction isEven(n: number): boolean {\n  return n % 2 === 0;\n}\n\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1: number[], shape2: number[]): boolean {\n  shape1 = shape1.slice(-2);\n  shape2 = shape2.slice(-2);\n\n  if (util.arraysEqual(shape1, shape2)) {\n    return true;\n  }\n\n  if (!shape1.length || !shape2.length) {  // One of the shapes is a scalar.\n    return true;\n  }\n\n  if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n      shape2[1] === 0) {\n    return true;\n  }\n\n  if (shape1.length !== shape2.length) {  // One of the shapes is a vector.\n    const shape1Cols = shape1.slice(-1)[0];\n    const shape2Cols = shape2.slice(-1)[0];\n    if (shape1Cols === shape2Cols) {\n      return true;\n    }\n\n    if (isEven(shape1Cols) && isEven(shape2Cols) &&\n        (shape1[0] === 1 || shape2[0] === 1)) {\n      return true;\n    }\n  }\n  return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\n\n// We cache webgl params because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nlet MAX_TEXTURE_SIZE: number;\nlet MAX_TEXTURES_IN_SHADER: number;\n\nexport function getWebGLMaxTextureSize(webGLVersion: number): number {\n  if (MAX_TEXTURE_SIZE == null) {\n    const gl = getWebGLContext(webGLVersion);\n    MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n  }\n  return MAX_TEXTURE_SIZE;\n}\n\nexport function resetMaxTextureSize() {\n  MAX_TEXTURE_SIZE = null;\n}\nexport function resetMaxTexturesInShader() {\n  MAX_TEXTURES_IN_SHADER = null;\n}\n\nexport function getMaxTexturesInShader(webGLVersion: number): number {\n  if (MAX_TEXTURES_IN_SHADER == null) {\n    const gl = getWebGLContext(webGLVersion);\n    MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n  }\n  // We cap at 16 to avoid spurious runtime \"memory exhausted\" error.\n  return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\n\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion: number):\n    number {\n  if (webGLVersion === 0) {\n    return 0;\n  }\n\n  let queryTimerVersion: number;\n  const gl = getWebGLContext(webGLVersion);\n\n  if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n      webGLVersion === 2) {\n    queryTimerVersion = 2;\n  } else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n    queryTimerVersion = 1;\n  } else {\n    queryTimerVersion = 0;\n  }\n  return queryTimerVersion;\n}\n\nexport function hasExtension(gl: WebGLRenderingContext, extensionName: string) {\n  const ext = gl.getExtension(extensionName);\n  return ext != null;\n}\n\nexport function isWebGLVersionEnabled(webGLVersion: 1|2) {\n  try {\n    const gl = getWebGLContext(webGLVersion);\n    if (gl != null) {\n      return true;\n    }\n  } catch (e) {\n    console.log('Error when getting WebGL context: ', e);\n    return false;\n  }\n  return false;\n}\n\nexport function isCapableOfRenderingToFloatTexture(webGLVersion: number):\n    boolean {\n  if (webGLVersion === 0) {\n    return false;\n  }\n\n  const gl = getWebGLContext(webGLVersion);\n\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, 'OES_texture_float')) {\n      return false;\n    }\n  } else {\n    if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n      return false;\n    }\n  }\n\n  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n  return isFrameBufferComplete;\n}\n\n/**\n * Check if we can download values from a float/half-float texture.\n *\n * Note that for performance reasons we use binding a texture to a framebuffer\n * as a proxy for ability to download float values later using readPixels. The\n * texture params of this texture will not match those in readPixels exactly\n * but if we are unable to bind some kind of float texture to the frameBuffer\n * then we definitely will not be able to read float values from it.\n */\nexport function isDownloadFloatTextureEnabled(webGLVersion: number): boolean {\n  if (webGLVersion === 0) {\n    return false;\n  }\n\n  const gl = getWebGLContext(webGLVersion);\n\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, 'OES_texture_float')) {\n      return false;\n    }\n    if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n      return false;\n    }\n  } else {\n    if (hasExtension(gl, 'EXT_color_buffer_float')) {\n      return createFloatTextureAndBindToFramebuffer(gl);\n    }\n\n    const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n    if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n      const textureHalfFloatExtension =\n          gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n      return createHalfFloatTextureAndBindToFramebuffer(\n          gl, textureHalfFloatExtension);\n    }\n\n    return false;\n  }\n\n  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n  return isFrameBufferComplete;\n}\n\nfunction createFloatTextureAndBindToFramebuffer(gl: WebGLRenderingContext):\n    boolean {\n  const texConfig = getTextureConfig(gl);\n\n  const texture = gl.createTexture();\n  gl.bindTexture(gl.TEXTURE_2D, texture);\n\n  const width = 1;\n  const height = 1;\n  gl.texImage2D(\n      gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0,\n      texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n\n  const frameBuffer = gl.createFramebuffer();\n  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n  gl.framebufferTexture2D(\n      gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n  const isFrameBufferComplete =\n      gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n\n  gl.bindTexture(gl.TEXTURE_2D, null);\n  gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n  gl.deleteTexture(texture);\n  gl.deleteFramebuffer(frameBuffer);\n\n  return isFrameBufferComplete;\n}\n\nfunction createHalfFloatTextureAndBindToFramebuffer(\n    // tslint:disable-next-line:no-any\n    gl: WebGLRenderingContext, textureHalfFloatExtension: any): boolean {\n  const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n  const texture = gl.createTexture();\n  gl.bindTexture(gl.TEXTURE_2D, texture);\n\n  const width = 1;\n  const height = 1;\n  gl.texImage2D(\n      gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0,\n      texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n\n  const frameBuffer = gl.createFramebuffer();\n  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n  gl.framebufferTexture2D(\n      gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n  const isFrameBufferComplete =\n      gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n\n  gl.bindTexture(gl.TEXTURE_2D, null);\n  gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n  gl.deleteTexture(texture);\n  gl.deleteFramebuffer(frameBuffer);\n\n  return isFrameBufferComplete;\n}\n\nexport function isWebGLFenceEnabled(webGLVersion: number) {\n  if (webGLVersion !== 2) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n\n  // tslint:disable-next-line:no-any\n  const isEnabled = (gl as any).fenceSync != null;\n  return isEnabled;\n}\n\nexport function assertNotComplex(\n    tensor: TensorInfo|TensorInfo[], opName: string): void {\n  if (!Array.isArray(tensor)) {\n    tensor = [tensor];\n  }\n  tensor.forEach(t => {\n    if (t != null) {\n      util.assert(\n          t.dtype !== 'complex64',\n          () => `${opName} does not support complex64 tensors ` +\n              'in the WebGL backend.');\n    }\n  });\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {device_util, env} from '@tensorflow/tfjs-core';\n\nimport {getMaxTexturesInShader, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isCapableOfRenderingToFloatTexture, isDownloadFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled} from './webgl_util';\n\nconst ENV = env();\n\n/**\n * This file contains WebGL-specific flag registrations.\n */\n\n/**\n * True if WebGL is supported.\n */\nENV.registerFlag('HAS_WEBGL', () => ENV.getNumber('WEBGL_VERSION') > 0);\n\n/** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */\nENV.registerFlag('WEBGL_VERSION', () => {\n  if (isWebGLVersionEnabled(2)) {\n    return 2;\n  } else if (isWebGLVersionEnabled(1)) {\n    return 1;\n  }\n  return 0;\n});\n\n/** Whether to check for numerical representation problems. */\nENV.registerFlag('WEBGL_CHECK_NUMERICAL_PROBLEMS', () => false);\n\nENV.registerFlag(\n    'WEBGL_BUFFER_SUPPORTED', () => ENV.get('WEBGL_VERSION') === 2);\n\n/** Whether the WebGL backend will sometimes forward ops to the CPU. */\nENV.registerFlag('WEBGL_CPU_FORWARD', () => true);\n\n/** Whether the WebGL backend will always use f16 textures for rendering. */\nENV.registerFlag('WEBGL_FORCE_F16_TEXTURES', () => false);\n\n/** Whether to turn all packing related flags on. */\nENV.registerFlag('WEBGL_PACK', () => ENV.getBool('HAS_WEBGL'));\n\n/** Whether we will pack the batchnormalization op. */\nENV.registerFlag('WEBGL_PACK_NORMALIZATION', () => ENV.getBool('WEBGL_PACK'));\n\n/** Whether we will pack the clip op. */\nENV.registerFlag('WEBGL_PACK_CLIP', () => ENV.getBool('WEBGL_PACK'));\n\n/** Whether we will pack the depthwise conv op. */\nENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => ENV.getBool('WEBGL_PACK'));\n\n/** Whether we will pack binary ops. */\nENV.registerFlag(\n    'WEBGL_PACK_BINARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n\n/** Whether we will pack unary ops. */\nENV.registerFlag(\n    'WEBGL_PACK_UNARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n\n/** Whether we will pack array ops. */\nENV.registerFlag(\n    'WEBGL_PACK_ARRAY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n\n/** Whether we will pack image ops. */\nENV.registerFlag(\n    'WEBGL_PACK_IMAGE_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n\n/** Whether we will pack reduce ops. */\nENV.registerFlag('WEBGL_PACK_REDUCE', () => ENV.getBool('WEBGL_PACK'));\n\n/** Whether packed WebGL kernels lazily unpack their outputs. */\nENV.registerFlag('WEBGL_LAZILY_UNPACK', () => ENV.getBool('WEBGL_PACK'));\n\n/** Whether we will use the im2col algorithm to speed up convolutions. */\nENV.registerFlag('WEBGL_CONV_IM2COL', () => ENV.getBool('WEBGL_PACK'));\n\n/** The maximum texture dimension. */\nENV.registerFlag(\n    'WEBGL_MAX_TEXTURE_SIZE',\n    () => getWebGLMaxTextureSize(ENV.getNumber('WEBGL_VERSION')));\n\n/** The maximum texture dimension. */\nENV.registerFlag(\n    'WEBGL_MAX_TEXTURES_IN_SHADER',\n    () => getMaxTexturesInShader(ENV.getNumber('WEBGL_VERSION')));\n\n/**\n * The disjoint_query_timer extension version.\n * 0: disabled, 1: EXT_disjoint_timer_query, 2:\n * EXT_disjoint_timer_query_webgl2.\n * In Firefox with WebGL 2.0,\n * EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n * WebGL 1.0 extension.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', () => {\n  const webGLVersion = ENV.getNumber('WEBGL_VERSION');\n\n  if (webGLVersion === 0) {\n    return 0;\n  }\n  return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\n\n/**\n * Whether the timer object from the disjoint_query_timer extension gives\n * timing information that is reliable.\n */\nENV.registerFlag(\n    'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE',\n    () => ENV.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n        !device_util.isMobile());\n\n/**\n * Whether the device is physically capable of rendering to float32 textures.\n */\nENV.registerFlag(\n    'WEBGL_RENDER_FLOAT32_CAPABLE',\n    () => isCapableOfRenderingToFloatTexture(ENV.getNumber('WEBGL_VERSION')));\n\n/**\n * Whether rendering to float32 textures is enabled. If disabled, renders to\n * float16 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', () => {\n  return ENV.getBool('WEBGL_FORCE_F16_TEXTURES') ?\n      false :\n      ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');\n});\n\n/**\n * Whether downloading float textures is enabled (16 or 32 bit). If disabled,\n * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.\n */\nENV.registerFlag(\n    'WEBGL_DOWNLOAD_FLOAT_ENABLED',\n    () => isDownloadFloatTextureEnabled(ENV.getNumber('WEBGL_VERSION')));\n\n/** Whether the fence API is available. */\nENV.registerFlag(\n    'WEBGL_FENCE_API_ENABLED',\n    () => isWebGLFenceEnabled(ENV.getNumber('WEBGL_VERSION')));\n\n/**\n * Tensors with size <= than this will be uploaded as uniforms, not textures.\n */\nENV.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', () => {\n  // Use uniform uploads only when 32bit floats are supported. In\n  // 16bit\n  // environments there are problems with comparing a 16bit texture value\n  // with a 32bit uniform value.\n  const useUniforms = ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED');\n  return useUniforms ? 4 : 0;\n});\n\n/**\n * If the total number of bytes allocated on the GPU is greater than this\n * number, we will aggressively delete textures upon disposal with\n * gl.deleteMatrixTexture, rather than making them available for reuse.\n *\n * Default value -1 indicates that we will never aggressively delete textures.\n */\nENV.registerFlag(\n    'WEBGL_DELETE_TEXTURE_THRESHOLD',\n    () => {\n      return -1;\n    },\n    threshold => {\n      if (threshold < 0 && threshold !== -1) {\n        throw new Error(\n            `WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never ` +\n            `delete) or at least 0, but got ${threshold}.`);\n      }\n    });\n\n/**\n * Trigger a manual GL command flush if the threshold of time has passed since\n * previous Kernel execution. This can be useful for Andorid device where GL\n * command flush are delayed un til the end of javascript task. This value is\n * measured in millisecond. Typically you want to set this value to close to 1.\n *\n * Default value 1 for mobile chrome, and -1 for rest cases. -1 indicates that\n * we will not enforce manual flush and depend on system default flush schedule.\n */\nENV.registerFlag(\n    'WEBGL_FLUSH_THRESHOLD',\n    () => {\n      return device_util.isMobile() && ENV.getBool('IS_CHROME') ? 1 : -1;\n    },\n    threshold => {\n      if (threshold < 0 && threshold !== -1) {\n        throw new Error(\n            `WEBGL_FLUSH_THRESHOLD must be -1 (indicating never ` +\n            `manual flush) or at least 0, but got ${threshold}.`);\n      }\n    });\n\n/**\n * Threshold for input tensor size that determines whether WebGL backend will\n * delegate computation to CPU.\n *\n * Default value is 128.\n */\nENV.registerFlag('CPU_HANDOFF_SIZE_THRESHOLD', () => 128);\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {env} from '@tensorflow/tfjs-core';\n\nexport type GLSL = {\n  version: string,\n  attribute: string,\n  varyingVs: string,\n  varyingFs: string,\n  texture2D: string,\n  output: string,\n  defineOutput: string,\n  defineSpecialNaN: string,\n  defineSpecialInf: string,\n  defineRound: string\n};\n\nexport function getGlslDifferences(): GLSL {\n  let version: string;\n  let attribute: string;\n  let varyingVs: string;\n  let varyingFs: string;\n  let texture2D: string;\n  let output: string;\n  let defineOutput: string;\n  let defineSpecialNaN: string;\n  let defineSpecialInf: string;\n  let defineRound: string;\n\n  if (env().getNumber('WEBGL_VERSION') === 2) {\n    version = '#version 300 es';\n    attribute = 'in';\n    varyingVs = 'out';\n    varyingFs = 'in';\n    texture2D = 'texture';\n    output = 'outputColor';\n    defineOutput = 'out vec4 outputColor;';\n\n    // Use custom isnan definition to work across differences between\n    // implementations on various platforms. While this should happen in ANGLE\n    // we still see differences between android and windows (on chrome) when\n    // using isnan directly.\n    defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n    // In webgl 2 we do not need to specify a custom isinf so there is no\n    // need for a special INFINITY constant.\n    defineSpecialInf = ``;\n    defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n  } else {\n    version = '';\n    attribute = 'attribute';\n    varyingVs = 'varying';\n    varyingFs = 'varying';\n    texture2D = 'texture2D';\n    output = 'gl_FragColor';\n    defineOutput = '';\n    // WebGL1 has no built in isnan so we define one here.\n    defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n    defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n    defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n  }\n\n  return {\n    version,\n    attribute,\n    varyingVs,\n    varyingFs,\n    texture2D,\n    output,\n    defineOutput,\n    defineSpecialNaN,\n    defineSpecialInf,\n    defineRound\n  };\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {util} from '@tensorflow/tfjs-core';\n\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(\n    coords: string[], shape: number[], index = 'index'): string {\n  const strides = util.computeStrides(shape);\n  return strides\n      .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n      })\n      .join('');\n}\n\nfunction buildVec(x: string[]): string {\n  if (x.length === 1) {\n    return `${x[0]}`;\n  }\n  return `vec${x.length}(${x.join(',')})`;\n}\n\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x: string[], y: string[]): string {\n  if (x.length !== y.length) {\n    throw new Error(\n        `Vectors to be dotted must be of the same length -` +\n        `got ${x.length} and ${y.length}`);\n  }\n\n  const slices: string[] = [];\n  const nearestVec4 = Math.floor(x.length / 4);\n  const nearestVec4Remainder = x.length % 4;\n\n  for (let i = 0; i < nearestVec4; i++) {\n    const xSlice = x.slice(i * 4, i * 4 + 4);\n    const ySlice = y.slice(i * 4, i * 4 + 4);\n    slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n  }\n\n  if (nearestVec4Remainder !== 0) {\n    let xSlice = x.slice(nearestVec4 * 4);\n    let ySlice = y.slice(nearestVec4 * 4);\n    if (xSlice.length === 1) {\n      xSlice = xSlice.map(d => `float(${d})`);\n      ySlice = ySlice.map(d => `float(${d})`);\n    }\n    slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n  }\n\n  return slices.map((d, i) => `dot(${d})`).join('+');\n}\n\n/**\n * Produces GLSL that computes the flat index from 3D coordinates.\n */\nexport function getFlatIndexFrom3D(shape: [number, number, number]): string {\n  const strides = util.computeStrides(shape).map(d => d.toString());\n\n  return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\n\nexport const ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\nimport * as shader_util from './shader_compiler_util';\nimport {getDenseTexShape, PackingScheme} from './tex_util';\n\nexport class DecodeMatrixProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: [number, number, number];\n  packedInputs = false;\n  packedOutput = true;\n  outPackingScheme = PackingScheme.DENSE;\n\n  constructor(outputShape: [number, number, number]) {\n    const texShape = getDenseTexShape(outputShape);\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n\n    this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${\n        shader_util.getLogicalCoordinatesFromFlatIndex(\n            ['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\nimport * as shader_util from './shader_compiler_util';\nimport {getDenseTexShape, PackingScheme} from './tex_util';\n\nexport class DecodeMatrixPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  packedInputs = true;\n  packedOutput = true;\n  outputShape: [number, number, number];\n  outPackingScheme = PackingScheme.DENSE;\n\n  constructor(outputShape: [number, number, number]) {\n    const texShape = getDenseTexShape(outputShape);\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n\n    this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${\n        shader_util.getLogicalCoordinatesFromFlatIndex(\n            ['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {ENCODE_FLOAT_SNIPPET} from './shader_compiler_util';\nimport {TextureUsage} from './tex_util';\n\nexport class EncodeFloatProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n  outTexUsage = TextureUsage.DOWNLOAD;\n\n  constructor(outputShape: number[]) {\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {ENCODE_FLOAT_SNIPPET} from './shader_compiler_util';\nimport {TextureUsage} from './tex_util';\n\nexport class EncodeFloatPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n  packedInputs = true;\n  packedOutput = false;\n  outTexUsage = TextureUsage.DOWNLOAD;\n\n  constructor(outputShape: [number, number, number]) {\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\nimport * as shader_util from './shader_compiler_util';\n\nexport class EncodeMatrixProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(\n      outputShape: [number, number, number], texShape: [number, number],\n      inputIsUnsignedByte = false) {\n    const glsl = getGlslDifferences();\n    const [height, width] = texShape;\n    this.outputShape = outputShape;\n\n    let output = `result`;\n    if (inputIsUnsignedByte) {\n      output = `floor(result * 255. + 0.5)`;\n    }\n\n    this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / ${width};\n        int c = imod(flatIndex, ${width});\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\nimport * as shader_util from './shader_compiler_util';\n\n/*\nThis is how the shader encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n */\n\nexport class EncodeMatrixPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n  packedInputs = false;\n  packedOutput = true;\n\n  constructor(\n      outputShape: [number, number, number], texShape: [number, number],\n      inputIsUnsignedByte = false) {\n    const glsl = getGlslDifferences();\n    const [height, width] = texShape;\n    this.outputShape = outputShape;\n\n    let mainLoop = '';\n    let output = 'result';\n    if (inputIsUnsignedByte) {\n      output = 'floor(result * 255. + 0.5)';\n    }\n\n    for (let row = 0; row <= 1; row++) {\n      for (let col = 0; col <= 1; col++) {\n        const channel = row * 2 + col;\n\n        mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${outputShape[2]}) {\n            localCoords[2] += ${col};\n            if(localCoords[1] + ${row} < ${outputShape[1]}) {\n              localCoords[1] += ${row};\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n\n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / ${width};\n              c = imod(flatIndex, ${width});\n              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n              values = ${glsl.texture2D}(A, uv);\n\n              if(offset == 0) {\n                result[${channel}] = values[0];\n              } else if(offset == 1) {\n                result[${channel}] = values[1];\n              } else if(offset == 2) {\n                result[${channel}] = values[2];\n              } else {\n                result[${channel}] = values[3];\n              }\n            }\n          }\n        `;\n      }\n    }\n\n    this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n\n        ${mainLoop}\n\n        ${glsl.output} = ${output};\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {PixelData, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {getGlslDifferences} from './glsl_version';\nimport * as tex_util from './tex_util';\nimport {TextureConfig} from './tex_util';\nimport * as webgl_util from './webgl_util';\n\nexport function createVertexShader(gl: WebGLRenderingContext): WebGLShader {\n  const glsl = getGlslDifferences();\n  const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n  return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\n\nexport function createVertexBuffer(gl: WebGLRenderingContext): WebGLBuffer {\n  // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n  const vertexArray = new Float32Array(\n      [-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n  return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\n\nexport function createIndexBuffer(gl: WebGLRenderingContext): WebGLBuffer {\n  // OpenGL (and WebGL) have \"CCW == front\" winding\n  const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n  return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\n\nfunction createAndConfigureTexture(\n    gl: WebGLRenderingContext, width: number, height: number,\n    internalFormat: number, textureFormat: number,\n    textureType: number): WebGLTexture {\n  webgl_util.validateTextureSize(width, height);\n  const texture = webgl_util.createTexture(gl);\n\n  const tex2d = gl.TEXTURE_2D;\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.texImage2D(\n          tex2d, 0, internalFormat, width, height, 0, textureFormat,\n          textureType, null));\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n  return texture;\n}\n\nexport function getInternalFormatForFloat32MatrixTexture(\n    textureConfig: TextureConfig) {\n  return textureConfig.internalFormatFloat;\n}\n\nexport function createFloat32MatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height,\n      getInternalFormatForFloat32MatrixTexture(textureConfig),\n      textureConfig.textureFormatFloat, gl.FLOAT);\n}\n\nexport function getInternalFormatForFloat16MatrixTexture(\n    textureConfig: TextureConfig) {\n  return textureConfig.internalFormatHalfFloat;\n}\n\nexport function createFloat16MatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height,\n      getInternalFormatForFloat16MatrixTexture(textureConfig),\n      textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\n\nexport function getInternalFormatForUnsignedBytesMatrixTexture(\n    textureConfig: TextureConfig) {\n  return textureConfig.downloadTextureFormat;\n}\n\nexport function createUnsignedBytesMatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height,\n      getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA,\n      gl.UNSIGNED_BYTE);\n}\n\nexport function getInternalFormatForPackedMatrixTexture(\n    textureConfig: TextureConfig) {\n  return textureConfig.internalFormatPackedFloat;\n}\n\nexport function createPackedMatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig),\n      gl.RGBA, gl.FLOAT);\n}\n\nexport function getInternalFormatForFloat16PackedMatrixTexture(\n    textureConfig: TextureConfig) {\n  return textureConfig.internalFormatPackedHalfFloat;\n}\n\nexport function createFloat16PackedMatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height,\n      getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA,\n      textureConfig.textureTypeHalfFloat);\n}\n\nexport function bindVertexProgramAttributeStreams(\n    gl: WebGLRenderingContext, program: WebGLProgram,\n    vertexBuffer: WebGLBuffer): boolean {\n  const posOffset = 0;               // x is the first buffer element\n  const uvOffset = 3 * 4;            // uv comes after [x y z]\n  const stride = (3 * 4) + (2 * 4);  // xyz + uv, each entry is 4-byte float.\n  webgl_util.callAndCheck(\n      gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n  const success = webgl_util.bindVertexBufferToProgramAttribute(\n      gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n  return success &&\n      webgl_util.bindVertexBufferToProgramAttribute(\n          gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\n\nexport function uploadDenseMatrixToTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, width: number,\n    height: number, data: TypedArray, textureConfig: TextureConfig) {\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n\n  let dataForUpload: TypedArray, texelDataType: number, internalFormat: number;\n  if (data instanceof Uint8Array) {\n    dataForUpload = new Uint8Array(width * height * 4);\n    texelDataType = gl.UNSIGNED_BYTE;\n    internalFormat = gl.RGBA;\n  } else {\n    dataForUpload = new Float32Array(width * height * 4);\n    texelDataType = gl.FLOAT;\n    internalFormat = textureConfig.internalFormatPackedFloat;\n  }\n\n  dataForUpload.set(data);\n\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.texImage2D(\n          gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA,\n          texelDataType, dataForUpload));\n\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nexport function uploadPixelDataToTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture,\n    pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|\n    HTMLVideoElement|ImageBitmap) {\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  if ((pixels as PixelData).data instanceof Uint8Array) {\n    webgl_util.callAndCheck(\n        gl,\n        () => gl.texImage2D(\n            gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA,\n            gl.UNSIGNED_BYTE, (pixels as PixelData).data));\n  } else {\n    webgl_util.callAndCheck(\n        gl,\n        () => gl.texImage2D(\n            gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE,\n            pixels as ImageData | HTMLImageElement | HTMLCanvasElement |\n                HTMLVideoElement|ImageBitmap));\n  }\n\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nexport function createBufferFromOutputTexture(\n    gl2: WebGL2RenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLBuffer {\n  // Create and bind the buffer.\n  const buffer = gl2.createBuffer();\n  webgl_util.callAndCheck(\n      gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n\n  // Initialize the buffer to the size of the texture in bytes.\n  const bytesPerFloat = 4;\n  const valuesPerTexel = 4;\n  const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n\n  webgl_util.callAndCheck(\n      gl2,\n      () => gl2.bufferData(\n          gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n\n  // Enqueue a command on the GPU command queue to copy of texture into the\n  // buffer.\n  webgl_util.callAndCheck(\n      gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n\n  webgl_util.callAndCheck(\n      gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n\n  return buffer;\n}\n\nexport function downloadFloat32MatrixFromBuffer(\n    gl: WebGLRenderingContext, buffer: WebGLBuffer,\n    size: number): Float32Array {\n  const gl2 = gl as WebGL2RenderingContext;\n\n  const downloadTarget = new Float32Array(size);\n\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n\n  return downloadTarget;\n}\n\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig) {\n  const [w, h] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n  const numChannels = 4;\n  const downloadTarget = new Uint8Array(\n      tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.readPixels(\n          0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE,\n          downloadTarget));\n\n  // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n  // decoding of the 4 bytes that back each 32 bit float.\n  return new Float32Array(downloadTarget.buffer);\n}\n\nexport function downloadPackedMatrixFromBuffer(\n    gl: WebGLRenderingContext, buffer: WebGLBuffer, batch: number, rows: number,\n    cols: number, physicalRows: number, physicalCols: number,\n    textureConfig: TextureConfig): Float32Array {\n  const gl2 = gl as WebGL2RenderingContext;\n\n  const downloadTarget =\n      new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(\n          physicalRows, physicalCols));\n\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n\n  return downloadTarget;\n}\n\nexport function downloadMatrixFromPackedOutputTexture(\n    gl: WebGLRenderingContext, physicalRows: number,\n    physicalCols: number): Float32Array {\n  const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.readPixels(\n          0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n\n  return packedRGBA;\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, PixelData, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {getWebGLContext, setWebGLContext} from './canvas_util';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport {TextureConfig} from './tex_util';\nimport {WebGL1DisjointQueryTimerExtension, WebGL2DisjointQueryTimerExtension} from './webgl_types';\nimport * as webgl_util from './webgl_util';\n\nexport interface FenceContext {\n  query: WebGLQuery|WebGLSync;\n  isFencePassed(): boolean;\n}\n\nexport class GPGPUContext {\n  gl: WebGLRenderingContext;\n  textureFloatExtension: {};\n  textureHalfFloatExtension: {};\n  colorBufferFloatExtension: {};\n  colorBufferHalfFloatExtension: {};\n  disjointQueryTimerExtension: WebGL2DisjointQueryTimerExtension|\n      WebGL1DisjointQueryTimerExtension;\n  vertexBuffer: WebGLBuffer;\n  indexBuffer: WebGLBuffer;\n  framebuffer: WebGLFramebuffer;\n  outputTexture: WebGLTexture|null = null;\n  program: WebGLProgram|null = null;\n  private disposed = false;\n  private disjoint: boolean;\n  private vertexShader: WebGLShader;\n  textureConfig: TextureConfig;\n\n  constructor(gl?: WebGLRenderingContext) {\n    const glVersion = env().getNumber('WEBGL_VERSION');\n    if (gl != null) {\n      this.gl = gl;\n      setWebGLContext(glVersion, gl);\n    } else {\n      this.gl = getWebGLContext(glVersion);\n    }\n    // WebGL 2.0 enables texture floats without an extension.\n    let COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';\n    const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n      const TEXTURE_FLOAT = 'OES_texture_float';\n      const TEXTURE_HALF_FLOAT = 'OES_texture_half_float';\n\n      this.textureFloatExtension =\n          webgl_util.getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n      if (webgl_util.hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n        this.textureHalfFloatExtension =\n            webgl_util.getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n      } else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n        throw new Error(\n            'GL context does not support half float textures, yet the ' +\n            'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n      }\n\n      this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n      if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n        this.colorBufferHalfFloatExtension =\n            webgl_util.getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n      } else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n        throw new Error(\n            'GL context does not support color renderable half floats, yet ' +\n            'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n      }\n    } else {\n      COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';\n      if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n        this.colorBufferFloatExtension =\n            this.gl.getExtension(COLOR_BUFFER_FLOAT);\n      } else if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n        this.colorBufferHalfFloatExtension =\n            this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n      } else {\n        throw new Error('GL context does not support color renderable floats');\n      }\n    }\n\n    this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n    this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n    this.framebuffer = webgl_util.createFramebuffer(this.gl);\n\n    this.textureConfig =\n        tex_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n  }\n\n  private get debug(): boolean {\n    return env().getBool('DEBUG');\n  }\n\n  public dispose() {\n    if (this.disposed) {\n      return;\n    }\n    if (this.program != null) {\n      console.warn(\n          'Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n          ' This is probably a resource leak, delete the program with ' +\n          'GPGPUContext.deleteProgram before disposing.');\n    }\n    if (this.outputTexture != null) {\n      console.warn(\n          'Disposing a GPGPUContext that still has a bound output matrix ' +\n          'texture.  This is probably a resource leak, delete the output ' +\n          'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n          'disposing.');\n    }\n    const gl = this.gl;\n    webgl_util.callAndCheck(gl, () => gl.finish());\n    webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n    webgl_util.callAndCheck(\n        gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n    webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n    this.disposed = true;\n  }\n\n  public createFloat32MatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createFloat32MatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public createFloat16MatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createFloat16MatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public createUnsignedBytesMatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createUnsignedBytesMatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public uploadPixelDataToTexture(\n      texture: WebGLTexture,\n      pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|\n      ImageBitmap) {\n    this.throwIfDisposed();\n    gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n  }\n\n  public uploadDenseMatrixToTexture(\n      texture: WebGLTexture, width: number, height: number, data: TypedArray) {\n    this.throwIfDisposed();\n    gpgpu_util.uploadDenseMatrixToTexture(\n        this.gl, texture, width, height, data, this.textureConfig);\n  }\n\n  public createFloat16PackedMatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createFloat16PackedMatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public createPackedMatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createPackedMatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public deleteMatrixTexture(texture: WebGLTexture) {\n    this.throwIfDisposed();\n    if (this.outputTexture === texture) {\n      webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n      this.outputTexture = null;\n    }\n    webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n  }\n\n  public downloadByteEncodedFloatMatrixFromOutputTexture(\n      texture: WebGLTexture, rows: number, columns: number): Float32Array {\n    return this.downloadMatrixDriver(\n        texture,\n        () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(\n            this.gl, rows, columns, this.textureConfig));\n  }\n\n  public downloadPackedMatrixFromBuffer(\n      buffer: WebGLBuffer, batch: number, rows: number, columns: number,\n      physicalRows: number, physicalCols: number): Float32Array {\n    return gpgpu_util.downloadPackedMatrixFromBuffer(\n        this.gl, buffer, batch, rows, columns, physicalRows, physicalCols,\n        this.textureConfig);\n  }\n\n  public downloadFloat32MatrixFromBuffer(buffer: WebGLBuffer, size: number):\n      Float32Array {\n    return gpgpu_util.downloadFloat32MatrixFromBuffer(this.gl, buffer, size);\n  }\n\n  public createBufferFromTexture(\n      texture: WebGLTexture, rows: number, columns: number): WebGLBuffer {\n    this.bindTextureToFrameBuffer(texture);\n    const result = gpgpu_util.createBufferFromOutputTexture(\n        this.gl as WebGL2RenderingContext, rows, columns, this.textureConfig);\n    this.unbindTextureToFrameBuffer();\n    return result;\n  }\n\n  public createAndWaitForFence(): Promise<void> {\n    const fenceContext = this.createFence(this.gl);\n    return this.pollFence(fenceContext);\n  }\n\n  private createFence(gl: WebGLRenderingContext): FenceContext {\n    let query: WebGLQuery|WebGLSync;\n    let isFencePassed: () => boolean;\n\n    if (env().getBool('WEBGL_FENCE_API_ENABLED')) {\n      const gl2 = gl as WebGL2RenderingContext;\n\n      const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n      gl.flush();\n\n      isFencePassed = () => {\n        const status = gl2.clientWaitSync(sync, 0, 0);\n        return status === gl2.ALREADY_SIGNALED ||\n            status === gl2.CONDITION_SATISFIED;\n      };\n\n      query = sync;\n    } else if (\n        env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      query = this.beginQuery();\n      this.endQuery();\n      isFencePassed = () => this.isQueryAvailable(\n          query,\n          env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    } else {\n      // If we have no way to fence, return true immediately. This will fire in\n      // WebGL 1.0 when there is no disjoint query timer. In this case, because\n      // the fence passes immediately, we'll immediately ask for a download of\n      // the texture, which will cause the UI thread to hang.\n      isFencePassed = () => true;\n    }\n\n    return {query, isFencePassed};\n  }\n\n  public downloadMatrixFromPackedTexture(\n      texture: WebGLTexture, physicalRows: number,\n      physicalCols: number): Float32Array {\n    return this.downloadMatrixDriver(\n        texture,\n        () => gpgpu_util.downloadMatrixFromPackedOutputTexture(\n            this.gl, physicalRows, physicalCols));\n  }\n\n  private vertexAttrsAreBound = false;\n\n  public createProgram(fragmentShaderSource: string): WebGLProgram {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    const fragmentShader: WebGLShader =\n        webgl_util.createFragmentShader(gl, fragmentShaderSource);\n    if (this.vertexShader == null) {\n      this.vertexShader = gpgpu_util.createVertexShader(gl);\n    }\n    const program: WebGLProgram = webgl_util.createProgram(gl);\n    webgl_util.callAndCheck(\n        gl, () => gl.attachShader(program, this.vertexShader));\n    webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n    webgl_util.linkProgram(gl, program);\n    if (this.debug) {\n      webgl_util.validateProgram(gl, program);\n    }\n    if (!this.vertexAttrsAreBound) {\n      this.setProgram(program);\n      this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(\n          gl, this.program, this.vertexBuffer);\n    }\n    return program;\n  }\n\n  public deleteProgram(program: WebGLProgram) {\n    this.throwIfDisposed();\n    if (program === this.program) {\n      this.program = null;\n    }\n    if (program != null) {\n      webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n    }\n  }\n\n  public setProgram(program: WebGLProgram|null) {\n    this.throwIfDisposed();\n    this.program = program;\n    if ((this.program != null) && this.debug) {\n      webgl_util.validateProgram(this.gl, this.program);\n    }\n    webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n  }\n\n  public getUniformLocation(\n      program: WebGLProgram, uniformName: string,\n      shouldThrow = true): WebGLUniformLocation {\n    this.throwIfDisposed();\n    if (shouldThrow) {\n      return webgl_util.getProgramUniformLocationOrThrow(\n          this.gl, program, uniformName);\n    } else {\n      return webgl_util.getProgramUniformLocation(\n          this.gl, program, uniformName);\n    }\n  }\n\n  public getAttributeLocation(program: WebGLProgram, attribute: string):\n      number {\n    this.throwIfDisposed();\n    return webgl_util.callAndCheck(\n        this.gl, () => this.gl.getAttribLocation(program, attribute));\n  }\n\n  public getUniformLocationNoThrow(program: WebGLProgram, uniformName: string):\n      WebGLUniformLocation {\n    this.throwIfDisposed();\n    return this.gl.getUniformLocation(program, uniformName);\n  }\n\n  public setInputMatrixTexture(\n      inputMatrixTexture: WebGLTexture, uniformLocation: WebGLUniformLocation,\n      textureUnit: number) {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    webgl_util.bindTextureToProgramUniformSampler(\n        this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n  }\n\n  public setOutputMatrixTexture(\n      outputMatrixTexture: WebGLTexture, rows: number, columns: number) {\n    this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n  }\n\n  public setOutputPackedMatrixTexture(\n      outputPackedMatrixTexture: WebGLTexture, rows: number, columns: number) {\n    this.throwIfDisposed();\n    const [width, height] =\n        tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n  }\n\n  public setOutputMatrixWriteRegion(\n      startRow: number, numRows: number, startColumn: number,\n      numColumns: number) {\n    this.setOutputMatrixWriteRegionDriver(\n        startColumn, startRow, numColumns, numRows);\n  }\n\n  public setOutputPackedMatrixWriteRegion(\n      startRow: number, numRows: number, startColumn: number,\n      numColumns: number) {\n    throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n  }\n\n  public debugValidate() {\n    if (this.program != null) {\n      webgl_util.validateProgram(this.gl, this.program);\n    }\n    webgl_util.validateFramebuffer(this.gl);\n  }\n\n  public executeProgram() {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    const gl = this.gl;\n    if (this.debug) {\n      this.debugValidate();\n    }\n    webgl_util.callAndCheck(\n        gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n  }\n\n  public blockUntilAllProgramsCompleted() {\n    this.throwIfDisposed();\n    webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n  }\n\n  private getQueryTimerExtension(): WebGL1DisjointQueryTimerExtension\n      |WebGL2DisjointQueryTimerExtension {\n    if (this.disjointQueryTimerExtension == null) {\n      this.disjointQueryTimerExtension =\n          webgl_util.getExtensionOrThrow(\n              this.gl,\n              env().getNumber(\n                  'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                  'EXT_disjoint_timer_query_webgl2' :\n                  'EXT_disjoint_timer_query') as\n              WebGL1DisjointQueryTimerExtension |\n          WebGL2DisjointQueryTimerExtension;\n    }\n    return this.disjointQueryTimerExtension;\n  }\n\n  private getQueryTimerExtensionWebGL2(): WebGL2DisjointQueryTimerExtension {\n    return this.getQueryTimerExtension();\n  }\n\n  private getQueryTimerExtensionWebGL1(): WebGL1DisjointQueryTimerExtension {\n    return this.getQueryTimerExtension() as WebGL1DisjointQueryTimerExtension;\n  }\n\n  beginQuery(): WebGLQuery {\n    if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.getQueryTimerExtensionWebGL2();\n\n      const query = gl2.createQuery();\n      gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n      return query;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    const query = ext.createQueryEXT() as WebGLQuery;\n    ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n    return query;\n  }\n\n  endQuery() {\n    if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.getQueryTimerExtensionWebGL2();\n      gl2.endQuery(ext.TIME_ELAPSED_EXT);\n      return;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n  }\n\n  public async waitForQueryAndGetTime(query: WebGLQuery): Promise<number> {\n    await util.repeatedTry(\n        () => this.disposed ||  // while testing contexts are created / disposed\n                                // in rapid succession, so without this check we\n                                // may poll for the query timer indefinitely\n            this.isQueryAvailable(\n                query,\n                env().getNumber(\n                    'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n    return this.getQueryTime(\n        query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n  }\n\n  private getQueryTime(query: WebGLQuery, queryTimerVersion: number): number {\n    if (queryTimerVersion === 0) {\n      return null;\n    }\n\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n\n      const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n      // Return milliseconds.\n      return timeElapsedNanos / 1000000;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n\n      const timeElapsedNanos =\n          ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n      // Return milliseconds.\n      return timeElapsedNanos / 1000000;\n    }\n  }\n\n  private isQueryAvailable(query: WebGLQuery, queryTimerVersion: number):\n      boolean {\n    if (queryTimerVersion === 0) {\n      return true;\n    }\n\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.getQueryTimerExtensionWebGL2();\n\n      const available =\n          gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n\n      return available && !this.disjoint;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n\n      const available =\n          ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n\n      return available && !this.disjoint;\n    }\n  }\n\n  pollFence(fenceContext: FenceContext) {\n    return new Promise<void>(resolve => {\n      this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n    });\n  }\n\n  private itemsToPoll: PollItem[] = [];\n\n  pollItems(): void {\n    // Find the last query that has finished.\n    const index = linearSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n    for (let i = 0; i <= index; ++i) {\n      const {resolveFn} = this.itemsToPoll[i];\n      resolveFn();\n    }\n    this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n  }\n\n  private addItemToPoll(isDoneFn: () => boolean, resolveFn: () => void) {\n    this.itemsToPoll.push({isDoneFn, resolveFn});\n    if (this.itemsToPoll.length > 1) {\n      // We already have a running loop that polls.\n      return;\n    }\n    // Start a new loop that polls.\n    util.repeatedTry(() => {\n      this.pollItems();\n      // End the loop if no more items to poll.\n      return this.itemsToPoll.length === 0;\n    });\n  }\n\n  private bindTextureToFrameBuffer(texture: WebGLTexture) {\n    this.throwIfDisposed();\n    webgl_util.bindColorTextureToFramebuffer(\n        this.gl, texture, this.framebuffer);\n    if (this.debug) {\n      webgl_util.validateFramebuffer(this.gl);\n    }\n  }\n\n  private unbindTextureToFrameBuffer() {\n    if (this.outputTexture != null) {\n      webgl_util.bindColorTextureToFramebuffer(\n          this.gl, this.outputTexture, this.framebuffer);\n      if (this.debug) {\n        webgl_util.validateFramebuffer(this.gl);\n      }\n    } else {\n      webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n    }\n  }\n\n  private downloadMatrixDriver(\n      texture: WebGLTexture,\n      downloadAndDecode: () => Float32Array): Float32Array {\n    this.bindTextureToFrameBuffer(texture);\n    const result = downloadAndDecode();\n    this.unbindTextureToFrameBuffer();\n\n    return result;\n  }\n\n  private setOutputMatrixTextureDriver(\n      outputMatrixTextureMaybePacked: WebGLTexture, width: number,\n      height: number) {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    webgl_util.bindColorTextureToFramebuffer(\n        gl, outputMatrixTextureMaybePacked, this.framebuffer);\n    if (this.debug) {\n      webgl_util.validateFramebuffer(gl);\n    }\n    this.outputTexture = outputMatrixTextureMaybePacked;\n    webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n    webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n  }\n\n  private setOutputMatrixWriteRegionDriver(\n      x: number, y: number, width: number, height: number) {\n    this.throwIfDisposed();\n    webgl_util.callAndCheck(\n        this.gl, () => this.gl.scissor(x, y, width, height));\n  }\n\n  private throwIfDisposed() {\n    if (this.disposed) {\n      throw new Error('Attempted to use disposed GPGPUContext.');\n    }\n  }\n\n  private throwIfNoProgram() {\n    if (this.program == null) {\n      throw new Error('No GPU program is currently set.');\n    }\n  }\n}\n\ntype PollItem = {\n  isDoneFn: () => boolean,\n  resolveFn: () => void\n};\n\n/**\n * Finds the index of the last true element using linear search.\n * Note: We can't do binary search because Chrome expects us to explicitly\n * test all fences before download:\n * https://github.com/tensorflow/tfjs/issues/1145\n */\nexport function linearSearchLastTrue(arr: Array<() => boolean>): number {\n  let i = 0;\n  for (; i < arr.length; ++i) {\n    const isDone = arr[i]();\n    if (!isDone) {\n      break;\n    }\n  }\n  return i - 1;\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, util} from '@tensorflow/tfjs-core';\nconst {getBroadcastDims} = backend_util;\nimport {getGlslDifferences, GLSL} from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\n\nexport type ShapeInfo = {\n  logicalShape: number[],\n  texShape: [number, number],\n  isUniform: boolean,\n  isPacked: boolean,\n  flatOffset: number\n};\n\nexport type InputInfo = {\n  name: string,\n  shapeInfo: ShapeInfo\n};\n\nexport function makeShader(\n    inputsInfo: InputInfo[], outputShape: ShapeInfo, userCode: string,\n    usesPackedTextures: boolean): string {\n  const prefixSnippets: string[] = [];\n  inputsInfo.forEach(x => {\n    const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n\n    // Snippet when we decided to upload the values as uniform.\n    if (x.shapeInfo.isUniform) {\n      prefixSnippets.push(\n          `uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`);\n    } else {\n      prefixSnippets.push(`uniform sampler2D ${x.name};`);\n      prefixSnippets.push(`uniform int offset${x.name};`);\n    }\n  });\n  const inputPrefixSnippet = prefixSnippets.join('\\n');\n\n  const inputSamplingSnippet =\n      inputsInfo\n          .map(x => getInputSamplingSnippet(x, outputShape, usesPackedTextures))\n          .join('\\n');\n  const outTexShape = outputShape.texShape;\n  const glsl = getGlslDifferences();\n  const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n  let outputSamplingSnippet: string;\n  let floatTextureSetOutputSnippet: string;\n  let shaderPrefix = getShaderPrefix(glsl);\n\n  if (outputShape.isPacked) {\n    outputSamplingSnippet =\n        getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n    floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n  } else {\n    outputSamplingSnippet =\n        getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n    floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n  }\n\n  if (usesPackedTextures) {\n    shaderPrefix += SHADER_PACKED_PREFIX;\n  }\n\n  const source = [\n    shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,\n    inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n  ].join('\\n');\n  return source;\n}\n\nfunction getSamplerFromInInfo(inInfo: InputInfo): string {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getSamplerScalar(inInfo);\n    case 1:\n      return getSampler1D(inInfo);\n    case 2:\n      return getSampler2D(inInfo);\n    case 3:\n      return getSampler3D(inInfo);\n    case 4:\n      return getSampler4D(inInfo);\n    case 5:\n      return getSampler5D(inInfo);\n    case 6:\n      return getSampler6D(inInfo);\n    default:\n      throw new Error(\n          `${shape.length}-D input sampling` +\n          ` is not yet supported`);\n  }\n}\n\nfunction getPackedSamplerFromInInfo(inInfo: InputInfo): string {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getPackedSamplerScalar(inInfo);\n    case 1:\n      return getPackedSampler1D(inInfo);\n    case 2:\n      return getPackedSampler2D(inInfo);\n    case 3:\n      return getPackedSampler3D(inInfo);\n    default:\n      return getPackedSamplerND(inInfo);\n  }\n}\n\nfunction getInputSamplingSnippet(\n    inInfo: InputInfo, outShapeInfo: ShapeInfo,\n    usesPackedTextures = false): string {\n  let res = '';\n  if (usesPackedTextures) {\n    res += getPackedSamplerFromInInfo(inInfo);\n  } else {\n    res += getSamplerFromInInfo(inInfo);\n  }\n\n  const inShape = inInfo.shapeInfo.logicalShape;\n  const outShape = outShapeInfo.logicalShape;\n  if (inShape.length <= outShape.length) {\n    if (usesPackedTextures) {\n      res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n    } else {\n      res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n    }\n  }\n  return res;\n}\n\nfunction getPackedOutputSamplingSnippet(\n    outShape: number[], outTexShape: [number, number]): string {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutputPacked1DCoords(outShape as [number], outTexShape);\n    case 2:\n      return getOutputPacked2DCoords(outShape as [number, number], outTexShape);\n    case 3:\n      return getOutputPacked3DCoords(\n          outShape as [number, number, number], outTexShape);\n    default:\n      return getOutputPackedNDCoords(outShape, outTexShape);\n  }\n}\n\nfunction getOutputSamplingSnippet(\n    outShape: number[], outTexShape: [number, number]): string {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutput1DCoords(outShape as [number], outTexShape);\n    case 2:\n      return getOutput2DCoords(outShape as [number, number], outTexShape);\n    case 3:\n      return getOutput3DCoords(\n          outShape as [number, number, number], outTexShape);\n    case 4:\n      return getOutput4DCoords(\n          outShape as [number, number, number, number], outTexShape);\n    case 5:\n      return getOutput5DCoords(\n          outShape as [number, number, number, number, number], outTexShape);\n    case 6:\n      return getOutput6DCoords(\n          outShape as [number, number, number, number, number, number],\n          outTexShape);\n    default:\n      throw new Error(\n          `${outShape.length}-D output sampling is not yet supported`);\n  }\n}\n\nfunction getFloatTextureSampleSnippet(glsl: GLSL): string {\n  return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\n\nfunction getFloatTextureSetRSnippet(glsl: GLSL): string {\n  return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\n\nfunction getFloatTextureSetRGBASnippet(glsl: GLSL): string {\n  return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\n\nfunction getShaderPrefix(glsl: GLSL): string {\n  const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n\n  return SHADER_PREFIX;\n}\n\nconst SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\n\nfunction getOutputScalarCoords() {\n  return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\n\nfunction getOutputPacked1DCoords(\n    shape: [number], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (packedTexShape[0] === 1) {\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n  }\n\n  if (packedTexShape[1] === 1) {\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n  }\n\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\n\nfunction getOutput1DCoords(\n    shape: [number], texShape: [number, number]): string {\n  if (texShape[0] === 1) {\n    return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n  }\n  if (texShape[1] === 1) {\n    return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n  }\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\n\nfunction getOutputPacked3DCoords(\n    shape: [number, number, number], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\n\nfunction getOutput3DCoords(\n    shape: [number, number, number], texShape: [number, number]): string {\n  const coordsFromIndexSnippet =\n      shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n\nfunction getOutputPackedNDCoords(\n    shape: number[], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n  const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n  const texelsInBatch =\n      texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n  let texelsInBatchN = texelsInBatch;\n  let batches = ``;\n  let coords = 'b, r, c';\n\n  for (let b = 2; b < shape.length - 1; b++) {\n    texelsInBatchN *= shape[shape.length - b - 1];\n    batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n    coords = `b${b}, ` + coords;\n  }\n\n  return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords});\n    }\n  `;\n}\n\nfunction getOutput4DCoords(\n    shape: [number, number, number, number],\n    texShape: [number, number]): string {\n  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n      ['r', 'c', 'd', 'd2'], shape);\n\n  return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\n\nfunction getOutput5DCoords(\n    shape: [number, number, number, number, number],\n    texShape: [number, number]): string {\n  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n      ['r', 'c', 'd', 'd2', 'd3'], shape);\n\n  return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\n\nfunction getOutput6DCoords(\n    shape: [number, number, number, number, number, number],\n    texShape: [number, number]): string {\n  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n      ['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n\n  return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\n\nfunction getOutputPacked2DCoords(\n    shape: [number, number], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (util.arraysEqual(shape, texShape)) {\n    return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${\n        packedTexShape[1]}));\n      }\n    `;\n  }\n\n  // texels needed to accommodate a logical row\n  const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n\n  /**\n   * getOutputCoords\n   *\n   * resTexRC: The rows and columns of the texels. If you move over one\n   * texel to the right in the packed texture, you are moving over one column\n   * (not two).\n   *\n   * index: The texel index\n   */\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\n\nfunction getOutput2DCoords(\n    shape: [number, number], texShape: [number, number]): string {\n  if (util.arraysEqual(shape, texShape)) {\n    return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n  }\n  if (shape[1] === 1) {\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n  }\n  if (shape[0] === 1) {\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n  }\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\n\nfunction getFlatOffsetUniformName(texName: string): string {\n  return `offset${texName}`;\n}\n\nfunction getPackedSamplerScalar(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const glsl = getGlslDifferences();\n  return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\n\nfunction getSamplerScalar(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  if (inputInfo.shapeInfo.isUniform) {\n    return `float ${funcName}() {return ${texName};}`;\n  }\n  const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n  if (texNumR === 1 && texNumC === 1) {\n    return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n  }\n\n  const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n  const offset = getFlatOffsetUniformName(texName);\n  return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getPackedSampler1D(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const glsl = getGlslDifferences();\n\n  return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler1D(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const tNumR = texShape[0];\n  const tNumC = texShape[1];\n\n  if (tNumC === 1 && tNumR === 1) {\n    return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (tNumC === 1) {\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (tNumR === 1) {\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getPackedSampler2D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const glsl = getGlslDifferences();\n  if (texShape != null && util.arraysEqual(shape, texShape)) {\n    return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n  }\n\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const valuesPerRow = Math.ceil(shape[1] / 2);\n\n  return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${\n      packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler2D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n\n  if (texShape != null && util.arraysEqual(shape, texShape)) {\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = ['row', 'col'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const offset = getFlatOffsetUniformName(texName);\n  if (texNumC === 1) {\n    // index is used directly as physical (no risk of float16 overflow).\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  if (texNumR === 1) {\n    // index is used directly as physical (no risk of float16 overflow).\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n\n  return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\n\nfunction getPackedSampler3D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n  if (shape[0] === 1) {\n    const squeezedShape = shape.slice(1);\n    const keptDims = [1, 2];\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = ['b', 'row', 'col'];\n    return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n\n  const valuesPerRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n  const glsl = getGlslDifferences();\n\n  return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler3D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride0 = shape[1] * shape[2];\n  const stride1 = shape[2];\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = ['row', 'col', 'depth'];\n    return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  if (texNumC === stride0 && flatOffset == null) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n  }\n\n  if (texNumC === stride1 && flatOffset == null) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n\n  const offset = getFlatOffsetUniformName(texName);\n  return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\n\nfunction getPackedSamplerND(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const rank = shape.length;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n\n  const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n  let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n  let params = `int b, int row, int col`;\n  let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n  for (let b = 2; b < rank - 1; b++) {\n    params = `int b${b}, ` + params;\n    texelsInBatch *= shape[rank - b - 1];\n    index = `b${b} * ${texelsInBatch} + ` + index;\n  }\n  const glsl = getGlslDifferences();\n  return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler4D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride2 = shape[3];\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = ['row', 'col', 'depth', 'depth2'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n\n  if (texNumC === stride0 && flatOffset == null) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride2 && flatOffset == null) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n\n  const offset = getFlatOffsetUniformName(texName);\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler5D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride3 = shape[4];\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n\n  if (texNumC === stride0 && flatOffset == null) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n\n  if (texNumC === stride3 && flatOffset == null) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n\n  const offset = getFlatOffsetUniformName(texName);\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler6D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  const stride4 = shape[5];\n  const stride3 = shape[4] * stride4;\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0 && flatOffset == null) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride4 && flatOffset == null) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getUniformSampler(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n\n  if (inSize < 2) {\n    return `return ${texName};`;\n  }\n  return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\n\nfunction getPackedSamplerAtOutputCoords(\n    inputInfo: InputInfo, outShapeInfo: ShapeInfo) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n\n  const broadcastDims = getBroadcastDims(\n      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n\n  const type = getCoordsDataType(outRank);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet: string;\n  const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n\n  if (inRank === 0) {\n    coordsSnippet = '';\n  } else if (outRank < 2 && broadcastDims.length >= 1) {\n    coordsSnippet = 'coords = 0;';\n  } else {\n    coordsSnippet =\n        broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n            .join('\\n');\n  }\n  let unpackedCoordsSnippet = '';\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = 'coords';\n  } else {\n    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n                                .map((s, i) => `coords.${fields[i + rankDiff]}`)\n                                .join(', ');\n  }\n\n  let output = `return outputValue;`;\n  const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n  const isInputScalar = inSize === 1;\n  const outSize = util.sizeFromShape(outShapeInfo.logicalShape);\n  const isOutputScalar = outSize === 1;\n\n  if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n    output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n  } else if (isInputScalar && !isOutputScalar) {\n    if (outRank === 1) {\n      output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n    } else {\n      output = `\n        return vec4(outputValue.x);\n      `;\n    }\n  } else if (broadcastDims.length) {\n    const rows = inRank - 2;\n    const cols = inRank - 1;\n\n    if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n      output = `return vec4(outputValue.x);`;\n    } else if (broadcastDims.indexOf(rows) > -1) {\n      output = `return vec4(outputValue.x, outputValue.y, ` +\n          `outputValue.x, outputValue.y);`;\n    } else if (broadcastDims.indexOf(cols) > -1) {\n      output = `return vec4(outputValue.xx, outputValue.zz);`;\n    }\n  }\n\n  return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\n\nfunction getSamplerAtOutputCoords(\n    inputInfo: InputInfo, outShapeInfo: ShapeInfo) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n  const outTexShape = outShapeInfo.texShape;\n  const inTexShape = inputInfo.shapeInfo.texShape;\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n\n  if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&\n      inputInfo.shapeInfo.flatOffset == null &&\n      util.arraysEqual(inTexShape, outTexShape)) {\n    return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n  }\n\n  const type = getCoordsDataType(outRank);\n  const broadcastDims = getBroadcastDims(\n      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet: string;\n  const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n\n  if (inRank === 0) {\n    coordsSnippet = '';\n  } else if (outRank < 2 && broadcastDims.length >= 1) {\n    coordsSnippet = 'coords = 0;';\n  } else {\n    coordsSnippet =\n        broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n            .join('\\n');\n  }\n  let unpackedCoordsSnippet = '';\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = 'coords';\n  } else {\n    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n                                .map((s, i) => `coords.${fields[i + rankDiff]}`)\n                                .join(', ');\n  }\n\n  return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\n\nexport function getCoordsDataType(rank: number): string {\n  if (rank <= 1) {\n    return 'int';\n  } else if (rank === 2) {\n    return 'ivec2';\n  } else if (rank === 3) {\n    return 'ivec3';\n  } else if (rank === 4) {\n    return 'ivec4';\n  } else if (rank === 5) {\n    return 'ivec5';\n  } else if (rank === 6) {\n    return 'ivec6';\n  } else {\n    throw Error(`GPU for rank ${rank} is not yet supported`);\n  }\n}\n\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(\n    inInfo: InputInfo, squeezedShape: number[]): InputInfo {\n  // Deep copy.\n  const newInputInfo: InputInfo = JSON.parse(JSON.stringify(inInfo));\n  newInputInfo.shapeInfo.logicalShape = squeezedShape;\n  return newInputInfo;\n}\n\nfunction getSqueezedParams(params: string[], keptDims: number[]): string {\n  return keptDims.map(d => params[d]).join(', ');\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, Tensor, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport * as shader_compiler from './shader_compiler';\nimport {InputInfo, ShapeInfo} from './shader_compiler';\nimport {PackingScheme, TextureData, TextureUsage} from './tex_util';\n\nexport interface GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[];\n  userCode: string;\n  /** If true, this program expects packed input textures. Defaults to false. */\n  packedInputs?: boolean;\n  /** If true, this program produces a packed texture. Defaults to false. */\n  packedOutput?: boolean;\n  /**\n   * Affects what type of texture we allocate for the output. Defaults to\n   * `TextureUsage.RENDER`.\n   */\n  outTexUsage?: TextureUsage;\n  /**\n   * The type of scheme to use when packing texels for the output values.\n   * See `PackingScheme` for details. Defaults to `PackingScheme.SHARED_BATCH`.\n   */\n  outPackingScheme?: PackingScheme;\n}\n\nexport interface GPGPUBinary {\n  webGLProgram: WebGLProgram;\n  program: GPGPUProgram;\n  uniformLocations: {[name: string]: WebGLUniformLocation};\n  source: string;\n  inShapeInfos: ShapeInfo[];\n  outShapeInfo: ShapeInfo;\n  infLoc: WebGLUniformLocation;\n  nanLoc: WebGLUniformLocation;\n}\n\nexport interface TensorData {\n  shape: number[];\n  texData: TextureData;\n  isUniform: boolean;\n  // Available when we decide to upload as uniform instead of texture.\n  uniformValues?: TypedArray;\n}\n\nexport function compileProgram<T extends Tensor, K extends Tensor>(\n    gpgpu: GPGPUContext, program: GPGPUProgram, inputs: TensorData[],\n    output: TensorData): GPGPUBinary {\n  const userCode = program.userCode;\n  const inputInfos: InputInfo[] = inputs.map((input, i) => {\n    const shapeInfo: ShapeInfo = {\n      logicalShape: input.shape,\n      texShape: input.isUniform ? null : input.texData.texShape,\n      isUniform: input.isUniform,\n      isPacked: input.isUniform ? false : input.texData.isPacked,\n      flatOffset: null\n    };\n    if (input.texData != null && input.texData.slice != null &&\n        input.texData.slice.flatOffset > 0) {\n      shapeInfo.flatOffset = input.texData.slice.flatOffset;\n    }\n    return {name: program.variableNames[i], shapeInfo};\n  });\n  const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n  const outShapeInfo: ShapeInfo = {\n    logicalShape: output.shape,\n    texShape: output.texData.texShape,\n    isUniform: false,\n    isPacked: output.texData.isPacked,\n    flatOffset: null\n  };\n  const source = shader_compiler.makeShader(\n      inputInfos, outShapeInfo, userCode, program.packedInputs);\n\n  const webGLProgram = gpgpu.createProgram(source);\n\n  // Add special uniforms (NAN, INFINITY)\n  let infLoc: WebGLUniformLocation = null;\n  const nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);\n  if (env().getNumber('WEBGL_VERSION') === 1) {\n    infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);\n  }\n\n  // Add user-defined uniforms\n  const uniformLocations: {[name: string]: WebGLUniformLocation} = {};\n  for (let i = 0; i < program.variableNames.length; i++) {\n    const varName = program.variableNames[i];\n    const shouldThrow = false;\n    uniformLocations[varName] =\n        gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n    uniformLocations[`offset${varName}`] =\n        gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n  }\n\n  return {\n    program,\n    source,\n    webGLProgram,\n    uniformLocations,\n    inShapeInfos,\n    outShapeInfo,\n    infLoc,\n    nanLoc,\n  };\n}\n\nfunction validateBinaryAndProgram(\n    shapeInfos: ShapeInfo[], inputs: TensorData[]) {\n  if (shapeInfos.length !== inputs.length) {\n    throw Error(\n        `Binary was compiled with ${shapeInfos.length} inputs, but ` +\n        `was executed with ${inputs.length} inputs`);\n  }\n\n  shapeInfos.forEach((s, i) => {\n    const shapeA = s.logicalShape;\n    const input = inputs[i];\n    const shapeB = input.shape;\n\n    if (!util.arraysEqual(shapeA, shapeB)) {\n      throw Error(\n          `Binary was compiled with different shapes than ` +\n          `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n    }\n    // The input is uploaded as uniform.\n    if (s.isUniform && input.isUniform) {\n      return;\n    }\n\n    const texShapeA = s.texShape;\n    const texShapeB = input.isUniform ? null : input.texData.texShape;\n    if (!util.arraysEqual(texShapeA, texShapeB)) {\n      throw Error(\n          `Binary was compiled with different texture shapes than the` +\n          ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n    }\n  });\n}\n\nexport function runProgram<T extends Tensor, K extends Tensor>(\n    gpgpu: GPGPUContext, binary: GPGPUBinary, inputs: TensorData[],\n    output: TensorData,\n    customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) =>\n        void): void {\n  validateBinaryAndProgram(binary.inShapeInfos, inputs);\n  validateBinaryAndProgram([binary.outShapeInfo], [output]);\n\n  const outTex = output.texData.texture;\n  const outTexShape = output.texData.texShape;\n  if (output.texData.isPacked) {\n    gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n  } else {\n    gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n  }\n  gpgpu.setProgram(binary.webGLProgram);\n\n  // Set special uniforms (NAN, INFINITY)\n  if (env().getNumber('WEBGL_VERSION') === 1) {\n    if (binary.infLoc !== null) {\n      gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n    }\n  }\n  if (binary.nanLoc !== null) {\n    gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n  }\n\n  // Set user-defined inputs\n  inputs.forEach((input, i) => {\n    const varName = binary.program.variableNames[i];\n    const varLoc = binary.uniformLocations[varName];\n    const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n\n    if (varLoc == null) {\n      // The compiler inferred that this variable is not used in this shader.\n      return;\n    }\n\n    if (input.isUniform) {\n      // Upload the values of the tensor as uniform.\n      if (util.sizeFromShape(input.shape) < 2) {\n        gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);\n      } else {\n        let vals = input.uniformValues;\n        if (!(vals instanceof Float32Array)) {\n          vals = new Float32Array(vals);\n        }\n        gpgpu.gl.uniform1fv(varLoc, vals);\n      }\n      return;\n    }\n\n    // If the input was sliced, upload the flat offset index.\n    if (input.texData.slice != null && varOffsetLoc != null) {\n      gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);\n    }\n\n    gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);\n  });\n\n  if (customSetup != null) {\n    customSetup(gpgpu, binary.webGLProgram);\n  }\n  gpgpu.executeProgram();\n}\n\nexport function makeShaderKey(\n    program: GPGPUProgram, inputs: TensorData[], output: TensorData): string {\n  let keyInputs = '';\n  inputs.concat(output).forEach(x => {\n    const hasOffset = x.texData != null && x.texData.slice != null &&\n        x.texData.slice.flatOffset > 0;\n    const texShape = x.isUniform ? 'uniform' : x.texData.texShape;\n    keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n  });\n  const keyUserCode = program.userCode;\n  let key = program.constructor.name;\n  // Fast string concat. See https://jsperf.com/string-concatenation/14.\n  key += '_' + keyInputs + '_' + keyUserCode;\n  return key;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function simpleAbsImpl(vals) {\n    const resultValues = new Float32Array(vals.length);\n    for (let i = 0; i < vals.length; ++i) {\n        resultValues[i] = Math.abs(vals[i]);\n    }\n    return resultValues;\n}\nexport const abs = (args) => {\n    const { x } = args.inputs;\n    const cpuBackend = args.backend;\n    assertNotComplex(x, 'abs');\n    let resultValues = new Float32Array(util.sizeFromShape(x.shape));\n    const values = cpuBackend.data.get(x.dataId).values;\n    resultValues = simpleAbsImpl(values);\n    return cpuBackend.makeOutput(resultValues, x.shape, 'float32');\n};\nexport const absConfig = {\n    kernelName: Abs,\n    backendName: 'cpu',\n    kernelFunc: abs,\n};\n//# sourceMappingURL=Abs.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for binary ops. Supports broadcast.\n */\nexport function createSimpleBinaryKernelImpl(op) {\n    return (aShape, bShape, aVals, bVals, dtype) => {\n        const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultRank = newShape.length;\n        const resultStrides = util.computeStrides(newShape);\n        const resultSize = util.sizeFromShape(newShape);\n        const result = util.getTypedArrayFromDType(dtype, resultSize);\n        const aRank = aShape.length;\n        const bRank = bShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bStrides = util.computeStrides(bShape);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < result.length; ++i) {\n                result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            for (let i = 0; i < result.length; ++i) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                result[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return [result, newShape];\n    };\n}\n//# sourceMappingURL=binary_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/binary_utils';\nexport const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));\nexport const addComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal + bReal, imag: aImag + bImag };\n}));\nexport const add = binaryKernelFunc(Add, addImpl, addComplexImpl);\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'cpu',\n    kernelFunc: add\n};\n//# sourceMappingURL=Add.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer, util } from '@tensorflow/tfjs-core';\nexport function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {\n    const weightsSize = util.sizeFromShape(weightsShape);\n    const outVals = util.makeZerosTypedArray(size, weightsDtype);\n    for (let i = 0; i < xVals.length; i++) {\n        const value = xVals[i];\n        if (value < 0) {\n            throw new Error('Input x must be non-negative!');\n        }\n        if (value >= size) {\n            continue;\n        }\n        if (weightsSize > 0) {\n            outVals[value] += weightsVals[i];\n        }\n        else {\n            outVals[value] += 1;\n        }\n    }\n    return outVals;\n}\nexport function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {\n    const numRows = xBuf.shape[0];\n    const numCols = xBuf.shape[1];\n    const outBuf = buffer([numRows, size], weightsBuf.dtype);\n    for (let i = 0; i < numRows; i++) {\n        for (let j = 0; j < numCols; j++) {\n            const value = xBuf.get(i, j);\n            if (value < 0) {\n                throw new Error('Input x must be non-negative!');\n            }\n            if (value >= size) {\n                continue;\n            }\n            if (binaryOutput) {\n                outBuf.set(1, i, value);\n            }\n            else {\n                if (weightsBuf.size > 0) {\n                    outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value);\n                }\n                else {\n                    outBuf.set(outBuf.get(i, value) + 1, i, value);\n                }\n            }\n        }\n    }\n    return outBuf;\n}\n//# sourceMappingURL=Bincount_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for unary op.\n */\nexport function createSimpleUnaryImpl(op) {\n    return (values, dtype, attrs) => {\n        const newValues = util.getTypedArrayFromDType(dtype, values.length);\n        for (let i = 0; i < values.length; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return newValues;\n    };\n}\n//# sourceMappingURL=unary_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nexport const ceil = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nexport const ceilConfig = {\n    kernelName: Ceil,\n    backendName: 'cpu',\n    kernelFunc: ceil,\n};\n//# sourceMappingURL=Ceil.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nexport function concatImpl(inputs, outShape, dtype, simplyConcat) {\n    const outVals = util.getArrayFromDType(dtype, util.sizeFromShape(outShape));\n    if (simplyConcat && dtype !== 'string') {\n        // Use built-in TypedArray.set() method for speed.\n        let offset = 0;\n        inputs.forEach(input => {\n            const size = util.sizeFromShape(input.shape);\n            outVals.set(input.vals, offset);\n            offset += size;\n        });\n    }\n    else {\n        let colOffset = 0;\n        inputs.forEach(input => {\n            const decodedData = dtype === 'string' ?\n                backend_util.fromUint8ToStringArray(input.vals) :\n                input.vals;\n            let tIdx = 0;\n            for (let row = 0; row < input.shape[0]; ++row) {\n                const resIdx = row * outShape[1] + colOffset;\n                for (let col = 0; col < input.shape[1]; ++col) {\n                    outVals[resIdx + col] = decodedData[tIdx++];\n                }\n            }\n            colOffset += input.shape[1];\n        });\n    }\n    return outVals;\n}\n//# sourceMappingURL=Concat_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Equal } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const equalImpl = createSimpleBinaryKernelImpl((a, b) => (a === b) ? 1 : 0);\nexport const equal = binaryKernelFunc(Equal, equalImpl, null /* complexImpl */, 'bool');\nexport const equalConfig = {\n    kernelName: Equal,\n    backendName: 'cpu',\n    kernelFunc: equal\n};\n//# sourceMappingURL=Equal.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nexport const exp = unaryKernelFuncFromImpl(Exp, expImpl);\nexport const expConfig = {\n    kernelName: Exp,\n    backendName: 'cpu',\n    kernelFunc: exp,\n};\n//# sourceMappingURL=Exp.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nexport const expm1 = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nexport const expm1Config = {\n    kernelName: Expm1,\n    backendName: 'cpu',\n    kernelFunc: expm1,\n};\n//# sourceMappingURL=Expm1.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nexport const floor = unaryKernelFuncFromImpl(Floor, floorImpl);\nexport const floorConfig = {\n    kernelName: Floor,\n    backendName: 'cpu',\n    kernelFunc: floor,\n};\n//# sourceMappingURL=Floor.js.map","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) {\n    const outBuf = buffer([numSlices, sliceSize], dtype);\n    for (let i = 0; i < numSlices; i++) {\n        const index = [];\n        let flattenIndex = 0;\n        for (let j = 0; j < sliceRank; j++) {\n            const dim = indicesData[i * sliceRank + j];\n            flattenIndex += dim * strides[j];\n            index.push(dim);\n        }\n        if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {\n            throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`);\n        }\n        for (let k = 0; k < sliceSize; k++) {\n            outBuf.values[i * sliceSize + k] =\n                paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));\n        }\n    }\n    return outBuf;\n}\n//# sourceMappingURL=GatherNd_Impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {\n    const outBuf = buffer(flattenOutputShape, xBuf.dtype);\n    for (let i = 0; i < outBuf.size; ++i) {\n        const newLoc = outBuf.indexToLoc(i);\n        const originalLoc = newLoc.slice();\n        const batchIdx = originalLoc[0];\n        const indicesIdx = originalLoc[2];\n        const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);\n        originalLoc[2] = indicesBuf.values[indicesIndex];\n        const originalIndex = xBuf.locToIndex(originalLoc);\n        outBuf.values[i] = xBuf.values[originalIndex];\n    }\n    return outBuf;\n}\n//# sourceMappingURL=GatherV2_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Greater } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const greaterImpl = createSimpleBinaryKernelImpl((a, b) => (a > b) ? 1 : 0);\nexport const greater = binaryKernelFunc(Greater, greaterImpl, null /* complexImpl */, 'bool');\nexport const greaterConfig = {\n    kernelName: Greater,\n    backendName: 'cpu',\n    kernelFunc: greater\n};\n//# sourceMappingURL=Greater.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GreaterEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const greaterEqualImpl = createSimpleBinaryKernelImpl((a, b) => (a >= b) ? 1 : 0);\nexport const greaterEqual = binaryKernelFunc(GreaterEqual, greaterEqualImpl, null /* complexImpl */, 'bool');\nexport const greaterEqualConfig = {\n    kernelName: GreaterEqual,\n    backendName: 'cpu',\n    kernelFunc: greaterEqual\n};\n//# sourceMappingURL=GreaterEqual.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Less } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const lessImpl = createSimpleBinaryKernelImpl((a, b) => (a < b) ? 1 : 0);\nexport const less = binaryKernelFunc(Less, lessImpl, null /* complexImpl */, 'bool');\nexport const lessConfig = {\n    kernelName: Less,\n    backendName: 'cpu',\n    kernelFunc: less\n};\n//# sourceMappingURL=Less.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LessEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const lessEqualImpl = createSimpleBinaryKernelImpl((a, b) => (a <= b) ? 1 : 0);\nexport const lessEqual = binaryKernelFunc(LessEqual, lessEqualImpl, null /* complexImpl */, 'bool');\nexport const lessEqualConfig = {\n    kernelName: LessEqual,\n    backendName: 'cpu',\n    kernelFunc: lessEqual\n};\n//# sourceMappingURL=LessEqual.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function linSpaceImpl(start, stop, num) {\n    const step = (stop - start) / (num - 1);\n    const values = util.makeZerosTypedArray(num, 'float32');\n    values[0] = start;\n    for (let i = 1; i < values.length; i++) {\n        values[i] = values[i - 1] + step;\n    }\n    return values;\n}\n//# sourceMappingURL=LinSpace_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nexport const log = unaryKernelFuncFromImpl(Log, logImpl);\nexport const logConfig = {\n    kernelName: Log,\n    backendName: 'cpu',\n    kernelFunc: log,\n};\n//# sourceMappingURL=Log.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function maxImpl(aVals, reduceSize, outShape, dtype) {\n    const vals = util.getTypedArrayFromDType(dtype, util.sizeFromShape(outShape));\n    for (let i = 0; i < vals.length; ++i) {\n        const offset = i * reduceSize;\n        let max = aVals[offset];\n        for (let j = 0; j < reduceSize; ++j) {\n            const value = aVals[offset + j];\n            if (Number.isNaN(value) ||\n                value > max) { // comparison with NaN always return false\n                max = value;\n            }\n        }\n        vals[i] = max;\n    }\n    return vals;\n}\n//# sourceMappingURL=Max_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Maximum } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const maximumImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => Math.max(aValue, bValue)));\nexport const maximum = binaryKernelFunc(Maximum, maximumImpl);\nexport const maximumConfig = {\n    kernelName: Maximum,\n    backendName: 'cpu',\n    kernelFunc: maximum\n};\n//# sourceMappingURL=Maximum.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Minimum } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const minimumImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => Math.min(aValue, bValue)));\nexport const minimum = binaryKernelFunc(Minimum, minimumImpl);\nexport const minimumConfig = {\n    kernelName: Minimum,\n    backendName: 'cpu',\n    kernelFunc: minimum\n};\n//# sourceMappingURL=Minimum.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/binary_utils';\nexport const multiplyImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));\nexport const multiplyComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return {\n        real: aReal * bReal - aImag * bImag,\n        imag: aReal * bImag + aImag * bReal\n    };\n}));\nexport const multiply = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'cpu',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Neg, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { multiplyImpl } from './Multiply';\nexport function negImpl(xVals, xShape, xDtype) {\n    const minusOne = util.createScalarValue(-1, xDtype);\n    return multiplyImpl([], xShape, minusOne, xVals, xDtype);\n}\nexport function neg(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'neg');\n    const xVals = backend.data.get(x.dataId).values;\n    const [res, newShape] = negImpl(xVals, x.shape, x.dtype);\n    return backend.makeTensorInfo(newShape, x.dtype, res);\n}\nexport const negConfig = {\n    kernelName: Neg,\n    backendName: 'cpu',\n    kernelFunc: neg\n};\n//# sourceMappingURL=Neg.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const notEqualImpl = createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));\nexport const notEqual = binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'cpu',\n    kernelFunc: notEqual\n};\n//# sourceMappingURL=NotEqual.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function transposeImpl(xVals, xShape, dtype, perm, newShape) {\n    const xRank = xShape.length;\n    const xSize = util.sizeFromShape(xShape);\n    const xStrides = util.computeStrides(xShape);\n    const newStrides = util.computeStrides(newShape);\n    const result = util.getTypedArrayFromDType(dtype, util.sizeFromShape(newShape));\n    for (let i = 0; i < xSize; ++i) {\n        const loc = util.indexToLoc(i, xRank, xStrides);\n        // Permute location.\n        const newLoc = new Array(loc.length);\n        for (let i = 0; i < newLoc.length; i++) {\n            newLoc[i] = loc[perm[i]];\n        }\n        const newIndex = util.locToIndex(newLoc, xRank, newStrides);\n        result[newIndex] = xVals[i];\n    }\n    return result;\n}\n//# sourceMappingURL=Transpose_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Prod, upcastType, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { transpose } from './Transpose';\nexport function prodImpl(xShape, xDtype, xVals, reductionAxes) {\n    const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(xShape, reductionAxes);\n    const outDtype = upcastType(xDtype, 'int32');\n    const outVals = util.makeZerosTypedArray(util.sizeFromShape(outShape), outDtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    for (let i = 0; i < outVals.length; ++i) {\n        const offset = i * reduceSize;\n        let prod = 1;\n        for (let j = 0; j < reduceSize; ++j) {\n            prod *= xVals[offset + j];\n        }\n        outVals[i] = prod;\n    }\n    return { outVals, outShape, outDtype };\n}\nexport function prod(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { axis, keepDims } = attrs;\n    assertNotComplex(x, 'prod');\n    const xRank = x.shape.length;\n    const axes = util.parseAxisParam(axis, x.shape);\n    const permutation = backend_util.getAxesPermutation(axes, xRank);\n    let reductionAxes = axes;\n    let permutedX = x;\n    const intermediateTensorInfos = [];\n    if (permutation != null) {\n        permutedX = transpose({ inputs: { x }, backend, attrs: { perm: permutation } });\n        intermediateTensorInfos.push(permutedX);\n        reductionAxes = backend_util.getInnerMostAxes(reductionAxes.length, xRank);\n    }\n    const xVals = backend.data.get(permutedX.dataId).values;\n    const { outVals, outShape, outDtype } = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);\n    let resultShape = outShape;\n    if (keepDims) {\n        resultShape = backend_util.expandShapeToKeepDim(outShape, axes);\n    }\n    intermediateTensorInfos.forEach(t => backend.disposeIntermediateTensorInfo(t));\n    return backend.makeTensorInfo(resultShape, outDtype, outVals);\n}\nexport const prodConfig = {\n    kernelName: Prod,\n    backendName: 'cpu',\n    kernelFunc: prod\n};\n//# sourceMappingURL=Prod.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function rangeImpl(start, stop, step, dtype) {\n    const sameStartStop = start === stop;\n    const increasingRangeNegativeStep = start < stop && step < 0;\n    const decreasingRangePositiveStep = stop < start && step > 1;\n    if (sameStartStop || increasingRangeNegativeStep ||\n        decreasingRangePositiveStep) {\n        return util.makeZerosTypedArray(0, dtype);\n    }\n    const numElements = Math.abs(Math.ceil((stop - start) / step));\n    const values = util.makeZerosTypedArray(numElements, dtype);\n    if (stop < start && step === 1) {\n        // Auto adjust the step's sign if it hasn't been set\n        // (or was set to 1)\n        step = -1;\n    }\n    values[0] = start;\n    for (let i = 1; i < values.length; i++) {\n        values[i] = values[i - 1] + step;\n    }\n    return values;\n}\n//# sourceMappingURL=Range_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nexport const rsqrt = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nexport const rsqrtConfig = {\n    kernelName: Rsqrt,\n    backendName: 'cpu',\n    kernelFunc: rsqrt,\n};\n//# sourceMappingURL=Rsqrt.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, buffer, Slice, slice_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function sliceImpl(vals, begin, size, shape, dtype) {\n    const isContinous = slice_util.isSliceContinous(shape, begin, size);\n    const length = util.sizeFromShape(size);\n    const xStrides = util.computeStrides(shape);\n    if (isContinous) {\n        const flatOffset = slice_util.computeFlatOffset(begin, xStrides);\n        if (dtype === 'string') {\n            return vals.slice(flatOffset, flatOffset + length);\n        }\n        return vals.subarray(flatOffset, flatOffset + length);\n    }\n    const decodedData = dtype === 'string' ?\n        backend_util.fromUint8ToStringArray(vals) :\n        vals;\n    const inBuf = buffer(shape, dtype, decodedData);\n    const outBuf = buffer(size, dtype);\n    for (let i = 0; i < outBuf.size; ++i) {\n        const outLoc = outBuf.indexToLoc(i);\n        const inLoc = outLoc.map((idx, j) => idx + begin[j]);\n        outBuf.set(inBuf.get(...inLoc), ...outLoc);\n    }\n    if (dtype === 'string') {\n        return backend_util.fromStringArrayToUint8(outBuf.values);\n    }\n    return outBuf.values;\n}\nexport function slice(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { begin, size } = attrs;\n    assertNotComplex(x, 'slice');\n    const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n    slice_util.assertParamsValid(x, $begin, $size);\n    const vals = backend.data.get(x.dataId).values;\n    const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n    return backend.makeTensorInfo($size, x.dtype, outVals);\n}\nexport const sliceConfig = {\n    kernelName: Slice,\n    backendName: 'cpu',\n    kernelFunc: slice\n};\n//# sourceMappingURL=Slice.js.map","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) {\n    const indicesCount = indicesShape[0];\n    const denseRows = denseShape[0];\n    const emptyRowIndicator = new Array(denseRows);\n    const reverseIndexMap = new Array(indicesCount);\n    const rank = indicesShape[1];\n    if (denseRows === 0) {\n        if (indicesCount !== 0) {\n            throw new Error(`Received SparseTensor with denseShape[0] = 0 but\n         indices.shape[0] = ${indicesCount}`);\n        }\n        const outputIndices = util.getArrayFromDType(indicesDType, 0);\n        const outputValues = util.getArrayFromDType(valuesDType, 0);\n        return [\n            outputIndices, [0, rank], outputValues, emptyRowIndicator, reverseIndexMap\n        ];\n    }\n    let rowsAreOrdered = true;\n    let lastIndicesRow = 0;\n    const csrOffset = new Array(denseRows).fill(0);\n    for (let i = 0; i < indicesCount; ++i) {\n        // indices is a 2d tensor with shape of [N, rank]\n        const row = indices[i * rank];\n        if (row < 0) {\n            throw new Error(`indices(${i}, 0) is invalid: ${row} < 0`);\n        }\n        if (row >= denseRows) {\n            throw new Error(`indices(${i}, 0) is invalid: ${row} >= ${denseRows}`);\n        }\n        ++csrOffset[row];\n        rowsAreOrdered = rowsAreOrdered && (row >= lastIndicesRow);\n        lastIndicesRow = row;\n    }\n    let allRowsFull = true;\n    for (let row = 0; row < denseRows; ++row) {\n        // csrOffset here describes the number of elements in this dense row\n        const rowEmpty = (csrOffset[row] === 0);\n        emptyRowIndicator[row] = rowEmpty;\n        allRowsFull = allRowsFull && !rowEmpty;\n        // In filled version, each row has at least one element.\n        csrOffset[row] = Math.max(csrOffset[row], 1);\n        // Update csrOffset to represent the number of elements up to and\n        // including denseRows + 1:\n        //  csrOffset[0] == #{elements of row 0}\n        //  csrOffset[1] == #{elements of row 1} + #{elements of row 0}\n        //  ..\n        //  csrOffset[i] == starting index for elements in row i + 1.\n        if (row > 0) {\n            csrOffset[row] += csrOffset[row - 1];\n        }\n    }\n    if (allRowsFull && rowsAreOrdered) {\n        const outputIndices = indices;\n        const outputValues = values;\n        for (let i = 0; i < indicesCount; ++i) {\n            reverseIndexMap[i] = i;\n        }\n        return [\n            outputIndices, [indicesCount, rank], outputValues, emptyRowIndicator,\n            reverseIndexMap\n        ];\n    }\n    else {\n        const fullIndicesCount = csrOffset[denseRows - 1];\n        const outputIndices = util.getArrayFromDType(indicesDType, fullIndicesCount * rank);\n        const outputValues = util.getArrayFromDType(valuesDType, fullIndicesCount);\n        const filledCount = new Array(denseRows).fill(0);\n        // Fill in values for rows that are not missing\n        for (let i = 0; i < indicesCount; ++i) {\n            // indices is a 2d tensor with shape of [N, rank]\n            const row = indices[i * rank];\n            const offset = filledCount[row];\n            const outputI = ((row === 0) ? 0 : csrOffset[row - 1]) + offset;\n            filledCount[row]++; // Increment the filled count for this row.\n            for (let j = 0; j < rank; ++j) {\n                // indices and outputIndices are 2d tensors with shape of [N, rank]\n                outputIndices[outputI * rank + j] = indices[i * rank + j];\n            }\n            outputValues[outputI] = values[i];\n            // We'll need this reverse index map to backprop correctly.\n            reverseIndexMap[i] = outputI;\n        }\n        // Fill in values for rows that are missing\n        for (let row = 0; row < denseRows; ++row) {\n            const rowCount = filledCount[row];\n            if (rowCount === 0) { // We haven't filled this row\n                const startingIndex = (row === 0) ? 0 : csrOffset[row - 1];\n                // Remaining index values were set to zero already.\n                // Just need to set the row index in the right location.\n                // outputIndices is a 2d tensor with shape of [N, rank]\n                outputIndices[startingIndex * rank + 0] = row;\n                for (let col = 1; col < rank; ++col) {\n                    outputIndices[startingIndex * rank + col] = 0;\n                }\n                outputValues[startingIndex] = defaultValue;\n            }\n        }\n        return [\n            outputIndices, [fullIndicesCount, rank], outputValues, emptyRowIndicator,\n            reverseIndexMap\n        ];\n    }\n}\n//# sourceMappingURL=SparseFillEmptyRows_impl.js.map","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) {\n    const denseSize = util.sizeFromShape(inputShape);\n    const nnz = inputIndicesShape[0];\n    const outputRank = targetShape.length;\n    // Compute the output shape. Determine product of specified dimensions, and\n    // find the index of the unspecified one.\n    const outputShape = [];\n    let product = 1;\n    let unknownIndex = -1;\n    for (let d = 0; d < outputRank; ++d) {\n        const size = targetShape[d];\n        if (size === -1) {\n            if (unknownIndex !== -1) {\n                throw new Error(`only one output dimension may be -1, not both ${unknownIndex} and ${d}`);\n            }\n            unknownIndex = d;\n            outputShape.push(1);\n        }\n        else {\n            if (size < 0) {\n                throw new Error(`size ${d} must be non-negative, not ${size}`);\n            }\n            product *= size;\n            outputShape.push(size);\n        }\n    }\n    if (unknownIndex !== -1) {\n        if (product <= 0) {\n            throw new Error('reshape cannot infer the missing ' +\n                'input size for an empty tensor unless all ' +\n                'specified input sizes are non-zero');\n        }\n        const missing = Math.trunc(denseSize / product);\n        if (product * missing !== denseSize) {\n            throw new Error(`Input to reshape is a SparseTensor with ${denseSize}\n          dense values, but the requested shape requires a multiple of ${product}. inputShape=${inputShape} outputShape= ${outputShape}`);\n        }\n        outputShape[unknownIndex] = missing;\n    }\n    const outputSize = util.sizeFromShape(outputShape);\n    if (outputSize !== denseSize) {\n        throw new Error(`Input to reshape is a tensor with ${denseSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`);\n    }\n    const inputRank = inputShape.length;\n    const inputStrides = [];\n    if (inputRank > 0) {\n        inputStrides[inputRank - 1] = 1;\n        for (let d = inputRank - 2; d >= 0; --d) {\n            inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];\n        }\n    }\n    const outputStrides = [];\n    if (outputRank > 0) {\n        outputStrides[outputRank - 1] = 1;\n        for (let d = outputRank - 2; d >= 0; --d) {\n            outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];\n        }\n    }\n    const newIndices = util.getArrayFromDType(inputDType, nnz * outputRank);\n    for (let i = 0; i < nnz; ++i) {\n        let id = 0;\n        for (let j = 0; j < inputRank; ++j) {\n            // inputIndices is a 2d tensor with shape of [nnz, inputRank]\n            id += inputIndices[i * inputRank + j] * inputStrides[j];\n        }\n        for (let j = 0; j < outputRank; ++j) {\n            // newIndices is a 2d tensor with shape of [nnz, outputRank]\n            newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);\n            id %= outputStrides[j];\n        }\n    }\n    return [newIndices, [nnz, outputRank], outputShape];\n}\n//# sourceMappingURL=SparseReshape_impl.js.map","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function sparseSegmentReductionImpl(input, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) {\n    const numIndices = indices.length;\n    if (numIndices !== segmentIds.length) {\n        throw new Error(`segmentIds and indices should have same size.`);\n    }\n    // Flatten the array to two dimensions\n    const inputFlat = [inputShape[0], input.length / inputShape[0]];\n    const numCol = inputFlat[1];\n    // Note that the current implementation assumes that segmentIds values are\n    // sorted.\n    const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;\n    const outputRows = lastSegmentIdPlusOne;\n    if (outputRows < 0) {\n        throw new Error(`segment ids must be >= 0`);\n    }\n    const outputShape = inputShape.slice();\n    outputShape[0] = outputRows;\n    const outputLength = outputShape.reduce((product, value) => product * value, 1);\n    // Output array is initialized with the value 0 by default.\n    const output = util.getArrayFromDType(inputDType, outputLength);\n    // Note that we do not initialize the output buffer with a default value, so\n    // we need to explicitly set missing indices to the default value.\n    if (numIndices === 0) {\n        if (outputRows > 0) {\n            output.fill(defaultValue);\n        }\n        return [output, outputShape];\n    }\n    if (outputRows <= 0) {\n        throw new Error(`segment ids must be >= 0`);\n    }\n    let start = 0, end = 1;\n    // Index from which the output is not initialized.\n    let uninitializedIndex = 0;\n    let outIndex = segmentIds[start];\n    while (true) {\n        // We initialize nextIndex to 0 to avoid may be uninitialized warning\n        let nextIndex = 0;\n        if (end < numIndices) {\n            nextIndex = segmentIds[end];\n            if (outIndex === nextIndex) {\n                ++end;\n                continue;\n            }\n            // We have a new segment here.  Verify that the segment ids are growing.\n            if (outIndex >= nextIndex) {\n                throw new Error(`segment ids are not increasing`);\n            }\n        }\n        if (outIndex < 0 || outIndex >= outputRows) {\n            throw new Error(`Segment id ${outIndex} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`);\n        }\n        // If there is a gap between two indices, we need to set that gap to the\n        // default value.\n        if (outIndex > uninitializedIndex) {\n            output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);\n        }\n        for (let i = start; i < end; ++i) {\n            const index = indices[i];\n            if (index < 0 || index >= inputFlat[0]) {\n                throw new Error(`Bad: indices[${i}] == ${indices[i]} out of range [0, ${inputFlat[0]})`);\n            }\n            for (let j = 0; j < numCol; j++) {\n                output[outIndex * numCol + j] += input[index * numCol + j];\n            }\n        }\n        if (isMean) {\n            for (let j = 0; j < numCol; j++) {\n                output[outIndex * numCol + j] /= end - start;\n            }\n        }\n        start = end;\n        ++end;\n        uninitializedIndex = outIndex + 1;\n        outIndex = nextIndex;\n        if (end > numIndices) {\n            break;\n        }\n    }\n    // Fill the gap at the end with the default value.\n    if (uninitializedIndex < outputRows) {\n        output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);\n    }\n    return [output, outputShape];\n}\n//# sourceMappingURL=SparseSegmentReduction_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function stridedSliceImpl(outShape, xBuf, strides, begin) {\n    const outBuf = buffer(outShape, xBuf.dtype);\n    for (let i = 0; i < outBuf.size; i++) {\n        const loc = outBuf.indexToLoc(i);\n        const newLoc = new Array(loc.length);\n        for (let j = 0; j < newLoc.length; j++) {\n            newLoc[j] = loc[j] * strides[j] + begin[j];\n        }\n        outBuf.set(xBuf.get(...newLoc), ...loc);\n    }\n    return outBuf;\n}\n//# sourceMappingURL=StridedSlice_impl.js.map","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * The StringNGramsOp class creates ngrams from ragged string data.\n * The constructor contains all attributes related to the operation such as\n * padding widths and strings, and the compute function can be used to\n * compute the ngrams for different ragged tensor inputs.\n */\nclass StringNGramsOp {\n    constructor(separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) {\n        this.separator = util.encodeString(separator);\n        this.nGramWidths = nGramWidths;\n        this.leftPad = util.encodeString(leftPad);\n        this.rightPad = util.encodeString(rightPad);\n        this.padWidth = padWidth;\n        this.preserveShort = preserveShortSequences;\n    }\n    getPadWidth(nGramWidth) {\n        // Ngrams can be padded with either a fixed pad width or a dynamic pad\n        // width depending on the 'padWidth' arg, but in no case should the padding\n        // ever be wider than 'nGramWidth' - 1.\n        return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);\n    }\n    getNumNGrams(length, nGramWidth) {\n        const padWidth = this.getPadWidth(nGramWidth);\n        return Math.max(0, ((length + 2 * padWidth) - nGramWidth) + 1);\n    }\n    createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) {\n        for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {\n            const padWidth = this.getPadWidth(nGramWidth);\n            const leftPadding = Math.max(0, padWidth - nGramIndex);\n            const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));\n            const numTokens = nGramWidth - (leftPadding + rightPadding);\n            const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);\n            // Calculate the total expected size of the nGram so we can reserve the\n            // correct amount of space in the string.\n            let nGramSize = 0;\n            // Size of the left padding.\n            nGramSize += leftPadding * this.leftPad.length;\n            // Size of the tokens.\n            for (let n = 0; n < numTokens; ++n) {\n                nGramSize += data[dataStartIndex + n].length;\n            }\n            // Size of the right padding.\n            nGramSize += rightPadding * this.rightPad.length;\n            // Size of the separators.\n            const numSeparators = leftPadding + rightPadding + numTokens - 1;\n            nGramSize += numSeparators * this.separator.length;\n            // Build the nGram.\n            output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);\n            const nGram = output[outputStartIndex + nGramIndex];\n            let nextNGramIndex = 0;\n            const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value);\n            for (let n = 0; n < leftPadding; ++n) {\n                appendToNGram(this.leftPad);\n                appendToNGram(this.separator);\n            }\n            // Only output first numTokens - 1 pairs of data and separator\n            for (let n = 0; n < numTokens - 1; ++n) {\n                appendToNGram(data[dataStartIndex + n]);\n                appendToNGram(this.separator);\n            }\n            // Handle case when there are no tokens or no right padding as these\n            // can result in consecutive separators.\n            if (numTokens > 0) {\n                // If we have tokens, then output last and then pair each separator\n                // with the right padding that follows, to ensure nGram ends either with\n                // the token or with the right pad.\n                appendToNGram(data[dataStartIndex + numTokens - 1]);\n                for (let n = 0; n < rightPadding; ++n) {\n                    appendToNGram(this.separator);\n                    appendToNGram(this.rightPad);\n                }\n            }\n            else {\n                // If we don't have tokens, then the last item inserted into the nGram\n                // has been the separator from the left padding loop above. Hence,\n                // output right pad and separator and make sure to finish with a\n                // padding, not a separator.\n                for (let n = 0; n < rightPadding - 1; ++n) {\n                    appendToNGram(this.rightPad);\n                    appendToNGram(this.separator);\n                }\n                appendToNGram(this.rightPad);\n            }\n        }\n    }\n    // Data and splits together form the definition of the ragged tensor,\n    // where data is 1 dimensional and contains the values of the tensor\n    // and splits denotes the indices at which each row starts.\n    compute(data, splits) {\n        // Validate that the splits are valid indices into data, only if there are\n        // splits specified.\n        const inputDataSize = data.length;\n        const splitsSize = splits.length;\n        if (splitsSize > 0) {\n            let prevSplit = splits[0];\n            if (prevSplit !== 0) {\n                throw new Error(`First split value must be 0, got ${prevSplit}`);\n            }\n            for (let i = 1; i < splitsSize; ++i) {\n                let validSplits = splits[i] >= prevSplit;\n                validSplits = validSplits && (splits[i] <= inputDataSize);\n                if (!validSplits) {\n                    throw new Error(`Invalid split value ${splits[i]}, must be in [${prevSplit}, ${inputDataSize}]`);\n                }\n                prevSplit = splits[i];\n            }\n            if (prevSplit !== inputDataSize) {\n                throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);\n            }\n        }\n        const numBatchItems = splitsSize - 1;\n        const nGramsSplits = util.getArrayFromDType('int32', splitsSize);\n        // If there is no data or size, return an empty ragged tensor.\n        if (inputDataSize === 0 || splitsSize === 0) {\n            const empty = new Array(inputDataSize);\n            for (let i = 0; i <= numBatchItems; ++i) {\n                nGramsSplits[i] = 0;\n            }\n            return [empty, nGramsSplits];\n        }\n        nGramsSplits[0] = 0;\n        for (let i = 1; i <= numBatchItems; ++i) {\n            const length = splits[i] - splits[i - 1];\n            let numNGrams = 0;\n            this.nGramWidths.forEach((nGramWidth) => {\n                numNGrams += this.getNumNGrams(length, nGramWidth);\n            });\n            if (this.preserveShort && length > 0 && numNGrams === 0) {\n                numNGrams = 1;\n            }\n            nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams;\n        }\n        const nGrams = new Array(nGramsSplits[numBatchItems]);\n        for (let i = 0; i < numBatchItems; ++i) {\n            const splitIndex = splits[i];\n            let outputStartIdx = nGramsSplits[i];\n            this.nGramWidths.forEach((nGramWidth) => {\n                const length = splits[i + 1] - splits[i];\n                const numNGrams = this.getNumNGrams(length, nGramWidth);\n                this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n                outputStartIdx += numNGrams;\n            });\n            // If we're preserving short sequences, check to see if no sequence was\n            // generated by comparing the current output start idx to the original\n            // one (nGramSplitsdata). If no ngrams were generated, then they will\n            // be equal (since we increment outputStartIdx by numNGrams every\n            // time we create a set of ngrams.)\n            if (this.preserveShort && outputStartIdx === nGramsSplits[i]) {\n                const dataLength = splits[i + 1] - splits[i];\n                // One legitimate reason to not have any ngrams when this.preserveShort\n                // is true is if the sequence itself is empty. In that case, move on.\n                if (dataLength === 0) {\n                    continue;\n                }\n                // We don't have to worry about dynamic padding sizes here: if padding\n                // was dynamic, every sequence would have had sufficient padding to\n                // generate at least one nGram.\n                const nGramWidth = dataLength + 2 * this.padWidth;\n                const numNGrams = 1;\n                this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n            }\n        }\n        return [nGrams, nGramsSplits];\n    }\n}\nexport function stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) {\n    return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences)\n        .compute(data, dataSplits);\n}\n//# sourceMappingURL=StringNGrams_impl.js.map","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nfunction split(str, delimiters, skipEmpty) {\n    if (!str.length) {\n        return [];\n    }\n    // When the delimiter is empty, the input is split into individual characters.\n    if (delimiters.length === 0) {\n        const result = new Array(str.length);\n        for (let i = 0; i < str.length; ++i) {\n            result[i] = str.subarray(i, i + 1);\n        }\n        return result;\n    }\n    // When there is one delimiter, the input is split only at that delimiter.\n    if (delimiters.length === 1) {\n        const delimiter = delimiters[0];\n        const result = [];\n        let f = str.indexOf(delimiter);\n        while (f !== -1) {\n            const token = str.subarray(0, f);\n            if (!skipEmpty || token.length !== 0) {\n                result.push(token);\n            }\n            str = str.subarray(f + 1);\n            f = str.indexOf(delimiter);\n        }\n        if (!skipEmpty || str.length !== 0) {\n            result.push(str);\n        }\n        return result;\n    }\n    // When there are multiple delimiters, the input is split at every instance\n    // one of the delimiters appears.\n    const result = [];\n    let tokenStart = 0;\n    for (let i = 0; i < str.length + 1; i++) {\n        if ((i === str.length) || (delimiters.indexOf(str[i]) !== -1)) {\n            const token = str.subarray(tokenStart, i);\n            if (!skipEmpty || token.length !== 0) {\n                result.push(token);\n            }\n            tokenStart = i + 1;\n        }\n    }\n    return result;\n}\nexport function stringSplitImpl(input, delimiter, skipEmpty) {\n    const batchSize = input.length;\n    // Empty delimiter means split the input character by character.\n    const tokens = [];\n    let outputSize = 0;\n    let maxNumEntries = 0;\n    const numIndices = new Array(batchSize);\n    for (let i = 0; i < batchSize; ++i) {\n        const parts = split(input[i], delimiter, skipEmpty);\n        const nEntries = parts.length;\n        numIndices[i] = nEntries;\n        outputSize += nEntries;\n        maxNumEntries = Math.max(maxNumEntries, nEntries);\n        tokens.push(...parts);\n    }\n    const indices = util.getArrayFromDType('int32', outputSize * 2);\n    const values = new Array(outputSize);\n    const shape = [batchSize, maxNumEntries];\n    let c = 0;\n    for (let i = 0; i < batchSize; ++i) {\n        for (let j = 0; j < numIndices[i]; ++j) {\n            // indices is a 2d tensor with shape of [outputSize, 2]\n            indices[c * 2] = i;\n            indices[c * 2 + 1] = j;\n            values[c] = tokens[c];\n            ++c;\n        }\n    }\n    return [indices, values, shape];\n}\n//# sourceMappingURL=StringSplit_impl.js.map","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function stringToHashBucketFastImpl(input, numBuckets) {\n    const output = util.getArrayFromDType('int32', input.length);\n    for (let i = 0; i < input.length; ++i) {\n        output[i] =\n            util.fingerPrint64(input[i]).modulo(numBuckets).getLowBitsUnsigned();\n    }\n    return output;\n}\n//# sourceMappingURL=StringToHashBucketFast_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/binary_utils';\nexport const subImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));\nexport const subComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal - bReal, imag: aImag - bImag };\n}));\nexport const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'cpu',\n    kernelFunc: sub\n};\n//# sourceMappingURL=Sub.js.map","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\nexport function tileImpl(xBuf, reps) {\n    const newShape = new Array(xBuf.rank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = xBuf.shape[i] * reps[i];\n    }\n    const result = buffer(newShape, xBuf.dtype);\n    for (let i = 0; i < result.values.length; ++i) {\n        const newLoc = result.indexToLoc(i);\n        const originalLoc = new Array(xBuf.rank);\n        for (let j = 0; j < originalLoc.length; j++) {\n            originalLoc[j] = newLoc[j] % xBuf.shape[j];\n        }\n        const originalIndex = xBuf.locToIndex(originalLoc);\n        result.values[i] = xBuf.values[originalIndex];\n    }\n    return result;\n}\n//# sourceMappingURL=Tile_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the TopK kernel shared between webgl and cpu. */\nimport { buffer, util } from '@tensorflow/tfjs-core';\nexport function topKImpl(x, xShape, xDtype, k, sorted) {\n    // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n    const lastDim = xShape[xShape.length - 1];\n    const [batch, size] = [x.length / lastDim, lastDim];\n    const allTopKVals = util.getTypedArrayFromDType(xDtype, batch * k);\n    const allTopKIndices = util.getTypedArrayFromDType('int32', batch * k);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = x.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        const outOffset = b * k;\n        const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n        const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n        for (let i = 0; i < k; i++) {\n            topKVals[i] = valAndInd[i].value;\n            topKIndices[i] = valAndInd[i].index;\n        }\n    }\n    // Reshape back to the original input shape, except that the last\n    // dimension is k.\n    const outputShape = xShape.slice();\n    outputShape[outputShape.length - 1] = k;\n    return [\n        buffer(outputShape, xDtype, allTopKVals),\n        buffer(outputShape, 'int32', allTopKIndices)\n    ];\n}\n//# sourceMappingURL=TopK_impl.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer, util } from '@tensorflow/tfjs-core';\nexport function uniqueImpl(values, axis, shape, dtype) {\n    // Normalize and validate axis.\n    const $axis = util.parseAxisParam(axis, shape)[0];\n    // Calculate the new shape that is suitable for extracting data along the\n    // given axis.\n    //\n    // The rank is 3.\n    // The size of the 1st dimension is the size of all the axes < the given axis.\n    // The size of the 2nd dimension is the same as the size of the given axis.\n    // The size of the 3rd dimension is the size of all the axes > the given axis.\n    //\n    // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n    // newShape would be: [2*3, 5, 4].\n    //\n    // Note that this is not the final output shape. This will be the shape for an\n    // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n    // values along the given axis. To demonstrate how it works, consider the\n    // following example:\n    //\n    // Input: a 3D tensor, with shape [1, 2, 3]\n    // [\n    //   [\n    //      [1,2,3],\n    //      [4,5,6]\n    //   ]\n    // ]\n    // Axis: 2 (the last axis).\n    // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n    //\n    // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n    // 1*2. The re-shaped data would look like:\n    //\n    // [\n    //   [\n    //     [1], [2], [3]\n    //   ],\n    //   [\n    //     [4], [5], [6]\n    //   ]\n    // ]\n    //\n    // Then, we can construct a 3-level nested loop by the following dimension\n    // order to extract the values along the axis (dimension1):\n    // i: dimension1       // 0,1,2 (newShape[1])\n    //   m: dimension0     // 0,1   (newShape[0])\n    //     n: dimension2   // 0     (newShape[2])\n    //\n    //                       m, i, n\n    //                      ---------\n    // Iteration 0: data at [0, 0, 0] => \"1\"\n    // Iteration 1: data at [1, 0, 0] => \"4\"\n    // We got [1,4].\n    // Iteration 2: data at [0, 1, 0] => \"2\"\n    // Iteration 3: data at [1, 1, 0] => \"5\"\n    // We got [2,5].\n    // Iteration 4: data at [0, 2, 0] => \"3\"\n    // Iteration 5: data at [1, 2, 0] => \"6\"\n    // We got [3,6].\n    const newShape = [1, shape[0], 1];\n    for (let i = 0; i < $axis; i++) {\n        newShape[0] *= shape[i];\n    }\n    newShape[1] = shape[$axis];\n    for (let i = $axis + 1; i < shape.length; i++) {\n        newShape[2] *= shape[i];\n    }\n    // A map from unique elements (their string representations) to their values\n    // in \"indices\" (below).\n    const uniqueElements = {};\n    // The indices of each unique element in the original tensor along the given\n    // axis. It is 1D and has the same size as the given axis.\n    const indices = new Int32Array(shape[$axis]);\n    // Create a buffer so we can easily extract value at a given location.\n    const inputBuffer = new TensorBuffer(newShape, dtype, values);\n    // The indices along the given axis that have unique elements. This is a\n    // de-duped version of \"indices\" above.\n    const uniqueIndices = [];\n    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n    for (let i = 0; i < shape[$axis]; i++) {\n        // Extract values along the axis.\n        let element;\n        if (is1DTensor) {\n            // Fast path for 1D tensor input.\n            element = values[i].toString();\n        }\n        else {\n            const axisValues = [];\n            for (let m = 0; m < newShape[0]; m++) {\n                for (let n = 0; n < newShape[2]; n++) {\n                    axisValues.push(inputBuffer.get(m, i, n));\n                }\n            }\n            element = axisValues.join(',');\n        }\n        // Dedup and update various indices.\n        if (uniqueElements[element] !== undefined) {\n            indices[i] = uniqueElements[element];\n        }\n        else {\n            const uniqueIndex = Object.keys(uniqueElements).length;\n            uniqueElements[element] = uniqueIndex;\n            indices[i] = uniqueIndex;\n            uniqueIndices.push(i);\n        }\n    }\n    // Now we know where each of the unique elements are located along the axis\n    // (uniqueIndices). Extract them from input buffer and store them in the\n    // output buffer.\n    const outputTmpShape = newShape.slice();\n    outputTmpShape[1] = Object.keys(uniqueElements).length;\n    const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n    uniqueIndices.forEach((uniqueElementIndex, i) => {\n        for (let m = 0; m < newShape[0]; m++) {\n            for (let n = 0; n < newShape[2]; n++) {\n                outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n            }\n        }\n    });\n    // The output shape can be calculated from the input shape with the size of\n    // the given axis replaced by the number of unique elements along that axis.\n    const outputShape = shape.slice();\n    outputShape[$axis] = outputTmpShape[1];\n    return {\n        outputValues: outputBuffer.values,\n        outputShape,\n        indices,\n    };\n}\n//# sourceMappingURL=Unique_impl.js.map","\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Import shared functionality from tfjs-backend-cpu without triggering\n// side effects.\n// tslint:disable-next-line: no-imports-from-dist\nimport * as shared from '@tensorflow/tfjs-backend-cpu/dist/shared';\n// tslint:disable-next-line: no-imports-from-dist\nimport {SimpleBinaryKernelImpl} from '@tensorflow/tfjs-backend-cpu/dist/shared';\n// tslint:disable-next-line: no-imports-from-dist\nimport {SimpleUnaryImpl} from '@tensorflow/tfjs-backend-cpu/dist/utils/unary_types';\n\nexport type SimpleBinaryKernelImplCPU = SimpleBinaryKernelImpl;\nexport type SimpleUnaryKernelImplCPU = SimpleUnaryImpl;\nconst {\n  addImpl: addImplCPU,\n  bincountImpl: bincountImplCPU,\n  bincountReduceImpl: bincountReduceImplCPU,\n  ceilImpl: ceilImplCPU,\n  concatImpl: concatImplCPU,\n  equalImpl: equalImplCPU,\n  expImpl: expImplCPU,\n  expm1Impl: expm1ImplCPU,\n  floorImpl: floorImplCPU,\n  gatherNdImpl: gatherNdImplCPU,\n  gatherV2Impl: gatherV2ImplCPU,\n  greaterImpl: greaterImplCPU,\n  greaterEqualImpl: greaterEqualImplCPU,\n  lessImpl: lessImplCPU,\n  lessEqualImpl: lessEqualImplCPU,\n  linSpaceImpl: linSpaceImplCPU,\n  logImpl: logImplCPU,\n  maxImpl: maxImplCPU,\n  maximumImpl: maximumImplCPU,\n  minimumImpl: minimumImplCPU,\n  multiplyImpl: multiplyImplCPU,\n  negImpl: negImplCPU,\n  notEqualImpl: notEqualImplCPU,\n  prodImpl: prodImplCPU,\n  rangeImpl: rangeImplCPU,\n  rsqrtImpl: rsqrtImplCPU,\n  simpleAbsImpl: simpleAbsImplCPU,\n  sliceImpl: sliceImplCPU,\n  sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU,\n  sparseReshapeImpl: sparseReshapeImplCPU,\n  sparseSegmentReductionImpl: sparseSegmentReductionImplCPU,\n  stridedSliceImpl: stridedSliceImplCPU,\n  stringNGramsImpl: stringNGramsImplCPU,\n  stringSplitImpl: stringSplitImplCPU,\n  stringToHashBucketFastImpl: stringToHashBucketFastImplCPU,\n  subImpl: subImplCPU,\n  tileImpl: tileImplCPU,\n  topKImpl: topKImplCPU,\n  transposeImpl: transposeImplCPU,\n  uniqueImpl: uniqueImplCPU,\n} = shared;\n\nexport {\n  addImplCPU,\n  bincountImplCPU,\n  bincountReduceImplCPU,\n  ceilImplCPU,\n  concatImplCPU,\n  equalImplCPU,\n  expImplCPU,\n  expm1ImplCPU,\n  floorImplCPU,\n  gatherNdImplCPU,\n  gatherV2ImplCPU,\n  greaterEqualImplCPU,\n  greaterImplCPU,\n  lessEqualImplCPU,\n  lessImplCPU,\n  linSpaceImplCPU,\n  logImplCPU,\n  maxImplCPU,\n  maximumImplCPU,\n  minimumImplCPU,\n  multiplyImplCPU,\n  negImplCPU,\n  notEqualImplCPU,\n  prodImplCPU,\n  simpleAbsImplCPU,\n  sliceImplCPU,\n  sparseFillEmptyRowsImplCPU,\n  sparseReshapeImplCPU,\n  sparseSegmentReductionImplCPU,\n  stridedSliceImplCPU,\n  stringNGramsImplCPU,\n  stringSplitImplCPU,\n  stringToHashBucketFastImplCPU,\n  subImplCPU,\n  rangeImplCPU,\n  rsqrtImplCPU,\n  tileImplCPU,\n  topKImplCPU,\n  transposeImplCPU,\n  uniqueImplCPU,\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport function getVecChannels(name: string, rank: number): string[] {\n  return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\n\nexport function getChannels(name: string, rank: number): string[] {\n  if (rank === 1) {\n    return [name];\n  }\n  return getVecChannels(name, rank);\n}\n\nexport function getSourceCoords(rank: number, dims: string[]): string {\n  if (rank === 1) {\n    return 'rc';\n  }\n\n  let coords = '';\n  for (let i = 0; i < rank; i++) {\n    coords += dims[i];\n    if (i < rank - 1) {\n      coords += ',';\n    }\n  }\n  return coords;\n}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getChannels} from './packing_util';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class PackProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n  packedInputs = false;\n  packedOutput = true;\n\n  constructor(\n      outputShape:\n          number[]) {  // TODO(https://github.com/tensorflow/tfjs/issues/893):\n                       // Only input / output 3D tensors.\n    this.outputShape = outputShape;\n    const rank = outputShape.length;\n\n    if (rank === 0) {\n      this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n    } else {\n      const channels = getChannels('rc', rank);\n      const dtype = getCoordsDataType(rank);\n      const outOfBoundsCondition =\n          getOutOfBoundsCondition(rank, outputShape, channels);\n      const setup = getSetup(\n          rank, outputShape[outputShape.length - 1],\n          outputShape[outputShape.length - 2], channels);\n      const output = getOutput(outputShape, channels);\n\n      this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n    }\n  }\n}\n\nfunction getSourceCoordsArr(rank: number, dims: string[]): string[] {\n  const coords = [];\n\n  for (let row = 0; row <= 1; row++) {\n    for (let col = 0; col <= 1; col++) {\n      let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n\n      for (let d = 2; d < rank; d++) {\n        coord = `${dims[dims.length - 1 - d]},` + coord;\n      }\n\n      coords.push(coord);\n    }\n  }\n  return coords;\n}\n\nfunction getOutOfBoundsCondition(\n    rank: number, shape: number[], dims: string[]): string {\n  if (rank === 1) {\n    return `rc > ${shape[0]}`;\n  }\n\n  let cond = '';\n  for (let i = rank - 2; i < rank; i++) {\n    cond += `${dims[i]} >= ${shape[i]}`;\n    if (i < rank - 1) {\n      cond += '||';\n    }\n  }\n\n  return cond;\n}\n\nfunction getSetup(\n    rank: number, cols: number, rows: number, dims: string[]): string {\n  if (rank === 1) {\n    return '';\n  }\n\n  const innerDims = dims.slice(-2);\n\n  return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\n\nfunction getOutput(shape: number[], dims: string[]): string {\n  const rank = shape.length;\n  const sourceCoords = getSourceCoordsArr(rank, dims);\n  if (rank === 1) {\n    return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n  }\n\n  return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport * as shader_util from './shader_compiler_util';\n\nexport class ReshapePackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  packedInputs = true;\n  packedOutput = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(outputShape: [number, number, number], inputShape: [\n    number, number, number\n  ]) {\n    this.outputShape = outputShape;\n\n    let mainLoop = ``;\n    for (let i = 0; i < 4; i++) {\n      let thisRC = `thisRC = rc;`;\n      if (i % 2 === 1) {\n        thisRC += `thisRC.z += 1;`;\n      }\n      if (i > 1) {\n        thisRC += `thisRC.y += 1;`;\n      }\n\n      mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n    }\n\n    this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n  }\n}\n\nfunction getReshapedInputCoords(shape: [number, number, number]): string {\n  const coordsFromIndexSnippet =\n      shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n\n  return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from '@tensorflow/tfjs-core';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {getInternalFormatForFloat16MatrixTexture, getInternalFormatForFloat16PackedMatrixTexture, getInternalFormatForFloat32MatrixTexture, getInternalFormatForPackedMatrixTexture, getInternalFormatForUnsignedBytesMatrixTexture} from './gpgpu_util';\nimport {getPackedMatrixTextureShapeWidthHeight, getUnpackedMatrixTextureShapeWidthHeight, PhysicalTextureType, TextureConfig, TextureUsage} from './tex_util';\n\nexport class TextureManager {\n  private numUsedTextures = 0;\n  private numFreeTextures = 0;\n  private _numBytesAllocated = 0;\n  private _numBytesFree = 0;  // How many bytes that have been allocated\n                              // are available for reuse.\n  private freeTextures: {[shape: string]: WebGLTexture[]} = {};\n  private logEnabled = false;\n  private usedTextures: {[shape: string]: WebGLTexture[]} = {};\n\n  constructor(private gpgpu: GPGPUContext) {}\n\n  acquireTexture(\n      shapeRC: [number, number], usage: TextureUsage,\n      isPacked: boolean): WebGLTexture {\n    const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n\n    const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    if (!(shapeKey in this.usedTextures)) {\n      this.usedTextures[shapeKey] = [];\n    }\n\n    const texBytes = computeBytes(\n        shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig,\n        isPacked);\n\n    if (this.freeTextures[shapeKey].length > 0) {\n      this.numFreeTextures--;\n      this.numUsedTextures++;\n      this._numBytesFree -= texBytes;\n      this.log();\n      const newTexture = this.freeTextures[shapeKey].shift();\n      this.usedTextures[shapeKey].push(newTexture);\n      return newTexture;\n    }\n\n    let newTexture: WebGLTexture;\n    if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n      newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n      newTexture =\n          this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n      newTexture =\n          this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n      newTexture =\n          this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (\n        physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n      newTexture =\n          this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n    }\n    this.usedTextures[shapeKey].push(newTexture);\n\n    this.numUsedTextures++;\n    this._numBytesAllocated += texBytes;\n    this.log();\n\n    return newTexture;\n  }\n\n  releaseTexture(\n      texture: WebGLTexture, shape: [number, number],\n      logicalTexType: TextureUsage, isPacked: boolean): void {\n    if (this.freeTextures == null) {\n      // Already disposed.\n      return;\n    }\n    const physicalTexType =\n        getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n    const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n\n    const texBytes = computeBytes(\n        shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig,\n        isPacked);\n    const deleteTexThreshold = env().get('WEBGL_DELETE_TEXTURE_THRESHOLD');\n    if (deleteTexThreshold !== -1 &&\n        this._numBytesAllocated > deleteTexThreshold) {\n      this.gpgpu.deleteMatrixTexture(texture);\n      this._numBytesAllocated -= texBytes;\n    } else {\n      this.freeTextures[shapeKey].push(texture);\n      this.numFreeTextures++;\n      this._numBytesFree += texBytes;\n    }\n\n    this.numUsedTextures--;\n\n    const texList = this.usedTextures[shapeKey];\n    const texIndex = texList.indexOf(texture);\n    if (texIndex < 0) {\n      throw new Error(\n          'Cannot release a texture that was never provided by this ' +\n          'texture manager');\n    }\n    texList.splice(texIndex, 1);\n    this.log();\n  }\n\n  private log() {\n    if (!this.logEnabled) {\n      return;\n    }\n    const total = this.numFreeTextures + this.numUsedTextures;\n    console.log(\n        'Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`,\n        `(${total})`);\n    const freeRatio = this._numBytesFree / this._numBytesAllocated;\n    console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n    console.log(`Bytes unused: ${this._numBytesFree} (${\n        Math.round(100 * freeRatio)}%)`);\n  }\n\n  get numBytesAllocated(): number {\n    return this._numBytesAllocated;\n  }\n\n  get numBytesFree(): number {\n    return this._numBytesFree;\n  }\n\n  getNumUsedTextures(): number {\n    return this.numUsedTextures;\n  }\n\n  getNumFreeTextures(): number {\n    return this.numFreeTextures;\n  }\n\n  dispose() {\n    if (this.freeTextures == null) {\n      // Already disposed.\n      return;\n    }\n    for (const texShape in this.freeTextures) {\n      this.freeTextures[texShape].forEach(tex => {\n        this.gpgpu.deleteMatrixTexture(tex);\n      });\n    }\n    for (const texShape in this.usedTextures) {\n      this.usedTextures[texShape].forEach(tex => {\n        this.gpgpu.deleteMatrixTexture(tex);\n      });\n    }\n    this.freeTextures = null;\n    this.usedTextures = null;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this._numBytesAllocated = 0;\n    this._numBytesFree = 0;\n  }\n}\n\nfunction numBytesForInternalFormat(\n    gl: WebGLRenderingContext, internalFormat: number): number {\n  // tslint:disable-next-line:no-any\n  const glany = gl as any;\n  if (internalFormat === glany.R32F) {\n    return 4;\n  } else if (internalFormat === glany.R16F) {\n    return 2;\n  } else if (internalFormat === glany.RGBA32F) {\n    return 16;\n  } else if (internalFormat === gl.RGBA) {\n    return 16;\n  } else if (internalFormat === glany.RGBA16F) {\n    return 8;\n  }\n  throw new Error(`Unknown internal format ${internalFormat}`);\n}\n\nexport function computeBytes(\n    shape: [number, number], physicalTexType: PhysicalTextureType,\n    gl: WebGLRenderingContext, textureConfig: TextureConfig,\n    isPacked: boolean): number {\n  // It is not possible to infer packed status from the texture type because\n  // depending on the textureConfig, different  texture types may resolve to the\n  // same internal format (e.g. in WebGL1, the internal format for\n  // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`\n  // explicitly.\n  const internalFormat =\n      internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n\n  let numElements: number;\n  if (isPacked) {\n    const [packedWidth, packedHeight] =\n        getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n    numElements = packedWidth * packedHeight;\n\n  } else {\n    const [width, height] =\n        getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n    numElements = width * height;\n  }\n\n  const bytesPerElement = numBytesForInternalFormat(gl, internalFormat);\n  return numElements * bytesPerElement;\n}\n\nfunction internalFormatForPhysicalTexType(\n    physicalTexType: PhysicalTextureType,\n    textureConfig: TextureConfig): number {\n  switch (physicalTexType) {\n    case PhysicalTextureType.PACKED_2X2_FLOAT32:\n      return getInternalFormatForPackedMatrixTexture(textureConfig);\n    case PhysicalTextureType.PACKED_2X2_FLOAT16:\n      return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n    case PhysicalTextureType.UNPACKED_FLOAT32:\n      return getInternalFormatForFloat32MatrixTexture(textureConfig);\n    case PhysicalTextureType.UNPACKED_FLOAT16:\n      return getInternalFormatForFloat16MatrixTexture(textureConfig);\n    case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n      return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n    default:\n      throw new Error(`Unknown physical texture type ${physicalTexType}`);\n  }\n}\n\nfunction getPhysicalTextureForRendering(isPacked: boolean):\n    PhysicalTextureType {\n  if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {\n    if (isPacked) {\n      return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT32;\n  }\n\n  if (isPacked) {\n    return PhysicalTextureType.PACKED_2X2_FLOAT16;\n  }\n  return PhysicalTextureType.UNPACKED_FLOAT16;\n}\n\nfunction getPhysicalFromLogicalTextureType(\n    logicalTexType: TextureUsage, isPacked: boolean): PhysicalTextureType {\n  if (logicalTexType === TextureUsage.UPLOAD) {\n    return PhysicalTextureType.PACKED_2X2_FLOAT32;\n  } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n    return getPhysicalTextureForRendering(isPacked);\n  } else if (\n      logicalTexType === TextureUsage.DOWNLOAD ||\n      logicalTexType === TextureUsage.PIXELS) {\n    return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n  }\n  throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\n\nfunction getKeyFromTextureShape(\n    shapeRowsCol: [number, number], physicalTexType: PhysicalTextureType,\n    isPacked: boolean): string {\n  return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class UnaryOpProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(aShape: number[], opSnippet: string) {\n    this.outputShape = aShape;\n    this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n  }\n}\n\nexport const CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\n\nexport const LINEAR = `return x;`;\n\nexport const ABS = `return abs(x);`;\n\nexport function STEP(alpha = 0.0) {\n  return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\n\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\n\nexport const RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\n\nexport const CLONE = 'return x;';\n\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport const LINEAR = `return x;`;\n\nexport const ELU = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\n\nexport const RELU = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\n\nexport const RELU6 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\n\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n\nexport class UnaryOpPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n  packedInputs = true;\n  packedOutput = true;\n\n  constructor(aShape: number[], opSnippet: string) {\n    this.outputShape = aShape;\n    this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getChannels, getSourceCoords} from './packing_util';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class UnpackProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  packedInputs = true;\n  packedOutput = false;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(outputShape: number[]) {\n    this.outputShape = outputShape;\n    const rank = outputShape.length;\n\n    const channels = getChannels('rc', rank);\n    const dtype = getCoordsDataType(rank);\n    const sourceCoords = getSourceCoords(rank, channels);\n    const innerDims = channels.slice(-2);\n    const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n\n    this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Import webgl flags.\nimport './flags_webgl';\n\nimport * as tf from '@tensorflow/tfjs-core';\nimport {backend_util, BackendValues, buffer, DataId, DataStorage, DataType, DataValues, engine, env, kernel_impls, KernelBackend, MemoryInfo, NumericDataType, Rank, RecursiveArray, scalar, ShapeMap, Tensor, Tensor2D, TensorBuffer, TensorInfo, tidy, TimingInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {getWebGLContext} from './canvas_util';\nimport {DecodeMatrixProgram} from './decode_matrix_gpu';\nimport {DecodeMatrixPackedProgram} from './decode_matrix_packed_gpu';\nimport {EncodeFloatProgram} from './encode_float_gpu';\nimport {EncodeFloatPackedProgram} from './encode_float_packed_gpu';\nimport {EncodeMatrixProgram} from './encode_matrix_gpu';\nimport {EncodeMatrixPackedProgram} from './encode_matrix_packed_gpu';\nimport {GPGPUContext} from './gpgpu_context';\nimport * as gpgpu_math from './gpgpu_math';\nimport {GPGPUBinary, GPGPUProgram, TensorData} from './gpgpu_math';\nimport {simpleAbsImplCPU} from './kernel_utils/shared';\nimport {PackProgram} from './pack_gpu';\nimport {ReshapePackedProgram} from './reshape_packed_gpu';\nimport * as tex_util from './tex_util';\nimport {TextureData, TextureUsage} from './tex_util';\nimport {TextureManager} from './texture_manager';\nimport * as unary_op from './unaryop_gpu';\nimport {UnaryOpProgram} from './unaryop_gpu';\nimport {UnaryOpPackedProgram} from './unaryop_packed_gpu';\nimport {UnpackProgram} from './unpack_gpu';\nimport * as webgl_util from './webgl_util';\n\nconst whereImpl = kernel_impls.whereImpl;\n\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n\ntype KernelInfo = {\n  name: string; query: Promise<number>;\n};\n\nexport type TimerNode = RecursiveArray<KernelInfo>|KernelInfo;\nexport interface CPUTimerQuery {\n  startMs: number;\n  endMs?: number;\n}\n\nexport interface WebGLMemoryInfo extends MemoryInfo {\n  numBytesInGPU: number;\n  // Tracks the total number of bytes allocated on the GPU, accounting for the\n  // physical texture type.\n  numBytesInGPUAllocated: number;\n  // Tracks byte size of textures that were created and then made available for\n  // reuse (disposed).\n  numBytesInGPUFree: number;\n  unreliable: boolean;\n}\n\nexport interface WebGLTimingInfo extends TimingInfo {\n  uploadWaitMs: number;\n  downloadWaitMs: number;\n}\n\nconst binaryCaches: {[webGLVersion: string]: {[key: string]: GPGPUBinary}} = {};\n\nexport function getBinaryCache(webGLVersion: number) {\n  if (webGLVersion in binaryCaches) {\n    return binaryCaches[webGLVersion];\n  }\n  binaryCaches[webGLVersion] = {};\n  return binaryCaches[webGLVersion];\n}\n\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD =\n    env().getNumber('CPU_HANDOFF_SIZE_THRESHOLD');\n\n// Empirically determined constant used to decide the number of MB on GPU\n// before we warn about high memory use. The MB are this constant * screen area\n// * dpi / 1024 / 1024.\nconst BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning(): number {\n  if (env().global.screen == null) {\n    return 1024;  // 1 GB.\n  }\n  return (env().global.screen.height * env().global.screen.width *\n          window.devicePixelRatio) *\n      BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\n\nexport class MathBackendWebGL extends KernelBackend {\n  texData: DataStorage<TextureData>;\n  gpgpu: GPGPUContext;\n\n  private static nextDataId = 0;\n  private nextDataId(): number {\n    return MathBackendWebGL.nextDataId++;\n  }\n  // Maps data ids that have a pending read operation, to list of subscribers.\n  private pendingRead = new WeakMap<DataId, Array<(arr: TypedArray) => void>>();\n  // List of data ids that are scheduled for disposal, but are waiting on a\n  // pending read operation.\n  private pendingDisposal = new WeakSet<DataId>();\n\n  // Used to count the number of 'shallow' sliced tensors that point to the\n  // same data id.\n  dataRefCount = new WeakMap<DataId, number>();\n  private numBytesInGPU = 0;\n\n  private canvas: HTMLCanvasElement|OffscreenCanvas;\n\n  private programTimersStack: TimerNode[];\n  private activeTimers: TimerNode[];\n  // Accumulated time spent (including blocking) in uploading data to webgl.\n  private uploadWaitMs = 0;\n  // Accumulated time spent (including blocking in downloading data from webgl.\n  private downloadWaitMs = 0;\n\n  // record the last manual GL Flush time.\n  private lastGlFlushTime = 0;\n\n  // Number of bits of precision of this backend.\n  private floatPrecisionValue: 32|16;\n\n  private textureManager: TextureManager;\n  private binaryCache: {[key: string]: GPGPUBinary};\n  private gpgpuCreatedLocally: boolean;\n  private numMBBeforeWarning: number;\n  private warnedAboutMemory = false;\n\n  constructor(gpgpu?: GPGPUContext) {\n    super();\n    if (!env().getBool('HAS_WEBGL')) {\n      throw new Error('WebGL is not supported on this device');\n    }\n\n    if (gpgpu == null) {\n      const gl = getWebGLContext(env().getNumber('WEBGL_VERSION'));\n      this.binaryCache = getBinaryCache(env().getNumber('WEBGL_VERSION'));\n      this.gpgpu = new GPGPUContext(gl);\n      this.canvas = gl.canvas;\n      this.gpgpuCreatedLocally = true;\n    } else {\n      this.gpgpu = gpgpu;\n      this.binaryCache = {};\n      this.gpgpuCreatedLocally = false;\n      this.canvas = gpgpu.gl.canvas;\n    }\n    this.textureManager = new TextureManager(this.gpgpu);\n    this.numMBBeforeWarning = numMBBeforeWarning();\n\n    this.texData = new DataStorage(this, engine());\n  }\n\n  numDataIds() {\n    return this.texData.numDataIds() - this.pendingDeletes;\n  }\n\n  write(values: BackendValues, shape: number[], dtype: DataType): DataId {\n    if (env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||\n        env().getBool('DEBUG')) {\n      this.checkNumericalProblems(values);\n    }\n    if (dtype === 'complex64' && values != null) {\n      throw new Error(\n          `Cannot write to a complex64 dtype. ` +\n          `Please use tf.complex(real, imag).`);\n    }\n    const dataId = {id: this.nextDataId()};\n    this.texData.set(\n        dataId,\n        {shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1});\n    return dataId;\n  }\n\n  /** Return refCount of a `TensorData`. */\n  refCount(dataId: DataId): number {\n    if (this.texData.has(dataId)) {\n      const tensorData = this.texData.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n\n  /** Increase refCount of a `TextureData`. */\n  incRef(dataId: DataId): void {\n    const texData = this.texData.get(dataId);\n    texData.refCount++;\n  }\n\n  /** Decrease refCount of a `TextureData`. */\n  decRef(dataId: DataId): void {\n    if (this.texData.has(dataId)) {\n      const texData = this.texData.get(dataId);\n      texData.refCount--;\n    }\n  }\n\n  move(\n      dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,\n      refCount: number): void {\n    if (env().getBool('DEBUG')) {\n      this.checkNumericalProblems(values);\n    }\n    if (dtype === 'complex64') {\n      throw new Error(\n          `Cannot write to a complex64 dtype. ` +\n          `Please use tf.complex(real, imag).`);\n    }\n    this.texData.set(\n        dataId, {shape, dtype, values, usage: TextureUsage.UPLOAD, refCount});\n  }\n\n  disposeIntermediateTensorInfo(tensorInfo: TensorInfo): void {\n    this.disposeData(tensorInfo.dataId);\n  }\n\n  readSync(dataId: DataId): BackendValues {\n    const texData = this.texData.get(dataId);\n    const {values, dtype, complexTensorInfos, slice, shape, isPacked} = texData;\n\n    // The presence of `slice` indicates this tensor is a shallow slice of a\n    // different tensor, and is using that original tensor's texture. Run\n    // `clone` in order to copy that texture and read from it.\n    if (slice != null) {\n      let program;\n      if (isPacked) {\n        program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n      } else {\n        program = new UnaryOpProgram(shape, unary_op.CLONE);\n      }\n      const res =\n          this.runWebGLProgram(program, [{dataId, shape, dtype}], dtype);\n      const data = this.readSync(res.dataId);\n      this.disposeIntermediateTensorInfo(res);\n      return data;\n    }\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n    if (dtype === 'string') {\n      return values;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start: number;\n    if (shouldTimeProgram) {\n      start = util.now();\n    }\n\n    let result: Float32Array;\n    if (dtype === 'complex64') {\n      const realValues =\n          this.readSync(complexTensorInfos.real.dataId) as Float32Array;\n      const imagValues =\n          this.readSync(complexTensorInfos.imag.dataId) as Float32Array;\n      result = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n    } else {\n      result = this.getValuesFromTexture(dataId);\n    }\n\n    if (shouldTimeProgram) {\n      this.downloadWaitMs += util.now() - start;\n    }\n    return this.convertAndCacheOnCPU(dataId, result);\n  }\n\n  async read(dataId: DataId): Promise<BackendValues> {\n    if (this.pendingRead.has(dataId)) {\n      const subscribers = this.pendingRead.get(dataId);\n      return new Promise<TypedArray>(resolve => subscribers.push(resolve));\n    }\n    const texData = this.texData.get(dataId);\n    const {values, shape, slice, dtype, complexTensorInfos, isPacked} = texData;\n\n    // The presence of `slice` indicates this tensor is a shallow slice of a\n    // different tensor, and is using that original tensor's texture. Run\n    // `clone` in order to copy that texture and read from it.\n    if (slice != null) {\n      let program;\n      if (isPacked) {\n        program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n      } else {\n        program = new UnaryOpProgram(shape, unary_op.CLONE);\n      }\n      const res =\n          this.runWebGLProgram(program, [{dataId, shape, dtype}], dtype);\n      const data = this.read(res.dataId);\n      this.disposeIntermediateTensorInfo(res);\n      return data;\n    }\n\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n\n    if (!env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n        env().getNumber('WEBGL_VERSION') === 2) {\n      throw new Error(\n          `tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n          `WEBGL_VERSION=2 not yet supported.`);\n    }\n\n    let buffer = null;\n    let tmpDownloadTarget: TensorInfo;\n\n    if (dtype !== 'complex64' && env().get('WEBGL_BUFFER_SUPPORTED')) {\n      // Possibly copy the texture into a buffer before inserting a fence.\n      tmpDownloadTarget = this.decode(dataId);\n      const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n\n      buffer = this.gpgpu.createBufferFromTexture(\n          tmpData.texture, ...tex_util.getDenseTexShape(shape));\n    }\n\n    this.pendingRead.set(dataId, []);\n\n    if (dtype !== 'complex64') {\n      // Create a fence and wait for it to resolve.\n      await this.gpgpu.createAndWaitForFence();\n    }\n\n    // Download the values from the GPU.\n    let vals: Float32Array;\n    if (dtype === 'complex64') {\n      const ps = await Promise.all([\n        this.read(complexTensorInfos.real.dataId),\n        this.read(complexTensorInfos.imag.dataId)\n      ]);\n\n      const realValues = ps[0];\n      const imagValues = ps[1];\n      vals = backend_util.mergeRealAndImagArrays(\n          realValues as Float32Array, imagValues as Float32Array);\n    } else if (buffer == null) {\n      vals = this.getValuesFromTexture(dataId);\n    } else {\n      const size = util.sizeFromShape(shape);\n      vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);\n    }\n    if (tmpDownloadTarget != null) {\n      this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n    }\n    const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n\n    const subscribers = this.pendingRead.get(dataId);\n    this.pendingRead.delete(dataId);\n\n    // Notify all pending reads.\n    subscribers.forEach(resolve => resolve(dTypeVals));\n    if (this.pendingDisposal.has(dataId)) {\n      this.pendingDisposal.delete(dataId);\n      if (this.disposeData(dataId)) {\n        engine().removeDataId(dataId, this);\n      }\n      this.pendingDeletes--;\n    }\n    return dTypeVals;\n  }\n\n  bufferSync<R extends Rank>(t: TensorInfo): TensorBuffer<R> {\n    const data = this.readSync(t.dataId);\n    let decodedData = data as DataValues;\n    if (t.dtype === 'string') {\n      try {\n        // Decode the bytes into string.\n        decodedData = (data as Uint8Array[]).map(d => util.decodeString(d));\n      } catch {\n        throw new Error('Failed to decode encoded string bytes into utf-8');\n      }\n    }\n    return buffer(t.shape as ShapeMap[R], t.dtype, decodedData) as\n        TensorBuffer<R>;\n  }\n\n  private checkNumericalProblems(values: BackendValues): void {\n    if (values == null) {\n      return;\n    }\n    for (let i = 0; i < values.length; i++) {\n      const num = values[i] as number;\n      if (!webgl_util.canBeRepresented(num)) {\n        if (env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {\n          throw Error(\n              `The value ${num} cannot be represented with your ` +\n              `current settings. Consider enabling float32 rendering: ` +\n              `'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n        }\n        throw Error(`The value ${num} cannot be represented on this device.`);\n      }\n    }\n  }\n\n  private getValuesFromTexture(dataId: DataId): Float32Array {\n    const {shape, dtype, isPacked} = this.texData.get(dataId);\n    const size = util.sizeFromShape(shape);\n    if (env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n      const tmpTarget = this.decode(dataId);\n      const tmpData = this.texData.get(tmpTarget.dataId);\n      const vals = this.gpgpu\n                       .downloadMatrixFromPackedTexture(\n                           tmpData.texture, ...tex_util.getDenseTexShape(shape))\n                       .subarray(0, size);\n\n      this.disposeIntermediateTensorInfo(tmpTarget);\n\n      return vals;\n    }\n\n    const shouldUsePackedProgram =\n        env().getBool('WEBGL_PACK') && isPacked === true;\n    const outputShape =\n        shouldUsePackedProgram ? webgl_util.getShapeAs3D(shape) : shape;\n    const program = shouldUsePackedProgram ?\n        new EncodeFloatPackedProgram(outputShape as [number, number, number]) :\n        new EncodeFloatProgram(outputShape);\n    const output = this.runWebGLProgram(\n        program, [{shape: outputShape, dtype, dataId}], 'float32');\n    const tmpData = this.texData.get(output.dataId);\n    const vals =\n        this.gpgpu\n            .downloadByteEncodedFloatMatrixFromOutputTexture(\n                tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])\n            .subarray(0, size);\n    this.disposeIntermediateTensorInfo(output);\n\n    return vals;\n  }\n\n  timerAvailable(): boolean {\n    return env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0;\n  }\n\n  async time(f: () => void): Promise<WebGLTimingInfo> {\n    const oldActiveTimers = this.activeTimers;\n    const newActiveTimers: TimerNode[] = [];\n\n    let outerMostTime = false;\n    if (this.programTimersStack == null) {\n      this.programTimersStack = newActiveTimers;\n      outerMostTime = true;\n    } else {\n      this.activeTimers.push(newActiveTimers);\n    }\n    this.activeTimers = newActiveTimers;\n\n    f();\n\n    // needing to split these up because util.flatten only accepts certain types\n    const flattenedActiveTimerQueries =\n        util.flatten(this.activeTimers.map((d: KernelInfo) => d.query))\n            .filter(d => d != null);\n    const flattenedActiveTimerNames =\n        util.flatten(this.activeTimers.map((d: KernelInfo) => d.name))\n            .filter(d => d != null);\n\n    this.activeTimers = oldActiveTimers;\n\n    if (outerMostTime) {\n      this.programTimersStack = null;\n    }\n\n    const res: WebGLTimingInfo = {\n      uploadWaitMs: this.uploadWaitMs,\n      downloadWaitMs: this.downloadWaitMs,\n      kernelMs: null,\n      wallMs: null  // will be filled by the engine\n    };\n\n    if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n      const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n\n      res['kernelMs'] = util.sum(kernelMs);\n      res['getExtraProfileInfo'] = () =>\n          kernelMs.map((d, i) => ({name: flattenedActiveTimerNames[i], ms: d}))\n              .map(d => `${d.name}: ${d.ms}`)\n              .join(', ');\n    } else {\n      res['kernelMs'] = {\n        error: 'WebGL query timers are not supported in this environment.'\n      };\n    }\n\n    this.uploadWaitMs = 0;\n    this.downloadWaitMs = 0;\n    return res;\n  }\n  memory(): WebGLMemoryInfo {\n    return {\n      unreliable: false,\n      numBytesInGPU: this.numBytesInGPU,\n      numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n      numBytesInGPUFree: this.textureManager.numBytesFree\n    } as WebGLMemoryInfo;\n  }\n\n  private startTimer(): WebGLQuery|CPUTimerQuery {\n    if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n      return this.gpgpu.beginQuery();\n    }\n    return {startMs: util.now(), endMs: null};\n  }\n\n  private endTimer(query: WebGLQuery|CPUTimerQuery): WebGLQuery|CPUTimerQuery {\n    if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n      this.gpgpu.endQuery();\n      return query;\n    }\n    (query as CPUTimerQuery).endMs = util.now();\n    return query;\n  }\n\n  private async getQueryTime(query: WebGLQuery|CPUTimerQuery): Promise<number> {\n    if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n      return this.gpgpu.waitForQueryAndGetTime(query as WebGLQuery);\n    }\n    const timerQuery = query as CPUTimerQuery;\n    return timerQuery.endMs - timerQuery.startMs;\n  }\n\n  private pendingDeletes = 0;\n\n  /**\n   * Decrease the RefCount on the dataId and dispose the memory if the dataId\n   * has 0 refCount. If there are pending read on the data, the disposal would\n   * added to the pending delete queue. Return true if the dataId is removed\n   * from backend or the backend does not contain the dataId, false if the\n   * dataId is not removed. Memory may or may not be released even when dataId\n   * is removed, which also depends on dataRefCount, see `releaseGPU`.\n   * @param dataId\n   * @oaram force Optional, remove the data regardless of refCount\n   */\n  disposeData(dataId: DataId, force = false): boolean {\n    if (this.pendingDisposal.has(dataId)) {\n      return false;\n    }\n\n    // No-op if already disposed.\n    if (!this.texData.has(dataId)) {\n      return true;\n    }\n\n    // if force flag is set, change refCount to 0, this would ensure disposal\n    // when added to the pendingDisposal queue. Memory may or may not be\n    // released, which also depends on dataRefCount, see `releaseGPU`.\n    if (force) {\n      this.texData.get(dataId).refCount = 0;\n    } else {\n      this.texData.get(dataId).refCount--;\n    }\n\n    if (!force && this.texData.get(dataId).refCount > 0) {\n      return false;\n    }\n\n    if (this.pendingRead.has(dataId)) {\n      this.pendingDisposal.add(dataId);\n      this.pendingDeletes++;\n      return false;\n    }\n\n    this.releaseGPUData(dataId);\n    const {complexTensorInfos} = this.texData.get(dataId);\n    if (complexTensorInfos != null) {\n      this.disposeData(complexTensorInfos.real.dataId, force);\n      this.disposeData(complexTensorInfos.imag.dataId, force);\n    }\n\n    this.texData.delete(dataId);\n\n    return true;\n  }\n\n  private releaseGPUData(dataId: DataId): void {\n    const {texture, dtype, texShape, usage, isPacked, slice} =\n        this.texData.get(dataId);\n    const key = slice && slice.origDataId || dataId;\n    const refCount = this.dataRefCount.get(key);\n\n    if (refCount > 1) {\n      this.dataRefCount.set(key, refCount - 1);\n    } else {\n      this.dataRefCount.delete(key);\n      if (texture != null) {\n        this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n        this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n      }\n    }\n\n    const texData = this.texData.get(dataId);\n    texData.texture = null;\n    texData.texShape = null;\n    texData.isPacked = false;\n    texData.slice = null;\n  }\n\n  getTexture(dataId: DataId): WebGLTexture {\n    this.uploadToGPU(dataId);\n    return this.texData.get(dataId).texture;\n  }\n\n  /**\n   * Returns internal information for the specific data bucket. Used in unit\n   * tests.\n   */\n  getDataInfo(dataId: DataId): TextureData {\n    return this.texData.get(dataId);\n  }\n\n  /*\n  Tests whether all the inputs to an op are small and on the CPU. This heuristic\n  determines when it would be faster to execute a kernel on the CPU. WebGL\n  kernels opt into running this check and forwarding when appropriate.\n  TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n  sustainable strategy for optimizing backend execution of ops.\n   */\n  shouldExecuteOnCPU(\n      inputs: TensorInfo[],\n      sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD): boolean {\n    return env().getBool('WEBGL_CPU_FORWARD') &&\n        inputs.every(\n            input => this.texData.get(input.dataId).texture == null &&\n                util.sizeFromShape(input.shape) < sizeThreshold);\n  }\n\n  getGPGPUContext(): GPGPUContext {\n    return this.gpgpu;\n  }\n\n  where(condition: Tensor): Tensor2D {\n    backend_util.warn(\n        'tf.where() in webgl locks the UI thread. ' +\n        'Call tf.whereAsync() instead');\n    const condVals = condition.dataSync();\n    return whereImpl(condition.shape, condVals);\n  }\n\n  private packedUnaryOp(x: TensorInfo, op: string, dtype: DataType) {\n    const program = new UnaryOpPackedProgram(x.shape, op);\n    const outInfo = this.compileAndRun(program, [x], dtype);\n    return engine().makeTensorFromDataId(\n        outInfo.dataId, outInfo.shape, outInfo.dtype);\n  }\n\n  // TODO(msoulanille) remove this once the backend has been modularized\n  // a copy is needed here to break a circular dependency.\n  // Also remove the op from unary_op.\n  abs<T extends Tensor>(x: T): T {\n    // TODO: handle cases when x is complex.\n    if (this.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {\n      const outValues =\n          simpleAbsImplCPU(this.texData.get(x.dataId).values as TypedArray);\n      return this.makeOutput(x.shape, x.dtype, outValues);\n    }\n\n    if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n      return this.packedUnaryOp(x, unary_op.ABS, x.dtype) as T;\n    }\n\n    const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n    const outInfo = this.compileAndRun(program, [x]);\n    return engine().makeTensorFromDataId(\n               outInfo.dataId, outInfo.shape, outInfo.dtype) as T;\n  }\n\n  makeTensorInfo(\n      shape: number[], dtype: DataType,\n      values?: BackendValues|string[]): TensorInfo {\n    let dataId;\n    if (dtype === 'string' && values != null && values.length > 0 &&\n        util.isString(values[0])) {\n      const encodedValues =\n          (values as {} as string[]).map(d => util.encodeString(d));\n\n      dataId = this.write(encodedValues, shape, dtype);\n    } else {\n      dataId = this.write(values as TypedArray, shape, dtype);\n    }\n\n    this.texData.get(dataId).usage = null;\n    return {dataId, shape, dtype};\n  }\n\n  private makeOutput<T extends Tensor>(\n      shape: number[], dtype: DataType, values?: BackendValues): T {\n    const {dataId} = this.makeTensorInfo(shape, dtype, values);\n    return engine().makeTensorFromDataId(dataId, shape, dtype, this) as T;\n  }\n\n  private unpackTensor(input: TensorInfo): TensorInfo {\n    const program = new UnpackProgram(input.shape);\n    return this.runWebGLProgram(program, [input], input.dtype);\n  }\n\n  private packTensor(input: TensorInfo): TensorInfo {\n    const program = new PackProgram(input.shape);\n    const preventEagerUnpackingOutput = true;\n    return this.runWebGLProgram(\n        program, [input], input.dtype, null /* customSetup */,\n        preventEagerUnpackingOutput);\n  }\n\n  private packedReshape(input: TensorInfo, afterShape: number[]): TensorInfo {\n    const input3DShape = [\n      webgl_util.getBatchDim(input.shape),\n      ...webgl_util.getRowsCols(input.shape)\n    ] as [number, number, number];\n    const input3D: TensorInfo = {\n      dtype: input.dtype,\n      shape: input3DShape,\n      dataId: input.dataId\n    };\n    const afterShapeAs3D = [\n      webgl_util.getBatchDim(afterShape), ...webgl_util.getRowsCols(afterShape)\n    ] as [number, number, number];\n\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const output = this.runWebGLProgram(\n        program, [input3D], input.dtype, null /* customSetup */,\n        preventEagerUnpackingOfOutput);\n    return {dataId: output.dataId, shape: afterShape, dtype: output.dtype};\n  }\n\n  private decode(dataId: DataId): TensorInfo {\n    const texData = this.texData.get(dataId);\n    const {isPacked, shape, dtype} = texData;\n    const shapeAs3D =\n        webgl_util.getShapeAs3D(shape) as [number, number, number];\n    let program;\n    if (isPacked) {\n      program = new DecodeMatrixPackedProgram(shapeAs3D);\n    } else {\n      program = new DecodeMatrixProgram(shapeAs3D);\n    }\n    const preventEagerUnpackingOfOutput = true;\n    const out = this.runWebGLProgram(\n        program, [{shape: shapeAs3D, dtype, dataId}], dtype,\n        null /* customSetup */, preventEagerUnpackingOfOutput);\n    return {dtype, shape, dataId: out.dataId};\n  }\n\n  runWebGLProgram(\n      program: GPGPUProgram, inputs: TensorInfo[], outputDtype: DataType,\n      customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void,\n      preventEagerUnpackingOfOutput = false): TensorInfo {\n    const output = this.makeTensorInfo(program.outputShape, outputDtype);\n    const outData = this.texData.get(output.dataId);\n    if (program.packedOutput) {\n      outData.isPacked = true;\n    }\n    if (program.outPackingScheme === tex_util.PackingScheme.DENSE) {\n      const texelShape = tex_util.getDenseTexShape(program.outputShape);\n      // For a densely packed output, we explicitly set texShape\n      // so it doesn't get assigned later according to our typical packing\n      // scheme wherein a single texel can only contain values from adjacent\n      // rows/cols.\n      outData.texShape = texelShape.map(d => d * 2) as [number, number];\n    }\n    if (program.outTexUsage != null) {\n      outData.usage = program.outTexUsage;\n    }\n    if (util.sizeFromShape(output.shape) === 0) {\n      // Short-circuit the computation since the result is empty (has 0 in its\n      // shape).\n      outData.values =\n          util.getTypedArrayFromDType(output.dtype as 'float32', 0);\n      return output;\n    }\n\n    const dataToDispose: TensorInfo[] = [];\n    const inputsData: TensorData[] = inputs.map(input => {\n      if (input.dtype === 'complex64') {\n        throw new Error(\n            `GPGPUProgram does not support complex64 input. For complex64 ` +\n            `dtypes, please separate the program into real and imaginary ` +\n            `parts.`);\n      }\n\n      let texData = this.texData.get(input.dataId);\n\n      if (texData.texture == null) {\n        if (!program.packedInputs &&\n            util.sizeFromShape(input.shape) <=\n                env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n          // Upload small tensors that live on the CPU as uniforms, not as\n          // textures. Do this only when the environment supports 32bit floats\n          // due to problems when comparing 16bit floats with 32bit floats.\n          // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n          // possible for packed shaders to sample from uniforms.\n          return {\n            shape: input.shape,\n            texData: null,\n            isUniform: true,\n            uniformValues: texData.values as TypedArray\n          };\n        }\n\n        // This ensures that if a packed program's inputs have not yet been\n        // uploaded to the GPU, they get uploaded as packed right off the bat.\n        if (program.packedInputs) {\n          texData.isPacked = true;\n          texData.shape = input.shape;\n        }\n      } else if (!!texData.isPacked !== !!program.packedInputs) {\n        input = texData.isPacked ? this.unpackTensor(input) :\n                                   this.packTensor(input);\n        dataToDispose.push(input);\n        texData = this.texData.get(input.dataId);\n      } else if (\n          texData.isPacked &&\n          !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n        // This is a special case where a texture exists for a tensor\n        // but the shapes are incompatible (due to packing constraints) because\n        // the tensor did not have a chance to go through the packed reshape\n        // shader. This only happens when we reshape the *same* tensor to form\n        // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n        // case will disappear once packed uploading is the default.\n\n        const savedInput = input;\n        const targetShape = input.shape;\n\n        input.shape = texData.shape;\n        input = this.packedReshape(input as Tensor, targetShape);\n        dataToDispose.push(input);\n        texData = this.texData.get(input.dataId);\n\n        savedInput.shape = targetShape;\n      }\n\n      this.uploadToGPU(input.dataId);\n      return {shape: input.shape, texData, isUniform: false};\n    });\n\n    this.uploadToGPU(output.dataId);\n    const outputData:\n        TensorData = {shape: output.shape, texData: outData, isUniform: false};\n    const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n    const binary = this.getAndSaveBinary(key, () => {\n      return gpgpu_math.compileProgram(\n          this.gpgpu, program, inputsData, outputData);\n    });\n    const shouldTimeProgram = this.activeTimers != null;\n    let query: WebGLQuery|CPUTimerQuery;\n    if (shouldTimeProgram) {\n      query = this.startTimer();\n    }\n\n    gpgpu_math.runProgram(\n        this.gpgpu, binary, inputsData, outputData, customSetup);\n\n    dataToDispose.forEach(info => this.disposeIntermediateTensorInfo(info));\n\n    if (shouldTimeProgram) {\n      query = this.endTimer(query);\n      this.activeTimers.push(\n          {name: program.constructor.name, query: this.getQueryTime(query)});\n    }\n\n    const glFlushThreshold = env().get('WEBGL_FLUSH_THRESHOLD');\n    // Manually GL flush requested\n    if (glFlushThreshold > 0) {\n      const time = util.now();\n      if ((time - this.lastGlFlushTime) > glFlushThreshold) {\n        this.gpgpu.gl.flush();\n        this.lastGlFlushTime = time;\n      }\n    }\n\n    if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&\n        preventEagerUnpackingOfOutput === false) {\n      const unpacked = this.unpackTensor(output);\n      this.disposeIntermediateTensorInfo(output);\n      return unpacked;\n    }\n    return output;\n  }\n\n  compileAndRun(\n      program: GPGPUProgram, inputs: TensorInfo[], outputDtype?: DataType,\n      customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void,\n      preventEagerUnpackingOfOutput = false): TensorInfo {\n    outputDtype = outputDtype || inputs[0].dtype;\n    const outInfo = this.runWebGLProgram(\n        program, inputs, outputDtype, customSetup,\n        preventEagerUnpackingOfOutput);\n    return outInfo;\n  }\n\n  private getAndSaveBinary(key: string, getBinary: () => GPGPUBinary):\n      GPGPUBinary {\n    if (!(key in this.binaryCache)) {\n      this.binaryCache[key] = getBinary();\n    }\n    return this.binaryCache[key];\n  }\n\n  getTextureManager(): TextureManager {\n    return this.textureManager;\n  }\n\n  private disposed = false;\n\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    // Avoid disposing the compiled webgl programs during unit testing because\n    // it slows down test execution.\n    if (!env().getBool('IS_TEST')) {\n      const allKeys = Object.keys(this.binaryCache);\n      allKeys.forEach(key => {\n        this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n        delete this.binaryCache[key];\n      });\n    }\n    this.textureManager.dispose();\n    if (this.canvas != null &&\n        (typeof (HTMLCanvasElement) !== 'undefined' &&\n         this.canvas instanceof HTMLCanvasElement)) {\n      this.canvas.remove();\n    } else {\n      this.canvas = null;\n    }\n    if (this.gpgpuCreatedLocally) {\n      this.gpgpu.program = null;\n      this.gpgpu.dispose();\n    }\n    this.disposed = true;\n  }\n\n  floatPrecision(): 16|32 {\n    if (this.floatPrecisionValue == null) {\n      this.floatPrecisionValue = tidy(() => {\n        if (!env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n          // Momentarily switching DEBUG flag to false so we don't throw an\n          // error trying to upload a small value.\n          const debugFlag = env().getBool('DEBUG');\n          env().set('DEBUG', false);\n          const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n          env().set('DEBUG', debugFlag);\n\n          if (underflowCheckValue > 0) {\n            return 32;\n          }\n        }\n        return 16;\n      });\n    }\n    return this.floatPrecisionValue;\n  }\n\n  /** Returns the smallest representable number.  */\n  epsilon(): number {\n    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n  }\n\n  uploadToGPU(dataId: DataId): void {\n    const texData = this.texData.get(dataId);\n    const {shape, dtype, values, texture, usage, isPacked} = texData;\n\n    if (texture != null) {\n      // Array is already on GPU. No-op.\n      return;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start: number;\n    if (shouldTimeProgram) {\n      start = util.now();\n    }\n\n    let texShape = texData.texShape;\n    if (texShape == null) {\n      texShape = webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n      texData.texShape = texShape;\n    }\n\n    if (values != null) {\n      const shapeAs3D = webgl_util.getShapeAs3D(shape);\n\n      let program;\n      let width = texShape[1], height = texShape[0];\n      const isByteArray = values instanceof Uint8Array;\n\n      if (isPacked) {\n        [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n            texShape[0], texShape[1]);\n        program = new EncodeMatrixPackedProgram(\n            shapeAs3D, [height, width], isByteArray);\n      } else {\n        program =\n            new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);\n      }\n\n      const tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);\n      if (isByteArray) {\n        this.texData.get(tempDenseInputHandle.dataId).usage =\n            TextureUsage.PIXELS;\n      } else {\n        this.texData.get(tempDenseInputHandle.dataId).usage =\n            TextureUsage.UPLOAD;\n      }\n      this.gpgpu.uploadDenseMatrixToTexture(\n          this.getTexture(tempDenseInputHandle.dataId), width, height,\n          values as TypedArray);\n\n      // We want the output to remain packed regardless of the value of\n      // WEBGL_PACK.\n      const preventEagerUnpacking = true;\n      const encodedOutputTarget = this.runWebGLProgram(\n          program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);\n\n      // Have the original texture assume the identity of the encoded output.\n      const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n      texData.texture = outputTexData.texture;\n      texData.texShape = outputTexData.texShape;\n      texData.isPacked = outputTexData.isPacked;\n      texData.usage = outputTexData.usage;\n\n      this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n      this.texData.delete(encodedOutputTarget.dataId);\n\n      // Once uploaded, don't store the values on cpu.\n      texData.values = null;\n      if (shouldTimeProgram) {\n        this.uploadWaitMs += util.now() - start;\n      }\n    } else {\n      const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n      texData.texture = newTexture;\n    }\n  }\n\n  private convertAndCacheOnCPU(dataId: DataId, float32Values?: Float32Array):\n      TypedArray {\n    const texData = this.texData.get(dataId);\n    const {dtype} = texData;\n\n    this.releaseGPUData(dataId);\n\n    if (float32Values != null) {\n      texData.values = float32ToTypedArray(float32Values, dtype as 'float32');\n    }\n    return texData.values as TypedArray;\n  }\n\n  private acquireTexture(\n      texShape: [number, number], texType: TextureUsage, dtype: DataType,\n      isPacked: boolean): WebGLTexture {\n    this.numBytesInGPU += this.computeBytes(texShape, dtype);\n    if (!this.warnedAboutMemory &&\n        this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n      const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n      this.warnedAboutMemory = true;\n      console.warn(\n          `High memory usage in GPU: ${mb} MB, ` +\n          `most likely due to a memory leak`);\n    }\n    return this.textureManager.acquireTexture(texShape, texType, isPacked);\n  }\n\n  private computeBytes(shape: [number, number], dtype: DataType) {\n    return shape[0] * shape[1] * util.bytesPerElement(dtype);\n  }\n}\n\nfunction float32ToTypedArray<D extends NumericDataType>(\n    a: Float32Array, dtype: D): tf.DataTypeMap[D] {\n  if (dtype === 'float32' || dtype === 'complex64') {\n    return a as tf.DataTypeMap[D];\n  } else if (dtype === 'int32' || dtype === 'bool') {\n    const result = (dtype === 'int32') ? new Int32Array(a.length) :\n                                         new Uint8Array(a.length);\n    for (let i = 0; i < result.length; ++i) {\n      result[i] = Math.round(a[i]);\n    }\n    return result as tf.DataTypeMap[D];\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\n","/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '3.7.0';\nexport {version};\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from '@tensorflow/tfjs-core';\n\nimport * as gpgpu_util from './gpgpu_util';\nimport * as webgl_util from './webgl_util';\n\nexport {MathBackendWebGL, WebGLMemoryInfo, WebGLTimingInfo} from './backend_webgl';\nexport {setWebGLContext} from './canvas_util';\nexport {GPGPUContext} from './gpgpu_context';\nexport {GPGPUProgram} from './gpgpu_math';\n// WebGL specific utils.\nexport {gpgpu_util, webgl_util};\n\n/**\n * Enforce use of half precision textures if available on the platform.\n *\n * @doc {heading: 'Environment', namespace: 'webgl'}\n */\nexport function forceHalfFloat(): void {\n  env().set('WEBGL_FORCE_F16_TEXTURES', true);\n}\n","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// base.ts is the webgl backend without auto kernel registration.\n\nimport {device_util, registerBackend} from '@tensorflow/tfjs-core';\nimport {MathBackendWebGL} from './backend_webgl';\nexport {version as version_webgl} from './version';\n\nif (device_util.isBrowser()) {\n  registerBackend('webgl', () => new MathBackendWebGL(), 2 /* priority */);\n}\n\n// Export webgl utilities\nexport * from './webgl';\n\n// Export forceHalfFlost under webgl namespace for the union bundle.\nimport {forceHalfFloat} from './webgl';\nexport const webgl = {forceHalfFloat};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport const CHECK_NAN_SNIPPET = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\n\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport class BinaryOpProgram implements GPGPUProgram {\n  variableNames = ['A', 'B'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(op: string, aShape: number[], bShape: number[]) {\n    this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n    this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, util} from '@tensorflow/tfjs-core';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getChannels} from './packing_util';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport const CHECK_NAN_SNIPPET = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\n\nexport const ELU_DER = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\n\nexport const NOT_EQUAL = `\n  return vec4(notEqual(a, b));\n`;\n\nexport class BinaryOpPackedProgram implements GPGPUProgram {\n  variableNames = ['A', 'B'];\n  outputShape: number[];\n  userCode: string;\n  supportsBroadcasting = true;\n  packedInputs = true;\n  packedOutput = true;\n\n  constructor(\n      op: string, aShape: number[], bShape: number[],\n      checkOutOfBounds = false) {\n    this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n    const rank = this.outputShape.length;\n    let checkOutOfBoundsString = '';\n    if (checkOutOfBounds) {\n      if (rank === 0 || util.sizeFromShape(this.outputShape) === 1) {\n        checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n      } else {\n        const dtype = getCoordsDataType(rank);\n        checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n        if (rank === 1) {\n          checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n        } else {\n          const channels = getChannels('coords', rank);\n          checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n        }\n      }\n    }\n\n    this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Identity, IdentityInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nexport function identity(\n    args: {inputs: IdentityInputs, backend: MathBackendWebGL}): TensorInfo {\n  const {inputs, backend} = args;\n  const {x} = inputs;\n\n  backend.incRef(x.dataId);\n\n  return {dataId: x.dataId, shape: x.shape, dtype: x.dtype};\n}\n\nexport const identityConfig: KernelConfig = {\n  kernelName: Identity,\n  backendName: 'webgl',\n  kernelFunc: identity as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Complex, ComplexInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {identity} from './Identity';\n\n/**\n * In WebGL data is stored in GPU textures which can't be efficiently copied, so\n * complex tensors share data with their real and imaginary components. Complex\n * tensors' reference to the components is tracked by refCount on the individual\n * component. The refCounts are increased by the identity call.\n *\n * When a complex tensor is disposed, it will reduce the refCount on the\n * components by calling disposeData on each.\n */\nexport function complex(\n    args: {inputs: ComplexInputs, backend: MathBackendWebGL}): TensorInfo {\n  const {inputs, backend} = args;\n  const {real, imag} = inputs;\n\n  const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n  const complex = backend.texData.get(complexInfo.dataId);\n\n  const realTensorInfo = identity({inputs: {x: real}, backend});\n\n  const imagTensorInfo = identity({inputs: {x: imag}, backend});\n\n  complex.complexTensorInfos = {real: realTensorInfo, imag: imagTensorInfo};\n\n  return complexInfo;\n}\n\nexport const complexConfig: KernelConfig = {\n  kernelName: Complex,\n  backendName: 'webgl',\n  kernelFunc: complex as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, KernelConfig, KernelFunc, LeakyRelu, LeakyReluAttrs, LeakyReluInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {BinaryOpProgram} from '../binaryop_gpu';\nimport {BinaryOpPackedProgram} from '../binaryop_packed_gpu';\n\nexport const LEAKYRELU = `return (a < 0.) ? b * a : a;`;\nexport const LEAKYRELU_PACKED = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\n\nexport function leakyRelu(args: {\n  inputs: LeakyReluInputs,\n  backend: MathBackendWebGL,\n  attrs: LeakyReluAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {alpha} = attrs;\n\n  const $alpha = backend.makeTensorInfo(\n      [], 'float32',\n      util.createScalarValue(alpha as {} as 'float32', 'float32'));\n\n  const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n      new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) :\n      new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape);\n  const result = backend.runWebGLProgram(program, [x, $alpha], x.dtype);\n\n  backend.disposeIntermediateTensorInfo($alpha);\n\n  return result;\n}\n\nexport const leakyReluConfig: KernelConfig = {\n  kernelName: LeakyRelu,\n  backendName: 'webgl',\n  kernelFunc: leakyRelu as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, KernelConfig, KernelFunc, Prelu, PreluInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {BinaryOpProgram} from '../binaryop_gpu';\nimport {BinaryOpPackedProgram} from '../binaryop_packed_gpu';\n\nexport const PRELU = `return (a < 0.) ? b * a : a;`;\nexport const PRELU_PACKED = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\n\nexport function prelu(args: {inputs: PreluInputs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend} = args;\n  const {x, alpha} = inputs;\n\n  const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n      new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha.shape) :\n      new BinaryOpProgram(PRELU, x.shape, alpha.shape);\n  return backend.runWebGLProgram(program, [x, alpha], x.dtype);\n}\n\nexport const preluConfig: KernelConfig = {\n  kernelName: Prelu,\n  backendName: 'webgl',\n  kernelFunc: prelu as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, BinaryInputs, DataType, env, KernelFunc, TypedArray, UnaryInputs, upcastType} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {BinaryOpProgram} from '../binaryop_gpu';\nimport {BinaryOpPackedProgram} from '../binaryop_packed_gpu';\nimport {complex} from '../kernels/Complex';\nimport {LEAKYRELU, LEAKYRELU_PACKED} from '../kernels/LeakyRelu';\nimport {PRELU, PRELU_PACKED} from '../kernels/Prelu';\nimport * as unary_op from '../unaryop_gpu';\nimport {UnaryOpProgram} from '../unaryop_gpu';\nimport * as unary_packed_op from '../unaryop_packed_gpu';\nimport {UnaryOpPackedProgram} from '../unaryop_packed_gpu';\n\nimport {SimpleBinaryKernelImplCPU, SimpleUnaryKernelImplCPU} from './shared';\n\nexport const CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\n\nexport const CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\n\nexport const CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\n\ntype UnaryKernelFuncConfig = {\n  opSnippet: string,\n  packedOpSnippet?: string,\n  cpuKernelImpl?: SimpleUnaryKernelImplCPU,\n  dtype?: DataType\n};\n\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param opSnippet Op snippet to create `UnaryOpProgram`.\n * @param packedOpSnippet Op snippet to create `UnaryOpPackedProgram`.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function unaryKernelFunc(\n    {opSnippet, packedOpSnippet, cpuKernelImpl, dtype}: UnaryKernelFuncConfig):\n    KernelFunc {\n  return ({inputs, backend}) => {\n    const {x} = inputs as UnaryInputs;\n    const webglBackend = backend as MathBackendWebGL;\n\n    const $dtype = dtype || x.dtype;\n    if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {\n      const xData = webglBackend.texData.get(x.dataId);\n      const outValues = cpuKernelImpl(xData.values as TypedArray, $dtype);\n      return webglBackend.makeTensorInfo(x.shape, $dtype, outValues);\n    }\n\n    const shouldUsePackedProgram =\n        env().getBool('WEBGL_PACK_UNARY_OPERATIONS') && packedOpSnippet != null;\n    let program: UnaryOpProgram|UnaryOpPackedProgram;\n    if (shouldUsePackedProgram) {\n      program = new UnaryOpPackedProgram(x.shape, packedOpSnippet);\n    } else {\n      program = new UnaryOpProgram(x.shape, opSnippet);\n    }\n\n    return webglBackend.runWebGLProgram(program, [x], $dtype);\n  };\n}\n\ntype BinaryKernelFuncConfig = {\n  opSnippet: string,\n  packedOpSnippet?: string,\n  checkOutOfBounds?: boolean,\n  supportsComplex?: boolean,\n  cpuKernelImpl?: SimpleBinaryKernelImplCPU,\n  dtype?: DataType\n};\n\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param opSnippet Op snippet to create `BinaryOpProgram`.\n * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.\n * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true\n *     when creating BinaryOpPackedProgram.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc({\n  opSnippet,\n  packedOpSnippet,\n  checkOutOfBounds = false,\n  supportsComplex = false,\n  cpuKernelImpl,\n  dtype\n}: BinaryKernelFuncConfig): KernelFunc {\n  return ({inputs, backend}) => {\n    const {a, b} = inputs as BinaryInputs;\n    const webglBackend = backend as MathBackendWebGL;\n\n    if (supportsComplex && a.dtype === 'complex64') {\n      const aData = webglBackend.texData.get(a.dataId);\n      const bData = webglBackend.texData.get(b.dataId);\n\n      const [real, imag] = [\n        [aData.complexTensorInfos.real, bData.complexTensorInfos.real],\n        [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]\n      ].map(complexParts => {\n        const [aPart, bPart] = complexParts;\n\n        const aHandle = {\n          dataId: aPart.dataId,\n          dtype: aPart.dtype,\n          shape: a.shape\n        };\n        const bHandle = {\n          dataId: bPart.dataId,\n          dtype: bPart.dtype,\n          shape: b.shape\n        };\n\n        const program = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n        return webglBackend.runWebGLProgram(\n            program, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n      });\n\n      const complexOutput =\n          complex({inputs: {real, imag}, backend: webglBackend});\n\n      webglBackend.disposeIntermediateTensorInfo(real);\n      webglBackend.disposeIntermediateTensorInfo(imag);\n\n      // TODO(annxingyuan): Implement CPU forwarding for complex inputs.\n\n      return complexOutput;\n    }\n\n    const $dtype = dtype || upcastType(a.dtype, b.dtype);\n    if ((a.dtype === 'string' || b.dtype === 'string' ||\n         webglBackend.shouldExecuteOnCPU([a, b])) &&\n        cpuKernelImpl != null) {\n      const aVals = webglBackend.texData.get(a.dataId).values as TypedArray;\n      const bVals = webglBackend.texData.get(b.dataId).values as TypedArray;\n\n      const decodedAVals = a.dtype === 'string' ?\n          // tslint:disable-next-line: no-any\n          backend_util.fromUint8ToStringArray(aVals as any as Uint8Array[]) :\n          aVals;\n      const decodedBVals = a.dtype === 'string' ?\n          // tslint:disable-next-line: no-any\n          backend_util.fromUint8ToStringArray(bVals as any as Uint8Array[]) :\n          bVals;\n      const [outValues, outShape] =\n          cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);\n\n      const out = webglBackend.makeTensorInfo(outShape, $dtype);\n      const outData = webglBackend.texData.get(out.dataId);\n      outData.values = outValues;\n      return out;\n    }\n\n    const shouldUsePackedProgram =\n        env().getBool('WEBGL_PACK_BINARY_OPERATIONS') &&\n        packedOpSnippet != null;\n    let program: BinaryOpProgram|BinaryOpPackedProgram;\n    if (shouldUsePackedProgram) {\n      program = new BinaryOpPackedProgram(\n          packedOpSnippet, a.shape, b.shape, checkOutOfBounds);\n    } else {\n      program = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n    }\n\n    return webglBackend.runWebGLProgram(program, [a, b], $dtype);\n  };\n}\n\nexport function mapActivationToShaderProgram(\n    activation: backend_util.Activation, packed = false): string {\n  if (activation === 'linear') {\n    if (packed) {\n      return unary_packed_op.LINEAR;\n    }\n    return unary_op.LINEAR;\n  } else if (activation === 'relu') {\n    if (packed) {\n      return unary_packed_op.RELU;\n    }\n    return unary_op.RELU;\n  } else if (activation === 'elu') {\n    if (packed) {\n      return unary_packed_op.ELU;\n    }\n    return unary_op.ELU;\n  } else if (activation === 'relu6') {\n    if (packed) {\n      return unary_packed_op.RELU6;\n    }\n    return unary_op.RELU6;\n  } else if (activation === 'prelu') {\n    if (packed) {\n      return PRELU_PACKED;\n    }\n    return PRELU;\n  } else if (activation === 'leakyrelu') {\n    if (packed) {\n      return LEAKYRELU_PACKED;\n    }\n    return LEAKYRELU;\n  } else if (activation === 'sigmoid') {\n    if (packed) {\n      return unary_packed_op.SIGMOID;\n    }\n    return unary_op.SIGMOID;\n  }\n  throw new Error(`Activation ${\n      activation} has not been implemented for the WebGL backend.`);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MatMulPackedProgram implements GPGPUProgram {\n  variableNames = ['matrixA', 'matrixB'];\n  packedInputs = true;\n  packedOutput = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      aShape: [number, number, number], bShape: [number, number, number],\n      outputShape: [number, number, number], transposeA = false,\n      transposeB = false, addBias = false, activation: string = null,\n      hasPreluActivation = false, hasLeakyreluActivation = false) {\n    this.outputShape = outputShape;\n\n    const sharedDim = transposeA ? aShape[1] : aShape[2];\n    const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n\n    const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';\n    const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';\n    const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n    const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n\n    let activationSnippet = '', applyActivationSnippet = '';\n    if (activation) {\n      if (hasPreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n      } else if (hasLeakyreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${activation}\n        }`;\n      } else {\n        activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n      }\n\n      applyActivationSnippet = `result = activation(result);`;\n    }\n\n    const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n    if (addBias) {\n      this.variableNames.push('bias');\n    }\n\n    if (hasPreluActivation) {\n      this.variableNames.push('preluActivationWeights');\n    }\n\n    if (hasLeakyreluActivation) {\n      this.variableNames.push('leakyreluAlpha');\n    }\n\n    let batchASnippet = 'rc.x';\n    let batchBSnippet = 'rc.x';\n    if (aShape[0] < bShape[0]) {\n      batchASnippet = `int(min(float(rc.x), ${aShape[0] - 1}.))`;\n    } else if (bShape[0] < aShape[0]) {\n      batchBSnippet = `int(min(float(rc.x), ${bShape[0] - 1}.))`;\n    }\n\n    this.userCode = `\n      ${activationSnippet}\n\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          int batchA = ${batchASnippet};\n          int batchB = ${batchBSnippet};\n          vec4 a = getMatrixA(batchA, ${aSample});\n          vec4 b = getMatrixB(batchB, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n  REAL: 'return areal * breal - aimag * bimag;',\n  IMAG: 'return areal * bimag + aimag * breal;'\n};\n\nexport class BinaryOpComplexProgram implements GPGPUProgram {\n  variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(op: string, aShape: number[], bShape: number[]) {\n    this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n\n    this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, BinaryInputs, env, KernelConfig, Multiply, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport * as binaryop_complex_gpu from '../binaryop_complex_gpu';\nimport {BinaryOpComplexProgram} from '../binaryop_complex_gpu';\nimport {BinaryOpProgram} from '../binaryop_gpu';\nimport {BinaryOpPackedProgram} from '../binaryop_packed_gpu';\nimport {multiplyImplCPU as cpuMultiply} from '../kernel_utils/shared';\n\nimport {complex} from './Complex';\n\nconst MUL = 'return a * b;';\n\nexport function multiply(\n    args: {inputs: BinaryInputs, backend: MathBackendWebGL}): TensorInfo {\n  const {inputs, backend} = args;\n  const {a, b} = inputs;\n  const dtype = backend_util.upcastType(a.dtype, b.dtype);\n\n  if (a.dtype === 'complex64') {\n    const aData = backend.texData.get(a.dataId);\n    const bData = backend.texData.get(b.dataId);\n\n    const realProgram = new BinaryOpComplexProgram(\n        binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n    const imagProgram = new BinaryOpComplexProgram(\n        binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n\n    const inputs = [\n      {\n        dataId: aData.complexTensorInfos.real.dataId,\n        dtype: aData.complexTensorInfos.real.dtype,\n        shape: a.shape\n      },\n      {\n        dataId: aData.complexTensorInfos.imag.dataId,\n        dtype: aData.complexTensorInfos.imag.dtype,\n        shape: a.shape\n      },\n      {\n        dataId: bData.complexTensorInfos.real.dataId,\n        dtype: bData.complexTensorInfos.real.dtype,\n        shape: b.shape\n      },\n      {\n        dataId: bData.complexTensorInfos.imag.dataId,\n        dtype: bData.complexTensorInfos.imag.dtype,\n        shape: b.shape\n      }\n    ];\n\n    const realPart = backend.runWebGLProgram(realProgram, inputs, 'float32');\n    const imagPart = backend.runWebGLProgram(imagProgram, inputs, 'float32');\n\n    const complexOutput =\n        complex({inputs: {real: realPart, imag: imagPart}, backend});\n\n    backend.disposeIntermediateTensorInfo(realPart);\n    backend.disposeIntermediateTensorInfo(imagPart);\n\n    // TODO(annxingyuan): CPU forwarding for complex inputs.\n    return complexOutput;\n  }\n\n  if (backend.shouldExecuteOnCPU([a, b])) {\n    const aData = backend.texData.get(a.dataId);\n    const bData = backend.texData.get(b.dataId);\n    const [outValues, outShape] = cpuMultiply(\n        a.shape, b.shape, aData.values as TypedArray,\n        bData.values as TypedArray, dtype);\n\n    const out = backend.makeTensorInfo(outShape, dtype);\n    const outData = backend.texData.get(out.dataId);\n    outData.values = outValues;\n    return out;\n  }\n\n  let program: BinaryOpProgram|BinaryOpPackedProgram;\n  if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n    program = new BinaryOpPackedProgram(MUL, a.shape, b.shape);\n  } else {\n    program = new BinaryOpProgram(MUL, a.shape, b.shape);\n  }\n\n  return backend.runWebGLProgram(program, [a, b], dtype);\n}\n\nexport const multiplyConfig: KernelConfig = {\n  kernelName: Multiply,\n  backendName: 'webgl',\n  kernelFunc: multiply\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {ReshapePackedProgram} from '../reshape_packed_gpu';\nimport {getBatchDim, getRowsCols} from '../webgl_util';\n\nexport function packedReshape(\n    input: TensorInfo, afterShape: number[],\n    backend: MathBackendWebGL): TensorInfo {\n  const input3DShape =\n      [getBatchDim(input.shape),\n       ...getRowsCols(input.shape)] as [number, number, number];\n  const input3D: TensorInfo = {\n    dtype: input.dtype,\n    shape: input3DShape,\n    dataId: input.dataId\n  };\n  const afterShapeAs3D =\n      [getBatchDim(afterShape),\n       ...getRowsCols(afterShape)] as [number, number, number];\n\n  const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n  const preventEagerUnpackingOfOutput = true;\n  const output = backend.runWebGLProgram(\n      program, [input3D], input.dtype, null /* customSetup */,\n      preventEagerUnpackingOfOutput);\n  return {dataId: output.dataId, shape: afterShape, dtype: output.dtype};\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Reshape, ReshapeAttrs, ReshapeInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {packedReshape} from '../kernel_utils/reshape';\nimport {isReshapeFree} from '../webgl_util';\n\nexport function reshape(args: {\n  inputs: ReshapeInputs,\n  backend: MathBackendWebGL,\n  attrs: ReshapeAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {shape} = attrs;\n  const webglBackend = backend;\n\n  const xSize = util.sizeFromShape(x.shape);\n  const $shape = util.inferFromImplicitShape(shape, xSize);\n  const $xSize = util.sizeFromShape($shape);\n\n  util.assert(\n      xSize === $xSize,\n      () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n          `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n          `shape must have the same number of elements.`);\n\n  const xTexData = webglBackend.texData.get(x.dataId);\n  if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) &&\n      !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n    return packedReshape(x, $shape, webglBackend);\n  }\n\n  webglBackend.incRef(x.dataId);\n\n  return {dataId: x.dataId, shape: $shape, dtype: x.dtype};\n}\n\nexport const reshapeConfig: KernelConfig = {\n  kernelName: Reshape,\n  backendName: 'webgl',\n  kernelFunc: reshape as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MeanProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(reduceInfo: backend_util.ReduceInfo, divisor?: number) {\n    const {windowSize, batchSize, inSize, outSize} = reduceInfo;\n    this.outputShape = [batchSize, outSize];\n\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n\n    let updateSnippet = `sumValue += dot(values, ones);`;\n    if (divisor != null) {\n      const denominator = 1 / divisor;\n      updateSnippet = `sumValue += dot(values * ${\n          util.isInt(denominator) ? denominator.toPrecision(2) :\n                                    denominator}, ones);`;\n    }\n\n    let checkOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return 0.0;\n        }\n      `;\n    }\n\n    this.userCode = `\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${updateSnippet}\n        }\n        setOutput(sumValue);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ReduceProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      reduceInfo: backend_util.ReduceInfo,\n      reduceType: 'all'|'any'|'max'|'min'|'sum'|'prod') {\n    const {windowSize, batchSize, inSize, outSize} = reduceInfo;\n    this.outputShape = [batchSize, outSize];\n\n    let initializationValue = '0.0';\n    let compareOp = ``;\n\n    if (reduceType === 'prod') {\n      initializationValue = '1.0';\n    } else if (reduceType === 'min') {\n      // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n      initializationValue = '1.0 / 1e-20';\n      compareOp = `min`;\n    } else if (reduceType === 'max') {\n      // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n      initializationValue = '-1.0 / 1e-20';\n      compareOp = `max`;\n    }\n\n    let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n        'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n\n    if (reduceType === 'sum') {\n      returnValue = `sumValue`;\n    } else if (reduceType === 'prod') {\n      returnValue = `prodValue`;\n    } else if (reduceType === 'all') {\n      returnValue = `allValue`;\n    } else if (reduceType === 'any') {\n      returnValue = `anyValue`;\n    }\n\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n\n    let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n        if (${reduceType === 'min'} || ${reduceType === 'max'}) {\n          minMaxValue = ${compareOp}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `;\n\n    let vecType = `vec4`;\n\n    if (reduceType === 'all') {\n      initializationValue = '1.0';\n      updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    } else if (reduceType === 'any') {\n      initializationValue = '0.0';\n      updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    }\n\n    let checkOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DataType, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {MeanProgram} from '../mean_gpu';\nimport {ReduceProgram} from '../reduce_gpu';\n\ntype ReduceTypes = 'all'|'any'|'max'|'min'|'sum'|'prod'|'mean';\n\n// Returns an array of configuration objects that describe each stage of the\n// reduction.\nfunction getReductionStages(inShape: number[]):\n    Array<{inSize: number, windowSize: number, outSize: number}> {\n  const stages = [];\n\n  while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n    const outSize: number =\n        stages.length ? stages[stages.length - 1].outSize : inShape[1];\n    const windowSize = backend_util.computeOptimalWindowSize(outSize);\n    stages.push({\n      inSize: outSize,\n      windowSize,\n      outSize: Math.ceil(outSize / windowSize)\n    });\n  }\n\n  return stages;\n}\n\nexport function reduce(\n    x: TensorInfo, dtype: DataType, reductionType: ReduceTypes,\n    backend: MathBackendWebGL): TensorInfo {\n  const reductionStages = getReductionStages(x.shape);\n\n  let result = x;\n  for (let i = 0; i < reductionStages.length; i++) {\n    const {inSize, windowSize, outSize} = reductionStages[i];\n\n    let program: ReduceProgram|MeanProgram;\n    let previousResult: TensorInfo;\n    if (reductionType === 'mean') {\n      program = i === 0 ?\n          new MeanProgram(\n              {windowSize, inSize, batchSize: x.shape[0], outSize}, inSize) :\n          new MeanProgram({windowSize, inSize, batchSize: x.shape[0], outSize});\n    } else {\n      program = new ReduceProgram(\n          {windowSize, inSize, batchSize: x.shape[0], outSize}, reductionType);\n    }\n\n    previousResult = result;\n    result = backend.runWebGLProgram(program, [result], dtype);\n\n    if (previousResult.dataId !== x.dataId) {\n      backend.disposeIntermediateTensorInfo(previousResult);\n    }\n  }\n\n  return result;\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class TransposeProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  constructor(aShape: number[], newDim: number[]) {\n    const outputShape: number[] = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[newDim[i]];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const switched = getSwitchedCoords(newDim);\n\n    this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n  }\n}\n\nfunction getSwitchedCoords(newDim: number[]): string {\n  const rank = newDim.length;\n  if (rank > 6) {\n    throw Error(`Transpose for rank ${rank} is not yet supported`);\n  }\n  const originalOrder =\n      ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n  const switchedCoords = new Array(rank);\n  for (let i = 0; i < newDim.length; i++) {\n    switchedCoords[newDim[i]] = originalOrder[i];\n  }\n  return switchedCoords.join();\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getVecChannels} from './packing_util';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class TransposePackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n  packedInputs = true;\n  packedOutput = true;\n\n  constructor(aShape: number[], newDim: number[]) {\n    const outputShape: number[] = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[newDim[i]];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    if (this.rank > 6) {\n      throw Error(\n          `Packed transpose for rank ${this.rank} is not yet supported.`);\n    }\n    const dtype = getCoordsDataType(this.rank);\n\n    const outputOrder = getVecChannels('rc', this.rank);\n    const switchedOrder = new Array(this.rank);\n    for (let i = 0; i < newDim.length; i++) {\n      switchedOrder[newDim[i]] = outputOrder[i];\n    }\n    const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n    const nextColumn =\n        `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n    const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n\n    this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {transposeImplCPU} from '../kernel_utils/shared';\nimport {TransposeProgram} from '../transpose_gpu';\nimport {TransposePackedProgram} from '../transpose_packed_gpu';\n\nexport function transposeImpl(\n    x: TensorInfo, perm: number[], backend: MathBackendWebGL): TensorInfo {\n  const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n      new TransposePackedProgram(x.shape, perm) :\n      new TransposeProgram(x.shape, perm);\n  return backend.runWebGLProgram(program, [x], x.dtype);\n}\n\nexport {transposeImplCPU};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, sumOutType, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {reduce} from '../kernel_utils/reduce';\nimport {reshape} from './Reshape';\n\nimport {transposeImpl} from './Transpose_impl';\n\nexport function sumImpl(\n    x: TensorInfo, axis: number|number[], keepDims: boolean,\n    backend: MathBackendWebGL): TensorInfo {\n  const reductionIndices = axis;\n\n  const xRank = x.shape.length;\n\n  const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n  const sumInputIsTransposed = permutedAxes != null;\n\n  let sumInput = x;\n  if (sumInputIsTransposed) {\n    sumInput = transposeImpl(x, permutedAxes, backend);\n\n    axes = backend_util.getInnerMostAxes(axes.length, xRank);\n  }\n\n  backend_util.assertAxesAreInnerMostDims('sum', axes, xRank);\n  const [sumOutShape, reduceShape] =\n      backend_util.computeOutAndReduceShapes(sumInput.shape, axes);\n\n  let outShape = sumOutShape;\n  if (keepDims) {\n    // rather than reshape at the end, set the target shape here.\n    outShape = backend_util.expandShapeToKeepDim(sumOutShape, origAxes);\n  }\n\n  const inSize = util.sizeFromShape(reduceShape);\n  const xSize = util.sizeFromShape(x.shape);\n  const batchSize = xSize / inSize;\n  const reshapedInput = reshape(\n      {inputs: {x: sumInput}, attrs: {shape: [batchSize, inSize]}, backend});\n\n  const outType = sumOutType(x.dtype);\n\n  const reduced = reduce(reshapedInput, outType, 'sum', backend);\n  const out =\n      reshape({inputs: {x: reduced}, attrs: {shape: outShape}, backend});\n\n  backend.disposeIntermediateTensorInfo(reshapedInput);\n  backend.disposeIntermediateTensorInfo(reduced);\n  if (sumInputIsTransposed) {\n    backend.disposeIntermediateTensorInfo(sumInput);\n  }\n\n  return out;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Sum, SumAttrs, SumInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {sumImpl} from './Sum_impl';\n\nexport function sum(\n    args: {inputs: SumInputs, attrs: SumAttrs, backend: MathBackendWebGL}) {\n  const {inputs, backend, attrs} = args;\n\n  const {x} = inputs;\n  const {axis, keepDims} = attrs;\n\n  return sumImpl(x, axis, keepDims, backend);\n}\n\nexport const sumConfig: KernelConfig = {\n  kernelName: Sum,\n  backendName: 'webgl',\n  kernelFunc: sum as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, TensorInfo, Transpose, TransposeAttrs, TransposeInputs, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {transposeImpl} from './Transpose_impl';\nimport {transposeImplCPU as cpuTranspose} from './Transpose_impl';\n\nexport function transpose(args: {\n  inputs: TransposeInputs,\n  attrs: TransposeAttrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {perm} = attrs;\n  const webglBackend = backend;\n\n  const xRank = x.shape.length;\n\n  const newShape: number[] = new Array(xRank);\n  for (let i = 0; i < newShape.length; i++) {\n    newShape[i] = x.shape[perm[i]];\n  }\n\n  let out: TensorInfo;\n  if (webglBackend.shouldExecuteOnCPU([x])) {\n    const xTexData = webglBackend.texData.get(x.dataId);\n    const values = xTexData.values as TypedArray;\n    const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape);\n\n    out = webglBackend.makeTensorInfo(newShape, x.dtype);\n    const outData = webglBackend.texData.get(out.dataId);\n    outData.values = outValues;\n  } else {\n    out = transposeImpl(x, perm, webglBackend);\n  }\n  return out;\n}\n\nexport const transposeConfig: KernelConfig = {\n  kernelName: Transpose,\n  backendName: 'webgl',\n  kernelFunc: transpose as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, TensorInfo, upcastType, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {mapActivationToShaderProgram} from '../kernel_utils/kernel_funcs_utils';\nimport {MatMulPackedProgram} from '../mulmat_packed_gpu';\n\nimport {multiply} from './Multiply';\nimport {reshape} from './Reshape';\nimport {sum} from './Sum';\nimport {transpose} from './Transpose';\n\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\n\ntype BatchMatMulConfig = {\n  a: TensorInfo,\n  b: TensorInfo,\n  transposeA: boolean,\n  transposeB: boolean,\n  backend: MathBackendWebGL,\n  bias?: TensorInfo,\n  preluActivationWeights?: TensorInfo,\n  leakyreluAlpha?: number,\n  activation?: backend_util.Activation\n};\n\nexport function batchMatMulImpl({\n  a,\n  b,\n  transposeA,\n  transposeB,\n  backend,\n  bias = null,\n  preluActivationWeights = null,\n  leakyreluAlpha = 0,\n  activation = null\n}: BatchMatMulConfig): TensorInfo {\n  const aRank = a.shape.length;\n  const bRank = b.shape.length;\n\n  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n\n  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n\n  const outerDimsA = a.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n\n  const batchDimA = util.sizeFromShape(outerDimsA);\n  const batchDimB = util.sizeFromShape(outerDimsB);\n\n  const batchDimsCompatible =\n      batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;\n\n  util.assert(\n      aRank >= 2 && bRank >= 2 && batchDimsCompatible,\n      () => `Error in matMul: the input batch dimensions must either be the ` +\n          `same or at least one input batch dimension must be 1. Got input ` +\n          `batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);\n\n  const outShapeOuterDims =\n      batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2);\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n\n  util.assert(\n      innerShapeA === innerShapeB,\n      () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n          `${innerShapeB}) of Tensors with shapes ${a.shape} and ` +\n          `${b.shape} and transposeA=${transposeA}` +\n          ` and transposeB=${transposeB} must match.`);\n\n  const a3dShape: [number, number, number] = transposeA ?\n      [batchDimA, innerShapeA, outerShapeA] :\n      [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape: [number, number, number] = transposeB ?\n      [batchDimB, outerShapeB, innerShapeB] :\n      [batchDimB, innerShapeB, outerShapeB];\n\n  // The rest of the implementation is designed to operate on rank-3 tensors\n  const a3d = reshape({inputs: {x: a}, backend, attrs: {shape: a3dShape}});\n  const b3d = reshape({inputs: {x: b}, backend, attrs: {shape: b3dShape}});\n\n  const intermediates: TensorInfo[] = [a3d, b3d];\n\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation === 'leakyrelu';\n  const fusedActivation = activation != null ?\n      mapActivationToShaderProgram(activation, true) :\n      null;\n  const containsFusedOps = hasBias || hasPreluActivationWeights ||\n      hasLeakyreluAlpha || fusedActivation != null;\n  let out: TensorInfo;\n\n  // Since the matrices are vectors, it is faster to call mul().sum()\n  // because sum() is O(sqrt(N)) due to divide-and-conquer.\n  if ((outerShapeA === 1 || outerShapeB === 1) &&\n      sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) {\n    let aVec = a3d;\n    let bVec = b3d;\n    if (transposeA) {\n      aVec = transpose({inputs: {x: a3d}, backend, attrs: {perm: [0, 2, 1]}});\n      intermediates.push(aVec);\n    }\n    if (transposeB) {\n      bVec = transpose({inputs: {x: b3d}, backend, attrs: {perm: [0, 2, 1]}});\n      intermediates.push(bVec);\n    }\n\n    const shouldReshapeA = outerShapeB !== 1;\n    const shouldReshapeB = outerShapeB === 1;\n\n    let aVec3d = aVec;\n    if (shouldReshapeA) {\n      aVec3d = reshape({\n        inputs: {x: aVec},\n        backend,\n        attrs: {shape: [batchDim, sharedDim, 1]}\n      });\n\n      intermediates.push(aVec3d);\n    }\n\n    const axis = outerShapeB === 1 ? 2 : 1;\n\n    let bVec3d = bVec;\n    if (shouldReshapeB) {\n      bVec3d = reshape({\n        inputs: {x: bVec},\n        backend,\n        attrs: {shape: [batchDim, 1, sharedDim]}\n      });\n\n      intermediates.push(bVec3d);\n    }\n\n    const product = multiply({inputs: {a: aVec3d, b: bVec3d}, backend});\n    out = sum({inputs: {x: product}, backend, attrs: {axis, keepDims: true}});\n    intermediates.push(product);\n  } else {\n    const dtype = upcastType(a.dtype, b.dtype);\n\n    const program = new MatMulPackedProgram(\n        a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA,\n        transposeB, hasBias, fusedActivation, hasPreluActivationWeights,\n        hasLeakyreluAlpha);\n\n    const inputs: TensorInfo[] = [a3d, b3d];\n    if (bias != null) {\n      inputs.push(bias);\n    }\n    if (hasPreluActivationWeights) {\n      inputs.push(preluActivationWeights);\n    }\n    if (hasLeakyreluAlpha) {\n      const $leakyreluAlpha = backend.makeTensorInfo(\n          [], 'float32',\n          util.createScalarValue(leakyreluAlpha as {} as 'float32', 'float32'));\n      inputs.push($leakyreluAlpha);\n      intermediates.push($leakyreluAlpha);\n    }\n\n    out = backend.runWebGLProgram(program, inputs, dtype);\n  }\n\n  const outReshaped =\n      reshape({inputs: {x: out}, backend, attrs: {shape: outShape}});\n  intermediates.push(out);\n  for (const i of intermediates) {\n    backend.disposeIntermediateTensorInfo(i);\n  }\n  return outReshaped;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {batchMatMulImpl} from './BatchMatMul_impl';\n\nexport function _fusedMatMul(args: {\n  inputs: _FusedMatMulInputs,\n  attrs: _FusedMatMulAttrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {a, b, bias, preluActivationWeights} = inputs;\n  const {transposeA, transposeB, activation, leakyreluAlpha} = attrs;\n\n  return batchMatMulImpl({\n    a,\n    b,\n    transposeA,\n    transposeB,\n    backend,\n    bias,\n    preluActivationWeights,\n    leakyreluAlpha,\n    activation\n  });\n}\n\nexport const _fusedMatMulConfig: KernelConfig = {\n  kernelName: _FusedMatMul,\n  backendName: 'webgl',\n  kernelFunc: _fusedMatMul as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Abs, AbsInputs, env, KernelConfig, KernelFunc, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {simpleAbsImplCPU} from '../kernel_utils/shared';\nimport {UnaryOpProgram} from '../unaryop_gpu';\nimport {UnaryOpPackedProgram} from '../unaryop_packed_gpu';\n\nconst ABS = `return abs(x);`;\n\nexport function abs(args: {inputs: AbsInputs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend} = args;\n  const {x} = inputs;\n\n  // TODO: handle cases when x is complex. Once the cpu implementation\n  // can handle complex values, refactor to use unaryKernelFunc.\n  if (backend.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {\n    const xData = backend.texData.get(x.dataId);\n    const outValues = simpleAbsImplCPU(xData.values as TypedArray);\n    return backend.makeTensorInfo(x.shape, x.dtype, outValues);\n  }\n\n  let program: UnaryOpProgram|UnaryOpPackedProgram;\n  if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n    program = new UnaryOpPackedProgram(x.shape, ABS);\n  } else {\n    program = new UnaryOpProgram(x.shape, ABS);\n  }\n  return backend.runWebGLProgram(program, [x], x.dtype);\n}\n\nexport const absConfig: KernelConfig = {\n  kernelName: Abs,\n  backendName: 'webgl',\n  kernelFunc: abs as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Acos, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {CHECK_NAN_SNIPPET} from '../unaryop_gpu';\n\nconst ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\n\nexport const acos = unaryKernelFunc({opSnippet: ACOS});\n\nexport const acosConfig: KernelConfig = {\n  kernelName: Acos,\n  backendName: 'webgl',\n  kernelFunc: acos,\n};\n","\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Acosh, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {CHECK_NAN_SNIPPET} from '../unaryop_gpu';\n\nconst ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`;\n\nexport const acosh = unaryKernelFunc({opSnippet: ACOSH});\n\nexport const acoshConfig: KernelConfig = {\n  kernelName: Acosh,\n  backendName: 'webgl',\n  kernelFunc: acosh,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Add, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {addImplCPU as cpuAdd} from '../kernel_utils/shared';\n\nconst ADD = 'return a + b;';\n\nexport const addKernelFunc = binaryKernelFunc({\n  opSnippet: ADD,\n  packedOpSnippet: ADD,\n  supportsComplex: true,\n  cpuKernelImpl: cpuAdd\n});\n\nexport const addConfig: KernelConfig = {\n  kernelName: Add,\n  backendName: 'webgl',\n  kernelFunc: addKernelFunc\n};\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class AddNProgram implements GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(outputShape: number[], shapes: number[][]) {\n    this.outputShape = outputShape;\n    this.variableNames = shapes.map((_, i) => `T${i}`);\n\n    const snippets: string[] = [];\n    // Get target elements from every input tensor.\n    this.variableNames.forEach(variable => {\n      snippets.push(`float v${variable} = get${variable}AtOutCoords();`);\n    });\n\n    // Calculate the sum of all elements.\n    const operation = this.variableNames\n                          .map(variable => {\n                            return `v${variable}`;\n                          })\n                          .join(' + ');\n\n    this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class AddNPackedProgram implements GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[] = [];\n  userCode: string;\n  packedInputs = true;\n  packedOutput = true;\n\n  constructor(outputShape: number[], shapes: number[][]) {\n    this.outputShape = outputShape;\n    this.variableNames = shapes.map((_, i) => `T${i}`);\n\n    const snippets: string[] = [];\n    // Get target elements from every input tensor.\n    this.variableNames.forEach(variable => {\n      snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);\n    });\n\n    // Calculate the sum of all elements.\n    const operation = this.variableNames\n                          .map(variable => {\n                            return `v${variable}`;\n                          })\n                          .join(' + ');\n\n    this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AddN, AddNInputs, env, KernelConfig, KernelFunc, TensorInfo, upcastType} from '@tensorflow/tfjs-core';\n\nimport {AddNProgram} from '../addn_gpu';\nimport {AddNPackedProgram} from '../addn_packed_gpu';\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {identity} from './Identity';\n\nexport function addN(args: {inputs: AddNInputs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend} = args;\n\n  const tensors = inputs;\n  if (tensors.length === 1) {\n    return identity({inputs: {x: tensors[0]}, backend});\n  }\n\n  // Limit the number of uploaded textures for optimization.\n  if (tensors.length > env().get('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n    const midIndex = Math.floor(tensors.length / 2);\n    const leftSide = addN({inputs: tensors.slice(0, midIndex), backend});\n    const rightSide = addN({inputs: tensors.slice(midIndex), backend});\n    return addN({inputs: [leftSide, rightSide], backend});\n  }\n\n  const dtype =\n      tensors.map(t => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n  const shapes = tensors.map(t => t.shape);\n  // We can make sure shapes are identical in op level.\n  const usePackedOp = env().getBool('WEBGL_PACK');\n  const program = usePackedOp ?\n      new AddNPackedProgram(tensors[0].shape, shapes) :\n      new AddNProgram(tensors[0].shape, shapes);\n  return backend.runWebGLProgram(program, tensors, dtype);\n}\n\nexport const addNConfig: KernelConfig = {\n  kernelName: AddN,\n  backendName: 'webgl',\n  kernelFunc: addN as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {All, AllAttrs, AllInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {reduce} from '../kernel_utils/reduce';\n\nimport {reshape} from './Reshape';\nimport {transpose} from './Transpose';\n\nexport function all(\n    args: {inputs: AllInputs, backend: MathBackendWebGL, attrs: AllAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {axis, keepDims} = attrs;\n\n  const xRank = x.shape.length;\n\n  const origAxes = util.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});\n    axes = backend_util.getInnerMostAxes(axes.length, xRank);\n  }\n\n  backend_util.assertAxesAreInnerMostDims('all', axes, xRank);\n  const [outShape, reduceShape] =\n      backend_util.computeOutAndReduceShapes(permutedX.shape, axes);\n  const inSize = util.sizeFromShape(reduceShape);\n\n  const a2D =\n      reshape({inputs: {x: permutedX}, backend, attrs: {shape: [-1, inSize]}});\n  const reduced = reduce(a2D, a2D.dtype, 'all', backend);\n\n  let res;\n  if (keepDims) {\n    const newShape = backend_util.expandShapeToKeepDim(outShape, origAxes);\n    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: newShape}});\n  } else {\n    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: outShape}});\n  }\n\n  backend.disposeIntermediateTensorInfo(a2D);\n  backend.disposeIntermediateTensorInfo(reduced);\n\n  if (permutedAxes != null) {\n    backend.disposeIntermediateTensorInfo(permutedX);\n  }\n\n  return res;\n}\n\nexport const allConfig: KernelConfig = {\n  kernelName: All,\n  backendName: 'webgl',\n  kernelFunc: all as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Any, AnyAttrs, AnyInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {reduce} from '../kernel_utils/reduce';\nimport {reshape} from './Reshape';\nimport {transpose} from './Transpose';\n\nexport function any(\n    args: {inputs: AnyInputs, backend: MathBackendWebGL, attrs: AnyAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {axis, keepDims} = attrs;\n\n  const xRank = x.shape.length;\n\n  const origAxes = util.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});\n    axes = backend_util.getInnerMostAxes(axes.length, xRank);\n  }\n\n  backend_util.assertAxesAreInnerMostDims('any', axes, xRank);\n  const [outShape, reduceShape] =\n      backend_util.computeOutAndReduceShapes(permutedX.shape, axes);\n  const inSize = util.sizeFromShape(reduceShape);\n\n  const a2D =\n      reshape({inputs: {x: permutedX}, backend, attrs: {shape: [-1, inSize]}});\n  const reduced = reduce(a2D, a2D.dtype, 'any', backend);\n\n  let res;\n  if (keepDims) {\n    const newShape = backend_util.expandShapeToKeepDim(outShape, origAxes);\n    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: newShape}});\n  } else {\n    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: outShape}});\n  }\n\n  backend.disposeIntermediateTensorInfo(a2D);\n  backend.disposeIntermediateTensorInfo(reduced);\n\n  if (permutedAxes != null) {\n    backend.disposeIntermediateTensorInfo(permutedX);\n  }\n\n  return res;\n}\n\nexport const anyConfig: KernelConfig = {\n  kernelName: Any,\n  backendName: 'webgl',\n  kernelFunc: any as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ArgMinMaxProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      reduceInfo: backend_util.ReduceInfo, op: 'max'|'min',\n      firstPass: boolean) {\n    const {windowSize, batchSize, outSize} = reduceInfo;\n    if (!firstPass) {\n      this.variableNames.push('bestIndicesA');\n    }\n    this.outputShape = [batchSize, outSize];\n    const compOp = (op === 'max') ? '>' : '<';\n    const indexSnippet = firstPass ?\n        'inOffset + i;' :\n        'round(getBestIndicesA(batch, inOffset + i));';\n\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {util} from '@tensorflow/tfjs-core';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getChannels} from './packing_util';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ArgMinMaxPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n  packedInputs = true;\n  packedOutput = true;\n\n  constructor(\n      shape: number[], windowSize: number, op: 'max'|'min',\n      firstPass: boolean) {\n    util.assert(\n        shape.length > 2,\n        () => `Packed arg${\n            op.charAt(0).toUpperCase() +\n            op.slice(1)} supports only inputs with rank above 2.`);\n    const inSize = shape[shape.length - 1];\n    const outSize = Math.ceil(inSize / windowSize);\n    this.outputShape = shape.slice(0, -1);\n    if (outSize > 1) {\n      this.outputShape.push(outSize);\n    }\n    if (!firstPass) {\n      this.variableNames.push('bestIndicesA');\n    }\n    const outShape = this.outputShape;\n    const rank = outShape.length;\n    const dtype = getCoordsDataType(rank);\n    const coords = getChannels('coords', rank);\n\n    let sourceLocSetup;\n    let sourceRank;\n    if (outSize === 1) {\n      sourceRank = rank + 1;\n      const sourceLocDType = getCoordsDataType(sourceRank);\n      sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 2]};`;\n    } else {\n      sourceRank = rank;\n      sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords[rank - 2]};`;\n    }\n    const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);\n    const inChannel = '.' + channels[sourceRank - 1];  // e.g. \".b\" for rank 3.\n    const intChannels = channels.map(x => 'int ' + x);\n    const srcRCoords =\n        getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');\n    const srcGCoords =\n        getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');\n    const srcBCoords =\n        getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');\n    const srcACoords =\n        getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');\n\n    const compOp = (op === 'max') ? 'greaterThan' : 'lessThan';\n    const fetchCandidateIdx = firstPass ? '' : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n\n    const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n\n    const getBestIndicesAChannelSnippet = firstPass ? '' : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n\n    this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, env, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {ArgMinMaxProgram} from '../argminmax_gpu';\nimport {ArgMinMaxPackedProgram} from '../argminmax_packed_gpu';\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {reshape} from '../kernels/Reshape';\n\nfunction argReduce(\n    backend: MathBackendWebGL, x: TensorInfo, reduceType: 'max'|'min',\n    bestIndicesA: TensorInfo = null): TensorInfo {\n  let batchSize = x.shape[0];\n  let inSize = x.shape[1];\n  if (bestIndicesA != null) {\n    batchSize = bestIndicesA.shape[0];\n    inSize = bestIndicesA.shape[1];\n  }\n  const windowSize = backend_util.computeOptimalWindowSize(inSize);\n  const reduceInfo =\n      {windowSize, inSize, batchSize, outSize: Math.ceil(inSize / windowSize)};\n  const program =\n      new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n  const inputs = [x];\n  if (bestIndicesA != null) {\n    inputs.push(bestIndicesA);\n  }\n  const output = backend.runWebGLProgram(program, inputs, 'int32');\n  // No need to run another GPGPU program.\n  if (output.shape[1] === 1) {\n    return output;\n  }\n  const result = argReduce(backend, x, reduceType, output);\n  backend.disposeIntermediateTensorInfo(output);\n  return result;\n}\n\nfunction argReducePacked(\n    backend: MathBackendWebGL, x: TensorInfo, reduceType: 'max'|'min',\n    bestIndicesA: TensorInfo = null): TensorInfo {\n  const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n  const inSize = inShape[inShape.length - 1];\n  const windowSize = backend_util.computeOptimalWindowSize(inSize);\n  const program = new ArgMinMaxPackedProgram(\n      inShape, windowSize, reduceType, bestIndicesA == null);\n  const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n  const output = backend.runWebGLProgram(program, inputs, 'int32');\n  if (output.shape.length === x.shape.length) {\n    const result = argReducePacked(backend, x, reduceType, output);\n    backend.disposeIntermediateTensorInfo(output);\n    return result;\n  }\n  return output;\n}\n\nexport function argMinMaxReduce(\n    backend: MathBackendWebGL, x: TensorInfo, axis: number,\n    reduceType: 'min'|'max'): TensorInfo {\n  const axes = [axis];\n  backend_util.assertAxesAreInnerMostDims(\n      'arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes,\n      x.shape.length);\n  if (!env().getBool('WEBGL_PACK_REDUCE') || x.shape.length <= 2) {\n    const intermediateTensorInfos = [];\n    const [outShape, reduceShape] =\n        backend_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = reshape({inputs: {x}, backend, attrs: {shape: [-1, inSize]}});\n    intermediateTensorInfos.push(a2D);\n\n    const reduced = argReduce(backend, a2D, reduceType);\n    intermediateTensorInfos.push(reduced);\n    const reshaped =\n        reshape({inputs: {x: reduced}, backend, attrs: {shape: outShape}});\n\n    intermediateTensorInfos.forEach(\n        t => backend.disposeIntermediateTensorInfo(t));\n    return reshaped;\n  }\n  return argReducePacked(backend, x, reduceType);\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ArgMax, ArgMaxAttrs, ArgMaxInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {argMinMaxReduce} from '../kernel_utils/arg_min_max';\n\nimport {transpose} from './Transpose';\n\nexport function argMax(\n    args:\n        {inputs: ArgMaxInputs, backend: MathBackendWebGL, attrs: ArgMaxAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {axis} = attrs;\n\n  let axes = util.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});\n    intermediateTensorInfos.push($x);\n    axes = backend_util.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n\n  backend_util.assertAxesAreInnerMostDims('argMax', [axes[0]], $x.shape.length);\n  const out = argMinMaxReduce(backend, $x, axes[0], 'max');\n\n  intermediateTensorInfos.forEach(\n      t => backend.disposeIntermediateTensorInfo(t));\n  return out;\n}\n\nexport const argMaxConfig: KernelConfig = {\n  kernelName: ArgMax,\n  backendName: 'webgl',\n  kernelFunc: argMax as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ArgMin, ArgMinAttrs, ArgMinInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {argMinMaxReduce} from '../kernel_utils/arg_min_max';\nimport {transpose} from './Transpose';\n\nexport function argMin(\n    args:\n        {inputs: ArgMinInputs, backend: MathBackendWebGL, attrs: ArgMinAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {axis} = attrs;\n\n  let axes = util.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});\n    intermediateTensorInfos.push($x);\n    axes = backend_util.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n\n  backend_util.assertAxesAreInnerMostDims('argMin', [axes[0]], $x.shape.length);\n\n  const out = argMinMaxReduce(backend, $x, axes[0], 'min');\n\n  intermediateTensorInfos.forEach(\n      t => backend.disposeIntermediateTensorInfo(t));\n  return out;\n}\n\nexport const argMinConfig: KernelConfig = {\n  kernelName: ArgMin,\n  backendName: 'webgl',\n  kernelFunc: argMin as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Asin, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {CHECK_NAN_SNIPPET} from '../unaryop_gpu';\n\nconst ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\n\nexport const asin = unaryKernelFunc({opSnippet: ASIN});\n\nexport const asinConfig: KernelConfig = {\n  kernelName: Asin,\n  backendName: 'webgl',\n  kernelFunc: asin,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Asinh, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {CHECK_NAN_SNIPPET} from '../unaryop_gpu';\n\nconst ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\n\nexport const asinh = unaryKernelFunc({opSnippet: ASINH});\n\nexport const asinhConfig: KernelConfig = {\n  kernelName: Asinh,\n  backendName: 'webgl',\n  kernelFunc: asinh,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Atan, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {CHECK_NAN_SNIPPET} from '../unaryop_gpu';\n\nconst ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\n\nexport const atan = unaryKernelFunc({opSnippet: ATAN});\n\nexport const atanConfig: KernelConfig = {\n  kernelName: Atan,\n  backendName: 'webgl',\n  kernelFunc: atan,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Atan2} from '@tensorflow/tfjs-core';\nimport {KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {binaryKernelFunc, CHECK_NAN_SNIPPET_BINARY, CHECK_NAN_SNIPPET_BINARY_PACKED} from '../kernel_utils/kernel_funcs_utils';\n\nconst ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\n\nconst ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\n\nexport const atan2 =\n    binaryKernelFunc({opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED});\n\nexport const atan2Config: KernelConfig = {\n  kernelName: Atan2,\n  backendName: 'webgl',\n  kernelFunc: atan2,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Atanh, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {CHECK_NAN_SNIPPET} from '../unaryop_gpu';\n\nconst ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\n\nexport const atanh = unaryKernelFunc({opSnippet: ATANH});\n\nexport const atanhConfig: KernelConfig = {\n  kernelName: Atanh,\n  backendName: 'webgl',\n  kernelFunc: atanh,\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Pool2DProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      convInfo: backend_util.Conv2DInfo, poolType: 'max'|'avg',\n      computePositions: boolean, flattenPositions = false,\n      includeBatchInIndex = false) {\n    if (poolType === 'avg' && computePositions) {\n      throw new Error('Cannot compute positions for average pool.');\n    }\n\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.outputShape = convInfo.outShape;\n\n    const isAvgPool = poolType === 'avg';\n    const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${\n        convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n    const flattenPositionStr =\n        `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n\n    let initializationValue = '0.0';\n    if (!isAvgPool) {\n      // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n      initializationValue = '-1.0 / 1e-20';\n    }\n\n    if (computePositions) {\n      const compareOp = '>=';\n\n      this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${\n          flattenPositions ? (includeBatchInIndex ? batchFlattenPositionStr :\n                                                    flattenPositionStr) :\n                             `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n      return;\n    }\n\n    const compareOp = 'max';\n\n    let returnValue = `${poolType}(${poolType}(${poolType}(` +\n        'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n    if (poolType === 'avg') {\n      returnValue = `avgValue / count`;\n    }\n\n    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n    const filterWidthVec4Remainder = filterWidth % 4;\n\n    const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n}\n\nexport class Pool3DProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      convInfo: backend_util.Conv3DInfo, poolType: 'max'|'avg',\n      computePositions: boolean, flattenPositions = false,\n      includeBatchInIndex = false) {\n    if (poolType === 'avg' && computePositions) {\n      throw new Error('Cannot compute positions for average pool.');\n    }\n\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.outputShape = convInfo.outShape;\n\n    const isAvgPool = poolType === 'avg';\n\n    let initializationValue = '0.0';\n    if (!isAvgPool) {\n      // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n      initializationValue = '-1.0 / 1e-20';\n    }\n\n    if (computePositions) {\n      const compareOp = '>=';\n\n      this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${\n          flattenPositions ?\n              (includeBatchInIndex ?\n                   `(((batch * ${convInfo.inDepth} + xD) * ${\n                       convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${\n                       convInfo.inChannels} + ch` :\n                   `((xD * ${convInfo.inHeight} + xR) * ${\n                       convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`) :\n              `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n      return;\n    }\n\n    const compareOp = 'max';\n\n    let returnValue = `${poolType}(${poolType}(${poolType}(` +\n        'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n    if (poolType === 'avg') {\n      returnValue = `avgValue / count`;\n    }\n\n    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n    const filterWidthVec4Remainder = filterWidth % 4;\n\n    const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n\n    this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {AvgPool, AvgPoolAttrs, AvgPoolInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Pool2DProgram} from '../pool_gpu';\nimport {assertNotComplex} from '../webgl_util';\nimport {identity} from './Identity';\n\nexport function avgPool(args: {\n  inputs: AvgPoolInputs,\n  backend: MathBackendWebGL,\n  attrs: AvgPoolAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  assertNotComplex(x, 'avgPool');\n  const {filterSize, strides, pad, dimRoundingMode} = attrs;\n  const dilations = 1;\n\n  util.assert(\n      backend_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  const convInfo = backend_util.computePool2DInfo(\n      x.shape as [number, number, number, number], filterSize, strides,\n      dilations, pad, dimRoundingMode);\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n      util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    return identity({inputs: {x}, backend});\n  }\n  const avgPoolProgram = new Pool2DProgram(convInfo, 'avg', false);\n  return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');\n}\n\nexport const avgPoolConfig: KernelConfig = {\n  kernelName: AvgPool,\n  backendName: 'webgl',\n  kernelFunc: avgPool as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {AvgPool3D, AvgPool3DAttrs, AvgPool3DInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Pool3DProgram} from '../pool_gpu';\n\nexport function avgPool3D(args: {\n  inputs: AvgPool3DInputs,\n  backend: MathBackendWebGL,\n  attrs: AvgPool3DAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {filterSize, strides, pad, dimRoundingMode, dataFormat} = attrs;\n  const dilations: [number, number, number] = [1, 1, 1];\n\n  const convInfo = backend_util.computePool3DInfo(\n      x.shape as [number, number, number, number, number], filterSize, strides,\n      dilations, pad, dimRoundingMode, dataFormat);\n  const avgPoolProgram = new Pool3DProgram(convInfo, 'avg', false);\n  return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');\n}\n\nexport const avgPool3DConfig: KernelConfig = {\n  kernelName: AvgPool3D,\n  backendName: 'webgl',\n  kernelFunc: avgPool3D as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class AvgPool2DBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: backend_util.Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class AvgPool3DBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: backend_util.Conv3DInfo) {\n    this.outputShape = convInfo.inShape;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n\n    const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {AvgPool3DGrad, AvgPool3DGradAttrs, AvgPool3DGradInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {AvgPool3DBackpropProgram} from '../avg_pool_backprop_gpu';\nimport {MathBackendWebGL} from '../backend_webgl';\n\nexport function avgPool3DGrad(args: {\n  inputs: AvgPool3DGradInputs,\n  backend: MathBackendWebGL,\n  attrs: AvgPool3DGradAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {dy, input} = inputs;\n  const x = input;\n  const {filterSize, strides, pad, dimRoundingMode} = attrs;\n  const dilations: [number, number, number] = [1, 1, 1];\n\n  const convInfo = backend_util.computePool3DInfo(\n      x.shape as [number, number, number, number, number], filterSize, strides,\n      dilations, pad, dimRoundingMode);\n  const avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n  return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\n\nexport const avgPoolGrad3DConfig: KernelConfig = {\n  kernelName: AvgPool3DGrad,\n  backendName: 'webgl',\n  kernelFunc: avgPool3DGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {AvgPoolGrad, AvgPoolGradAttrs, AvgPoolGradInputs, backend_util, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {AvgPool2DBackpropProgram} from '../avg_pool_backprop_gpu';\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {assertNotComplex} from '../webgl_util';\n\nexport function avgPoolGrad(args: {\n  inputs: AvgPoolGradInputs,\n  backend: MathBackendWebGL,\n  attrs: AvgPoolGradAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {dy, input} = inputs;\n  const x = input;\n  assertNotComplex([dy, input], 'avgPoolGrad');\n  const {filterSize, strides, pad} = attrs;\n\n  const convInfo = backend_util.computePool2DInfo(\n      x.shape as [number, number, number, number], filterSize, strides,\n      1 /* dilations */, pad);\n  const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n  return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\n\nexport const avgPoolGradConfig: KernelConfig = {\n  kernelName: AvgPoolGrad,\n  backendName: 'webgl',\n  kernelFunc: avgPoolGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {batchMatMulImpl} from './BatchMatMul_impl';\n\nexport function batchMatMul(args: {\n  inputs: BatchMatMulInputs,\n  attrs: BatchMatMulAttrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {a, b} = inputs;\n  const {transposeA, transposeB} = attrs;\n\n  return batchMatMulImpl({a, b, transposeA, transposeB, backend});\n}\n\nexport const batchMatMulConfig: KernelConfig = {\n  kernelName: BatchMatMul,\n  backendName: 'webgl',\n  kernelFunc: batchMatMul as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class BatchNormProgram implements GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      xShape: number[], meanShape: number[], varianceShape: number[],\n      offsetShape: number[]|null, scaleShape: number[]|null,\n      varianceEpsilon: number) {\n    this.variableNames = ['x', 'mean', 'variance'];\n    backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n    backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n\n    let offsetSnippet = '0.0';\n    if (offsetShape != null) {\n      backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push('offset');\n      offsetSnippet = 'getOffsetAtOutCoords()';\n    }\n\n    let scaleSnippet = '1.0';\n    if (scaleShape != null) {\n      backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push('scale');\n      scaleSnippet = 'getScaleAtOutCoords()';\n    }\n\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class BatchNormPackedProgram implements GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[];\n  userCode: string;\n  packedInputs = true;\n  packedOutput = true;\n\n  constructor(\n      xShape: number[], meanShape: number[], varianceShape: number[],\n      offsetShape: number[]|null, scaleShape: number[]|null,\n      varianceEpsilon: number) {\n    this.variableNames = ['x', 'mean', 'variance'];\n    backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n    backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n\n    let offsetSnippet = 'vec4(0.0)';\n    if (offsetShape != null) {\n      backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push('offset');\n      offsetSnippet = 'getOffsetAtOutCoords()';\n    }\n\n    let scaleSnippet = 'vec4(1.0)';\n    if (scaleShape != null) {\n      backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push('scale');\n      scaleSnippet = 'getScaleAtOutCoords()';\n    }\n\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n  }\n}\n","\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, FusedBatchNorm, FusedBatchNormAttrs, FusedBatchNormInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {BatchNormProgram} from '../batchnorm_gpu';\nimport {BatchNormPackedProgram} from '../batchnorm_packed_gpu';\n\nexport const batchNorm: (params: {\n  inputs: FusedBatchNormInputs,\n  backend: MathBackendWebGL,\n  attrs: FusedBatchNormAttrs\n}) => TensorInfo = ({inputs, backend, attrs}) => {\n  const {x, mean, variance, offset, scale} = inputs;\n\n  util.assert(\n      mean.shape.length === variance.shape.length,\n      () => 'Batch normalization gradient requires mean and variance to have ' +\n          'equal ranks.');\n  util.assert(\n      offset == null || mean.shape.length === offset.shape.length,\n      () => 'Batch normalization gradient requires mean and offset to have ' +\n          'equal ranks.');\n  util.assert(\n      scale == null || mean.shape.length === scale.shape.length,\n      () => 'Batch normalization gradient requires mean and scale to have ' +\n          'equal ranks.');\n\n  let {varianceEpsilon} = attrs;\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 0.001;\n  }\n\n  const finalInputs = [x, mean, variance];\n\n  let offsetShape = null;\n  if (offset != null) {\n    offsetShape = offset.shape;\n    finalInputs.push(offset);\n  }\n\n  let scaleShape = null;\n  if (scale != null) {\n    scaleShape = scale.shape;\n    finalInputs.push(scale);\n  }\n\n  const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n      new BatchNormPackedProgram(\n          x.shape, mean.shape, variance.shape, offsetShape, scaleShape,\n          varianceEpsilon) :\n      new BatchNormProgram(\n          x.shape, mean.shape, variance.shape, offsetShape, scaleShape,\n          varianceEpsilon);\n  const output =\n      backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n\n  return output;\n};\n\nexport const batchNormConfig: KernelConfig = {\n  kernelName: FusedBatchNorm,\n  backendName: 'webgl',\n  kernelFunc: batchNorm as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class SliceProgram implements GPGPUProgram {\n  variableNames = ['source'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  // Caching uniform location for speed.\n  startLoc: WebGLUniformLocation;\n\n  constructor(destSize: number[]) {\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n\n    const dtype = getCoordsDataType(this.rank);\n    const uniformPart = `uniform int start[${this.rank}];`;\n    const sourceCoords = getCoords(this.rank);\n\n    let body: string;\n    const coordSum = destSize.map((_, i) => {\n      return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n    });\n    body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join('\\n')}\n      `;\n    this.userCode = `\n      ${uniformPart}\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n  }\n\n  getCustomSetupFunc(start: number[]) {\n    if (start.length !== this.rank) {\n      throw Error(\n          `The rank (${this.rank}) of the program must match the ` +\n          `length of start (${start.length})`);\n    }\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.startLoc == null) {\n        this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n        if (this.startLoc == null) {\n          // This means the compiler has optimized and realized it doesn't need\n          // the uniform.\n          return;\n        }\n      }\n      gpgpu.gl.uniform1iv(this.startLoc, start);\n    };\n  }\n}\n\nconst coords = ['x', 'y', 'z', 'w', 'u', 'v'];\n\nfunction getCoords(rank: number): string {\n  if (rank === 1) {\n    return 'sourceLoc';\n  } else if (rank <= 6) {\n    return coords.slice(0, rank).map(x => 'sourceLoc.' + x).join(',');\n  } else {\n    throw Error(`Slicing for rank ${rank} is not yet supported`);\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getChannels} from './packing_util';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class SlicePackedProgram implements GPGPUProgram {\n  variableNames = ['source'];\n  packedInputs = true;\n  packedOutput = true;\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  // Caching uniform location for speed.\n  startLoc: WebGLUniformLocation;\n\n  constructor(destSize: number[]) {\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n\n    const dtype = getCoordsDataType(this.rank);\n    const coords = getChannels('coords', this.rank);\n    const sourceLoc = getChannels('sourceLoc', this.rank);\n\n    const innerDims =\n        this.rank === 1 ? 'sourceLoc' : `vec2(${sourceLoc.slice(-2).join()})`;\n    const getChannel =\n        `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n    const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n    const lowerRow = this.rank === 1 ? '' : `\n      --${coords[this.rank - 1]};\n      if (++${coords[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n\n    const sourceLocSetup = this.rank <= 4 ?\n        `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` :\n        destSize.map((_, i) => `${sourceLoc[i]} = ${coords[i]} + start[${i}];`)\n            .join('\\n');\n    this.userCode = `\n      uniform int start[${this.rank}];\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n  }\n\n  getCustomSetupFunc(start: number[]) {\n    if (start.length !== this.rank) {\n      throw Error(\n          `The rank (${this.rank}) of the program must match the ` +\n          `length of start (${start.length})`);\n    }\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.startLoc == null) {\n        this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n        if (this.startLoc == null) {\n          // This means the compiler has optimized and realized it doesn't need\n          // the uniform.\n          return;\n        }\n      }\n      gpgpu.gl.uniform1iv(this.startLoc, start);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, KernelConfig, KernelFunc, Slice, slice_util, SliceAttrs, SliceInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {sliceImplCPU} from '../kernel_utils/shared';\nimport {SliceProgram} from '../slice_gpu';\nimport {SlicePackedProgram} from '../slice_packed_gpu';\n\nfunction shallowSlice(\n    x: TensorInfo, begin: number[], size: number[], backend: MathBackendWebGL) {\n  const xTexData = backend.texData.get(x.dataId);\n  const t = backend.makeTensorInfo(size, x.dtype);\n  const newTexData = backend.texData.get(t.dataId);\n  // Copy texture data from the original tensor.\n  Object.assign(newTexData, xTexData);\n  newTexData.refCount = 1;\n  newTexData.shape = size;\n  newTexData.dtype = x.dtype;\n  let flatOffset =\n      slice_util.computeFlatOffset(begin, util.computeStrides(x.shape));\n  if (xTexData.slice) {\n    // We are slicing an already sliced tensor, so we have to accumulate\n    // the offset.\n    flatOffset += xTexData.slice.flatOffset;\n  }\n  newTexData.slice = {\n    flatOffset,\n    // Point to the original dataId, which is used to do ref counting.\n    origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n  };\n\n  // Increase the ref count for that data bucket.\n  const refCount = backend.dataRefCount.get(newTexData.slice.origDataId) || 1;\n  backend.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n  return t;\n}\n\nexport function slice(\n    args: {inputs: SliceInputs, backend: MathBackendWebGL, attrs: SliceAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {begin, size} = attrs;\n\n  const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n  slice_util.assertParamsValid(x, $begin, $size);\n\n  if (util.sizeFromShape($size) === 0) {\n    return backend.makeTensorInfo($size, x.dtype, []);\n  }\n\n  // Run on cpu if dtype is string. For string, the backend represents it\n  // as Uint8Array[], where each Uint8Array is a character. Given that the\n  // computation is only on the outer array, uploading the whole data onto\n  // gpu is wasteful. Also, currently webgl doesn't have a design to\n  // upload and retrieve Uint8Array[] between cpu and gpu. Therefore, we\n  // just run the kernel on cpu if dtype is string.\n  if (backend.shouldExecuteOnCPU([x]) || x.dtype === 'string') {\n    const xTexData = backend.texData.get(x.dataId);\n    const outValues = sliceImplCPU(\n        xTexData.values as TypedArray, $begin, $size, x.shape, x.dtype);\n    return backend.makeTensorInfo($size, x.dtype, outValues);\n  }\n\n  const {isPacked} = backend.texData.get(x.dataId);\n  const isContinous = slice_util.isSliceContinous(x.shape, $begin, $size);\n  if (isPacked || !isContinous) {\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new SlicePackedProgram($size) :\n        new SliceProgram($size);\n    const customSetup = program.getCustomSetupFunc($begin);\n    return backend.runWebGLProgram(program, [x], x.dtype, customSetup);\n  }\n  backend.uploadToGPU(x.dataId);\n  return shallowSlice(x, $begin, $size, backend);\n}\n\nexport const sliceConfig: KernelConfig = {\n  kernelName: Slice,\n  backendName: 'webgl',\n  kernelFunc: slice as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, BatchToSpaceND, BatchToSpaceNDAttrs, BatchToSpaceNDInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {reshape} from './Reshape';\nimport {slice} from './Slice';\nimport {transpose} from './Transpose';\n\nexport const batchToSpaceND = (args: {\n  inputs: BatchToSpaceNDInputs,\n  backend: MathBackendWebGL,\n  attrs: BatchToSpaceNDAttrs\n}): TensorInfo => {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {blockShape, crops} = attrs;\n\n  util.assert(\n      x.shape.length <= 4,\n      () => 'batchToSpaceND for rank > 4 with a WebGL backend not ' +\n          'implemented yet');\n  const prod = blockShape.reduce((a, b) => a * b);\n\n  const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n  const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted =\n      backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n  const sliceBeginCoords =\n      backend_util.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize =\n      backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n\n  const toDispose = [];\n\n  const reshapedIntermediate =\n      reshape({inputs: {x}, backend, attrs: {shape: reshaped}});\n  const transposedIntermediate = transpose(\n      {inputs: {x: reshapedIntermediate}, backend, attrs: {perm: permuted}});\n  const reshapedIntermediate2 = reshape({\n    inputs: {x: transposedIntermediate},\n    backend,\n    attrs: {shape: reshapedPermuted}\n  });\n  const sliced = slice({\n    inputs: {x: reshapedIntermediate2},\n    backend,\n    attrs: {begin: sliceBeginCoords, size: sliceSize}\n  });\n\n  toDispose.push(reshapedIntermediate);\n  toDispose.push(transposedIntermediate);\n  toDispose.push(reshapedIntermediate2);\n\n  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));\n\n  return sliced;\n};\n\nexport const batchToSpaceNDConfig: KernelConfig = {\n  kernelName: BatchToSpaceND,\n  backendName: 'webgl',\n  kernelFunc: batchToSpaceND as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Bincount, BincountAttrs, BincountInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {bincountImplCPU} from '../kernel_utils/shared';\n\nexport function bincount(args: {\n  inputs: BincountInputs,\n  backend: MathBackendWebGL,\n  attrs: BincountAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x, weights} = inputs;\n  const {size} = attrs;\n\n  const xVals = backend.readSync(x.dataId) as TypedArray;\n  const weightsVals = backend.readSync(weights.dataId) as TypedArray;\n\n  const outVals =\n      bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);\n\n  return backend.makeTensorInfo([size], weights.dtype, outVals);\n}\n\nexport const bincountConfig: KernelConfig = {\n  kernelName: Bincount,\n  backendName: 'webgl',\n  kernelFunc: bincount as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {NotEqual} from '@tensorflow/tfjs-core';\nimport {KernelConfig} from '@tensorflow/tfjs-core';\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {notEqualImplCPU} from '../kernel_utils/shared';\n\nconst NOT_EQUAL = `return float(a != b);`;\n\nexport const notEqual = binaryKernelFunc(\n    {opSnippet: NOT_EQUAL, cpuKernelImpl: notEqualImplCPU, dtype: 'bool'});\n\nexport const notEqualConfig: KernelConfig = {\n  kernelName: NotEqual,\n  backendName: 'webgl',\n  kernelFunc: notEqual,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Real, RealInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {identity} from './Identity';\n\nexport function real(args: {inputs: RealInputs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend} = args;\n  const {input} = inputs;\n  const inputData = backend.texData.get(input.dataId);\n\n  return identity({inputs: {x: inputData.complexTensorInfos.real}, backend});\n}\n\nexport const realConfig: KernelConfig = {\n  kernelName: Real,\n  backendName: 'webgl',\n  kernelFunc: real as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {UnaryOpProgram} from '../unaryop_gpu';\n\nconst TO_INT = `return float(int(x));`;\n\nexport function int(input: TensorInfo, backend: MathBackendWebGL): TensorInfo {\n  const program = new UnaryOpProgram(input.shape, TO_INT);\n  const output = backend.runWebGLProgram(program, [input], 'int32');\n  return {dataId: output.dataId, shape: output.shape, dtype: output.dtype};\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport {BinaryInputs, Cast, CastAttrs, CastInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {complex} from './Complex';\nimport {identity} from './Identity';\nimport {notEqual} from './NotEqual';\nimport {real} from './Real';\n\nimport {int} from '../kernel_utils/int';\n\nexport function cast(\n    args: {inputs: CastInputs, backend: MathBackendWebGL, attrs: CastAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {dtype} = attrs;\n\n  // Casting to complex64.\n  if (dtype === 'complex64') {\n    if (x.dtype === 'complex64') {\n      return identity({inputs: {x}, backend});\n    }\n\n    // TODO(annxingyuan): Import kernel function once zeros is modularized.\n    const zerosTensor = tf.zeros(x.shape);\n    const floatX = cast({inputs: {x}, backend, attrs: {dtype: 'float32'}});\n\n    const result =\n        complex({inputs: {real: floatX, imag: zerosTensor}, backend});\n\n    zerosTensor.dispose();\n    backend.disposeIntermediateTensorInfo(floatX);\n\n    return result;\n  }\n\n  // Casting from complex64\n  if (x.dtype === 'complex64') {\n    const realPart = real({inputs: {input: x}, backend});\n    const result = cast({inputs: {x: realPart}, backend, attrs: {dtype}});\n    backend.disposeIntermediateTensorInfo(realPart);\n    return result;\n  }\n\n  if (!util.hasEncodingLoss(x.dtype, dtype)) {\n    // We don't change the underlying data, since we cast to higher\n    // precision.\n    const result = identity({inputs: {x}, backend});\n    return {dataId: result.dataId, shape: result.shape, dtype};\n  }\n\n  if (dtype === 'int32') {\n    return int(x, backend);\n  }\n\n  if (dtype === 'bool') {\n    const zerosTensorInfo = backend.makeTensorInfo(\n        [], 'bool', util.getTypedArrayFromDType('bool', 1));\n\n    const binaryInputs: BinaryInputs = {a: x, b: zerosTensorInfo};\n\n    const result = notEqual({inputs: binaryInputs, backend}) as TensorInfo;\n    backend.disposeIntermediateTensorInfo(zerosTensorInfo);\n    return result;\n  }\n\n  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\n\nexport const castConfig: KernelConfig = {\n  kernelName: Cast,\n  backendName: 'webgl',\n  kernelFunc: cast as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Ceil, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {ceilImplCPU} from '../kernel_utils/shared';\n\nconst CEIL = `return ceil(x);`;\n\nexport const ceil = unaryKernelFunc(\n    {opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU});\n\nexport const ceilConfig: KernelConfig = {\n  kernelName: Ceil,\n  backendName: 'webgl',\n  kernelFunc: ceil as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ClipProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  // Caching uniform locations for speed.\n  minLoc: WebGLUniformLocation;\n  maxLoc: WebGLUniformLocation;\n\n  constructor(aShape: number[]) {\n    this.outputShape = aShape;\n    this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n  }\n\n  getCustomSetupFunc(min: number, max: number) {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.minLoc == null) {\n        this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n        this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n      }\n      gpgpu.gl.uniform1f(this.minLoc, min);\n      gpgpu.gl.uniform1f(this.maxLoc, max);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ClipPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  packedInputs = true;\n  packedOutput = true;\n  userCode: string;\n  outputShape: number[];\n\n  // Caching uniform locations for speed.\n  minLoc: WebGLUniformLocation;\n  maxLoc: WebGLUniformLocation;\n\n  constructor(aShape: number[]) {\n    this.outputShape = aShape;\n    this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n  }\n\n  getCustomSetupFunc(min: number, max: number) {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.minLoc == null) {\n        this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n        this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n      }\n      gpgpu.gl.uniform1f(this.minLoc, min);\n      gpgpu.gl.uniform1f(this.maxLoc, max);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ClipByValue, ClipByValueAttrs, ClipByValueInputs, env, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {ClipProgram} from '../clip_gpu';\nimport {ClipPackedProgram} from '../clip_packed_gpu';\n\nexport function clipByValue(args: {\n  inputs: ClipByValueInputs,\n  backend: MathBackendWebGL,\n  attrs: ClipByValueAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {clipValueMin, clipValueMax} = attrs;\n\n  let program;\n  if (env().getBool('WEBGL_PACK_CLIP')) {\n    program = new ClipPackedProgram(x.shape);\n  } else {\n    program = new ClipProgram(x.shape);\n  }\n  const customSetup = program.getCustomSetupFunc(clipValueMin, clipValueMax);\n  return backend.runWebGLProgram(program, [x], x.dtype, customSetup);\n}\n\nexport const clipByValueConfig: KernelConfig = {\n  kernelName: ClipByValue,\n  backendName: 'webgl',\n  kernelFunc: clipByValue as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ComplexAbsProgram implements GPGPUProgram {\n  variableNames = ['real', 'imag'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(shape: number[]) {\n    this.outputShape = shape;\n    this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ComplexAbs, ComplexAbsInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {ComplexAbsProgram} from '../complex_abs_gpu';\n\n// Returns a TensorInfo with the complex shape and the dataId of the\n// underlying part. We need to do this because a reshaped complex tensor is\n// not reflected in its parts.\nfunction makeComplexComponentTensorInfo(\n    complexTensor: TensorInfo, complexPart: TensorInfo): TensorInfo {\n  return {\n    dataId: complexPart.dataId,\n    dtype: complexPart.dtype,\n    shape: complexTensor.shape\n  };\n}\n\nexport function complexAbs(\n    args: {inputs: ComplexAbsInputs, backend: MathBackendWebGL}): TensorInfo {\n  const {inputs, backend} = args;\n  const {x} = inputs;\n\n  const xData = backend.texData.get(x.dataId);\n\n  const program = new ComplexAbsProgram(x.shape);\n  const programInputs = [\n    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),\n    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag),\n  ];\n\n  return backend.runWebGLProgram(\n      program, programInputs, programInputs[0].dtype);\n}\n\nexport const complexAbsConfig: KernelConfig = {\n  kernelName: ComplexAbs,\n  backendName: 'webgl',\n  kernelFunc: complexAbs as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ConcatProgram implements GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[] = [];\n  userCode: string;\n\n  // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n  constructor(shapes: Array<[number, number]>) {\n    this.outputShape = backend_util.computeOutShape(shapes, 1 /* axis */);\n    this.variableNames = shapes.map((_, i) => `T${i}`);\n\n    const offsets: number[] = new Array(shapes.length - 1);\n    offsets[0] = shapes[0][1];\n    for (let i = 1; i < offsets.length; i++) {\n      offsets[i] = offsets[i - 1] + shapes[i][1];\n    }\n\n    const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n    for (let i = 1; i < offsets.length; i++) {\n      const shift = offsets[i - 1];\n      snippets.push(\n          `else if (yC < ${offsets[i]}) ` +\n          `setOutput(getT${i}(yR, yC-${shift}));`);\n    }\n    const lastIndex = offsets.length;\n    const lastShift = offsets[offsets.length - 1];\n    snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join('\\n        ')}\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getChannels} from './packing_util';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ConcatPackedProgram implements GPGPUProgram {\n  variableNames: string[];\n  packedInputs = true;\n  packedOutput = true;\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(shapes: number[][], axis: number) {\n    this.outputShape = backend_util.computeOutShape(shapes, axis);\n    const shape = this.outputShape;\n    const rank = shape.length;\n    const dtype = getCoordsDataType(rank);\n    const coords = getChannels('coords', rank);\n    const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);\n    this.variableNames = shapes.map((_, i) => `T${i}`);\n\n    const offsets: number[] = new Array(shapes.length - 1);\n    offsets[0] = shapes[0][axis];\n    for (let i = 1; i < offsets.length; i++) {\n      offsets[i] = offsets[i - 1] + shapes[i][axis];\n    }\n\n    const channel = channels[axis];\n    const lastChannels = channels.slice(-2);\n    const allChannels = channels.join();\n\n    let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n    for (let i = 1; i < offsets.length; i++) {\n      const shift = offsets[i - 1];\n      // Note: the >= comparison below may seem unnecessary given the check\n      // above but is needed to workaround branch execution issues on some\n      // devices. It makes all the conditions exclusive without relying on\n      // execution order.\n      getValueSnippet += `\n        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {\n          return getChannel(\n            getT${i}(${shiftedChannels(channels, channel, shift)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift)}));\n        }`;\n    }\n    const lastIndex = offsets.length;\n    const shift = offsets[offsets.length - 1];\n    getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n\n    this.userCode = `\n      float getValue(${channels.map(x => 'int ' + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords}), 0., 0., 0.);\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} + 1;\n        if (${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords});\n        }\n\n        ${coords[rank - 2]} = ${coords[rank - 2]} + 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords});\n        }\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} - 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords});\n        }\n        setOutput(result);\n      }\n    `;\n  }\n}\n\n/**\n * Return an expression for coordinates into a vector where a given channel\n * will be offset by [shift].\n *\n * @param channels the channels to consider\n * @param channel the channel we want shifted\n * @param shift  the amount to subtract from the channel.\n *\n * @returns a string of the form 'x, y-[shift], z' where any one channel can\n * have the shift applied.\n */\nfunction shiftedChannels(channels: string[], channel: string, shift: number) {\n  const channelIdx = channels.indexOf(channel);\n  const res = channels.map((c, idx) => {\n    if (idx === channelIdx) {\n      return `${c} - ${shift}`;\n    } else {\n      return c;\n    }\n  });\n  return res.join();\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Imag, ImagInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {identity} from './Identity';\n\nexport function imag(args: {inputs: ImagInputs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend} = args;\n  const {input} = inputs;\n  const inputData = backend.texData.get(input.dataId);\n\n  return identity({inputs: {x: inputData.complexTensorInfos.imag}, backend});\n}\n\nexport const imagConfig: KernelConfig = {\n  kernelName: Imag,\n  backendName: 'webgl',\n  kernelFunc: imag as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, ConcatInputs, env, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {ConcatProgram} from '../concat_gpu';\nimport {ConcatPackedProgram} from '../concat_packed_gpu';\nimport {concatImplCPU} from '../kernel_utils/shared';\n\nimport {complex} from './Complex';\nimport {imag} from './Imag';\nimport {real} from './Real';\nimport {reshape} from './Reshape';\n\nexport function concatImpl(\n    inputs: ConcatInputs, axis: number, backend: MathBackendWebGL): TensorInfo {\n  const dtype = inputs[0].dtype;\n  if (dtype === 'complex64') {\n    const reals = inputs.map((t) => real({inputs: {input: t}, backend}));\n    const imags = inputs.map((t) => imag({inputs: {input: t}, backend}));\n\n    const realConcated = concatImpl(reals, axis, backend);\n    const imagConcated = concatImpl(imags, axis, backend);\n\n    const result =\n        complex({inputs: {real: realConcated, imag: imagConcated}, backend});\n\n    reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n    imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n    backend.disposeIntermediateTensorInfo(realConcated);\n    backend.disposeIntermediateTensorInfo(imagConcated);\n\n    return result;\n  }\n\n  let runOnCpu = backend.shouldExecuteOnCPU(inputs);\n\n  // Run on cpu if dtype is string. For string, the backend represents it\n  // as Uint8Array[], where each Uint8Array is a character. Given that the\n  // computation is only on the outer array, uploading the whole data onto\n  // gpu is wasteful. Also, currently webgl doesn't have a design to\n  // upload and retrieve Uint8Array[] between cpu and gpu. Therefore, we\n  // just run the kernel on cpu if dtype is string.\n  if (dtype === 'string') {\n    runOnCpu = true;\n  }\n\n  if (runOnCpu) {\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const tensors2D = inputs.map(t => {\n      const innerSize = util.sizeFromShape(t.shape.slice(axis));\n      const shape = [-1, innerSize];\n      return reshape({inputs: {x: t}, backend, attrs: {shape}});\n    });\n\n    const inputsValShapes = tensors2D.map(t => {\n      return {vals: backend.readSync(t.dataId), shape: t.shape};\n    });\n\n    // Concats 2d tensors along axis=1.\n    const outShape =\n        backend_util.computeOutShape(tensors2D.map(t => t.shape), 1 /* axis */);\n    const simplyConcat = tensors2D[0].shape[0] === 1;\n    const outVals =\n        concatImplCPU(inputsValShapes, outShape, dtype, simplyConcat);\n\n    const finalOutShape =\n        backend_util.computeOutShape(inputs.map(t => t.shape), axis);\n\n    const outInfo = backend.makeTensorInfo(finalOutShape, dtype, outVals);\n\n    tensors2D.forEach(t => backend.disposeIntermediateTensorInfo(t));\n\n    return outInfo;\n  }\n\n  if (inputs.length > env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n    const midIndex = Math.floor(inputs.length / 2);\n    const leftSide = concatImpl(inputs.slice(0, midIndex), axis, backend);\n    const rightSide = concatImpl(inputs.slice(midIndex), axis, backend);\n\n    const result = concatImpl([leftSide, rightSide], axis, backend);\n\n    backend.disposeIntermediateTensorInfo(leftSide);\n    backend.disposeIntermediateTensorInfo(rightSide);\n\n    return result;\n  }\n\n  if (env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') &&\n      inputs[0].shape.length > 1) {\n    const program = new ConcatPackedProgram(inputs.map(t => t.shape), axis);\n    return backend.runWebGLProgram(program, inputs, dtype);\n  }\n\n  const {tensors2D, outShape} = computeTensors2D(inputs, axis, backend);\n  const program =\n      new ConcatProgram(tensors2D.map(t => t.shape as [number, number]));\n  const result = backend.runWebGLProgram(program, tensors2D, dtype);\n\n  tensors2D.forEach(r => backend.disposeIntermediateTensorInfo(r));\n  const reshapedResult =\n      reshape({inputs: {x: result}, attrs: {shape: outShape}, backend});\n  backend.disposeIntermediateTensorInfo(result);\n\n  return reshapedResult;\n}\n\nfunction computeTensors2D(\n    inputs: ConcatInputs, axis: number, backend: MathBackendWebGL) {\n  // Any concat of n-dimensional tensors across any axis can be reduced to\n  // a concatenation of two-dimensional tensors across the axis 1 by first\n  // partitioning the axes of the original tensors into those less than the\n  // axis to be concatenated and the rest. Then reshape the tensors\n  // into a two-dimensional tensor by collapsing these two sets of axes and\n  // concatenate the resulting matrices across the axis 1, finally reshaping\n  // the result to have the proper shape.\n  const outShape = backend_util.computeOutShape(inputs.map(t => t.shape), axis);\n  const tensors2D = inputs.map(\n      x => reshape({\n        inputs: {x},\n        attrs: {shape: [-1, util.sizeFromShape(x.shape.slice(axis))]},\n        backend\n      }));\n\n  return {tensors2D, outShape};\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Concat, ConcatAttrs, ConcatInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {concatImpl} from './Concat_impl';\nimport {identity} from './Identity';\n\nexport function concat(\n    args:\n        {inputs: ConcatInputs, attrs: ConcatAttrs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {axis} = attrs;\n\n  const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n  const outShape =\n      backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n\n  if (util.sizeFromShape(outShape) === 0) {\n    return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n  }\n\n  // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n  const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity({inputs: {x: $inputs[0]}, backend});\n  }\n\n  const shapes = $inputs.map(t => t.shape);\n  backend_util.assertParamsConsistent(shapes, $axis);\n\n  return concatImpl($inputs, $axis, backend);\n}\n\nexport const concatConfig: KernelConfig = {\n  kernelName: Concat,\n  backendName: 'webgl',\n  kernelFunc: concat as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Conv2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      convInfo: backend_util.Conv2DInfo, addBias = false,\n      activation: string = null, hasPreluActivationWeights = false,\n      hasLeakyreluAlpha = false) {\n    this.outputShape = convInfo.outShape;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n    const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n\n    const rowDim = isChannelsLast ? 1 : 2;\n    const colDim = isChannelsLast ? 2 : 3;\n    const channelDim = isChannelsLast ? 3 : 1;\n\n    let activationSnippet = '', applyActivationSnippet = '';\n    if (activation) {\n      if (hasPreluActivationWeights) {\n        activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n      } else if (hasLeakyreluAlpha) {\n        activationSnippet = `float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${activation}\n        }`;\n      } else {\n        activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n      }\n\n      applyActivationSnippet = `result = activation(result);`;\n    }\n\n    const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n    if (addBias) {\n      this.variableNames.push('bias');\n    }\n\n    if (hasPreluActivationWeights) {\n      this.variableNames.push('preluActivationWeights');\n    }\n\n    if (hasLeakyreluAlpha) {\n      this.variableNames.push('leakyreluAlpha');\n    }\n\n    this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n  }\n}\n\nexport class Conv3DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: backend_util.Conv3DInfo) {\n    this.outputShape = convInfo.outShape;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n\n    this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${\n        strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Im2ColPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  packedInputs = true;\n  packedOutput = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      outputShape: number[], inputShape: number[],\n      convInfo: backend_util.Conv2DInfo) {\n    this.outputShape = outputShape;\n\n    const {\n      filterWidth,\n      inChannels,\n      strideWidth,\n      strideHeight,\n      padInfo,\n      outWidth,\n      dilationWidth,\n      dilationHeight,\n      dataFormat\n    } = convInfo;\n    const {left, top} = padInfo;\n    const itemsPerBlockRow = inChannels * filterWidth;\n    const glsl = getGlslDifferences();\n    const isChannelsLast = dataFormat === 'channelsLast';\n    const rowDim = isChannelsLast ? 0 : 1;\n    const colDim = isChannelsLast ? 1 : 2;\n\n    let unrolled = ``;\n\n    for (let row = 0; row <= 1; row++) {\n      for (let col = 0; col <= 1; col++) {\n        unrolled += `\n          blockIndex = rc.y + ${col};\n          pos = rc.x + ${row};\n\n          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {\n            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${\n            top};\n            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${\n            strideWidth}. - ${left}.);\n              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${\n            itemsPerBlockRow}.) / ${inChannels}.));\n\n              if(d1 < ${inputShape[colDim]} && d1 >= 0) {\n\n                ch = int(mod(float(pos), ${inChannels}.));\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n      }\n    }\n\n    this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, env, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Im2ColPackedProgram} from '../im2col_packed_gpu';\nimport {mapActivationToShaderProgram} from '../kernel_utils/kernel_funcs_utils';\nimport {MatMulPackedProgram} from '../mulmat_packed_gpu';\nimport * as webgl_util from '../webgl_util';\n\nimport {batchMatMulImpl, MATMUL_SHARED_DIM_THRESHOLD} from './BatchMatMul_impl';\nimport {identity} from './Identity';\nimport {reshape} from './Reshape';\n\ntype Conv2DConfig = {\n  x: TensorInfo,\n  filter: TensorInfo,\n  convInfo: backend_util.Conv2DInfo,\n  backend: MathBackendWebGL,\n  bias?: TensorInfo,\n  preluActivationWeights?: TensorInfo,\n  leakyreluAlpha?: number,\n  activation?: backend_util.Activation\n};\n\n// For 1x1 kernels that iterate through every point in the input, convolution\n// can be expressed as matrix multiplication (without need for memory\n// remapping).\nexport function conv2dByMatMul({\n  x,\n  filter,\n  convInfo,\n  backend,\n  bias = null,\n  preluActivationWeights = null,\n  leakyreluAlpha = 0,\n  activation = null\n}: Conv2DConfig) {\n  // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the\n  // result from 2D to 4D.\n  const xShape = x.shape;\n  const xTexData = backend.texData.get(x.dataId);\n  const sharedMatMulDim = convInfo.inChannels;\n  const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n  const outerShapeFilter = convInfo.outChannels;\n  const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n  const transposeA = false;\n  const transposeB = false;\n\n  let out: TensorInfo;\n  const intermediates: TensorInfo[] = [];\n\n  // TODO: Once reduction ops are packed, batchMatMul will always be packed\n  // and we can remove this condition.\n  const batchMatMulWillBeUnpacked =\n      (outerShapeX === 1 || outerShapeFilter === 1) &&\n      sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n  const reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;\n\n  if (batchMatMulWillBeUnpacked || !env().getBool('WEBGL_LAZILY_UNPACK') ||\n      !env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ||\n      !reshapeWillBeExpensive) {\n    const targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] :\n                                         xShape[0] * xShape[2] * xShape[3];\n    const xReshaped = reshape({\n      inputs: {x},\n      backend,\n      attrs: {shape: [1, targetShape, convInfo.inChannels]}\n    });\n    const filterReshaped = reshape({\n      inputs: {x: filter},\n      backend,\n      attrs: {shape: [1, convInfo.inChannels, convInfo.outChannels]}\n    });\n    const result = batchMatMulImpl({\n      a: xReshaped,\n      b: filterReshaped,\n      transposeA,\n      transposeB,\n      backend,\n      bias,\n      activation,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n\n    out = reshape(\n        {inputs: {x: result}, backend, attrs: {shape: convInfo.outShape}});\n\n    intermediates.push(xReshaped);\n    intermediates.push(filterReshaped);\n    intermediates.push(result);\n  } else {\n    // Following optimization is specific to packed |x| with odd row count\n    // (For example, in channelLast mode, 'row count' refers to x.shape[2]):\n    // we avoid expensive packed 2x2 reshape by padding row count to next,\n    // even number. When x.shape[2] is odd, the result of packed batchMatMul is\n    // the same (has the same texture layout and and values in the texture) as\n    // it is for even x.shape[2] + 1. We make the odd-rows tensor to look like\n    // even-rows tensor before the operation and, after the batchMatMul,\n    // fix the even-rows result to have odd number of rows.\n    const targetShape = isChannelsLast ?\n        xShape[0] * xShape[1] * (xShape[2] + 1) :\n        xShape[0] * xShape[2] * (xShape[3] + 1);\n    const xReshaped: TensorInfo = {\n      dataId: x.dataId,\n      shape: [1, targetShape, convInfo.inChannels],\n      dtype: x.dtype\n    };\n    // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.\n    // Decrementing row count, after batchMatMul->...->compileProgram leads to\n    // invalid row count within the reference in GPGPUBinary.inShapeInfos.\n    // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos\n    // in compileProgram method, but that would affect compilation of all\n    // programs - instead, provide a copy here, with even row count, before\n    // calling batchMatMul->...->compileProgram and after that, the original\n    // xTexData.shape is restored.\n    const originalXTexDataShape = xTexData.shape;\n    xTexData.shape = xTexData.shape.slice();\n    xTexData.shape[xTexData.shape.length - 2]++;\n    util.assert(\n        webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape),\n        () => `packed reshape ${xTexData.shape} to ${\n            xReshaped.shape} isn't free`);\n    const filterReshaped = reshape({\n      inputs: {x: filter},\n      backend,\n      attrs: {shape: [1, convInfo.inChannels, convInfo.outChannels]}\n    });\n    intermediates.push(filterReshaped);\n    const pointwiseConv = batchMatMulImpl({\n      a: xReshaped,\n      b: filterReshaped,\n      backend,\n      transposeA,\n      transposeB,\n      bias,\n      activation,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n\n    const pointwiseConvTexData = backend.texData.get(pointwiseConv.dataId);\n    util.assert(\n        pointwiseConvTexData.isPacked,\n        () => 'batchMatMul result is expected to be packed');\n    // Restore the input shape to original.\n    xTexData.shape = originalXTexDataShape;\n    // Set the output shape - there is no need for expensive reshape as data\n    // layout is already correct.\n    pointwiseConvTexData.shape = convInfo.outShape;\n\n    out = identity({inputs: {x: pointwiseConv}, backend});\n    out.shape = convInfo.outShape;\n\n    intermediates.push(pointwiseConv);\n  }\n\n  for (const i of intermediates) {\n    backend.disposeIntermediateTensorInfo(i);\n  }\n\n  return out;\n}\n\n// Implements the im2row algorithm as outlined in \"High Performance\n// Convolutional Neural Networks for Document Processing\" (Suvisoft, 2006)\nexport function conv2dWithIm2Row({\n  x,\n  filter,\n  convInfo,\n  backend,\n  bias = null,\n  preluActivationWeights = null,\n  leakyreluAlpha = 0,\n  activation = null\n}: Conv2DConfig) {\n  // Rearranges conv2d input so each block to be convolved over forms the\n  // column of a new matrix with shape [filterWidth * filterHeight *\n  // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n  // output channel forms a row of a new matrix with shape [outChannels,\n  // filterWidth * filterHeight * inChannels]. The convolution is then\n  // computed by multiplying these matrices and reshaping the result.\n  const {\n    filterWidth,\n    filterHeight,\n    inChannels,\n    outWidth,\n    outHeight,\n    dataFormat\n  } = convInfo;\n\n  const isChannelsLast = dataFormat === 'channelsLast';\n\n  const sharedDim = filterWidth * filterHeight * inChannels;\n  const numCols = outHeight * outWidth;\n  const x2ColShape = [sharedDim, numCols];\n  const transposeA = true;\n  const transposeB = false;\n\n  const intermediates: TensorInfo[] = [];\n\n  const xSqueezed =\n      reshape({inputs: {x}, backend, attrs: {shape: x.shape.slice(1)}});\n  const w2Row = reshape({\n    inputs: {x: filter},\n    backend,\n    attrs: {shape: [1, sharedDim, util.sizeFromShape(filter.shape) / sharedDim]}\n  });\n\n  intermediates.push(xSqueezed);\n  intermediates.push(w2Row);\n\n  const im2ColProgram =\n      new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);\n  const im2Col = backend.runWebGLProgram(im2ColProgram, [xSqueezed], 'float32');\n  const im2ColReshaped = reshape({\n    inputs: {x: im2Col},\n    backend,\n    attrs: {shape: [1, x2ColShape[0], x2ColShape[1]]}\n  });\n\n  intermediates.push(im2Col);\n  intermediates.push(im2ColReshaped);\n\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation === 'leakyrelu';\n  const fusedActivation =\n      activation ? mapActivationToShaderProgram(activation, true) : null;\n  const matmulProgram = new MatMulPackedProgram(\n      im2ColReshaped.shape as [number, number, number],\n      w2Row.shape as [number, number, number],\n      [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias,\n      fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n  const inputs: TensorInfo[] = [im2ColReshaped, w2Row];\n  if (bias) {\n    inputs.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    inputs.push(preluActivationWeights);\n  }\n  if (hasLeakyreluAlpha) {\n    const $leakyreluAlpha = backend.makeTensorInfo(\n        [], 'float32',\n        util.createScalarValue(leakyreluAlpha as {} as 'float32', 'float32'));\n    inputs.push($leakyreluAlpha);\n    intermediates.push($leakyreluAlpha);\n  }\n  const product = backend.runWebGLProgram(matmulProgram, inputs, 'float32');\n\n  const outShape = isChannelsLast ?\n      [1, outHeight, outWidth, convInfo.outChannels] :\n      [1, convInfo.outChannels, outHeight, outWidth];\n  const out =\n      reshape({inputs: {x: product}, backend, attrs: {shape: outShape}});\n\n  intermediates.push(product);\n  for (const i of intermediates) {\n    backend.disposeIntermediateTensorInfo(i);\n  }\n\n  return out;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Conv2D, Conv2DAttrs, Conv2DInputs, env, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Conv2DProgram} from '../conv_gpu';\nimport {conv2dByMatMul, conv2dWithIm2Row} from './Conv2D_impl';\nimport {reshape} from './Reshape';\n\nexport function conv2d(\n    args:\n        {inputs: Conv2DInputs, attrs: Conv2DAttrs, backend: MathBackendWebGL}) {\n  const {inputs, backend, attrs} = args;\n  const {x, filter} = inputs;\n  const {strides, pad, dataFormat, dilations, dimRoundingMode} = attrs;\n\n  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util.computeConv2DInfo(\n      x.shape as [number, number, number, number],\n      filter.shape as [number, number, number, number], strides, dilations, pad,\n      dimRoundingMode, false /* depthwise */, $dataFormat);\n  let out: TensorInfo;\n\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n      convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n      convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n      (convInfo.padInfo.type === 'SAME' || convInfo.padInfo.type === 'VALID')) {\n    out = conv2dByMatMul({x, filter, convInfo, backend});\n  } else if (env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n    out = conv2dWithIm2Row({x, filter, convInfo, backend});\n  } else {\n    const program = new Conv2DProgram(convInfo);\n    out = backend.runWebGLProgram(program, [x, filter], 'float32');\n  }\n\n  const outReshaped =\n      reshape({inputs: {x: out}, backend, attrs: {shape: convInfo.outShape}});\n  backend.disposeIntermediateTensorInfo(out);\n\n  return outReshaped;\n}\n\nexport const conv2DConfig: KernelConfig = {\n  kernelName: Conv2D,\n  backendName: 'webgl',\n  kernelFunc: conv2d as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Conv2DDerFilterProgram implements GPGPUProgram {\n  variableNames = ['x', 'dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: backend_util.Conv2DInfo) {\n    this.outputShape = convInfo.filterShape;\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv2DDerInputProgram implements GPGPUProgram {\n  variableNames = ['dy', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: backend_util.Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n\n    const rowDim = isChannelsLast ? 1 : 2;\n    const colDim = isChannelsLast ? 2 : 3;\n    const channelDim = isChannelsLast ? 3 : 1;\n\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv3DDerFilterProgram implements GPGPUProgram {\n  variableNames = ['x', 'dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: backend_util.Conv3DInfo) {\n    this.outputShape = convInfo.filterShape;\n\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv3DDerInputProgram implements GPGPUProgram {\n  variableNames = ['dy', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: backend_util.Conv3DInfo) {\n    this.outputShape = convInfo.inShape;\n\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n\n    const padFront = filterDepth - 1 - convInfo.padInfo.front;\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Conv2DBackpropFilter, Conv2DBackpropFilterAttrs, Conv2DBackpropFilterInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Conv2DDerFilterProgram} from '../conv_backprop_gpu';\n\nexport function conv2DBackpropFilter(args: {\n  inputs: Conv2DBackpropFilterInputs,\n  attrs: Conv2DBackpropFilterAttrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {x, dy} = inputs;\n  const {strides, pad, dataFormat, dimRoundingMode, filterShape} = attrs;\n\n  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util.computeConv2DInfo(\n      x.shape as [number, number, number, number], filterShape, strides,\n      1 /* dilations */, pad, dimRoundingMode, false /* depthwise */,\n      $dataFormat);\n\n  const program = new Conv2DDerFilterProgram(convInfo);\n  return backend.runWebGLProgram(program, [x, dy], 'float32');\n}\n\nexport const conv2DBackpropFilterConfig: KernelConfig = {\n  kernelName: Conv2DBackpropFilter,\n  backendName: 'webgl',\n  kernelFunc: conv2DBackpropFilter as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Conv2DBackpropInput, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Conv2DDerInputProgram} from '../conv_backprop_gpu';\n\nexport function conv2DBackpropInput(args: {\n  inputs: Conv2DBackpropInputInputs,\n  attrs: Conv2DBackpropInputAttrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {dy, filter} = inputs;\n  const {inputShape, strides, pad, dataFormat, dimRoundingMode} = attrs;\n\n  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util.computeConv2DInfo(\n      inputShape, filter.shape as [number, number, number, number], strides,\n      1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);\n\n  const program = new Conv2DDerInputProgram(convInfo);\n  return backend.runWebGLProgram(program, [dy, filter], 'float32');\n}\n\nexport const conv2DBackpropInputConfig: KernelConfig = {\n  kernelName: Conv2DBackpropInput,\n  backendName: 'webgl',\n  kernelFunc: conv2DBackpropInput as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Conv3D, Conv3DAttrs, Conv3DInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Conv3DProgram} from '../conv_gpu';\n\nexport function conv3D(\n    args:\n        {inputs: Conv3DInputs, attrs: Conv3DAttrs, backend: MathBackendWebGL}) {\n  const {inputs, backend, attrs} = args;\n  const {x, filter} = inputs;\n  const {strides, pad, dilations} = attrs;\n\n  const convInfo = backend_util.computeConv3DInfo(\n      x.shape as [number, number, number, number, number],\n      filter.shape as [number, number, number, number, number], strides,\n      dilations, pad);\n\n  const program = new Conv3DProgram(convInfo);\n  return backend.runWebGLProgram(program, [x, filter], 'float32');\n}\n\nexport const conv3DConfig: KernelConfig = {\n  kernelName: Conv3D,\n  backendName: 'webgl',\n  kernelFunc: conv3D as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Conv3DBackpropFilterV2, Conv3DBackpropFilterV2Attrs, Conv3DBackpropFilterV2Inputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Conv3DDerFilterProgram} from '../conv_backprop_gpu';\n\nexport function conv3DBackpropFilterV2(args: {\n  inputs: Conv3DBackpropFilterV2Inputs,\n  attrs: Conv3DBackpropFilterV2Attrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {x, dy} = inputs;\n  const {strides, pad, filterShape} = attrs;\n\n  const convInfo = backend_util.computeConv3DInfo(\n      x.shape as [number, number, number, number, number], filterShape, strides,\n      1 /* dilations */, pad);\n\n  const program = new Conv3DDerFilterProgram(convInfo);\n  return backend.runWebGLProgram(program, [x, dy], 'float32');\n}\n\nexport const conv3DBackpropFilterV2Config: KernelConfig = {\n  kernelName: Conv3DBackpropFilterV2,\n  backendName: 'webgl',\n  kernelFunc: conv3DBackpropFilterV2 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Conv3DBackpropInputV2, Conv3DBackpropInputV2Attrs, Conv3DBackpropInputV2Inputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Conv3DDerInputProgram} from '../conv_backprop_gpu';\n\nexport function conv3DBackpropInput(args: {\n  inputs: Conv3DBackpropInputV2Inputs,\n  attrs: Conv3DBackpropInputV2Attrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {dy, filter} = inputs;\n  const {pad, strides, inputShape} = attrs;\n\n  const convInfo = backend_util.computeConv3DInfo(\n      inputShape, filter.shape as [number, number, number, number, number],\n      strides, 1 /* dilations */, pad);\n\n  const program = new Conv3DDerInputProgram(convInfo);\n  return backend.runWebGLProgram(program, [dy, filter], 'float32');\n}\n\nexport const conv3DBackpropInputConfig: KernelConfig = {\n  kernelName: Conv3DBackpropInputV2,\n  backendName: 'webgl',\n  kernelFunc: conv3DBackpropInput as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Cos, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\n\nexport const cos = unaryKernelFunc({opSnippet: COS});\n\nexport const cosConfig: KernelConfig = {\n  kernelName: Cos,\n  backendName: 'webgl',\n  kernelFunc: cos,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Cosh, KernelConfig} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\n\nexport const cosh = unaryKernelFunc({opSnippet: COSH});\n\nexport const coshConfig: KernelConfig = {\n  kernelName: Cosh,\n  backendName: 'webgl',\n  kernelFunc: cosh,\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class CropAndResizeProgram implements GPGPUProgram {\n  variableNames = ['Image', 'Boxes', 'BoxInd'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      imageShape: [number, number, number, number], boxShape: [number, number],\n      cropSize: [number, number], method: 'bilinear'|'nearest',\n      extrapolationValue: number) {\n    const [batch, imageHeight, imageWidth, depth] = imageShape;\n    const [numBoxes, ] = boxShape;\n    const [cropHeight, cropWidth] = cropSize;\n    this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n    const methodId = method === 'bilinear' ? 1 : 0;\n\n    const [inputHeightFloat, inputWidthFloat] =\n        [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n\n    const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n        [\n          `${(imageHeight - 1) / (cropHeight - 1)}`,\n          '(y2-y1) * height_ratio',\n          `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n        ] :\n        [\n          '0.0',\n          '0.0',\n          `0.5 * (y1+y2) * ${inputHeightFloat}`,\n        ];\n    const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n        [\n          `${(imageWidth - 1) / (cropWidth - 1)}`,\n          '(x2-x1) * width_ratio',\n          `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n        ] :\n        [\n          '0.0',\n          '0.0',\n          `0.5 * (x1+x2) * ${inputWidthFloat}`,\n        ];\n\n    // Reference implementation\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n    this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {CropAndResizeProgram} from '../crop_and_resize_gpu';\n\nexport const cropAndResize = (args: {\n  inputs: CropAndResizeInputs,\n  backend: MathBackendWebGL,\n  attrs: CropAndResizeAttrs\n}): TensorInfo => {\n  const {inputs, backend, attrs} = args;\n  const {image, boxes, boxInd} = inputs;\n  const {cropSize, method, extrapolationValue} = attrs;\n\n  const program = new CropAndResizeProgram(\n      image.shape as [number, number, number, number],\n      boxes.shape as [number, number], cropSize, method, extrapolationValue);\n  return backend.runWebGLProgram(program, [image, boxes, boxInd], 'float32');\n};\n\nexport const cropAndResizeConfig: KernelConfig = {\n  kernelName: CropAndResize,\n  backendName: 'webgl',\n  kernelFunc: cropAndResize as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class CumSumProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  // Caching uniform location for speed.\n  index: WebGLUniformLocation;\n\n  constructor(shape: number[], exclusive: boolean, reverse: boolean) {\n    this.outputShape = shape;\n    const rank = shape.length;\n    const val = exclusive ? '0.0' : `getX(${getCoords(rank, 'coords')})`;\n    const length = shape[shape.length - 1];\n    let condition = '';\n    let idxString = '';\n    // When exclusive is set, the cumsum op becomes roll op that copies the\n    // value from the previous index based on the direction specified by the\n    // reverse flag.\n    if (exclusive) {\n      condition = reverse ? `end != ${length - 1}` : 'end != 0';\n      idxString = reverse ? 'end + 1' : 'end - 1';\n    } else {\n      condition = reverse ? `end + pow2 < ${length}` : 'end >= pow2';\n      idxString = (reverse ? 'end + pow2' : 'end - pow2');\n    }\n\n    this.userCode = `\n      uniform float index;\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n  }\n\n  getCustomSetupFunc(index: number) {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.index == null) {\n        this.index = gpgpu.getUniformLocation(webGLProgram, 'index');\n      }\n      gpgpu.gl.uniform1f(this.index, index);\n    };\n  }\n}\n\nfunction getCoords(rank: number, name: string): string {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.x, ${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.x, ${name}.y, ${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n  } else {\n    throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n  }\n}\n\nfunction getFinalCoord(rank: number, name: string): string {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.w`;\n  } else {\n    throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Cumsum, CumsumAttrs, CumsumInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {CumSumProgram} from '../cumsum_gpu';\n\nimport {identity} from './Identity';\nimport {transpose} from './Transpose';\n\nexport function cumsum(\n    args:\n        {inputs: CumsumInputs, backend: MathBackendWebGL, attrs: CumsumAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {axis, exclusive, reverse} = attrs;\n\n  const xRank = x.shape.length;\n  const permutation = backend_util.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation != null) {\n    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutation}});\n  }\n  const permutedAxis = backend_util.getInnerMostAxes(1, xRank)[0];\n\n  if (permutedAxis !== xRank - 1) {\n    throw new Error(\n        `WebGL cumsum shader expects an inner-most axis=${\n            x.shape.length - 1} ` +\n        `but got axis=${axis}`);\n  }\n  const size = permutedX.shape[permutedAxis];\n  let result = identity({inputs: {x: permutedX}, backend});\n  // Use cumsum parallel algorithm, ref:\n  // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda\n\n  for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n    const program = new CumSumProgram(permutedX.shape, false, reverse);\n    const customSetup = program.getCustomSetupFunc(i);\n    const prevResult = result;\n    result =\n        backend.runWebGLProgram(program, [result], result.dtype, customSetup);\n    backend.disposeIntermediateTensorInfo(prevResult);\n  }\n  // For exclusive cumsum, shift the end result in the direction of sum\n  // and add 0 to the front index.\n  if (exclusive) {\n    const program = new CumSumProgram(permutedX.shape, exclusive, reverse);\n    const prevResult = result;\n    result = backend.runWebGLProgram(program, [result], result.dtype);\n    backend.disposeIntermediateTensorInfo(prevResult);\n  }\n\n  if (permutation != null) {\n    const reversePermutation = backend_util.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose(\n        {inputs: {x: result}, backend, attrs: {perm: reversePermutation}});\n\n    backend.disposeIntermediateTensorInfo(result);\n    backend.disposeIntermediateTensorInfo(permutedX);\n\n    return reverseTransposedResult;\n  }\n\n  return result;\n}\n\nexport const cumsumConfig: KernelConfig = {\n  kernelName: Cumsum,\n  backendName: 'webgl',\n  kernelFunc: cumsum as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DenseBincount, DenseBincountAttrs, DenseBincountInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {bincountImplCPU, bincountReduceImplCPU} from '../kernel_utils/shared';\n\nexport function denseBincount(args: {\n  inputs: DenseBincountInputs,\n  backend: MathBackendWebGL,\n  attrs: DenseBincountAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x, weights} = inputs;\n  const {size, binaryOutput} = attrs;\n\n  if (x.shape.length === 1) {\n    const xVals = backend.readSync(x.dataId) as TypedArray;\n    const weightsVals = backend.readSync(weights.dataId) as TypedArray;\n\n    const outVals =\n        bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);\n\n    return backend.makeTensorInfo([size], weights.dtype, outVals);\n  } else if (x.shape.length === 2) {\n    const xBuf = backend.bufferSync(x);\n    const weightsBuf = backend.bufferSync(weights);\n\n    const outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size, binaryOutput);\n\n    return backend.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);\n  }\n\n  throw new Error(\n      `Error in denseBincount: input must be at most rank 2, but got rank` +\n      `${x.shape.length}.`);\n}\n\nexport const denseBincountConfig: KernelConfig = {\n  kernelName: DenseBincount,\n  backendName: 'webgl',\n  kernelFunc: denseBincount as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthToSpaceProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[] = [];\n  userCode: string;\n  blockSize: number;\n  dataFormat: string;\n\n  constructor(\n      outputShape: number[], blockSize: number, dataFormat: 'NHWC'|'NCHW') {\n    this.outputShape = outputShape;\n    this.blockSize = blockSize;\n    this.dataFormat = dataFormat;\n    this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n  }\n\n  private getHeightCoordString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `coords[1]`;\n    } else {\n      return `coords[2]`;\n    }\n  }\n\n  private getWidthCoordString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `coords[2]`;\n    } else {\n      return `coords[3]`;\n    }\n  }\n\n  private getDepthCoordString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `coords[3]`;\n    } else {\n      return `coords[1]`;\n    }\n  }\n\n  private getOutputDepthSize(): number {\n    if (this.dataFormat === 'NHWC') {\n      return this.outputShape[3];\n    } else {\n      return this.outputShape[1];\n    }\n  }\n\n  private getInputSamplingString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `getX(b, in_h, in_w, in_d)`;\n    } else {\n      return `getX(b, in_d, in_h, in_w)`;\n    }\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DepthToSpace, DepthToSpaceAttrs, DepthToSpaceInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {DepthToSpaceProgram} from '../depth_to_space_gpu';\n\nexport function depthToSpace(args: {\n  inputs: DepthToSpaceInputs,\n  backend: MathBackendWebGL,\n  attrs: DepthToSpaceAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {blockSize, dataFormat} = attrs;\n\n  util.assert(\n      blockSize > 1,\n      () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n\n  const batchSize = x.shape[0];\n  const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n  const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n  const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n\n  const outputShape = (dataFormat === 'NHWC') ?\n      [batchSize, outputHeight, outputWidth, outputDepth] :\n      [batchSize, outputDepth, outputHeight, outputWidth];\n\n  const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n  return backend.runWebGLProgram(program, [x], x.dtype);\n}\n\nexport const depthToSpaceConfig: KernelConfig = {\n  kernelName: DepthToSpace,\n  backendName: 'webgl',\n  kernelFunc: depthToSpace as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConv2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      convInfo: backend_util.Conv2DInfo, addBias = false,\n      activation: string = null, hasPreluActivation = false,\n      hasLeakyReluAlpha = false) {\n    this.outputShape = convInfo.outShape;\n\n    const xNumRows = convInfo.inHeight;\n    const xNumCols = convInfo.inWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n    let activationSnippet = '', applyActivationSnippet = '';\n    if (activation) {\n      if (hasPreluActivation) {\n        activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n      } else if (hasLeakyReluAlpha) {\n        activationSnippet = `float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${activation}\n        }`;\n      } else {\n        activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n      }\n\n      applyActivationSnippet = `result = activation(result);`;\n    }\n\n    const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n    if (addBias) {\n      this.variableNames.push('bias');\n    }\n\n    if (hasPreluActivation) {\n      this.variableNames.push('preluActivationWeights');\n    }\n    if (hasLeakyReluAlpha) {\n      this.variableNames.push('leakyreluAlpha');\n    }\n\n    this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, util} from '@tensorflow/tfjs-core';\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConvPacked2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  packedInputs = true;\n  packedOutput = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      convInfo: backend_util.Conv2DInfo, addBias = false,\n      activation: string = null, hasPreluActivation = false,\n      hasLeakyReluAlpha = false) {\n    this.outputShape = convInfo.outShape;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    const xNumRows = convInfo.inHeight;\n    const xNumCols = convInfo.inWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const texelsAcross = filterWidth;\n\n    let mainLoop = `\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;\n\n    for (let c = 0; c < filterWidth; c++) {\n      mainLoop += `\n          vec4 xTexelC${c * 2};\n          int xTexelC${c * 2}Ready;\n          vec4 xC${c};`;\n    }\n\n    /**\n     * This vectorized implementation works by gathering the values needed for\n     * each output channel's dot product into vec4's and then multiplying them\n     * all together (this happens in the final double for-loop below). Most of\n     * the main loop consists of constructing these vec4's with the minimum\n     * number of texture2D calls, which means making use of all four returned\n     * values from a texture2D call at once.\n     */\n    for (let r = 0; r < filterHeight; r++) {\n      for (let c = 0; c < filterWidth; c++) {\n        mainLoop += `\n          xTexelC${c * 2} = vec4(0.0);\n          xTexelC${c * 2}Ready = 0;\n          xC${c} = vec4(0.0);`;\n      }\n      mainLoop += `\n        xR = xRCorner + ${r * dilationHeight};\n        if (xR >=0 && xR < ${xNumRows}) {\n      `;\n\n      for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {\n        const colIndex = texelC * 2;\n        const c = colIndex * dilationWidth;\n\n        mainLoop += `\n          xC = xCCorner + ${c};\n          `;\n\n        if (strideWidth === 1) {\n          if (colIndex < filterWidth) {\n            // If padding is odd, the outer texels have to be composed.\n            if (padLeft % 2 === 1) {\n              // TODO: Ensure vec4 previous does not result in redundant sample,\n              // and avoid setting xTexelRC's that exceed the boundary in the\n              // first place rather than resetting them to vec4(0)).\n\n              // To compute xCOffset:\n              // - If padding is odd, we must add 1 to ensure we ask for an\n              // even-numbered row.\n              // - We subtract 2 to access the previous texel.\n\n              mainLoop += `\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < ${xNumCols} && xTexelC${\n                  c}Ready == 0) {\n                  xTexelC${c} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= ${xNumCols}) {\n                    xTexelC${c}.zw = vec2(0.0);\n                  }\n                  xTexelC${c}Ready = 1;\n                }\n              `;\n              // This texel has been read in previous iteration if the dilation\n              // is 1.\n              if (dilationWidth === 1 && c > 0) {\n                mainLoop += `\n                xC${colIndex} = vec4(xTexelC${c - 2}.zw, xTexelC${c}.xy);\n                `;\n              } else {\n                mainLoop += `\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= ${xNumCols}) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${colIndex} = vec4(previous.zw, xTexelC${c}.xy);\n                  } else {\n                    xC${colIndex} = vec4(0.0, 0.0, xTexelC${c}.xy);\n                  }\n                  `;\n              }\n            } else {\n              // Padding is even, so xRC corresponds to a single texel.\n              mainLoop += `\n                if (xC >= 0 && xC < ${xNumCols} && xTexelC${c}Ready == 0) {\n                  xTexelC${c} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= ${xNumCols}) {\n                    xTexelC${c}.zw = vec2(0.0);\n                  }\n                  xTexelC${c}Ready = 1;\n                }\n\n                xC${colIndex} = xTexelC${c};\n                `;\n            }\n\n            if (c + 1 < filterWidth) {\n              // If dilation is even, the second entry should match the first\n              // (either both are composed or both are single samples). But if\n              // dilation is odd, then the second entry should be the opposite\n              // of the first (if the first is composed, the second is a single\n              // sample, and vice versa.)\n\n              const nextTexelOffset = padLeft % 2 === 0 ?\n                  util.nearestLargerEven(dilationWidth) :\n                  dilationWidth;\n\n              if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||\n                  (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {\n                mainLoop += `\n                  xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};\n\n                  if (xCOffset >= 0 && xCOffset < ${xNumCols} && xTexelC${\n                    c + 2}Ready == 0) {\n                    xTexelC${c + 2} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= ${xNumCols}) {\n                      xTexelC${c + 2}.zw = vec2(0.0);\n                    }\n                    xTexelC${c + 2}Ready = 1;\n                  }\n                  `;\n\n                // If dilation > 1 then the xRC's will not be able to share any\n                // values, so each xRC will require two unique calls to getX.\n                if (dilationWidth > 1) {\n                  mainLoop += `\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < ${xNumCols} && xTexelC${\n                      c}Ready == 0) {\n                      xTexelC${c} = getX(batch, xR, xCOffset, d1);\n                      xTexelC${c}Ready = 1;\n                    }\n                    `;\n                }\n\n                mainLoop += `\n                  xC${colIndex + 1} = vec4(xTexelC${c}.zw, xTexelC${c + 2}.xy);\n                  `;\n              } else {\n                // If dilation is 1 and padding is odd, we have already read the\n                // texel when constructing the previous x value. Here we can\n                // simply skip the texture read.\n                if (nextTexelOffset === 1) {\n                  mainLoop += `\n                    xC${colIndex + 1} = xTexelC${c};\n                    `;\n                } else {\n                  mainLoop += `\n                    xCOffset = xC + ${nextTexelOffset};\n\n                    if (xCOffset >= 0 && xCOffset < ${xNumCols} && xTexelC${\n                      c + 2}Ready == 0) {\n                      xTexelC${c + 2} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= ${xNumCols}) {\n                        xTexelC${c + 2}.zw = vec2(0.0);\n                      }\n                      xTexelC${c + 2}Ready = 1;\n                    }\n\n                    xC${colIndex + 1} = xTexelC${c + 2};\n                    `;\n                }\n              }\n            }\n          }\n        } else {  // stride === 2\n          if (c < filterWidth) {\n            // Depending on whether padLeft is even or odd, we want either the\n            // xy or zw channels from X texels for xC${colIndex}. If padLeft is\n            // even, xC${colIndex +1} is simply the zw channels of texels we've\n            // already sampled. But if padLeft is odd, xC{$c + 1}.zw will\n            // need to come from the xy channels of a new texel, hence the `\n            // vec4\n            // final` initialized below.\n            if (padLeft % 2 === 1) {\n              mainLoop += `\n                xCOffset = xC + 1 - ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols} && xTexelC${\n                  c}Ready == 0) {\n                  xTexelC${c} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= ${xNumCols}) {\n                    xTexelC${c}.zw = vec2(0.0);\n                  }\n                  xTexelC${c}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < ${xNumCols} && xTexelC${\n                  c + 2}Ready == 0) {\n                  xTexelC${c + 2} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= ${xNumCols}) {\n                    xTexelC${c + 2}.zw = vec2(0.0);\n                  }\n                  xTexelC${c + 2}Ready = 1;\n                }\n\n                xC${colIndex} = vec4(xTexelC${c}.zw, xTexelC${c + 2}.zw);\n              `;\n\n              if (c + 1 < filterWidth) {\n                mainLoop += `\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + ${strideWidth};\n                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${colIndex + 1} = vec4(xTexelC${c + 2}.xy, final.xy);\n                `;\n              }\n            } else {\n              mainLoop += `\n                if(xC >= 0 && xC < ${xNumCols} && xTexelC${c}Ready == 0) {\n                  xTexelC${c} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= ${xNumCols}) {\n                    xTexelC${c}.zw = vec2(0.0);\n                  }\n                  xTexelC${c}Ready = 1;\n                }\n\n                xCOffset = xC + ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols} && xTexelC${\n                  c + 2}Ready == 0) {\n                  xTexelC${c + 2} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= ${xNumCols}) {\n                    xTexelC${c + 2}.zw = vec2(0.);\n                  }\n                  xTexelC${c + 2}Ready = 1;\n                }\n\n                xC${colIndex} = vec4(\n                  xTexelC${c}.xy, xTexelC${c + 2}.xy);\n              `;\n\n              if (c + 1 < filterWidth) {\n                mainLoop += `\n                  xC${colIndex + 1} = vec4(xTexelC${c}.zw, xTexelC${c + 2}.zw);\n                `;\n              }\n            }\n          }\n        }\n\n        // localize the dotProd accumulation within the loop, the theory is for\n        // GPU with limited cache, accumulate sum across large amount of\n        // veriables will cause lots of cache misses. (i.e. 5x5 filter will have\n        // 50 variables)\n        if (colIndex < filterWidth) {\n          mainLoop += `\n            wTexel = getW(${r}, ${c}, d1, q);\n            dotProd += xC${colIndex} * vec4(wTexel.xz, wTexel.xz);\n          `;\n\n          if (c + 1 < filterWidth) {\n            mainLoop += `\n              wTexel = getW(${r}, ${c + 1}, d1, q);\n              dotProd += xC${colIndex + 1} * vec4(wTexel.xz, wTexel.xz);\n            `;\n          }\n        }\n      }\n      mainLoop += `\n        }\n      `;\n    }\n\n    let activationSnippet = '', applyActivationSnippet = '';\n    if (activation) {\n      if (hasPreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n      } else if (hasLeakyReluAlpha) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${activation}\n        }`;\n      } else {\n        activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n      }\n\n      applyActivationSnippet = `result = activation(result);`;\n    }\n\n    const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n    if (addBias) {\n      this.variableNames.push('bias');\n    }\n\n    if (hasPreluActivation) {\n      this.variableNames.push('preluActivationWeights');\n    }\n    if (hasLeakyReluAlpha) {\n      this.variableNames.push('leakyreluAlpha');\n    }\n\n    this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${mainLoop}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs, env, KernelConfig, KernelFunc, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {DepthwiseConv2DProgram} from '../conv_gpu_depthwise';\nimport {DepthwiseConvPacked2DProgram} from '../conv_packed_gpu_depthwise';\n\nexport function depthwiseConv2dNative(args: {\n  inputs: DepthwiseConv2dNativeInputs,\n  attrs: DepthwiseConv2dNativeAttrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {x, filter} = inputs;\n  const {strides, pad, dilations, dimRoundingMode} = attrs;\n\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n\n  util.assert(\n      backend_util.eitherStridesOrDilationsAreOne(strides, $dilations),\n      () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n          `1. Got strides ${strides} and dilations '${$dilations}'`);\n\n  const convInfo = backend_util.computeConv2DInfo(\n      x.shape as [number, number, number, number],\n      filter.shape as [number, number, number, number], strides, $dilations,\n      pad, dimRoundingMode, true /* depthwise */);\n\n  let program: DepthwiseConv2DProgram|DepthwiseConvPacked2DProgram;\n  if (env().getBool('WEBGL_PACK_DEPTHWISECONV') && convInfo.strideWidth <= 2 &&\n      convInfo.outChannels / convInfo.inChannels === 1) {\n    program = new DepthwiseConvPacked2DProgram(convInfo);\n  } else {\n    program = new DepthwiseConv2DProgram(convInfo);\n  }\n\n  return backend.runWebGLProgram(program, [x, filter], 'float32');\n}\n\nexport const depthwiseConv2dNativeConfig: KernelConfig = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: 'webgl',\n  kernelFunc: depthwiseConv2dNative as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConv2DDerFilterProgram implements GPGPUProgram {\n  variableNames = ['x', 'dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: backend_util.Conv2DInfo) {\n    this.outputShape = convInfo.filterShape;\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class DepthwiseConv2DDerInputProgram implements GPGPUProgram {\n  variableNames = ['dy', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: backend_util.Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropFilterAttrs, DepthwiseConv2dNativeBackpropFilterInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {DepthwiseConv2DDerFilterProgram} from '../conv_backprop_gpu_depthwise';\n\nexport function depthwiseConv2dNativeBackpropFilter(args: {\n  inputs: DepthwiseConv2dNativeBackpropFilterInputs,\n  attrs: DepthwiseConv2dNativeBackpropFilterAttrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {x, dy} = inputs;\n  const {strides, dilations, pad, dimRoundingMode, filterShape} = attrs;\n\n  const convInfo = backend_util.computeConv2DInfo(\n      x.shape as [number, number, number, number], filterShape, strides,\n      dilations, pad, dimRoundingMode, true /* depthwise */);\n\n  const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n  return backend.runWebGLProgram(program, [x, dy], 'float32');\n}\n\nexport const depthwiseConv2dNativeBackpropFilterConfig: KernelConfig = {\n  kernelName: DepthwiseConv2dNativeBackpropFilter,\n  backendName: 'webgl',\n  kernelFunc: depthwiseConv2dNativeBackpropFilter as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DepthwiseConv2dNativeBackpropInput, DepthwiseConv2dNativeBackpropInputAttrs, DepthwiseConv2dNativeBackpropInputInputs, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {DepthwiseConv2DDerInputProgram} from '../conv_backprop_gpu_depthwise';\n\nexport function depthwiseConv2dNativeBackpropInput(args: {\n  inputs: DepthwiseConv2dNativeBackpropInputInputs,\n  attrs: DepthwiseConv2dNativeBackpropInputAttrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {dy, filter} = inputs;\n  const {strides, dilations, pad, dimRoundingMode, inputShape} = attrs;\n\n  const convInfo = backend_util.computeConv2DInfo(\n      inputShape, filter.shape as [number, number, number, number], strides,\n      dilations, pad, dimRoundingMode, true /* depthwise */);\n\n  const program = new DepthwiseConv2DDerInputProgram(convInfo);\n  return backend.runWebGLProgram(program, [dy, filter], 'float32');\n}\n\nexport const depthwiseConv2dNativeBackpropInputConfig: KernelConfig = {\n  kernelName: DepthwiseConv2dNativeBackpropInput,\n  backendName: 'webgl',\n  kernelFunc: depthwiseConv2dNativeBackpropInput as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DiagProgram implements GPGPUProgram {\n  variableNames = ['X'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(size: number) {\n    this.outputShape = [size, size];\n    this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Diag, DiagInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {DiagProgram} from '../diag_gpu';\nimport {reshape} from './Reshape';\n\nexport function diag(args: {inputs: DiagInputs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend} = args;\n  const {x} = inputs;\n\n  const outShape = [...x.shape, ...x.shape];\n  const xSize = util.sizeFromShape(x.shape);\n\n  const flat = reshape({inputs: {x}, backend, attrs: {shape: [xSize]}});\n\n  const program = new DiagProgram(xSize);\n  const res = backend.runWebGLProgram(program, [flat], flat.dtype);\n\n  const out = reshape({inputs: {x: res}, backend, attrs: {shape: outShape}});\n\n  backend.disposeIntermediateTensorInfo(flat);\n  backend.disposeIntermediateTensorInfo(res);\n\n  return out;\n}\n\nexport const diagConfig: KernelConfig = {\n  kernelName: Diag,\n  backendName: 'webgl',\n  kernelFunc: diag as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Dilation2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: backend_util.Conv2DInfo) {\n    this.outputShape = convInfo.outShape;\n\n    const {\n      inHeight,\n      inWidth,\n      padInfo,\n      strideHeight,\n      strideWidth,\n      filterHeight,\n      filterWidth,\n      dilationHeight,\n      dilationWidth\n    } = convInfo;\n\n    const {top: padTop, left: padLeft} = padInfo;\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${filterHeight}; h++) {\n          int hIn = hBeg + h * ${dilationHeight};\n\n          if (hIn >= 0 && hIn < ${inHeight}) {\n            for (int w = 0; w < ${filterWidth}; w++) {\n              int wIn = wBeg + w * ${dilationWidth};\n\n              if (wIn >= 0 && wIn < ${inWidth}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Dilation2D, Dilation2DAttrs, Dilation2DInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Dilation2DProgram} from '../dilation_gpu';\nimport {reshape} from './Reshape';\n\nexport function dilation2D(args: {\n  inputs: Dilation2DInputs,\n  attrs: Dilation2DAttrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {x, filter} = inputs;\n  const {strides, pad, dilations} = attrs;\n\n  const convInfo = backend_util.computeDilation2DInfo(\n      x.shape as [number, number, number, number],\n      filter.shape as [number, number, number], strides, pad,\n      'NHWC' /* dataFormat */, dilations);\n  let out: TensorInfo;\n\n  const program = new Dilation2DProgram(convInfo);\n  out = backend.runWebGLProgram(program, [x, filter], 'float32');\n\n  const outReshaped =\n      reshape({inputs: {x: out}, backend, attrs: {shape: convInfo.outShape}});\n  backend.disposeIntermediateTensorInfo(out);\n\n  return outReshaped;\n}\n\nexport const dilation2DConfig: KernelConfig = {\n  kernelName: Dilation2D,\n  backendName: 'webgl',\n  kernelFunc: dilation2D as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Einsum, EinsumAttrs, EinsumInputs, KernelConfig, KernelFunc, Tensor, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {multiply} from './Multiply';\nimport {reshape} from './Reshape';\nimport {sum} from './Sum';\nimport {transpose} from './Transpose';\n\nexport function einsum(\n    args:\n        {inputs: EinsumInputs, backend: MathBackendWebGL, attrs: EinsumAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {equation} = attrs;\n  const tensors = inputs as Tensor[];\n\n  const {allDims, summedDims, idDims} =\n      backend_util.decodeEinsumEquation(equation, tensors.length);\n  backend_util.checkEinsumDimSizes(allDims.length, idDims, tensors);\n  const {path, steps} = backend_util.getEinsumComputePath(summedDims, idDims);\n\n  const nSteps = steps.length;\n  let out: TensorInfo|null = null;\n  let numDimsRemaining = allDims.length;\n  const tensorsToDispose: TensorInfo[] = [];\n  for (let i = 0; i < nSteps; ++i) {\n    for (const idTerm of steps[i]) {\n      const {permutationIndices: perm, expandDims: dimsToExpand} =\n          backend_util.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);\n      let x: TensorInfo;\n      if (backend_util.isIdentityPermutation(perm)) {\n        x = tensors[idTerm];\n      } else {\n        x = transpose({inputs: {x: tensors[idTerm]}, backend, attrs: {perm}});\n        tensorsToDispose.push(x);\n      }\n      const targetShape: number[] = x.shape.slice();\n      for (let k = 0; k < dimsToExpand.length; ++k) {\n        targetShape.splice(dimsToExpand[k], 0, 1);\n      }\n\n      if (!util.arraysEqual(x.shape, targetShape)) {\n        x = reshape({inputs: {x}, backend, attrs: {shape: targetShape}});\n        tensorsToDispose.push(x);\n      }\n      if (out === null) {\n        out = x;\n      } else {\n        // tslint:disable-next-line: no-unnecessary-type-assertion\n        out = multiply({inputs: {a: x, b: out}, backend}) as TensorInfo;\n        tensorsToDispose.push(out);\n      }\n    }\n    if (i < nSteps - 1) {\n      if (path[i] >= 0) {\n        out = sum({\n          inputs: {x: out},\n          backend,\n          attrs: {\n            axis: path[i] - (allDims.length - numDimsRemaining),\n            keepDims: false\n          }\n        });\n        tensorsToDispose.push(out);\n      }\n      numDimsRemaining--;\n    }\n  }\n\n  // Clean up intermediate tensors.\n  for (const tensorInfo of tensorsToDispose) {\n    if (tensorInfo === out) {\n      continue;\n    }\n    backend.disposeIntermediateTensorInfo(tensorInfo);\n  }\n\n  return out;\n}\n\nexport const einsumConfig: KernelConfig = {\n  kernelName: Einsum,\n  backendName: 'webgl',\n  kernelFunc: einsum as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Elu, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\n\nconst ELU_PACKED = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\n\nconst elu = unaryKernelFunc({opSnippet: ELU, packedOpSnippet: ELU_PACKED});\n\nexport const eluConfig: KernelConfig = {\n  kernelName: Elu,\n  backendName: 'webgl',\n  kernelFunc: elu as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {EluGrad, EluGradInputs, env, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {BinaryOpProgram} from '../binaryop_gpu';\nimport {BinaryOpPackedProgram} from '../binaryop_packed_gpu';\n\nconst ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nconst ELU_DER_PACKED = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\n\nexport const eluGrad =\n    (args: {inputs: EluGradInputs, backend: MathBackendWebGL}): TensorInfo => {\n      const {inputs, backend} = args;\n      const {dy, y} = inputs;\n\n      const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n          new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) :\n          new BinaryOpProgram(ELU_DER, dy.shape, y.shape);\n      return backend.runWebGLProgram(program, [dy, y], dy.dtype);\n    };\n\nexport const eluGradConfig: KernelConfig = {\n  kernelName: EluGrad,\n  backendName: 'webgl',\n  kernelFunc: eluGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Equal, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {equalImplCPU} from '../kernel_utils/shared';\nconst PACKED_EQUAL = `\n  return vec4(equal(a, b));\n`;\n\nconst EQUAL = `return float(a == b);`;\n\nexport const equal = binaryKernelFunc({\n  opSnippet: EQUAL,\n  packedOpSnippet: PACKED_EQUAL,\n  dtype: 'bool',\n  cpuKernelImpl: equalImplCPU,\n});\n\nexport const equalConfig: KernelConfig = {\n  kernelName: Equal,\n  backendName: 'webgl',\n  kernelFunc: equal as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, Erf, KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util.ERF_P};\n  float a1 = ${backend_util.ERF_A1};\n  float a2 = ${backend_util.ERF_A2};\n  float a3 = ${backend_util.ERF_A3};\n  float a4 = ${backend_util.ERF_A4};\n  float a5 = ${backend_util.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\n\nexport const erf = unaryKernelFunc({opSnippet: ERF});\n\nexport const erfConfig: KernelConfig = {\n  kernelName: Erf,\n  backendName: 'webgl',\n  kernelFunc: erf,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Exp, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {expImplCPU} from '../kernel_utils/shared';\n\nexport const EXP = `return exp(x);`;\nexport const exp = unaryKernelFunc(\n    {opSnippet: EXP, packedOpSnippet: EXP, cpuKernelImpl: expImplCPU});\n\nexport const expConfig: KernelConfig = {\n  kernelName: Exp,\n  backendName: 'webgl',\n  kernelFunc: exp as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ExpandDims, ExpandDimsAttrs, ExpandDimsInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {reshape} from './Reshape';\n\nexport function expandDims(args: {\n  inputs: ExpandDimsInputs,\n  attrs: ExpandDimsAttrs,\n  backend: MathBackendWebGL\n}): TensorInfo {\n  const {inputs, attrs, backend} = args;\n  const {dim} = attrs;\n  const {input} = inputs;\n\n  const inputRank = input.shape.length;\n  const newShape = input.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    // Negative value is counted from the tail of rank.\n    util.assert(\n        -(inputRank + 1) <= dim,\n        () => `Axis must be in the interval [${- (inputRank + 1)}, ${\n            inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n\n  return reshape({inputs: {x: input}, backend, attrs: {shape: newShape}});\n}\n\nexport const expandDimsConfig: KernelConfig = {\n  kernelName: ExpandDims,\n  backendName: 'webgl',\n  kernelFunc: expandDims as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Expm1, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {expm1ImplCPU} from '../kernel_utils/shared';\n\nconst EXPM1 = `return exp(x) - 1.0;`;\n\nexport const expm1 = unaryKernelFunc(\n    {opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU});\n\nexport const expm1Config: KernelConfig = {\n  kernelName: Expm1,\n  backendName: 'webgl',\n  kernelFunc: expm1 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class FFTProgram implements GPGPUProgram {\n  variableNames = ['real', 'imag'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      component: 'real'|'imag', inputShape: [number, number],\n      inverse: boolean) {\n    const innerDim = inputShape[1];\n    this.outputShape = inputShape;\n\n    const exponentMultiplierSnippet =\n        inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n    const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n\n    let opString: string;\n    if (component === 'real') {\n      opString = 'return real * expR - imag * expI;';\n    } else if (component === 'imag') {\n      opString = 'return real * expI + imag * expR;';\n    } else {\n      throw new Error(\n          `FFT component must be either \"real\" or \"imag\", got ${component}.`);\n    }\n\n    this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${opString}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {FFTProgram} from '../fft_gpu';\n\nimport {complex} from './Complex';\nimport {reshape} from './Reshape';\n\nexport function fftImpl(\n    x: TensorInfo, inverse: boolean, backend: MathBackendWebGL): TensorInfo {\n  const xData = backend.texData.get(x.dataId);\n\n  const inputSize = util.sizeFromShape(x.shape);\n  // Collapse all outer dimensions to a single batch dimension.\n  const innerDimensionSize = x.shape[x.shape.length - 1];\n  const batch = inputSize / innerDimensionSize;\n\n  const input2D = reshape(\n      {inputs: {x}, backend, attrs: {shape: [batch, innerDimensionSize]}});\n\n  const xShape = input2D.shape as [number, number];\n  const realProgram = new FFTProgram('real', xShape, inverse);\n  const imagProgram = new FFTProgram('imag', xShape, inverse);\n\n  const inputs = [\n    {\n      dataId: xData.complexTensorInfos.real.dataId,\n      dtype: xData.complexTensorInfos.real.dtype,\n      shape: xShape\n    },\n    {\n      dataId: xData.complexTensorInfos.imag.dataId,\n      dtype: xData.complexTensorInfos.imag.dtype,\n      shape: xShape\n    }\n  ];\n\n  const realPart = backend.runWebGLProgram(realProgram, inputs, 'float32');\n  const imagPart = backend.runWebGLProgram(imagProgram, inputs, 'float32');\n\n  const complexOutput =\n      complex({inputs: {real: realPart, imag: imagPart}, backend});\n\n  backend.disposeIntermediateTensorInfo(realPart);\n  backend.disposeIntermediateTensorInfo(imagPart);\n\n  const complexOutputReshaped =\n      reshape({inputs: {x: complexOutput}, backend, attrs: {shape: x.shape}});\n\n  backend.disposeIntermediateTensorInfo(input2D);\n  backend.disposeIntermediateTensorInfo(complexOutput);\n  return complexOutputReshaped;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {FFT, FFTInputs, KernelConfig, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {fftImpl} from './FFT_impl';\n\nexport function fft(args: {inputs: FFTInputs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend} = args;\n  const {input} = inputs;\n\n  return fftImpl(input, false /* inverse */, backend);\n}\n\nexport const fftConfig: KernelConfig = {\n  kernelName: FFT,\n  backendName: 'webgl',\n  kernelFunc: fft\n};\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class FillProgram implements GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[] = [];\n  userCode: string;\n\n  valueLoc: WebGLUniformLocation;\n\n  constructor(shape: number[], value: number) {\n    this.variableNames = ['x'];\n    this.outputShape = shape;\n\n    this.userCode = `\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n  }\n\n  getCustomSetupFunc(value: number) {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.valueLoc == null) {\n        this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n      }\n      gpgpu.gl.uniform1f(this.valueLoc, value);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Fill, FillAttrs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {FillProgram} from '../fill_gpu';\n\nexport function fill(args: {backend: MathBackendWebGL, attrs: FillAttrs}):\n    TensorInfo {\n  const {backend, attrs} = args;\n  const {shape, value} = attrs;\n  let {dtype} = attrs;\n\n  dtype = dtype || util.inferDtype(value);\n\n  if (dtype === 'string') {\n    // String type should be handled in CPU memory.\n    const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n    values.fill(value as string);\n    return backend.makeTensorInfo(shape, dtype, values);\n  } else {\n    const program = new FillProgram(shape, value as number);\n    const customSetup = program.getCustomSetupFunc(value as number);\n    return backend.runWebGLProgram(program, [], dtype, customSetup);\n  }\n}\n\nexport const fillConfig: KernelConfig = {\n  kernelName: Fill,\n  backendName: 'webgl',\n  kernelFunc: fill as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class FlipLeftRightProgram implements GPGPUProgram {\n  variableNames = ['Image'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(imageShape: [number, number, number, number]) {\n    const imageWidth = imageShape[2];\n    this.outputShape = imageShape;\n\n    this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Tensor4D} from '@tensorflow/tfjs-core';\nimport {FlipLeftRight, FlipLeftRightInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {FlipLeftRightProgram} from '../flip_left_right_gpu';\n\nexport const flipLeftRightConfig: KernelConfig = {\n  kernelName: FlipLeftRight,\n  backendName: 'webgl',\n  kernelFunc: ({inputs, backend}) => {\n    const {image} = inputs as FlipLeftRightInputs;\n    const webglBackend = backend as MathBackendWebGL;\n\n    const program = new FlipLeftRightProgram((image as Tensor4D).shape);\n    const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n    return output;\n  }\n};\n","\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Floor, KernelConfig} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {floorImplCPU} from '../kernel_utils/shared';\n\nconst FLOOR = `return floor(x);`;\n\nexport const floor = unaryKernelFunc(\n    {opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU});\n\nexport const floorConfig: KernelConfig = {\n  kernelName: Floor,\n  backendName: 'webgl',\n  kernelFunc: floor,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {FloorDiv, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\n\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nconst INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\n\nconst INT_DIV_PACKED = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\n\nexport const floorDiv = binaryKernelFunc(\n    {opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: 'int32'});\n\nexport const floorDivConfig: KernelConfig = {\n  kernelName: FloorDiv,\n  backendName: 'webgl',\n  kernelFunc: floorDiv as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getGlslDifferences} from '../../glsl_version';\nimport {GPGPUProgram} from '../../gpgpu_math';\n\nexport class FromPixelsProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(outputShape: number[]) {\n    const glsl = getGlslDifferences();\n    const [height, width, ] = outputShape;\n    this.outputShape = outputShape;\n    this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getGlslDifferences} from '../../glsl_version';\nimport {GPGPUProgram} from '../../gpgpu_math';\n\nexport class FromPixelsPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n  packedInputs = false;\n  packedOutput = true;\n\n  constructor(outputShape: number[]) {\n    const glsl = getGlslDifferences();\n    const [height, width, ] = outputShape;\n    this.outputShape = outputShape;\n    this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';\nimport {FromPixels, FromPixelsAttrs, FromPixelsInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {TextureUsage} from '../tex_util';\n\nimport {FromPixelsProgram} from './FromPixels_utils/from_pixels_gpu';\nimport {FromPixelsPackedProgram} from './FromPixels_utils/from_pixels_packed_gpu';\n\nexport const fromPixelsConfig: KernelConfig = {\n  kernelName: FromPixels,\n  backendName: 'webgl',\n  kernelFunc: fromPixels as {} as KernelFunc,\n};\n\nlet fromPixels2DContext: CanvasRenderingContext2D;\n\nfunction fromPixels(args: {\n  inputs: FromPixelsInputs,\n  backend: MathBackendWebGL,\n  attrs: FromPixelsAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  let {pixels} = inputs;\n  const {numChannels} = attrs;\n\n  const isVideo = typeof (HTMLVideoElement) !== 'undefined' &&\n      pixels instanceof HTMLVideoElement;\n  const isImage = typeof (HTMLImageElement) !== 'undefined' &&\n      pixels instanceof HTMLImageElement;\n  const [width, height] = isVideo ?\n      [\n        (pixels as HTMLVideoElement).videoWidth,\n        (pixels as HTMLVideoElement).videoHeight\n      ] :\n      [pixels.width, pixels.height];\n\n  const texShape: [number, number] = [height, width];\n  const outShape = [height, width, numChannels];\n\n  if (isImage || isVideo) {\n    if (fromPixels2DContext == null) {\n      fromPixels2DContext = document.createElement('canvas').getContext('2d');\n    }\n\n    fromPixels2DContext.canvas.width = width;\n    fromPixels2DContext.canvas.height = height;\n    fromPixels2DContext.drawImage(\n        pixels as HTMLVideoElement | HTMLImageElement | ImageBitmap,\n        0, 0, width, height);\n    pixels = fromPixels2DContext.canvas;\n  }\n\n  const tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');\n  // This is a byte texture with pixels.\n  backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n  backend.gpgpu.uploadPixelDataToTexture(\n      backend.getTexture(tempPixelHandle.dataId), pixels as ImageData);\n  const program = env().getBool('WEBGL_PACK') ?\n      new FromPixelsPackedProgram(outShape) :\n      new FromPixelsProgram(outShape);\n  const res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');\n  backend.disposeData(tempPixelHandle.dataId);\n  return res;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, env, FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Conv2DProgram} from '../conv_gpu';\nimport {mapActivationToShaderProgram} from '../kernel_utils/kernel_funcs_utils';\n\nimport {conv2dByMatMul, conv2dWithIm2Row} from './Conv2D_impl';\nimport {reshape} from './Reshape';\n\nexport function fusedConv2d(args: {\n  inputs: FusedConv2DInputs,\n  attrs: FusedConv2DAttrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {x, filter, bias, preluActivationWeights} = inputs;\n  const {\n    strides,\n    pad,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation,\n    leakyreluAlpha\n  } = attrs;\n\n  const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util.computeConv2DInfo(\n      x.shape as [number, number, number, number],\n      filter.shape as [number, number, number, number], strides, dilations, pad,\n      dimRoundingMode, false /* depthwise */, $dataFormat);\n  let out: TensorInfo;\n  const intermediates: TensorInfo[] = [];\n\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n      convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n      convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n      (convInfo.padInfo.type === 'SAME' || convInfo.padInfo.type === 'VALID')) {\n    out = conv2dByMatMul({\n      x,\n      filter,\n      convInfo,\n      backend,\n      bias,\n      activation,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n  } else if (env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n    out = conv2dWithIm2Row({\n      x,\n      filter,\n      convInfo,\n      backend,\n      bias,\n      activation,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n  } else {\n    const hasBias = bias != null;\n    const hasPreluActivationWeights = preluActivationWeights != null;\n    const hasLeakyreluAlpha = activation === 'leakyrelu';\n    const fusedActivation =\n        activation ? mapActivationToShaderProgram(activation, false) : null;\n    const program = new Conv2DProgram(\n        convInfo, hasBias, fusedActivation, hasPreluActivationWeights,\n        hasLeakyreluAlpha);\n    const inputs: TensorInfo[] = [x, filter];\n    if (bias) {\n      inputs.push(bias);\n    }\n    if (preluActivationWeights) {\n      inputs.push(preluActivationWeights);\n    }\n    if (hasLeakyreluAlpha) {\n      const $leakyreluAlpha = backend.makeTensorInfo(\n          [], 'float32',\n          util.createScalarValue(leakyreluAlpha as {} as 'float32', 'float32'));\n      inputs.push($leakyreluAlpha);\n      intermediates.push($leakyreluAlpha);\n    }\n    out = backend.runWebGLProgram(program, inputs, 'float32');\n  }\n\n  const outReshaped =\n      reshape({inputs: {x: out}, backend, attrs: {shape: convInfo.outShape}});\n\n  intermediates.push(out);\n  intermediates.forEach(t => backend.disposeIntermediateTensorInfo(t));\n\n  return outReshaped;\n}\n\nexport const fusedConv2DConfig: KernelConfig = {\n  kernelName: FusedConv2D,\n  backendName: 'webgl',\n  kernelFunc: fusedConv2d as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, env, FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs, KernelConfig, KernelFunc, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {DepthwiseConv2DProgram} from '../conv_gpu_depthwise';\nimport {DepthwiseConvPacked2DProgram} from '../conv_packed_gpu_depthwise';\nimport {mapActivationToShaderProgram} from '../kernel_utils/kernel_funcs_utils';\n\nexport function fusedDepthwiseConv2D(args: {\n  inputs: FusedDepthwiseConv2DInputs,\n  attrs: FusedDepthwiseConv2DAttrs,\n  backend: MathBackendWebGL\n}) {\n  const {inputs, backend, attrs} = args;\n  const {x, filter, bias, preluActivationWeights} = inputs;\n  const {strides, pad, dilations, dimRoundingMode, activation, leakyreluAlpha} =\n      attrs;\n\n  const intermediates: TensorInfo[] = [];\n\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n\n  util.assert(\n      backend_util.eitherStridesOrDilationsAreOne(strides, $dilations),\n      () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n          `1. Got strides ${strides} and dilations '${$dilations}'`);\n\n  const convInfo = backend_util.computeConv2DInfo(\n      x.shape as [number, number, number, number],\n      filter.shape as [number, number, number, number], strides, $dilations,\n      pad, dimRoundingMode, true /* depthwise */);\n\n  const shouldPackDepthwiseConv = env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n      convInfo.strideWidth <= 2 &&\n      convInfo.outChannels / convInfo.inChannels === 1;\n  const fusedActivation = activation ?\n      mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :\n      null;\n  const programInputs: TensorInfo[] = [x, filter];\n\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation === 'leakyrelu';\n\n  if (hasBias) {\n    programInputs.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    programInputs.push(preluActivationWeights);\n  }\n  if (hasLeakyreluAlpha) {\n    const $leakyreluAlpha = backend.makeTensorInfo(\n        [], 'float32',\n        util.createScalarValue(leakyreluAlpha as {} as 'float32', 'float32'));\n    programInputs.push($leakyreluAlpha);\n    intermediates.push($leakyreluAlpha);\n  }\n\n  let program: DepthwiseConv2DProgram|DepthwiseConvPacked2DProgram;\n  if (shouldPackDepthwiseConv) {\n    program = new DepthwiseConvPacked2DProgram(\n        convInfo, hasBias, fusedActivation, hasPreluActivationWeights,\n        hasLeakyreluAlpha);\n  } else {\n    program = new DepthwiseConv2DProgram(\n        convInfo, hasBias, fusedActivation, hasPreluActivationWeights,\n        hasLeakyreluAlpha);\n  }\n\n  const result = backend.runWebGLProgram(program, programInputs, 'float32');\n\n  intermediates.forEach(t => backend.disposeIntermediateTensorInfo(t));\n\n  return result;\n}\n\nexport const fusedDepthwiseConv2DConfig: KernelConfig = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: 'webgl',\n  kernelFunc: fusedDepthwiseConv2D as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class GatherNDProgram implements GPGPUProgram {\n  variableNames = ['x', 'indices'];\n  outputShape: number[];\n  userCode: string;\n  constructor(\n      private sliceDim: number, private strides: number[], shape: number[]) {\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, GatherNd, GatherNdInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {GatherNDProgram} from '../gather_nd_gpu';\nimport {gatherNdImplCPU} from '../kernel_utils/shared';\n\nimport {reshape} from './Reshape';\n\nexport function gatherNd(\n    args: {inputs: GatherNdInputs, backend: MathBackendWebGL}): TensorInfo {\n  const {inputs, backend} = args;\n  const {params, indices} = inputs;\n\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const paramsSize = util.sizeFromShape(params.shape);\n\n  const [resultShape, numSlices, sliceSize, strides] =\n      backend_util.prepareAndValidate(params, indices);\n\n  const flattenIndices = reshape(\n      {inputs: {x: indices}, backend, attrs: {shape: [numSlices, sliceRank]}});\n  const flattenX = reshape({\n    inputs: {x: params},\n    backend,\n    attrs: {shape: [(util.sizeFromShape(params.shape) / sliceSize), sliceSize]}\n  });\n\n  if (backend.shouldExecuteOnCPU([params, indices]) ||\n      params.dtype === 'string') {\n    const indicesData = backend.readSync(indices.dataId) as TypedArray;\n    const paramsBuf = backend.bufferSync(params);\n    const outValue = gatherNdImplCPU(\n        indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize,\n        strides, params.shape, paramsSize);\n\n    return backend.makeTensorInfo(resultShape, params.dtype, outValue.values);\n  }\n  const program =\n      new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n  const res = backend.runWebGLProgram(\n      program, [flattenX, flattenIndices], flattenX.dtype);\n\n  const reshaped =\n      reshape({inputs: {x: res}, backend, attrs: {shape: resultShape}});\n\n  backend.disposeIntermediateTensorInfo(flattenIndices);\n  backend.disposeIntermediateTensorInfo(flattenX);\n  backend.disposeIntermediateTensorInfo(res);\n\n  return reshaped;\n}\n\nexport const gatherNdConfig: KernelConfig = {\n  kernelName: GatherNd,\n  backendName: 'webgl',\n  kernelFunc: gatherNd as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class GatherProgram implements GPGPUProgram {\n  variableNames = ['A', 'indices'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  constructor(aShape: number[], outputShape: number[]) {\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords(aShape, 2);\n\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n  }\n}\n\n// The input and output are always flattened into rank 4 tensors.\nfunction getSourceCoords(aShape: number[], axis: number): string {\n  const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n\n  const sourceCoords = [];\n  for (let i = 0; i < aShape.length; i++) {\n    if (i === 2) {\n      sourceCoords.push('int(getIndices(resRC.x, resRC.z))');\n    } else {\n      sourceCoords.push(`${currentCoords[i]}`);\n    }\n  }\n  return sourceCoords.join();\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, GatherV2, GatherV2Attrs, GatherV2Inputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {GatherProgram} from '../gather_gpu';\nimport {gatherV2ImplCPU} from '../kernel_utils/shared';\n\nimport {reshape} from './Reshape';\n\nexport function gatherV2(args: {\n  inputs: GatherV2Inputs,\n  backend: MathBackendWebGL,\n  attrs: GatherV2Attrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x, indices} = inputs;\n  const {axis, batchDims} = attrs;\n\n  const parsedAxis = util.parseAxisParam(axis, x.shape)[0];\n  const shapeInfo = backend_util.segment_util.collectGatherOpShapeInfo(\n      x, indices, parsedAxis, batchDims);\n\n  const indicesSize = util.sizeFromShape(indices.shape);\n\n  const toDispose = [];\n\n  const flattenX = reshape({\n    inputs: {x},\n    backend,\n    attrs: {\n      shape: [\n        shapeInfo.batchSize, shapeInfo.outerSize, shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    }\n  });\n\n  const flattenIndex = reshape({\n    inputs: {x: indices},\n    backend,\n    attrs: {shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize]}\n  });\n\n  toDispose.push(flattenX);\n  toDispose.push(flattenIndex);\n\n  const flattenOutputShape = [\n    shapeInfo.batchSize, shapeInfo.outerSize, indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n\n  if (backend.shouldExecuteOnCPU([x, indices]) || x.dtype === 'string') {\n    const indicesBuf = backend.bufferSync(flattenIndex);\n    const xBuf = backend.bufferSync(flattenX);\n    const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape);\n\n    toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));\n\n    return backend.makeTensorInfo(\n        shapeInfo.outputShape, outBuf.dtype, outBuf.values as TypedArray);\n  }\n\n  const program = new GatherProgram(flattenX.shape, flattenOutputShape);\n  const res = backend.runWebGLProgram(\n      program, [flattenX, flattenIndex], flattenX.dtype);\n  toDispose.push(res);\n\n  const reshaped = reshape(\n      {inputs: {x: res}, backend, attrs: {shape: shapeInfo.outputShape}});\n  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));\n  return reshaped;\n}\n\nexport const gatherV2Config: KernelConfig = {\n  kernelName: GatherV2,\n  backendName: 'webgl',\n  kernelFunc: gatherV2 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Greater, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\n\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {greaterImplCPU} from '../kernel_utils/shared';\n\nconst GREATER = `return float(a > b);`;\nconst GREATER_PACKED = `\n  return vec4(greaterThan(a, b));\n`;\n\nexport const greater = binaryKernelFunc({\n  opSnippet: GREATER,\n  packedOpSnippet: GREATER_PACKED,\n  cpuKernelImpl: greaterImplCPU,\n  dtype: 'bool'\n});\n\nexport const greaterConfig: KernelConfig = {\n  kernelName: Greater,\n  backendName: 'webgl',\n  kernelFunc: greater as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GreaterEqual, KernelConfig, KernelFunc} from '@tensorflow/tfjs-core';\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {greaterEqualImplCPU} from '../kernel_utils/shared';\n\nconst GREATER_EQUAL = `return float(a >= b);`;\nconst GREATER_EQUAL_PACKED = `\n  return vec4(greaterThanEqual(a, b));\n`;\n\nexport const greaterEqual = binaryKernelFunc({\n  opSnippet: GREATER_EQUAL,\n  packedOpSnippet: GREATER_EQUAL_PACKED,\n  dtype: 'bool',\n  cpuKernelImpl: greaterEqualImplCPU\n});\n\nexport const greaterEqualConfig: KernelConfig = {\n  kernelName: GreaterEqual,\n  backendName: 'webgl',\n  kernelFunc: greaterEqual as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IFFT, IFFTInputs, KernelConfig, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {fftImpl} from './FFT_impl';\n\nexport function ifft(args: {inputs: IFFTInputs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend} = args;\n  const {input} = inputs;\n\n  return fftImpl(input, true /* inverse */, backend);\n}\n\nexport const ifftConfig: KernelConfig = {\n  kernelName: IFFT,\n  backendName: 'webgl',\n  kernelFunc: ifft\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IsFinite, KernelConfig} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\n\nexport const isFinite = unaryKernelFunc({opSnippet: IS_FINITE, dtype: 'bool'});\n\nexport const isFiniteConfig: KernelConfig = {\n  kernelName: IsFinite,\n  backendName: 'webgl',\n  kernelFunc: isFinite,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IsInf, KernelConfig} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst IS_INF = `return float(isinf(x));`;\n\nexport const isInf = unaryKernelFunc({opSnippet: IS_INF, dtype: 'bool'});\n\nexport const isInfConfig: KernelConfig = {\n  kernelName: IsInf,\n  backendName: 'webgl',\n  kernelFunc: isInf,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IsNan, KernelConfig} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst IS_NAN = `return float(isnan(x));`;\n\nexport const isNaN = unaryKernelFunc({opSnippet: IS_NAN, dtype: 'bool'});\n\nexport const isNaNConfig: KernelConfig = {\n  kernelName: IsNan,\n  backendName: 'webgl',\n  kernelFunc: isNaN,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Less} from '@tensorflow/tfjs-core';\n\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {lessImplCPU} from '../kernel_utils/shared';\n\nconst LESS = `return float(a < b);`;\nconst LESS_PACKED = `\n  return vec4(lessThan(a, b));\n`;\n\nexport const less = binaryKernelFunc({\n  opSnippet: LESS,\n  packedOpSnippet: LESS_PACKED,\n  cpuKernelImpl: lessImplCPU,\n  dtype: 'bool'\n});\n\nexport const lessConfig: KernelConfig = {\n  kernelName: Less,\n  backendName: 'webgl',\n  kernelFunc: less as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, LessEqual} from '@tensorflow/tfjs-core';\n\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {lessEqualImplCPU} from '../kernel_utils/shared';\n\nexport const LESS_EQUAL = `return float(a <= b);`;\nexport const LESS_EQUAL_PACKED = `\n  return vec4(lessThanEqual(a, b));\n`;\n\nexport const lessEqual = binaryKernelFunc({\n  opSnippet: LESS_EQUAL,\n  packedOpSnippet: LESS_EQUAL_PACKED,\n  cpuKernelImpl: lessEqualImplCPU,\n  dtype: 'bool'\n});\n\nexport const lessEqualConfig: KernelConfig = {\n  kernelName: LessEqual,\n  backendName: 'webgl',\n  kernelFunc: lessEqual as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, LinSpace, LinSpaceAttrs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {linSpaceImplCPU} from '../kernel_utils/shared';\n\nexport function linSpace(\n    args: {backend: MathBackendWebGL, attrs: LinSpaceAttrs}): TensorInfo {\n  const {backend, attrs} = args;\n  const {start, stop, num} = attrs;\n\n  // TODO: Use CPU implementation due to the precision problem in Safari.\n  const outVals = linSpaceImplCPU(start, stop, num);\n  return backend.makeTensorInfo([outVals.length], 'float32', outVals);\n}\n\nexport const linSpaceConfig: KernelConfig = {\n  kernelName: LinSpace,\n  backendName: 'webgl',\n  kernelFunc: linSpace as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Log} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {logImplCPU} from '../kernel_utils/shared';\n\nconst LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\n\nconst LOG_PACKED = `\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n`;\n\nexport const log = unaryKernelFunc(\n    {opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU});\n\nexport const logConfig: KernelConfig = {\n  kernelName: Log,\n  backendName: 'webgl',\n  kernelFunc: log as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Log1p} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst LOG1P = `return log(1.0 + x);`;\n\nexport const log1p = unaryKernelFunc({opSnippet: LOG1P});\n\nexport const log1pConfig: KernelConfig = {\n  kernelName: Log1p,\n  backendName: 'webgl',\n  kernelFunc: log1p,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, LogicalAnd} from '@tensorflow/tfjs-core';\n\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nconst LOGICAL_AND_PACKED = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\n\nexport const logicalAnd = binaryKernelFunc({\n  opSnippet: LOGICAL_AND,\n  packedOpSnippet: LOGICAL_AND_PACKED,\n  dtype: 'bool'\n});\n\nexport const logicalAndConfig: KernelConfig = {\n  kernelName: LogicalAnd,\n  backendName: 'webgl',\n  kernelFunc: logicalAnd as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, LogicalNot} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst LOGICAL_NOT = `return float(!(x >= 1.0));`;\n\nexport const logicalNot = unaryKernelFunc({opSnippet: LOGICAL_NOT});\n\nexport const logicalNotConfig: KernelConfig = {\n  kernelName: LogicalNot,\n  backendName: 'webgl',\n  kernelFunc: logicalNot,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, LogicalOr} from '@tensorflow/tfjs-core';\n\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nconst LOGICAL_OR_PACKED = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\n\nexport const logicalOr = binaryKernelFunc(\n    {opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: 'bool'});\n\nexport const logicalOrConfig: KernelConfig = {\n  kernelName: LogicalOr,\n  backendName: 'webgl',\n  kernelFunc: logicalOr as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class LRNProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      xShape: number[], radius: number, bias: number, alpha: number,\n      beta: number) {\n    const rad = radius;\n    const maxD = xShape[3] - 1;\n    this.outputShape = xShape;\n\n    // optimize pow(bias + alpha * sum, -beta)\n    // src: https://github.com/tensorflow/tensorflow/..\n    // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n    // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n    let powOperator;\n    const basis = `float(${bias}) + float(${alpha}) * sum`;\n    if (beta === 0.5) {\n      powOperator = `inversesqrt(${basis})`;\n    } else if (beta === 1.0) {\n      powOperator = `1.0/(${basis})`;\n    } else {\n      powOperator = `exp(log(${basis}) * float(-${beta}));`;\n    }\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class LRNPackedProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[] = [];\n  userCode: string;\n  packedInputs = true;\n  packedOutput = true;\n\n  constructor(\n      xShape: number[], radius: number, bias: number, alpha: number,\n      beta: number) {\n    const rad = radius;\n    const maxD = xShape[3] - 1;\n    this.outputShape = xShape;\n\n    // optimize pow(bias + alpha * sum, -beta)\n    // src: https://github.com/tensorflow/tensorflow/..\n    // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n    // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n    let powOperator;\n    const basis = `float(${bias}) + float(${alpha}) * sum`;\n    if (beta === 0.5) {\n      powOperator = `inversesqrt(${basis})`;\n    } else if (beta === 1.0) {\n      powOperator = `1.0/(${basis})`;\n    } else {\n      powOperator = `exp(log(${basis}) * float(-${beta}));`;\n    }\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, KernelConfig, KernelFunc, LRN, LRNAttrs, LRNInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {LRNProgram} from '../lrn_gpu';\nimport {LRNPackedProgram} from '../lrn_packed_gpu';\n\nexport const lrn =\n    (args: {inputs: LRNInputs, backend: MathBackendWebGL, attrs: LRNAttrs}):\n        TensorInfo => {\n          const {inputs, backend, attrs} = args;\n          const {x} = inputs;\n          const {depthRadius, bias, alpha, beta} = attrs;\n\n          const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n              new LRNPackedProgram(x.shape, depthRadius, bias, alpha, beta) :\n              new LRNProgram(x.shape, depthRadius, bias, alpha, beta);\n          return backend.runWebGLProgram(program, [x], x.dtype);\n        };\n\n// tslint:disable-next-line: variable-name\nexport const LRNConfig: KernelConfig = {\n  kernelName: LRN,\n  backendName: 'webgl',\n  kernelFunc: lrn as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class LRNGradProgram implements GPGPUProgram {\n  variableNames = ['inputImage', 'outputImage', 'dy'];\n  outputShape: number[] = [];\n  userCode: string;\n  depthRadius: number;\n  bias: number;\n  alpha: number;\n  beta: number;\n  depth: number;\n\n  constructor(\n      inputShape: number[], depthRadius: number, bias: number, alpha: number,\n      beta: number) {\n    this.outputShape = inputShape;\n    this.depth = inputShape[3];\n    this.depthRadius = depthRadius;\n    this.bias = bias;\n    this.alpha = alpha;\n    this.beta = beta;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, LRNGrad, LRNGradAttrs, LRNGradInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {LRNGradProgram} from '../lrn_grad_gpu';\n\nexport const lrnGrad = (args: {\n  inputs: LRNGradInputs,\n  backend: MathBackendWebGL,\n  attrs: LRNGradAttrs\n}): TensorInfo => {\n  const {inputs, backend, attrs} = args;\n  const {x, y, dy} = inputs;\n  const {depthRadius, bias, alpha, beta} = attrs;\n\n  const program = new LRNGradProgram(x.shape, depthRadius, bias, alpha, beta);\n  return backend.runWebGLProgram(program, [x, y, dy], x.dtype);\n};\n\n// tslint:disable-next-line: variable-name\nexport const LRNGradConfig: KernelConfig = {\n  kernelName: LRNGrad,\n  backendName: 'webgl',\n  kernelFunc: lrnGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {reduce} from '../kernel_utils/reduce';\nimport {reshape} from '../kernels/Reshape';\n\nexport function maxImpl(\n    x: TensorInfo, reduceShape: number[], outShape: number[],\n    backend: MathBackendWebGL): TensorInfo {\n  const inSize = util.sizeFromShape(reduceShape);\n  const xSize = util.sizeFromShape(x.shape);\n  const batchSize = xSize / inSize;\n  const reshapedInput =\n      reshape({inputs: {x}, attrs: {shape: [batchSize, inSize]}, backend});\n\n  const reduced = reduce(reshapedInput, x.dtype, 'max', backend);\n  const reshapedOutput =\n      reshape({inputs: {x: reduced}, attrs: {shape: outShape}, backend});\n\n  backend.disposeIntermediateTensorInfo(reshapedInput);\n  backend.disposeIntermediateTensorInfo(reduced);\n\n  return reshapedOutput;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelFunc, Max, MaxAttrs, MaxInputs, TensorInfo} from '@tensorflow/tfjs-core';\nimport {backend_util, KernelConfig, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {maxImplCPU} from '../kernel_utils/shared';\n\nimport {maxImpl} from './Max_impl';\nimport {transposeImpl, transposeImplCPU} from './Transpose_impl';\n\nexport function max(\n    args: {inputs: MaxInputs, backend: MathBackendWebGL, attrs: MaxAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {reductionIndices, keepDims} = attrs;\n\n  const xRank = x.shape.length;\n\n  const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n  const maxInputIsTransposed = permutedAxes != null;\n  const shouldExecuteOnCPU = backend.shouldExecuteOnCPU([x]);\n\n  let maxInput = x;\n  if (maxInputIsTransposed) {\n    if (shouldExecuteOnCPU) {\n      const xTexData = backend.texData.get(maxInput.dataId);\n      const values = xTexData.values as TypedArray;\n\n      const newShape: number[] = new Array(xRank);\n      for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = x.shape[permutedAxes[i]];\n      }\n      const maxInputValues =\n          transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n\n      maxInput = backend.makeTensorInfo(newShape, x.dtype);\n      const maxInputData = backend.texData.get(maxInput.dataId);\n      maxInputData.values = maxInputValues;\n    } else {\n      maxInput = transposeImpl(x, permutedAxes, backend);\n    }\n\n    axes = backend_util.getInnerMostAxes(axes.length, xRank);\n  }\n\n  backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n  const [maxOutShape, reduceShape] =\n      backend_util.computeOutAndReduceShapes(maxInput.shape, axes);\n\n  let outShape = maxOutShape;\n  if (keepDims) {\n    // rather than reshape at the end, set the target shape here.\n    outShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n  }\n\n  let out;\n  if (shouldExecuteOnCPU) {\n    const xTexData = backend.texData.get(maxInput.dataId);\n    const values = xTexData.values as TypedArray;\n\n    const outValues =\n        maxImplCPU(values, util.sizeFromShape(reduceShape), outShape, x.dtype);\n\n    out = backend.makeTensorInfo(outShape, x.dtype);\n    const outData = backend.texData.get(out.dataId);\n    outData.values = outValues;\n  } else {\n    out = maxImpl(maxInput, reduceShape, outShape, backend);\n  }\n\n  if (maxInputIsTransposed) {\n    backend.disposeIntermediateTensorInfo(maxInput);\n  }\n\n  return out;\n}\n\nexport const maxConfig: KernelConfig = {\n  kernelName: Max,\n  backendName: 'webgl',\n  kernelFunc: max as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Maximum} from '@tensorflow/tfjs-core';\n\nimport {CHECK_NAN_SNIPPET} from '../binaryop_gpu';\nimport {CHECK_NAN_SNIPPET as CHECK_NAN_SNIPPET_PACKED} from '../binaryop_packed_gpu';\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {maximumImplCPU} from '../kernel_utils/shared';\n\nconst MAXIMUM = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\n\nconst MAXIMUM_PACKED = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET_PACKED + `\n  return result;\n`;\n\nexport const maximum = binaryKernelFunc({\n  opSnippet: MAXIMUM,\n  packedOpSnippet: MAXIMUM_PACKED,\n  cpuKernelImpl: maximumImplCPU\n});\n\nexport const maximumConfig: KernelConfig = {\n  kernelName: Maximum,\n  backendName: 'webgl',\n  kernelFunc: maximum as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {backend_util, KernelConfig, KernelFunc, MaxPool, MaxPoolAttrs, MaxPoolInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Pool2DProgram} from '../pool_gpu';\nimport {assertNotComplex} from '../webgl_util';\nimport {identity} from './Identity';\n\nexport function maxPool(args: {\n  inputs: MaxPoolInputs,\n  backend: MathBackendWebGL,\n  attrs: MaxPoolAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  assertNotComplex(x, 'maxPool');\n  const {filterSize, strides, pad, dimRoundingMode} = attrs;\n  const dilations = 1;\n\n  util.assert(\n      backend_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  const convInfo = backend_util.computePool2DInfo(\n      x.shape as [number, number, number, number], filterSize, strides,\n      dilations, pad, dimRoundingMode);\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n      util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    return identity({inputs: {x}, backend});\n  }\n  const maxPoolProgram = new Pool2DProgram(convInfo, 'max', false);\n  return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\n\nexport const maxPoolConfig: KernelConfig = {\n  kernelName: MaxPool,\n  backendName: 'webgl',\n  kernelFunc: maxPool as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {backend_util, KernelConfig, KernelFunc, MaxPool3D, MaxPool3DAttrs, MaxPool3DInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Pool3DProgram} from '../pool_gpu';\n\nexport function maxPool3d(args: {\n  inputs: MaxPool3DInputs,\n  backend: MathBackendWebGL,\n  attrs: MaxPool3DAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {filterSize, strides, pad, dataFormat, dimRoundingMode} = attrs;\n  const dilations: [number, number, number] = [1, 1, 1];\n\n  const convInfo = backend_util.computePool3DInfo(\n      x.shape as [number, number, number, number, number], filterSize, strides,\n      dilations, pad, dimRoundingMode, dataFormat);\n  const maxPoolProgram = new Pool3DProgram(convInfo, 'max', false);\n  return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\n\nexport const maxPool3DConfig: KernelConfig = {\n  kernelName: MaxPool3D,\n  backendName: 'webgl',\n  kernelFunc: maxPool3d as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MaxPool2DBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy', 'maxPos'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: backend_util.Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n\n    const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class MaxPool3DBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy', 'maxPos'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: backend_util.Conv3DInfo) {\n    this.outputShape = convInfo.inShape;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n\n    const lastIndex =\n        effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {backend_util, KernelConfig, KernelFunc, MaxPool3DGrad, MaxPool3DGradAttrs, MaxPool3DGradInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {MaxPool3DBackpropProgram} from '../max_pool_backprop_gpu';\nimport {Pool3DProgram} from '../pool_gpu';\n\nexport function maxPool3DGrad(args: {\n  inputs: MaxPool3DGradInputs,\n  backend: MathBackendWebGL,\n  attrs: MaxPool3DGradAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {dy, input} = inputs;\n  const x = input;\n  const {filterSize, strides, pad, dimRoundingMode} = attrs;\n  const dilations: [number, number, number] = [1, 1, 1];\n\n  const convInfo = backend_util.computePool3DInfo(\n      x.shape as [number, number, number, number, number], filterSize, strides,\n      dilations, pad, dimRoundingMode);\n\n  const maxPool3dPositionsProgram =\n      new Pool3DProgram(convInfo, 'max', true /* get positions */);\n  const maxPool3dPositions =\n      backend.runWebGLProgram(maxPool3dPositionsProgram, [x], x.dtype);\n  const maxPoolBackpropProgram = new MaxPool3DBackpropProgram(convInfo);\n  const result = backend.runWebGLProgram(\n      maxPoolBackpropProgram, [dy, maxPool3dPositions], x.dtype);\n  backend.disposeIntermediateTensorInfo(maxPool3dPositions);\n  return result;\n}\n\nexport const maxPoolGrad3DConfig: KernelConfig = {\n  kernelName: MaxPool3DGrad,\n  backendName: 'webgl',\n  kernelFunc: maxPool3DGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {backend_util, KernelConfig, KernelFunc, MaxPoolGrad, MaxPoolGradAttrs, MaxPoolGradInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {MaxPool2DBackpropProgram} from '../max_pool_backprop_gpu';\nimport {Pool2DProgram} from '../pool_gpu';\nimport {assertNotComplex} from '../webgl_util';\n\nexport function maxPoolGrad(args: {\n  inputs: MaxPoolGradInputs,\n  backend: MathBackendWebGL,\n  attrs: MaxPoolGradAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {dy, input, output} = inputs;\n  const x = input;\n  assertNotComplex([input, output], 'maxPoolGrad');\n  const {filterSize, strides, pad, dimRoundingMode} = attrs;\n\n  const convInfo = backend_util.computePool2DInfo(\n      x.shape as [number, number, number, number], filterSize, strides,\n      1 /* dilations */, pad, dimRoundingMode);\n  const getPositions = true;\n  const maxPoolPositionsProgram =\n      new Pool2DProgram(convInfo, 'max', getPositions);\n  const maxPoolPositions: TensorInfo =\n      backend.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n\n  const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n  const result = backend.runWebGLProgram(\n      maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);\n  backend.disposeIntermediateTensorInfo(maxPoolPositions);\n  return result;\n}\n\nexport const maxPoolGradConfig: KernelConfig = {\n  kernelName: MaxPoolGrad,\n  backendName: 'webgl',\n  kernelFunc: maxPoolGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Pool2DProgram} from '../pool_gpu';\n\nexport function maxPoolWithArgmaxImpl(\n    x: TensorInfo, includeBatchInIndex: boolean,\n    convInfo: backend_util.Conv2DInfo,\n    backend: MathBackendWebGL): TensorInfo[] {\n  let program = new Pool2DProgram(convInfo, 'max', false);\n  const poolOutput = backend.runWebGLProgram(program, [x], 'float32');\n\n  program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);\n  const indexOutput = backend.runWebGLProgram(program, [x], 'float32');\n  return [poolOutput, indexOutput];\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {MaxPoolWithArgmax, MaxPoolWithArgmaxAttrs, MaxPoolWithArgmaxInputs} from '@tensorflow/tfjs-core';\nimport {backend_util, KernelConfig, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {maxPoolWithArgmaxImpl} from './MaxPoolWithArgmax_impl';\n\nexport const maxPoolWithArgmaxConfig: KernelConfig = {\n  kernelName: MaxPoolWithArgmax,\n  backendName: 'webgl',\n  kernelFunc: ({inputs, attrs, backend}) => {\n    const {x} = inputs as MaxPoolWithArgmaxInputs;\n    const {filterSize, strides, pad, includeBatchInIndex} =\n        attrs as {} as MaxPoolWithArgmaxAttrs;\n    const webglBackend = backend as MathBackendWebGL;\n\n    util.assert(\n        x.shape.length === 4,\n        () => `Error in maxPool: input must be rank 4 but got rank ${\n            x.shape.length}.`);\n    const dilations: [number, number] = [1, 1];\n    util.assert(\n        backend_util.eitherStridesOrDilationsAreOne(strides, dilations),\n        () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n            `Got strides ${strides} and dilations '${dilations}'`);\n\n    const convInfo = backend_util.computePool2DInfo(\n        x.shape as [number, number, number, number], filterSize, strides,\n        dilations, pad);\n\n    const [result, indexes] =\n        maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, webglBackend);\n    return [result, indexes];\n  }\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {reduce} from '../kernel_utils/reduce';\nimport {reshape} from '../kernels/Reshape';\n\nexport function meanImpl(\n    x: TensorInfo, reduceShape: number[], outShape: number[],\n    backend: MathBackendWebGL): TensorInfo {\n  const inSize = util.sizeFromShape(reduceShape);\n  const xSize = util.sizeFromShape(x.shape);\n  const batchSize = xSize / inSize;\n  const reshapedInput =\n      reshape({inputs: {x}, attrs: {shape: [batchSize, inSize]}, backend});\n\n  const reduced = reduce(reshapedInput, 'float32', 'mean', backend);\n  const reshapedOutput =\n      reshape({inputs: {x: reduced}, attrs: {shape: outShape}, backend});\n\n  backend.disposeIntermediateTensorInfo(reshapedInput);\n  backend.disposeIntermediateTensorInfo(reduced);\n\n  return reshapedOutput;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, Mean, MeanAttrs, MeanInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {meanImpl} from './Mean_impl';\nimport {transposeImpl, transposeImplCPU} from './Transpose_impl';\n\nexport const meanConfig: KernelConfig = {\n  kernelName: Mean,\n  backendName: 'webgl',\n  kernelFunc: ({inputs, attrs, backend}) => {\n    const {x} = inputs as MeanInputs;\n    const {keepDims, axis} = attrs as {} as MeanAttrs;\n    const webglBackend = backend as MathBackendWebGL;\n\n    const xRank = x.shape.length;\n    const origAxes = util.parseAxisParam(axis, x.shape);\n\n    let axes = origAxes;\n    const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n    const meanInputIsTransposed = permutedAxes != null;\n    const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n\n    const intermediates: TensorInfo[] = [];\n\n    let meanInput = x;\n    if (meanInputIsTransposed) {\n      if (shouldExecuteOnCPU) {\n        const xTexData = webglBackend.texData.get(meanInput.dataId);\n        const values = xTexData.values as TypedArray;\n\n        const newShape: number[] = new Array(xRank);\n        for (let i = 0; i < newShape.length; i++) {\n          newShape[i] = x.shape[permutedAxes[i]];\n        }\n        const meanInputValues =\n            transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n\n        meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n        const meanInputData = webglBackend.texData.get(meanInput.dataId);\n        meanInputData.values = meanInputValues;\n      } else {\n        meanInput = transposeImpl(x, permutedAxes, webglBackend);\n      }\n\n      intermediates.push(meanInput);\n      axes = backend_util.getInnerMostAxes(axes.length, xRank);\n    }\n\n    backend_util.assertAxesAreInnerMostDims('sum', axes, xRank);\n    const [meanOutShape, reduceShape] =\n        backend_util.computeOutAndReduceShapes(meanInput.shape, axes);\n\n    let outShape = meanOutShape;\n    if (keepDims) {\n      // rather than reshape at the end, set the target shape here.\n      outShape = backend_util.expandShapeToKeepDim(meanOutShape, origAxes);\n    }\n\n    const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);\n    for (const i of intermediates) {\n      webglBackend.disposeIntermediateTensorInfo(i);\n    }\n\n    return out;\n  }\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, Min, MinAttrs, MinInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {reduce} from '../kernel_utils/reduce';\n\nimport {reshape} from './Reshape';\nimport {transpose} from './Transpose';\n\nexport function min(\n    args: {inputs: MinInputs, backend: MathBackendWebGL, attrs: MinAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {axis, keepDims} = attrs;\n\n  const xRank = x.shape.length;\n\n  const origAxes = util.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});\n    axes = backend_util.getInnerMostAxes(axes.length, x.shape.length);\n  }\n\n  backend_util.assertAxesAreInnerMostDims('min', axes, xRank);\n  const [outShape, reduceShape] =\n      backend_util.computeOutAndReduceShapes(permutedX.shape, axes);\n  const inSize = util.sizeFromShape(reduceShape);\n  const a2D =\n      reshape({inputs: {x: permutedX}, backend, attrs: {shape: [-1, inSize]}});\n  const reduced = reduce(a2D, a2D.dtype, 'min', backend);\n\n  let res;\n  if (keepDims) {\n    const newShape = backend_util.expandShapeToKeepDim(outShape, origAxes);\n    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: newShape}});\n  } else {\n    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: outShape}});\n  }\n\n  backend.disposeIntermediateTensorInfo(a2D);\n  backend.disposeIntermediateTensorInfo(reduced);\n\n  if (permutedAxes != null) {\n    backend.disposeIntermediateTensorInfo(permutedX);\n  }\n\n  return res;\n}\n\nexport const minConfig: KernelConfig = {\n  kernelName: Min,\n  backendName: 'webgl',\n  kernelFunc: min as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Minimum} from '@tensorflow/tfjs-core';\n\nimport {CHECK_NAN_SNIPPET} from '../binaryop_gpu';\nimport {CHECK_NAN_SNIPPET as CHECK_NAN_SNIPPET_PACKED} from '../binaryop_packed_gpu';\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {minimumImplCPU} from '../kernel_utils/shared';\n\nconst MINIMUM = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\n\nconst MINIMUM_PACKED = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET_PACKED + `\n  return result;\n`;\n\nexport const minimum = binaryKernelFunc({\n  opSnippet: MINIMUM,\n  packedOpSnippet: MINIMUM_PACKED,\n  cpuKernelImpl: minimumImplCPU\n});\n\nexport const minimumConfig: KernelConfig = {\n  kernelName: Minimum,\n  backendName: 'webgl',\n  kernelFunc: minimum as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class MirrorPadProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      xShape: number[], paddings: Array<[number, number]>,\n      mode: 'reflect'|'symmetric') {\n    this.outputShape = paddings.map(\n        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n    const rank = xShape.length;\n    const dtype = getCoordsDataType(rank);\n\n    const start = paddings.map(p => p[0]).join(',');\n    const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n    const unpackedCoords =\n        ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n    const offset = mode === 'reflect' ? 0 : 1;\n\n    if (rank === 1) {\n      this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - ${offset};\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + ${offset};\n          }\n          setOutput(getX(outC - start));\n        }\n      `;\n      return;\n    }\n    this.userCode = `\n      ${dtype} start = ${dtype}(${start});\n      ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outC = getOutputCoords();\n        for (int i = 0; i < ${rank}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${offset};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};\n          }\n        }\n        ${dtype} coords = outC - start;\n        setOutput(getX(${unpackedCoords}));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getChannels} from './packing_util';\nimport {getCoordsDataType} from './shader_compiler';\n\n/**\n * Example shader code for\n * `mirrorPad(tf.tensor1d([1, 2, 3], 'int32'), [[2, 2]], 'reflect')`\n * ```\n *    const int start = int(2);\n *    const int end = int(5);\n *\n *    void main() {\n *       int outputLoc = getOutputCoords();\n *       vec4 result = vec4(0.);\n *\n *       int rc = outputLoc;\n *\n *       int source = rc;\n *       if (source < start) {\n *         source = start * 2 - source - 0;\n *       } else if (source >= end) {\n *         source = (end - 1) * 2 - source + 0;\n *       }\n *       source -= start;\n *\n *       result[0] = getChannel(getX(source), source);\n *       rc += 1;\n *       if(rc < 6) {\n *          int source = rc;\n *          if (source < start) {\n *            source = start * 2 - source - 0;\n *          } else if (source >= end) {\n *            source = (end - 1) * 2 - source + 0;\n *          }\n *          source -= start;\n *\n *         result[1] = getChannel(getX(source), source);\n *       }\n *\n *       setOutput(result);\n *     }\n * ```\n */\nexport class MirrorPadPackedProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  packedInputs = true;\n  packedOutput = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      xShape: number[], paddings: Array<[number, number]>,\n      mode: 'reflect'|'symmetric') {\n    this.outputShape = paddings.map(\n        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n    const rank = xShape.length;\n    const dtype = getCoordsDataType(rank);\n\n    const start = paddings.map(p => p[0]).join(',');\n    const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n    const coords = getChannels('rc', rank);\n    const source = getChannels('source', rank);\n    const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n    const innerDims =\n        rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n    const offset = mode === 'reflect' ? 0 : 1;\n\n    let mainLoop = '';\n    if (rank === 1) {\n      const padSetup = `\n        ${dtype} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${offset};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${offset};\n        }\n        source -= start;\n      `;\n      mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n    } else {\n      const padSetup = `\n        ${dtype} source = rc;\n        ${dtype} lt = ${dtype}(lessThan(source, start));\n        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));\n        ${dtype} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${offset}) +\n                gte * ((end - 1) * 2 - source + ${offset});\n        source -= start;\n      `;\n\n      mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n        rc = outputLoc;\n        ${coords[rank - 2]} += 1;\n        if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {\n          ${padSetup}\n          result[2] = getChannel(getX(${source.join()}), ${innerDims});\n          ${coords[rank - 1]} += 1;\n          if(${cLimit}) {\n            ${padSetup}\n            result[3] = getChannel(getX(${source.join()}), ${innerDims});\n          }\n        }\n      `;\n    }\n\n    this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, KernelConfig, KernelFunc, MirrorPad, MirrorPadAttrs, MirrorPadInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {MirrorPadProgram} from '../mirror_pad_gpu';\nimport {MirrorPadPackedProgram} from '../mirror_pad_packed_gpu';\n\nexport const mirrorPadKernelFunc: (params: {\n  inputs: MirrorPadInputs,\n  backend: MathBackendWebGL,\n  attrs: MirrorPadAttrs\n}) => TensorInfo = ({inputs, backend, attrs}) => {\n  const {x} = inputs;\n  const {paddings, mode} = attrs;\n\n  const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n      new MirrorPadPackedProgram(x.shape, paddings, mode) :\n      new MirrorPadProgram(x.shape, paddings, mode);\n\n  const output = backend.runWebGLProgram(program, [x], x.dtype);\n\n  return output;\n};\n\nexport const mirrorPadConfig: KernelConfig = {\n  kernelName: MirrorPad,\n  backendName: 'webgl',\n  kernelFunc: mirrorPadKernelFunc as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Mod} from '@tensorflow/tfjs-core';\n\nimport {CHECK_NAN_SNIPPET} from '../binaryop_packed_gpu';\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\n\nconst MOD_PACKED = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\n\nexport const mod = binaryKernelFunc({\n  opSnippet: MOD,\n  packedOpSnippet: MOD_PACKED,\n});\n\nexport const modConfig: KernelConfig = {\n  kernelName: Mod,\n  backendName: 'webgl',\n  kernelFunc: mod as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MultinomialProgram implements GPGPUProgram {\n  variableNames = ['probs'];\n  outputShape: number[];\n  userCode: string;\n\n  // Caching uniform location for speed.\n  seedLoc: WebGLUniformLocation;\n\n  constructor(batchSize: number, numOutcomes: number, numSamples: number) {\n    this.outputShape = [batchSize, numSamples];\n\n    this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n  }\n\n  getCustomSetupFunc(seed: number) {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.seedLoc == null) {\n        this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n      }\n      gpgpu.gl.uniform1f(this.seedLoc, seed);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {RealDiv} from '@tensorflow/tfjs-core';\nimport {KernelConfig} from '@tensorflow/tfjs-core';\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\n// Without the equality check div produces 0.9999 for a = b, which when\n// floored can cause errors.\nconst DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\n\n// We do the same as in ./binaryop_gpu, with vec4 and ivec4.\n// On Linux, the vectorized implementation produces NaNs when a and b are 0.\nconst DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\n\nexport const realDiv = binaryKernelFunc(\n    {opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true});\n\nexport const realDivConfig: KernelConfig = {\n  kernelName: RealDiv,\n  backendName: 'webgl',\n  kernelFunc: realDiv,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Sub} from '@tensorflow/tfjs-core';\n\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {subImplCPU as cpuSub} from '../kernel_utils/shared';\n\nconst SUB = 'return a - b;';\n\nexport const sub = binaryKernelFunc({\n  opSnippet: SUB,\n  packedOpSnippet: SUB,\n  supportsComplex: true,\n  cpuKernelImpl: cpuSub\n});\n\nexport const subConfig: KernelConfig = {\n  kernelName: Sub,\n  backendName: 'webgl',\n  kernelFunc: sub\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, Softmax, SoftmaxAttrs, SoftmaxInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {exp} from './Exp';\nimport {max} from './Max';\nimport {realDiv} from './RealDiv';\nimport {reshape} from './Reshape';\nimport {sub} from './Sub';\nimport {sum} from './Sum';\n\nexport function softmax(args: {\n  inputs: SoftmaxInputs,\n  backend: MathBackendWebGL,\n  attrs: SoftmaxAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {logits} = inputs;\n  const {dim} = attrs;\n\n  const axes = util.parseAxisParam([dim], logits.shape);\n\n  const maxLogit = max({\n    inputs: {x: logits},\n    backend,\n    attrs: {reductionIndices: axes, keepDims: false}\n  });\n\n  const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n\n  const maxLogitsReshaped =\n      reshape({inputs: {x: maxLogit}, backend, attrs: {shape: expandedShape}});\n  const a =\n      sub({inputs: {a: logits, b: maxLogitsReshaped}, backend}) as TensorInfo;\n  const b = exp({inputs: {x: a}, backend}) as TensorInfo;\n  const sumExp =\n      sum({inputs: {x: b}, backend, attrs: {axis: axes, keepDims: false}});\n  const sumExpReshaped =\n      reshape({inputs: {x: sumExp}, backend, attrs: {shape: expandedShape}});\n\n  const res =\n      realDiv({inputs: {a: b, b: sumExpReshaped}, backend}) as TensorInfo;\n\n  backend.disposeIntermediateTensorInfo(maxLogit);\n  backend.disposeIntermediateTensorInfo(maxLogitsReshaped);\n  backend.disposeIntermediateTensorInfo(a);\n  backend.disposeIntermediateTensorInfo(b);\n  backend.disposeIntermediateTensorInfo(sumExp);\n  backend.disposeIntermediateTensorInfo(sumExpReshaped);\n\n  return res;\n}\n\nexport const softmaxConfig: KernelConfig = {\n  kernelName: Softmax,\n  backendName: 'webgl',\n  kernelFunc: softmax as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Multinomial, MultinomialAttrs, MultinomialInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {MultinomialProgram} from '../multinomial_gpu';\n\nimport {softmax} from './Softmax';\n\nexport function multinomial(args: {\n  inputs: MultinomialInputs,\n  backend: MathBackendWebGL,\n  attrs: MultinomialAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {logits} = inputs;\n  const {numSamples, seed, normalized} = attrs;\n\n  const probs = normalized ?\n      logits :\n      softmax(\n          {inputs: {logits}, backend, attrs: {dim: logits.shape.length - 1}});\n  const batchSize = probs.shape[0];\n  const numOutcomes = probs.shape[1];\n  const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n  const customSetup = program.getCustomSetupFunc(seed);\n\n  const res = backend.runWebGLProgram(program, [probs], 'int32', customSetup);\n  if (!normalized) {\n    backend.disposeIntermediateTensorInfo(probs);\n  }\n  return res;\n}\n\nexport const multinomialConfig: KernelConfig = {\n  kernelName: Multinomial,\n  backendName: 'webgl',\n  kernelFunc: multinomial as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, KernelConfig, KernelFunc, Neg, NegInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {negImplCPU} from '../kernel_utils/shared';\nimport {UnaryOpProgram} from '../unaryop_gpu';\nimport {UnaryOpPackedProgram} from '../unaryop_packed_gpu';\n\nconst NEG = `return -x;`;\n\n// This doesn't use unaryKernelFunc because negImplCPU is not of type\n// SimpleUnaryKernelImplCPU.\nexport function neg(args: {inputs: NegInputs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend} = args;\n  const {x} = inputs;\n\n  if (backend.shouldExecuteOnCPU([x])) {\n    const xData = backend.texData.get(x.dataId);\n    const [outValues, newShape] =\n        negImplCPU(xData.values as TypedArray, x.shape, x.dtype);\n    return backend.makeTensorInfo(newShape, x.dtype, outValues);\n  }\n\n  let program: UnaryOpProgram|UnaryOpPackedProgram;\n  if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n    program = new UnaryOpPackedProgram(x.shape, NEG);\n  } else {\n    program = new UnaryOpProgram(x.shape, NEG);\n  }\n\n  return backend.runWebGLProgram(program, [x], x.dtype);\n}\n\nexport const negConfig: KernelConfig = {\n  kernelName: Neg,\n  backendName: 'webgl',\n  kernelFunc: neg as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV3, NonMaxSuppressionV3Attrs, NonMaxSuppressionV3Inputs, TypedArray} from '@tensorflow/tfjs-core';\n\nconst nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;\nimport {MathBackendWebGL} from '../backend_webgl';\n\nexport function nonMaxSuppressionV3(args: {\n  inputs: NonMaxSuppressionV3Inputs,\n  backend: MathBackendWebGL,\n  attrs: NonMaxSuppressionV3Attrs\n}) {\n  backend_util.warn(\n      'tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n      'Call tf.nonMaxSuppressionAsync() instead');\n\n  const {inputs, backend, attrs} = args;\n  const {boxes, scores} = inputs;\n  const {maxOutputSize, iouThreshold, scoreThreshold} = attrs;\n\n  const boxesVals = backend.readSync(boxes.dataId) as TypedArray;\n  const scoresVals = backend.readSync(scores.dataId) as TypedArray;\n\n  const {selectedIndices} = nonMaxSuppressionV3Impl(\n      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n\n  return backend.makeTensorInfo(\n      [selectedIndices.length], 'int32', new Int32Array(selectedIndices));\n}\n\nexport const nonMaxSuppressionV3Config: KernelConfig = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: 'webgl',\n  kernelFunc: nonMaxSuppressionV3 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV4, NonMaxSuppressionV4Attrs, NonMaxSuppressionV4Inputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nexport function nonMaxSuppressionV4(args: {\n  inputs: NonMaxSuppressionV4Inputs,\n  backend: MathBackendWebGL,\n  attrs: NonMaxSuppressionV4Attrs\n}): [TensorInfo, TensorInfo] {\n  backend_util.warn(\n      'tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n      'Call tf.nonMaxSuppressionAsync() instead');\n\n  const {inputs, backend, attrs} = args;\n  const {boxes, scores} = inputs;\n  const {maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize} =\n      attrs;\n\n  const boxesVals = backend.readSync(boxes.dataId) as TypedArray;\n  const scoresVals = backend.readSync(scores.dataId) as TypedArray;\n\n  const {selectedIndices, validOutputs} = nonMaxSuppressionV4Impl(\n      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold,\n      padToMaxOutputSize);\n\n  return [\n    backend.makeTensorInfo(\n        [selectedIndices.length], 'int32', new Int32Array(selectedIndices)),\n    backend.makeTensorInfo([], 'int32', new Int32Array([validOutputs]))\n  ];\n}\n\nexport const nonMaxSuppressionV4Config: KernelConfig = {\n  kernelName: NonMaxSuppressionV4,\n  backendName: 'webgl',\n  kernelFunc: nonMaxSuppressionV4 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, kernel_impls, KernelConfig, KernelFunc, NonMaxSuppressionV5, NonMaxSuppressionV5Attrs, NonMaxSuppressionV5Inputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nimport {MathBackendWebGL} from '../backend_webgl';\n\nexport function nonMaxSuppressionV5(args: {\n  inputs: NonMaxSuppressionV5Inputs,\n  backend: MathBackendWebGL,\n  attrs: NonMaxSuppressionV5Attrs\n}): [TensorInfo, TensorInfo] {\n  backend_util.warn(\n      'tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n      'Call tf.nonMaxSuppressionAsync() instead');\n\n  const {inputs, backend, attrs} = args;\n  const {boxes, scores} = inputs;\n  const {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma} = attrs;\n\n  const boxesVals = backend.readSync(boxes.dataId) as TypedArray;\n  const scoresVals = backend.readSync(scores.dataId) as TypedArray;\n\n  const maxOutputSizeVal = maxOutputSize;\n  const iouThresholdVal = iouThreshold;\n  const scoreThresholdVal = scoreThreshold;\n  const softNmsSigmaVal = softNmsSigma;\n\n  const {selectedIndices, selectedScores} = nonMaxSuppressionV5Impl(\n      boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal,\n      scoreThresholdVal, softNmsSigmaVal);\n\n  return [\n    backend.makeTensorInfo(\n        [selectedIndices.length], 'int32', new Int32Array(selectedIndices)),\n    backend.makeTensorInfo(\n        [selectedScores.length], 'float32', new Float32Array(selectedScores))\n  ];\n}\n\nexport const nonMaxSuppressionV5Config: KernelConfig = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: 'webgl',\n  kernelFunc: nonMaxSuppressionV5 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class OneHotProgram implements GPGPUProgram {\n  variableNames = ['indices'];\n  outputShape: number[];\n  userCode: string;\n\n  // Caching uniform location for speed.\n  seedLoc: WebGLUniformLocation;\n\n  constructor(\n      numIndices: number, depth: number, onValue: number, offValue: number) {\n    this.outputShape = [numIndices, depth];\n\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, OneHot, OneHotAttrs, OneHotInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {OneHotProgram} from '../onehot_gpu';\nimport {reshape} from './Reshape';\n\nexport const oneHot = (args: {\n  inputs: OneHotInputs,\n  backend: MathBackendWebGL,\n  attrs: OneHotAttrs\n}): TensorInfo => {\n  const {inputs, backend, attrs} = args;\n  const {indices} = inputs;\n  const {depth, onValue, offValue} = attrs;\n\n  const indicesSize = util.sizeFromShape(indices.shape);\n  const program = new OneHotProgram(indicesSize, depth, onValue, offValue);\n  const reshaped =\n      reshape({inputs: {x: indices}, backend, attrs: {shape: [indicesSize]}});\n  const result = backend.runWebGLProgram(program, [reshaped], indices.dtype);\n  backend.disposeIntermediateTensorInfo(reshaped);\n\n  const outShape = [...indices.shape, depth];\n  const out = reshape({inputs: {x: result}, backend, attrs: {shape: outShape}});\n  backend.disposeIntermediateTensorInfo(result);\n  return out;\n};\n\nexport const oneHotConfig: KernelConfig = {\n  kernelName: OneHot,\n  backendName: 'webgl',\n  kernelFunc: oneHot as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, TensorInfo, ZerosLike, ZerosLikeInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {complex} from './Complex';\nimport {fill} from './Fill';\nimport {imag} from './Imag';\nimport {real} from './Real';\n\nexport function zerosLike(\n    args: {inputs: ZerosLikeInputs, backend: MathBackendWebGL}): TensorInfo {\n  const {inputs, backend} = args;\n  const {x} = inputs;\n\n  if (x.dtype === 'complex64') {\n    const realPart = real({inputs: {input: x}, backend});\n    const r = zerosLike({inputs: {x: realPart}, backend});\n    const imagPart = imag({inputs: {input: x}, backend});\n    const i = zerosLike({inputs: {x: imagPart}, backend});\n\n    const result = complex({inputs: {real: r, imag: i}, backend});\n\n    backend.disposeIntermediateTensorInfo(realPart);\n    backend.disposeIntermediateTensorInfo(r);\n    backend.disposeIntermediateTensorInfo(imagPart);\n    backend.disposeIntermediateTensorInfo(i);\n\n    return result;\n  } else {\n    return fill({\n      attrs: {\n        shape: x.shape,\n        dtype: x.dtype,\n        value: x.dtype === 'string' ? '' : 0\n      },\n      backend\n    });\n  }\n}\n\nexport const zerosLikeConfig: KernelConfig = {\n  kernelName: ZerosLike,\n  backendName: 'webgl',\n  kernelFunc: zerosLike as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, OnesLike, OnesLikeInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {complex} from './Complex';\nimport {fill} from './Fill';\nimport {imag} from './Imag';\nimport {real} from './Real';\nimport {zerosLike} from './ZerosLike';\n\nexport function onesLike(\n    args: {inputs: OnesLikeInputs, backend: MathBackendWebGL}): TensorInfo {\n  const {inputs, backend} = args;\n  const {x} = inputs;\n\n  if (x.dtype === 'string') {\n    throw new Error('onesLike is not supported under string dtype');\n  } else if (x.dtype === 'complex64') {\n    const realPart = real({inputs: {input: x}, backend});\n    const r = onesLike({inputs: {x: realPart}, backend});\n    const imagPart = imag({inputs: {input: x}, backend});\n    const i = zerosLike({inputs: {x: imagPart}, backend});\n\n    const result = complex({inputs: {real: r, imag: i}, backend});\n\n    backend.disposeIntermediateTensorInfo(realPart);\n    backend.disposeIntermediateTensorInfo(r);\n    backend.disposeIntermediateTensorInfo(imagPart);\n    backend.disposeIntermediateTensorInfo(i);\n\n    return result;\n  } else {\n    // TODO(cais, smilkov): Add WebGL shader for onesLike:\n    //   https://github.com/tensorflow/tfjs/issues/1293\n    return fill({attrs: {shape: x.shape, dtype: x.dtype, value: 1}, backend});\n  }\n}\n\nexport const onesLikeConfig: KernelConfig = {\n  kernelName: OnesLike,\n  backendName: 'webgl',\n  kernelFunc: onesLike as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Pack, PackAttrs, PackInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {concat} from './Concat';\nimport {expandDims} from './ExpandDims';\n\nexport function pack(\n    args: {inputs: PackInputs, backend: MathBackendWebGL, attrs: PackAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {axis} = attrs;\n\n  if (inputs.length === 1) {\n    return expandDims(\n        {inputs: {input: inputs[0]}, backend, attrs: {dim: axis}});\n  }\n\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n\n  inputs.forEach(t => {\n    util.assertShapesMatch(\n        shape, t.shape,\n        'All tensors passed to stack must have matching shapes');\n    util.assert(\n        dtype === t.dtype,\n        () => 'All tensors passed to stack must have matching dtypes');\n  });\n\n  const intermediateTensorInfos: TensorInfo[] = [];\n  const expandedTensors = inputs.map(t => {\n    const expandedT =\n        expandDims({inputs: {input: t}, backend, attrs: {dim: axis}});\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n\n  const result = concat({inputs: expandedTensors, backend, attrs: {axis}});\n\n  intermediateTensorInfos.forEach(\n      t => backend.disposeIntermediateTensorInfo(t));\n\n  return result;\n}\n\nexport const packConfig: KernelConfig = {\n  kernelName: Pack,\n  backendName: 'webgl',\n  kernelFunc: pack as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class PadProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n  valueLoc: WebGLUniformLocation;\n\n  constructor(\n      xShape: number[], paddings: Array<[number, number]>,\n      constantValue: number) {\n    this.outputShape = paddings.map(\n        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n    const rank = xShape.length;\n    const type = getCoordsDataType(rank);\n\n    const start = paddings.map(p => p[0]).join(',');\n    const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n    const unpackedCoords =\n        ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n\n    if (rank === 1) {\n      this.userCode = `\n        int start = ${start};\n        int end = ${end};\n        uniform float value;\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(value);\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n      return;\n    }\n    this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n      uniform float value;\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(value);\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n  }\n\n  getCustomSetupFunc(value: number) {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.valueLoc == null) {\n        this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n      }\n      gpgpu.gl.uniform1f(this.valueLoc, value);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getChannels} from './packing_util';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class PadPackedProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  packedInputs = true;\n  packedOutput = true;\n  outputShape: number[];\n  userCode: string;\n  valueLoc: WebGLUniformLocation;\n\n  constructor(\n      xShape: number[], paddings: Array<[number, number]>,\n      constantValue: number) {\n    this.outputShape = paddings.map(\n        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n    const rank = xShape.length;\n    const dtype = getCoordsDataType(rank);\n\n    const start = paddings.map(p => p[0]).join(',');\n    const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n    const coords = getChannels('rc', rank);\n    const source = getChannels('source', rank);\n    const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n    const innerDims =\n        rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n\n    const componentSetup = [\n      `${dtype} rc = outputLoc;`, `${coords[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n      rank === 1 ? '' : `}\n       rc = outputLoc;\n       ${coords[rank - 2]} += 1;\n       if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n      rank === 1 ? '' : `  ${coords[rank - 1]} += 1;\n         if(${cLimit}) {`\n    ];\n\n    const paddingArea = rank === 1 ?\n        'rc < start || rc >= end' :\n        'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';\n    let mainLoop = '';\n    for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n      mainLoop += `\n        ${componentSetup[i]}\n        if (${paddingArea}) {\n          result[${i}] = float(value);\n        } else {\n          ${dtype} source = rc - start;\n          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n    }\n    mainLoop += (rank === 1 ? `} ` : `}}`);\n\n    this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n      uniform float value;\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n  }\n\n  getCustomSetupFunc(value: number) {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.valueLoc == null) {\n        this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n      }\n      gpgpu.gl.uniform1f(this.valueLoc, value);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, KernelConfig, KernelFunc, PadV2, PadV2Attrs, PadV2Inputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {PadProgram} from '../pad_gpu';\nimport {PadPackedProgram} from '../pad_packed_gpu';\n\nexport const padV2 =\n    (args: {inputs: PadV2Inputs, backend: MathBackendWebGL, attrs: PadV2Attrs}):\n        TensorInfo => {\n          const {inputs, backend, attrs} = args;\n          const {x} = inputs;\n          const {paddings, constantValue} = attrs;\n\n          const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n              new PadPackedProgram(x.shape, paddings, constantValue) :\n              new PadProgram(x.shape, paddings, constantValue);\n          const customSetup = program.getCustomSetupFunc(constantValue);\n          return backend.runWebGLProgram(program, [x], x.dtype, customSetup);\n        };\n\nexport const padV2Config: KernelConfig = {\n  kernelName: PadV2,\n  backendName: 'webgl',\n  kernelFunc: padV2 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Pow} from '@tensorflow/tfjs-core';\n\nimport {CHECK_NAN_SNIPPET} from '../binaryop_packed_gpu';\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst POW = `\n  if(a < 0.0 && floor(b) < b){\n    return NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  return (round(mod(b, 2.0)) != 1) ?\n      pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\n\nconst POW_PACKED = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\n\nexport const pow =\n    binaryKernelFunc({opSnippet: POW, packedOpSnippet: POW_PACKED});\n\nexport const powConfig: KernelConfig = {\n  kernelName: Pow,\n  backendName: 'webgl',\n  kernelFunc: pow as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, Prod, ProdAttrs, ProdInputs, sumOutType, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {reduce} from '../kernel_utils/reduce';\nimport {prodImplCPU} from '../kernel_utils/shared';\n\nimport {reshape} from './Reshape';\nimport {transpose} from './Transpose';\n\nexport function prod(\n    args: {inputs: ProdInputs, backend: MathBackendWebGL, attrs: ProdAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {axis, keepDims} = attrs;\n\n  const xRank = x.shape.length;\n  const toDispose = [];\n\n  const origAxes = util.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutedAxes}});\n    axes = backend_util.getInnerMostAxes(axes.length, xRank);\n    toDispose.push(permutedX);\n  }\n\n  backend_util.assertAxesAreInnerMostDims('prod', axes, xRank);\n\n  let res;\n  if (backend.shouldExecuteOnCPU([permutedX])) {\n    const xVals = backend.texData.get(permutedX.dataId).values as TypedArray;\n    const {outVals, outShape, outDtype} =\n        prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes);\n    res = backend.makeTensorInfo(outShape, outDtype, outVals);\n  } else {\n    const [outShape, reduceShape] =\n        backend_util.computeOutAndReduceShapes(permutedX.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = reshape(\n        {inputs: {x: permutedX}, backend, attrs: {shape: [-1, inSize]}});\n    const outputDType = sumOutType(x.dtype);\n    const reduced = reduce(a2D, outputDType, 'prod', backend);\n    res = reshape({inputs: {x: reduced}, backend, attrs: {shape: outShape}});\n\n    toDispose.push(a2D);\n    toDispose.push(reduced);\n  }\n\n  if (keepDims) {\n    toDispose.push(res);\n    const newShape = backend_util.expandShapeToKeepDim(res.shape, origAxes);\n    res = reshape({inputs: {x: res}, backend, attrs: {shape: newShape}});\n  }\n\n  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));\n\n  return res;\n}\n\nexport const prodConfig: KernelConfig = {\n  kernelName: Prod,\n  backendName: 'webgl',\n  kernelFunc: prod as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Range, RangeAttrs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {rangeImplCPU} from '../kernel_utils/shared';\n\nexport const range =\n    (args: {backend: MathBackendWebGL, attrs: RangeAttrs}): TensorInfo => {\n      const {backend, attrs} = args;\n      const {start, stop, step, dtype} = attrs;\n      const values = rangeImplCPU(start, stop, step, dtype);\n      return backend.makeTensorInfo([values.length], dtype, values);\n    };\n\nexport const rangeConfig: KernelConfig = {\n  kernelName: Range,\n  backendName: 'webgl',\n  kernelFunc: range as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Reciprocal} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst RECIPROCAL = `return 1.0 / x;`;\n\nexport const reciprocal = unaryKernelFunc({opSnippet: RECIPROCAL});\n\nexport const reciprocalConfig: KernelConfig = {\n  kernelName: Reciprocal,\n  backendName: 'webgl',\n  kernelFunc: reciprocal,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Relu} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {CHECK_NAN_SNIPPET} from '../unaryop_gpu';\n\nconst RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\n\nconst RELU_PACKED = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\n\nexport const relu =\n    unaryKernelFunc({opSnippet: RELU, packedOpSnippet: RELU_PACKED});\n\nexport const reluConfig: KernelConfig = {\n  kernelName: Relu,\n  backendName: 'webgl',\n  kernelFunc: relu as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Relu6} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {CHECK_NAN_SNIPPET} from '../unaryop_gpu';\n\nconst RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\n\nconst RELU6_PACKED = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\n\nexport const relu6 =\n    unaryKernelFunc({opSnippet: RELU6, packedOpSnippet: RELU6_PACKED});\n\nexport const relu6Config: KernelConfig = {\n  kernelName: Relu6,\n  backendName: 'webgl',\n  kernelFunc: relu6 as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeBilinearProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      inputShape: [number, number, number, number], newHeight: number,\n      newWidth: number, alignCorners: boolean, halfPixelCenters: boolean) {\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n\n    const effectiveInSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    let sourceFracIndexRC: string;\n    if (halfPixelCenters) {\n      sourceFracIndexRC =\n          `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC` +\n          ` - vec2(0.5)`;\n    } else {\n      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeBilinearPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  packedInputs = true;\n  packedOutput = true;\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      inputShape: [number, number, number, number], newHeight: number,\n      newWidth: number, alignCorners: boolean, halfPixelCenters: boolean) {\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n\n    const effectiveInSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    let sourceFracIndexRC: string;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * ` +\n          `effectiveInputOverOutputRatioRC - vec3(0.5)`;\n    } else {\n      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n\n    this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, KernelConfig, KernelFunc, ResizeBilinear, ResizeBilinearAttrs, ResizeBilinearInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {ResizeBilinearProgram} from '../resize_bilinear_gpu';\nimport {ResizeBilinearPackedProgram} from '../resize_bilinear_packed_gpu';\n\nexport function resizeBilinear(args: {\n  inputs: ResizeBilinearInputs,\n  backend: MathBackendWebGL,\n  attrs: ResizeBilinearAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {images} = inputs;\n  const {alignCorners, halfPixelCenters, size} = attrs;\n\n  const [newHeight, newWidth] = size;\n\n  const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?\n      new ResizeBilinearPackedProgram(\n          images.shape as [number, number, number, number], newHeight, newWidth,\n          alignCorners, halfPixelCenters) :\n      new ResizeBilinearProgram(\n          images.shape as [number, number, number, number], newHeight, newWidth,\n          alignCorners, halfPixelCenters);\n  return backend.runWebGLProgram(program, [images], 'float32');\n}\n\nexport const resizeBilinearConfig: KernelConfig = {\n  kernelName: ResizeBilinear,\n  backendName: 'webgl',\n  kernelFunc: resizeBilinear as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeBilinearBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      dyShape: [number, number, number, number],\n      inputShape: [number, number, number, number], alignCorners: boolean) {\n    this.outputShape = inputShape;\n    const [, xHeight, xWidth, ] = inputShape;\n    const [, yHeight, yWidth] = dyShape;\n\n    // In the backwards pass, we want to find the pixels that were generated for\n    // each pixel in the input image the forward pass and add the corresponding\n    // coefficient from dy to the gradient (with some interpolation).\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n\n    // This defines the size of the window of values around a particular\n    // index in dy that we want to search for contributions to dx.\n    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, ResizeBilinearGrad, ResizeBilinearGradAttrs, ResizeBilinearGradInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {ResizeBilinearBackpropProgram} from '../resize_bilinear_backprop_gpu';\n\nexport function resizeBilinearGrad(args: {\n  inputs: ResizeBilinearGradInputs,\n  backend: MathBackendWebGL,\n  attrs: ResizeBilinearGradAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {images, dy} = inputs;\n  const {alignCorners} = attrs;\n\n  const program = new ResizeBilinearBackpropProgram(\n      dy.shape as [number, number, number, number],\n      images.shape as [number, number, number, number], alignCorners);\n\n  return backend.runWebGLProgram(program, [dy], dy.dtype);\n}\n\nexport const resizeBilinearGradConfig: KernelConfig = {\n  kernelName: ResizeBilinearGrad,\n  backendName: 'webgl',\n  kernelFunc: resizeBilinearGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeNearestNeighborProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      inputShape: [number, number, number, number], newHeight: number,\n      newWidth: number, alignCorners: boolean, halfPixelCenters: boolean) {\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n\n    const effectiveInSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    // When align corners is false, we rounds the value with floor.\n    const roundBase = alignCorners ? '0.5' : '0.0';\n\n    let sourceFracIndexRC: string;\n    if (halfPixelCenters) {\n      sourceFracIndexRC =\n          `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC` +\n          `, vec2(0.0))`;\n    } else {\n      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeNearestNeighborPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  packedInputs = true;\n  packedOutput = true;\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      inputShape: [number, number, number, number], newHeight: number,\n      newWidth: number, alignCorners: boolean, halfPixelCenters: boolean) {\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n\n    const effectiveInSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    // When align corners is false, we rounds the value with floor.\n    const roundBase = alignCorners ? '0.5' : '0.0';\n    let sourceFracIndexRC: string;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `max((vec3(yRC) + vec3(0.5)) * ` +\n          `effectiveInputOverOutputRatioRC, vec3(0.0))`;\n    } else {\n      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n\n    this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, KernelConfig, KernelFunc, ResizeNearestNeighbor, ResizeNearestNeighborAttrs, ResizeNearestNeighborInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {ResizeNearestNeighborProgram} from '../resize_nearest_neighbor_gpu';\nimport {ResizeNearestNeighborPackedProgram} from '../resize_nearest_neighbor_packed_gpu';\n\nexport function resizeNearestNeighbor(args: {\n  inputs: ResizeNearestNeighborInputs,\n  backend: MathBackendWebGL,\n  attrs: ResizeNearestNeighborAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {images} = inputs;\n  const {alignCorners, halfPixelCenters, size} = attrs;\n\n  const [newHeight, newWidth] = size;\n\n  const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?\n      new ResizeNearestNeighborPackedProgram(\n          images.shape as [number, number, number, number], newHeight, newWidth,\n          alignCorners, halfPixelCenters) :\n      new ResizeNearestNeighborProgram(\n          images.shape as [number, number, number, number], newHeight, newWidth,\n          alignCorners, halfPixelCenters);\n  return backend.runWebGLProgram(program, [images], images.dtype);\n}\n\nexport const resizeNearestNeighborConfig: KernelConfig = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: 'webgl',\n  kernelFunc: resizeNearestNeighbor as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeNearestNeigborBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      dyShape: [number, number, number, number],\n      inputShape: [number, number, number, number], alignCorners: boolean) {\n    this.outputShape = inputShape;\n    const [, xHeight, xWidth, ] = inputShape;\n    const [, yHeight, yWidth] = dyShape;\n\n    // In the backwards pass, we want to find the pixels that were generated for\n    // each pixel in the input image the forward pass and add the corresponding\n    // coefficient from dy to the gradient (with some interpolation).\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n\n    // This defines the size of the window of values around a particular\n    // index in dy that we want to search for contributions to dx.\n    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, ResizeNearestNeighborGrad, ResizeNearestNeighborGradAttrs, ResizeNearestNeighborGradInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {ResizeNearestNeigborBackpropProgram} from '../resize_nearest_neighbor_backprop_gpu';\n\nexport function resizeNearestNeighborGrad(args: {\n  inputs: ResizeNearestNeighborGradInputs,\n  backend: MathBackendWebGL,\n  attrs: ResizeNearestNeighborGradAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {images, dy} = inputs;\n  const {alignCorners} = attrs;\n\n  const program = new ResizeNearestNeigborBackpropProgram(\n      dy.shape as [number, number, number, number],\n      images.shape as [number, number, number, number], alignCorners);\n  return backend.runWebGLProgram(program, [dy], dy.dtype);\n}\n\nexport const resizeNearestNeighborGradConfig: KernelConfig = {\n  kernelName: ResizeNearestNeighborGrad,\n  backendName: 'webgl',\n  kernelFunc: resizeNearestNeighborGrad as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ReverseProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(xShape: number[], axis: number[]) {\n    const rank = xShape.length;\n    if (rank > 4) {\n      throw new Error(\n          `WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n    }\n    this.outputShape = xShape;\n\n    if (rank === 1) {\n      this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n      return;\n    }\n    const getInCoord = (i: number) => {\n      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n        return `${xShape[i]} - coords[${i}] - 1`;\n      }\n      return `coords[${i}]`;\n    };\n    const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n    const type = getCoordsDataType(rank);\n\n    this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getChannels} from './packing_util';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ReversePackedProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n  packedInputs = true;\n  packedOutput = true;\n\n  constructor(xShape: number[], axis: number[]) {\n    const rank = xShape.length;\n    if (rank > 4) {\n      throw new Error(\n          `WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n    }\n    this.outputShape = xShape;\n    const channels = getChannels('rc', rank);\n    const nextColumn =\n        `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n    const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n    const type = getCoordsDataType(rank);\n    if (rank === 1) {\n      this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n    } else {\n      this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n    }\n\n    function getR(channels: string[]): string {\n      return getChannel(channels);\n    }\n\n    function getG(channels: string[]): string {\n      channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n      return getChannel(channels);\n    }\n\n    function getB(channels: string[]): string {\n      channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n      return getChannel(channels);\n    }\n\n    function getA(channels: string[]): string {\n      channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n      channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n      return getChannel(channels);\n    }\n\n    function getChannel(channels: string[]): string {\n      const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels));\n      const inCoords = inCoordsArray.join(',');\n      const innerDims = inCoordsArray.slice(-2).join(',');\n      return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n    }\n\n    function getInCoord(i: number, channels1: string[]): string {\n      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n        return `${xShape[i]} - ${channels1[i]} - 1`;\n      } else {\n        return `${channels1[i]}`;\n      }\n    }\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env, KernelConfig, KernelFunc, Reverse, ReverseAttrs, ReverseInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {ReverseProgram} from '../reverse_gpu';\nimport {ReversePackedProgram} from '../reverse_packed_gpu';\n\nimport {identity} from './Identity';\n\nexport function reverse(args: {\n  inputs: ReverseInputs,\n  backend: MathBackendWebGL,\n  attrs: ReverseAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {dims} = attrs;\n\n  const xRank = x.shape.length;\n\n  const $dims = util.parseAxisParam(dims, x.shape);\n  if (xRank === 0) {\n    return identity({inputs: {x}, backend});\n  }\n\n  const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n      new ReversePackedProgram(x.shape, $dims) :\n      new ReverseProgram(x.shape, $dims);\n\n  return backend.runWebGLProgram(program, [x], x.dtype);\n}\n\nexport const reverseConfig: KernelConfig = {\n  kernelName: Reverse,\n  backendName: 'webgl',\n  kernelFunc: reverse as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class RotateProgram implements GPGPUProgram {\n  variableNames = ['Image'];\n  outputShape: number[] = [];\n  userCode: string;\n  paramsLoc: WebGLUniformLocation;\n  constructor(\n      imageShape: [number, number, number, number],\n      fillValue: number|[number, number, number]) {\n    const imageHeight = imageShape[1];\n    const imageWidth = imageShape[2];\n    this.outputShape = imageShape;\n\n    let fillSnippet = '';\n    if (typeof fillValue === 'number') {\n      fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n    } else {\n      fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(',')});\n        float outputValue = fill[coords[3]];`;\n    }\n\n    this.userCode = `\n        uniform vec4 params;\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${\n        imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n  }\n\n  getCustomSetupFunc(\n      centerX: number, centerY: number, sinFactor: number, cosFactor: number) {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.paramsLoc == null) {\n        this.paramsLoc =\n            gpgpu.getUniformLocationNoThrow(webGLProgram, 'params');\n      }\n      gpgpu.gl.uniform4f(\n          this.paramsLoc, centerX, centerY, sinFactor, cosFactor);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, Tensor4D} from '@tensorflow/tfjs-core';\nimport {RotateWithOffset, RotateWithOffsetAttrs, RotateWithOffsetInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {RotateProgram} from '../rotate_gpu';\n\nexport const rotateWithOffsetConfig: KernelConfig = {\n  kernelName: RotateWithOffset,\n  backendName: 'webgl',\n  kernelFunc: ({inputs, attrs, backend}) => {\n    const {image} = inputs as RotateWithOffsetInputs;\n    const {radians, fillValue, center} = attrs as {} as RotateWithOffsetAttrs;\n    const webglBackend = backend as MathBackendWebGL;\n\n    const program = new RotateProgram((image as Tensor4D).shape, fillValue);\n    const [centerX, centerY] =\n        backend_util.getImageCenter(center, image.shape[1], image.shape[2]);\n    const customSetup = program.getCustomSetupFunc(\n        centerX, centerY, Math.sin(radians), Math.cos(radians));\n    const output = webglBackend.runWebGLProgram(\n        program, [image], image.dtype, customSetup);\n    return output;\n  }\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Round} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\n\nexport const round = unaryKernelFunc({opSnippet: ROUND});\n\nexport const roundConfig: KernelConfig = {\n  kernelName: Round,\n  backendName: 'webgl',\n  kernelFunc: round,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Rsqrt} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\nimport {rsqrtImplCPU} from '../kernel_utils/shared';\n\nconst RSQRT = `return inversesqrt(x);`;\n\nexport const rsqrt =\n    unaryKernelFunc({opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU});\n\nexport const rsqrtConfig: KernelConfig = {\n  kernelName: Rsqrt,\n  backendName: 'webgl',\n  kernelFunc: rsqrt as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ScatterProgram implements GPGPUProgram {\n  variableNames = ['updates', 'indices', 'defaultValue'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      updateSize: number, sliceDim: number, indicesRank: number,\n      updatesRank: number, strides: number[], shape: number[],\n      summingDupeIndex = true) {\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    let indicesString = '';\n    if (indicesRank === 1) {\n      indicesString = 'i';\n    } else if (indicesRank === 2) {\n      indicesString = 'i, j';\n    }\n    const indicesSnippet = `getIndices(${indicesString})`;\n\n    let updatesString = '';\n    if (updatesRank === 1) {\n      updatesString = 'i';\n    } else if (updatesRank === 2) {\n      updatesString = 'i, coords[1]';\n    }\n    const updatesSnippet = `getUpdates(${updatesString})`;\n\n    const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, ScatterNd, ScatterNdAttrs, ScatterNdInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {ScatterProgram} from '../scatter_gpu';\nimport {reshape} from './Reshape';\n\nexport function scatterNd(args: {\n  inputs: ScatterNdInputs,\n  backend: MathBackendWebGL,\n  attrs: ScatterNdAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {indices, updates} = inputs;\n  const {shape} = attrs;\n\n  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n      backend_util.calculateShapes(updates, indices, shape);\n\n  const flattenShape = [outputSize / sliceSize, sliceSize];\n\n  if (outputSize === 0) {\n    return backend.makeTensorInfo(shape, indices.dtype);\n  }\n\n  const flattenIndices = reshape(\n      {inputs: {x: indices}, backend, attrs: {shape: [numUpdates, sliceRank]}});\n  const flattenX = reshape(\n      {inputs: {x: updates}, backend, attrs: {shape: [numUpdates, sliceSize]}});\n\n  const defaultValue = backend.makeTensorInfo(\n      [], 'float32', new Float32Array([0]));  // scalar(0)\n  const program = new ScatterProgram(\n      numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length,\n      strides, flattenShape);\n  const res = backend.runWebGLProgram(\n      program, [flattenX, flattenIndices, defaultValue], flattenX.dtype);\n\n  const reshaped = reshape({inputs: {x: res}, backend, attrs: {shape}});\n\n  backend.disposeIntermediateTensorInfo(flattenIndices);\n  backend.disposeIntermediateTensorInfo(flattenX);\n  backend.disposeIntermediateTensorInfo(res);\n  backend.disposeIntermediateTensorInfo(defaultValue);\n\n  return reshaped;\n}\n\nexport const scatterNdConfig: KernelConfig = {\n  kernelName: ScatterNd,\n  backendName: 'webgl',\n  kernelFunc: scatterNd as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class SelectProgram implements GPGPUProgram {\n  variableNames = ['c', 'a', 'b'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(cRank: number, shape: number[], rank: number) {\n    this.outputShape = shape;\n\n    let cCoords;\n    let abCoords;\n    if (rank > 4) {\n      throw Error(`Where for rank ${rank} is not yet supported`);\n    }\n\n    if (rank === 1) {\n      abCoords = `resRC`;\n      cCoords = `resRC`;\n    } else {\n      const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n      const cCoordVars = [];\n      const abCoordVars = [];\n      for (let i = 0; i < shape.length; i++) {\n        abCoordVars.push(`${currentCoords[i]}`);\n        if (i < cRank) {\n          cCoordVars.push(`${currentCoords[i]}`);\n        }\n      }\n      cCoords = cCoordVars.join();\n      abCoords = abCoordVars.join();\n    }\n\n    const dtype = getCoordsDataType(rank);\n\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Select, SelectInputs, TensorInfo, upcastType} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {SelectProgram} from '../select_gpu';\n\nexport function select(args: {inputs: SelectInputs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend} = args;\n  const {condition, t, e} = inputs;\n\n  const program =\n      new SelectProgram(condition.shape.length, t.shape, t.shape.length);\n  return backend.runWebGLProgram(\n      program, [condition, t, e], upcastType(t.dtype, e.dtype));\n}\n\nexport const selectConfig: KernelConfig = {\n  kernelName: Select,\n  backendName: 'webgl',\n  kernelFunc: select as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, Selu} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util.SELU_SCALEALPHA};\n  float scale = ${backend_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\n\nexport const selu = unaryKernelFunc({opSnippet: SELU});\n\nexport const seluConfig: KernelConfig = {\n  kernelName: Selu,\n  backendName: 'webgl',\n  kernelFunc: selu,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Sigmoid} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n\nexport const sigmoid = unaryKernelFunc({opSnippet: SIGMOID});\n\nexport const sigmoidConfig: KernelConfig = {\n  kernelName: Sigmoid,\n  backendName: 'webgl',\n  kernelFunc: sigmoid,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Sign} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\n// Sign does not propagate NANs.\nconst SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\n\nexport const sign = unaryKernelFunc({opSnippet: SIGN});\n\nexport const signConfig: KernelConfig = {\n  kernelName: Sign,\n  backendName: 'webgl',\n  kernelFunc: sign,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Sin} from '@tensorflow/tfjs-core';\n\nimport {CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\n\nexport const sin = unaryKernelFunc({opSnippet: SIN});\n\nexport const sinConfig: KernelConfig = {\n  kernelName: Sin,\n  backendName: 'webgl',\n  kernelFunc: sin,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Sinh} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\n\nexport const sinh = unaryKernelFunc({opSnippet: SINH});\n\nexport const sinhConfig: KernelConfig = {\n  kernelName: Sinh,\n  backendName: 'webgl',\n  kernelFunc: sinh,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Softplus} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\n\nexport const softplus = unaryKernelFunc({opSnippet: SOFTPLUS});\n\nexport const softplusConfig: KernelConfig = {\n  kernelName: Softplus,\n  backendName: 'webgl',\n  kernelFunc: softplus,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, SpaceToBatchND, SpaceToBatchNDAttrs, SpaceToBatchNDInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {padV2} from './PadV2';\nimport {reshape} from './Reshape';\nimport {transpose} from './Transpose';\n\nexport const spaceToBatchND = (args: {\n  inputs: SpaceToBatchNDInputs,\n  backend: MathBackendWebGL,\n  attrs: SpaceToBatchNDAttrs\n}): TensorInfo => {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {blockShape, paddings} = attrs;\n\n  util.assert(\n      x.shape.length <= 4,\n      () => 'spaceToBatchND for rank > 4 with a WebGL backend not ' +\n          'implemented yet');\n\n  const prod = blockShape.reduce((a, b) => a * b);\n\n  const completePaddings: Array<[number, number]> = [[0, 0]];\n  completePaddings.push(...paddings as Array<[number, number]>);\n  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n    completePaddings.push([0, 0]);\n  }\n\n  const toDispose = [];\n\n  const paddedX = padV2({\n    inputs: {x},\n    backend,\n    attrs: {paddings: completePaddings, constantValue: 0}\n  });\n\n  const reshapedPaddedShape =\n      backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n\n  const permutedReshapedPaddedPermutation = backend_util.getPermuted(\n      reshapedPaddedShape.length, blockShape.length, false);\n\n  const flattenShape =\n      backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n\n  const reshapedPaddedX = reshape(\n      {inputs: {x: paddedX}, backend, attrs: {shape: reshapedPaddedShape}});\n\n  const paddedXT = transpose({\n    inputs: {x: reshapedPaddedX},\n    backend,\n    attrs: {perm: permutedReshapedPaddedPermutation}\n  });\n\n  const result =\n      reshape({inputs: {x: paddedXT}, backend, attrs: {shape: flattenShape}});\n\n  toDispose.push(paddedX);\n  toDispose.push(reshapedPaddedX);\n  toDispose.push(paddedXT);\n\n  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));\n\n  return result;\n};\n\nexport const spaceToBatchNDConfig: KernelConfig = {\n  kernelName: SpaceToBatchND,\n  backendName: 'webgl',\n  kernelFunc: spaceToBatchND as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, SparseFillEmptyRows, SparseFillEmptyRowsInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {sparseFillEmptyRowsImplCPU} from '../kernel_utils/shared';\n\nexport function sparseFillEmptyRows(args: {\n  inputs: SparseFillEmptyRowsInputs,\n  backend: MathBackendWebGL\n}): [TensorInfo, TensorInfo, TensorInfo, TensorInfo] {\n  const {inputs, backend} = args;\n  const {indices, values, denseShape, defaultValue} = inputs;\n  if (denseShape.shape.length !== 1) {\n    throw new Error(`Dense shape must be a vector, saw:\n         ${denseShape.shape}`);\n  }\n  if (indices.shape.length !== 2) {\n    throw new Error(`Indices must be a matrix, saw:\n         ${indices.shape}`);\n  }\n  if (values.shape.length !== 1) {\n    throw new Error(`Values must be a vector, saw:\n         ${values.shape}`);\n  }\n  if (defaultValue.shape.length !== 0) {\n    throw new Error(`Default value must be a scalar, saw:\n        ${defaultValue.shape}`);\n  }\n\n  const $indices = backend.readSync(indices.dataId) as TypedArray;\n  const $values = backend.readSync(values.dataId) as TypedArray;\n  const $denseShape = backend.readSync(denseShape.dataId) as TypedArray;\n  const $defaultValue =\n      backend.readSync(defaultValue.dataId)[0] as number;\n\n  const [outputIndices, outputIndicesShape, outputValues,\n         emptyRowIndicator, reverseIndexMap] =\n      sparseFillEmptyRowsImplCPU(\n          $indices, indices.shape, indices.dtype, $values, values.dtype,\n          $denseShape, $defaultValue);\n  return [\n    backend.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),\n    backend.makeTensorInfo(\n        [outputIndicesShape[0]], values.dtype, outputValues),\n    backend.makeTensorInfo(\n        [emptyRowIndicator.length], 'bool',\n        new Uint8Array(\n            emptyRowIndicator.map((value: boolean) => Number(value)))),\n    backend.makeTensorInfo(\n        [reverseIndexMap.length], indices.dtype,\n        new Int32Array(reverseIndexMap)),\n  ];\n}\n\nexport const sparseFillEmptyRowsConfig: KernelConfig = {\n  kernelName: SparseFillEmptyRows,\n  backendName: 'webgl',\n  kernelFunc: sparseFillEmptyRows as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, SparseReshape, SparseReshapeInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {sparseReshapeImplCPU} from '../kernel_utils/shared';\n\nexport function sparseReshape(\n    args: {inputs: SparseReshapeInputs, backend: MathBackendWebGL}):\n    [TensorInfo, TensorInfo] {\n  const {inputs, backend} = args;\n  const {inputIndices, inputShape, newShape} = inputs;\n  if (inputIndices.shape.length !== 2) {\n    throw new Error(`Input indices should be a matrix but received shape ${\n        inputIndices.shape}`);\n  }\n  if (inputShape.shape.length !== 1) {\n    throw new Error(`Input shape should be a vector but received shape ${\n        inputShape.shape}`);\n  }\n\n  if (newShape.shape.length !== 1) {\n    throw new Error(\n        `Target shape should be a vector but received shape ${newShape.shape}`);\n  }\n\n  const $inputShape =\n      Array.from(backend.readSync(inputShape.dataId) as TypedArray);\n  const $inputIndices = backend.readSync(inputIndices.dataId) as TypedArray;\n  const targetShape =\n      Array.from(backend.readSync(newShape.dataId) as TypedArray);\n\n  const [newIndices, indicesShape, outputShape] = sparseReshapeImplCPU(\n      $inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape,\n      targetShape);\n  return [\n    backend.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),\n    backend.makeTensorInfo(\n        [outputShape.length], newShape.dtype, new Int32Array(outputShape)),\n  ];\n}\n\nexport const sparseReshapeConfig: KernelConfig = {\n  kernelName: SparseReshape,\n  backendName: 'webgl',\n  kernelFunc: sparseReshape,\n};\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, SparseSegmentMean, SparseSegmentMeanInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {sparseSegmentReductionImplCPU} from '../kernel_utils/shared';\n\nexport function sparseSegmentMean(\n    args: {inputs: SparseSegmentMeanInputs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend} = args;\n  const {data, indices, segmentIds} = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(\n        `Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n              ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n              ${segmentIds.shape}`);\n  }\n\n  const $data = backend.readSync(data.dataId) as TypedArray;\n  const $indices = backend.readSync(indices.dataId) as TypedArray;\n  const $segmentIds = backend.readSync(segmentIds.dataId) as TypedArray;\n\n  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU(\n      $data, data.shape, data.dtype, $indices, $segmentIds, true);\n  return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\n\nexport const sparseSegmentMeanConfig: KernelConfig = {\n  kernelName: SparseSegmentMean,\n  backendName: 'webgl',\n  kernelFunc: sparseSegmentMean as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, SparseSegmentSum, SparseSegmentSumInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {sparseSegmentReductionImplCPU} from '../kernel_utils/shared';\n\nexport function sparseSegmentSum(\n    args: {inputs: SparseSegmentSumInputs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {inputs, backend} = args;\n  const {data, indices, segmentIds} = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(\n        `Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n             ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n             ${segmentIds.shape}`);\n  }\n\n  const $data = backend.readSync(data.dataId) as TypedArray;\n  const $indices = backend.readSync(indices.dataId) as TypedArray;\n  const $segmentIds = backend.readSync(segmentIds.dataId) as TypedArray;\n\n  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU(\n      $data, data.shape, data.dtype, $indices, $segmentIds);\n  return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\n\nexport const sparseSegmentSumConfig: KernelConfig = {\n  kernelName: SparseSegmentSum,\n  backendName: 'webgl',\n  kernelFunc: sparseSegmentSum as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, SparseToDense, SparseToDenseAttrs, SparseToDenseInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {ScatterProgram} from '../scatter_gpu';\nimport {reshape} from './Reshape';\n\nexport function sparseToDense(args: {\n  inputs: SparseToDenseInputs,\n  backend: MathBackendWebGL,\n  attrs: SparseToDenseAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {sparseIndices, sparseValues, defaultValue} = inputs;\n  const {outputShape} = attrs;\n\n  const {sliceRank, numUpdates, strides, outputSize} =\n      backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n\n  const sumDupeIndices = false;\n  const program = new ScatterProgram(\n      numUpdates, sliceRank, sparseIndices.shape.length,\n      sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices);\n\n  const res = backend.runWebGLProgram(\n      program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype);\n\n  const reshaped =\n      reshape({inputs: {x: res}, backend, attrs: {shape: outputShape}});\n\n  backend.disposeIntermediateTensorInfo(res);\n  return reshaped;\n}\n\nexport const sparseToDenseConfig: KernelConfig = {\n  kernelName: SparseToDense,\n  backendName: 'webgl',\n  kernelFunc: sparseToDense as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, KernelConfig, KernelFunc, SplitV, SplitVAttrs, SplitVInputs, TensorInfo, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {slice} from './Slice';\n\nexport function splitV(\n    args:\n        {inputs: SplitVInputs, backend: MathBackendWebGL, attrs: SplitVAttrs}):\n    TensorInfo[] {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {numOrSizeSplits, axis} = attrs;\n\n  const $axis = util.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util.prepareSplitSize(x, numOrSizeSplits, $axis);\n\n  const xRank = x.shape.length;\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n\n  return splitSizes.map(s => {\n    const sliceSize = [...size];\n    sliceSize[$axis] = s;\n    const sliceT =\n        slice({inputs: {x}, backend, attrs: {begin, size: sliceSize}});\n    begin[$axis] += s;\n    return sliceT;\n  });\n}\n\nexport const splitVConfig: KernelConfig = {\n  kernelName: SplitV,\n  backendName: 'webgl',\n  kernelFunc: splitV as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Sqrt} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst SQRT = `return sqrt(x);`;\n\nexport const sqrt = unaryKernelFunc({opSnippet: SQRT});\n\nexport const sqrtConfig: KernelConfig = {\n  kernelName: Sqrt,\n  backendName: 'webgl',\n  kernelFunc: sqrt as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Square} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst SQUARE = `return x * x;`;\n\nexport const square = unaryKernelFunc({opSnippet: SQUARE});\n\nexport const squareConfig: KernelConfig = {\n  kernelName: Square,\n  backendName: 'webgl',\n  kernelFunc: square,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, SquaredDifference} from '@tensorflow/tfjs-core';\n\nimport {binaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\n\nexport const squaredDifference = binaryKernelFunc(\n    {opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE});\n\nexport const squaredDifferenceConfig: KernelConfig = {\n  kernelName: SquaredDifference,\n  backendName: 'webgl',\n  kernelFunc: squaredDifference,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, Step, StepAttrs, TensorInfo, UnaryInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {CHECK_NAN_SNIPPET, UnaryOpProgram} from '../unaryop_gpu';\n\nexport function step(\n    {inputs, attrs, backend}:\n        {inputs: UnaryInputs, attrs: StepAttrs, backend: MathBackendWebGL}):\n    TensorInfo {\n  const {x} = inputs;\n  const opSnippet = CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${attrs.alpha});\n  `;\n\n  const program = new UnaryOpProgram(x.shape, opSnippet);\n\n  return backend.runWebGLProgram(program, [x], x.dtype);\n}\n\nexport const stepConfig: KernelConfig = {\n  kernelName: Step,\n  backendName: 'webgl',\n  kernelFunc: step as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class StridedSliceProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(begin: number[], strides: number[], size: number[]) {\n    this.outputShape = size;\n    const rank = size.length;\n    const inputDtype = getCoordsDataType(size.length);\n    const dtype = getCoordsDataType(size.length);\n\n    let newCoords = '';\n    if (rank === 1) {\n      newCoords = 'coords * strides + begin';\n    } else {\n      let outputAxis = 0;\n      newCoords =\n          size.map((_, i) => {\n                outputAxis++;\n                return size.length === 1 ?\n                    `coords * strides[${i}] + begin[${i}]` :\n                    `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n              })\n              .join(',');\n    }\n\n    this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {buffer, KernelConfig, KernelFunc, Rank, slice_util, StridedSlice, StridedSliceAttrs, StridedSliceInputs, TensorBuffer, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {stridedSliceImplCPU} from '../kernel_utils/shared';\nimport {StridedSliceProgram} from '../strided_slice_gpu';\n\nimport {reshape} from './Reshape';\nimport {slice} from './Slice';\n\nexport function stridedSlice(args: {\n  inputs: StridedSliceInputs,\n  backend: MathBackendWebGL,\n  attrs: StridedSliceAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {\n    begin,\n    end,\n    strides,\n    beginMask,\n    endMask,\n    ellipsisMask,\n    newAxisMask,\n    shrinkAxisMask\n  } = attrs;\n\n  const {nonStrided, $begin, $strides, size, newShape, outShape} =\n      slice_util.sliceInfo(\n          x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,\n          newAxisMask, shrinkAxisMask);\n\n  const $x = reshape({inputs: {x}, backend, attrs: {shape: newShape}});\n\n  let result;\n  if (nonStrided) {\n    const sliced =\n        slice({inputs: {x: $x}, backend, attrs: {begin: $begin, size}});\n    result = reshape({inputs: {x: sliced}, backend, attrs: {shape: outShape}});\n\n    backend.disposeIntermediateTensorInfo(sliced);\n  } else if (outShape.some(axis => axis === 0)) {\n    result = backend.makeTensorInfo(outShape, x.dtype, []);\n  } else {\n    const shouldExecuteOnCPU = backend.shouldExecuteOnCPU([$x]);\n    if (shouldExecuteOnCPU) {\n      const xTexData = backend.texData.get($x.dataId);\n      const values = xTexData.values as TypedArray;\n      const xBuf = buffer($x.shape, $x.dtype, values) as TensorBuffer<Rank>;\n      const resultValues =\n          stridedSliceImplCPU(outShape, xBuf, $strides, $begin);\n      result = backend.makeTensorInfo(outShape, $x.dtype, resultValues.values);\n    } else {\n      const program = new StridedSliceProgram($begin, $strides, outShape);\n      result = backend.runWebGLProgram(program, [$x], $x.dtype);\n    }\n  }\n\n  const resultReshaped =\n      reshape({inputs: {x: result}, backend, attrs: {shape: outShape}});\n\n  backend.disposeIntermediateTensorInfo($x);\n  backend.disposeIntermediateTensorInfo(result);\n\n  return resultReshaped;\n}\n\nexport const stridedSliceConfig: KernelConfig = {\n  kernelName: StridedSlice,\n  backendName: 'webgl',\n  kernelFunc: stridedSlice as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, StringNGrams, StringNGramsAttrs, StringNGramsInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {stringNGramsImplCPU} from '../kernel_utils/shared';\n\nexport function stringNGrams(args: {\n  inputs: StringNGramsInputs,\n  backend: MathBackendWebGL,\n  attrs: StringNGramsAttrs\n}): [TensorInfo, TensorInfo] {\n  const {inputs, backend, attrs} = args;\n  const {\n    separator,\n    nGramWidths,\n    leftPad,\n    rightPad,\n    padWidth,\n    preserveShortSequences\n  } = attrs;\n  const {data, dataSplits} = inputs;\n  const $data = backend.readSync(data.dataId) as Uint8Array[];\n  const $dataSplits = backend.readSync(dataSplits.dataId) as Int32Array;\n\n  const [nGrams, nGramsSplits] = stringNGramsImplCPU(\n      $data, $dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth,\n      preserveShortSequences);\n  return [\n    backend.makeTensorInfo([nGrams.length], 'string', nGrams),\n    backend.makeTensorInfo(dataSplits.shape, 'int32', nGramsSplits),\n  ];\n}\n\nexport const stringNGramsConfig: KernelConfig = {\n  kernelName: StringNGrams,\n  backendName: 'webgl',\n  kernelFunc: stringNGrams as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, StringSplit, StringSplitAttrs, StringSplitInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {stringSplitImplCPU} from '../kernel_utils/shared';\n\nexport function stringSplit(args: {\n  inputs: StringSplitInputs,\n  backend: MathBackendWebGL,\n  attrs: StringSplitAttrs\n}): [TensorInfo, TensorInfo, TensorInfo] {\n  const {inputs, backend, attrs} = args;\n  const {skipEmpty} = attrs;\n  const {input, delimiter} = inputs;\n\n  if (input.dtype !== 'string') {\n    throw new Error('Input must be of datatype string');\n  }\n  if (input.shape.length !== 1) {\n    throw new Error(`Input must be a vector, got shape: ${input.shape}`);\n  }\n  if (delimiter.shape.length !== 0) {\n    throw new Error(\n        `Delimiter must be a scalar, got shape: ${delimiter.shape}`);\n  }\n\n  const $input = backend.readSync(input.dataId) as Uint8Array[];\n  const $delimiter = backend.readSync(delimiter.dataId)[0] as Uint8Array;\n\n  const [indices, values, shape] =\n      stringSplitImplCPU($input, $delimiter, skipEmpty);\n  const outputSize = values.length;\n  return [\n    backend.makeTensorInfo([outputSize, 2], 'int32', indices),\n    backend.makeTensorInfo([outputSize], 'string', values),\n    backend.makeTensorInfo([2], 'int32', new Int32Array(shape))\n  ];\n}\n\nexport const stringSplitConfig: KernelConfig = {\n  kernelName: StringSplit,\n  backendName: 'webgl',\n  kernelFunc: stringSplit as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, StringToHashBucketFast, StringToHashBucketFastAttrs, StringToHashBucketFastInputs, TensorInfo} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {stringToHashBucketFastImplCPU} from '../kernel_utils/shared';\n\nexport function stringToHashBucketFast(args: {\n  inputs: StringToHashBucketFastInputs,\n  backend: MathBackendWebGL,\n  attrs: StringToHashBucketFastAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {numBuckets} = attrs;\n  const {input} = inputs;\n\n  if (input.dtype !== 'string') {\n    throw new Error('Input must be of datatype string');\n  }\n  if (numBuckets <= 0) {\n    throw new Error(`Number of buckets must be at least 1`);\n  }\n\n  const $input = backend.readSync(input.dataId) as Uint8Array[];\n\n  const output = stringToHashBucketFastImplCPU($input, numBuckets);\n  return backend.makeTensorInfo(input.shape, 'int32', output);\n}\n\nexport const stringToHashBucketFastConfig: KernelConfig = {\n  kernelName: StringToHashBucketFast,\n  backendName: 'webgl',\n  kernelFunc: stringToHashBucketFast as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Tan} from '@tensorflow/tfjs-core';\n\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst TAN = `return tan(x);`;\n\nexport const tan = unaryKernelFunc({opSnippet: TAN});\n\nexport const tanConfig: KernelConfig = {\n  kernelName: Tan,\n  backendName: 'webgl',\n  kernelFunc: tan,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, Tanh} from '@tensorflow/tfjs-core';\nimport {unaryKernelFunc} from '../kernel_utils/kernel_funcs_utils';\n\nconst TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\n\nexport const tanh = unaryKernelFunc({opSnippet: TANH});\n\nexport const tanhConfig: KernelConfig = {\n  kernelName: Tanh,\n  backendName: 'webgl',\n  kernelFunc: tanh,\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class TileProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  constructor(aShape: number[], reps: number[]) {\n    const outputShape: number[] = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[i] * reps[i];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords(aShape);\n\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n  }\n}\n\nfunction getSourceCoords(aShape: number[]): string {\n  const rank = aShape.length;\n  if (rank > 5) {\n    throw Error(`Tile for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `imod(resRC, ${aShape[0]})`;\n  }\n\n  const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n\n  const sourceCoords = [];\n  for (let i = 0; i < aShape.length; i++) {\n    sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n  }\n  return sourceCoords.join();\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {buffer, KernelConfig, KernelFunc, TensorInfo, Tile, TileAttrs, TileInputs, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {tileImplCPU} from '../kernel_utils/shared';\nimport {TileProgram} from '../tile_gpu';\n\nexport function tile(\n    params: {inputs: TileInputs, backend: MathBackendWebGL, attrs: TileAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = params;\n  const {x} = inputs;\n  const {reps} = attrs;\n\n  // tile gpu program cannot handle rank > 5 case.\n  if (x.dtype === 'string' || x.shape.length > 5) {\n    // Even thought string tensor is always on CPU, just to be consistent on how\n    // to access tensor data.\n    const data = backend.readSync(x.dataId);\n    const value = x.dtype === 'string' ?\n        (data as Uint8Array[]).map(d => util.decodeString(d)) :\n        data as TypedArray;\n    const buf = buffer(x.shape, x.dtype, value);\n    const outBuf = tileImplCPU(buf, reps);\n    return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n  }\n\n  const program = new TileProgram(x.shape, reps);\n  const output = backend.runWebGLProgram(program, [x], x.dtype);\n\n  return output;\n}\n\nexport const tileConfig: KernelConfig = {\n  kernelName: Tile,\n  backendName: 'webgl',\n  kernelFunc: tile as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, NumericDataType, TensorInfo, TopK, TopKAttrs, TopKInputs, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {topKImplCPU} from '../kernel_utils/shared';\n\nexport function topK(\n    args: {inputs: TopKInputs, backend: MathBackendWebGL, attrs: TopKAttrs}):\n    TensorInfo[] {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {k, sorted} = attrs;\n\n  const xVals = backend.readSync(x.dataId) as TypedArray;\n  const [allTopKVals, allTopKIndices] =\n      topKImplCPU(xVals, x.shape, x.dtype as NumericDataType, k, sorted);\n\n  return [\n    backend.makeTensorInfo(\n        allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n    backend.makeTensorInfo(\n        allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n  ];\n}\n\nexport const topKConfig: KernelConfig = {\n  kernelName: TopK,\n  backendName: 'webgl',\n  kernelFunc: topK as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class TransformProgram implements GPGPUProgram {\n  variableNames = ['Image', 'Transforms'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      imageHeight: number, imageWidth: number,\n      interpolation: 'nearest'|'bilinear',\n      fillMode: 'constant'|'reflect'|'wrap'|'nearest', fillValue: number,\n      outShape: [number, number, number, number]) {\n    this.outputShape = outShape;\n    const interpolationModeId = interpolation === 'nearest' ? 1 : 2;\n    let fillModeId;\n    switch (fillMode) {\n      case 'constant':\n        fillModeId = 1;\n        break;\n      case 'reflect':\n        fillModeId = 2;\n        break;\n      case 'wrap':\n        fillModeId = 3;\n        break;\n      case 'nearest':\n        fillModeId = 4;\n        break;\n      default:\n        fillModeId = 1;\n        break;\n    }\n    this.userCode = `\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${fillModeId} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${fillModeId} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${fillModeId} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${\n        imageHeight} && 0 <= coordX && coordX < ${imageWidth}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${fillValue});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${fillValue});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${imageWidth}));\n                float mapY = mapCoord(inY, float(${imageHeight}));\n\n                if (${interpolationModeId} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `;\n  }\n}\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, TensorInfo, Transform, TransformAttrs, TransformInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {TransformProgram} from '../transform_gpu';\n\nexport function transform(args: {\n  inputs: TransformInputs,\n  backend: MathBackendWebGL,\n  attrs: TransformAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {image, transforms} = inputs;\n  const {interpolation, fillMode, fillValue, outputShape} = attrs;\n\n  const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n  const [outHeight, outWidth] =\n      outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape =\n      [batch, outHeight, outWidth,\n       numChannels] as [number, number, number, number];\n\n  const program = new TransformProgram(\n      imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape);\n  return backend.runWebGLProgram(program, [image, transforms], 'float32');\n}\n\nexport const transformConfig: KernelConfig = {\n  kernelName: Transform,\n  backendName: 'webgl',\n  kernelFunc: transform as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, TensorInfo, Unique, UniqueAttrs, UniqueInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {uniqueImplCPU} from '../kernel_utils/shared';\nimport {assertNotComplex} from '../webgl_util';\n\nexport function unique(\n    args:\n        {inputs: UniqueInputs, attrs: UniqueAttrs, backend: MathBackendWebGL}):\n    TensorInfo[] {\n  const {inputs, attrs, backend} = args;\n  const {axis} = attrs;\n  const {x} = inputs;\n  assertNotComplex(x, 'unique');\n\n  // For now, always forward calculation to the CPU backend.\n  console.warn(\n      'WARNING: ',\n      'UI might be locked temporarily as data is being downloaded');\n  const values = backend.readSync(x.dataId);\n  const {outputValues, outputShape, indices} =\n      uniqueImplCPU(values, axis, x.shape, x.dtype);\n  return [\n    backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n    backend.makeTensorInfo([indices.length], 'int32', indices),\n  ];\n}\n\nexport const uniqueConfig: KernelConfig = {\n  kernelName: Unique,\n  backendName: 'webgl',\n  kernelFunc: unique as {} as KernelFunc,\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, TensorInfo, Unpack, UnpackAttrs, UnpackInputs} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\n\nimport {reshape} from './Reshape';\nimport {slice} from './Slice';\n\nexport function unpack(\n    args:\n        {inputs: UnpackInputs, backend: MathBackendWebGL, attrs: UnpackAttrs}):\n    TensorInfo[] {\n  const {inputs, backend, attrs} = args;\n  const {value} = inputs;\n  let {axis} = attrs;\n\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n\n  const x = value;\n  const xRank = x.shape.length;\n\n  const num = value.shape[axis];\n  const outShape: number[] = new Array(xRank - 1);\n  let outIndex = 0;\n  for (let i = 0; i < xRank; i++) {\n    if (i !== axis) {\n      outShape[outIndex++] = x.shape[i];\n    }\n  }\n\n  const toDispose = [];\n\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  size[axis] = 1;\n  const res: TensorInfo[] = new Array(num);\n  for (let i = 0; i < res.length; i++) {\n    begin[axis] = i;\n    const sliced = slice({inputs: {x}, backend, attrs: {begin, size}});\n    const reshaped =\n        reshape({inputs: {x: sliced}, backend, attrs: {shape: outShape}});\n    res[i] = reshaped;\n\n    toDispose.push(sliced);\n  }\n\n  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));\n  return res;\n}\n\nexport const unpackConfig: KernelConfig = {\n  kernelName: Unpack,\n  backendName: 'webgl',\n  kernelFunc: unpack as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util} from '@tensorflow/tfjs-core';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class SegmentOpProgram implements GPGPUProgram {\n  variableNames = ['x', 'segmentIds'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      segOpInfo: backend_util.segment_util.SegOpInfo,\n      segOpType: 'unsortedSegmentSum') {\n    const windowSize = segOpInfo.windowSize;\n    const batchSize = segOpInfo.batchSize;\n    const inSize = segOpInfo.inSize;\n    const numSegments = segOpInfo.numSegments;\n    const outSize = numSegments * Math.ceil(inSize / windowSize);\n    this.outputShape = [batchSize, outSize];\n\n    const initializationValue = '0.0';\n    const returnValue = `sumValue`;\n\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n\n    const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n\n    let checkValueOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n\n    let checkSegmentIdOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n    }\n\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DataType, KernelConfig, KernelFunc, sumOutType, TensorInfo, UnsortedSegmentSum, UnsortedSegmentSumAttrs, UnsortedSegmentSumInputs, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {SegmentOpProgram} from '../segment_gpu';\n\nimport {range} from './Range';\nimport {reshape} from './Reshape';\nimport {tile} from './Tile';\nimport {transpose} from './Transpose';\n\nexport function unsortedSegmentSum(args: {\n  inputs: UnsortedSegmentSumInputs,\n  backend: MathBackendWebGL,\n  attrs: UnsortedSegmentSumAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x, segmentIds} = inputs;\n  const {numSegments} = attrs;\n\n  const xRank = x.shape.length;\n\n  const toDispose = [];\n\n  let axis = 0;\n  const permutation = backend_util.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation != null) {\n    permutedX = transpose({inputs: {x}, backend, attrs: {perm: permutation}});\n    toDispose.push(permutedX);\n    axis = backend_util.getInnerMostAxes(1, xRank)[0];\n  }\n\n  const outShape = backend_util.segment_util.computeOutShape(\n      permutedX.shape, axis, numSegments);\n  const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n  const a2D =\n      reshape({inputs: {x: permutedX}, backend, attrs: {shape: [-1, inSize]}});\n  toDispose.push(a2D);\n\n  const outputDType = sumOutType(x.dtype);\n\n  const segOpCompute =\n      (x: TensorInfo, segOpType: 'unsortedSegmentSum', segmentIds: TensorInfo,\n       dtype: DataType, numSegments: number): TensorInfo => {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize =\n            backend_util.segment_util.segOpComputeOptimalWindowSize(\n                inSize, numSegments);\n        const segOpInfo = {windowSize, inSize, batchSize, numSegments};\n        const program = new SegmentOpProgram(segOpInfo, segOpType);\n        const output = backend.compileAndRun(program, [x, segmentIds], dtype);\n        toDispose.push(output);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === numSegments) {\n          return output;\n        }\n        const rangeInfo = range({\n          backend,\n          attrs: {start: 0, stop: numSegments, step: 1, dtype: 'float32'}\n        });\n        const tileInfo = tile({\n          inputs: {x: rangeInfo},\n          backend,\n          attrs: {reps: [inSize / windowSize]}\n        });\n\n        toDispose.push(rangeInfo);\n        toDispose.push(tileInfo);\n\n        const result =\n            segOpCompute(output, segOpType, tileInfo, dtype, numSegments);\n        return result;\n      };\n\n  const segOpResult = segOpCompute(\n      a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments);\n\n  const reshaped =\n      reshape({inputs: {x: segOpResult}, backend, attrs: {shape: outShape}});\n\n  let result = reshaped;\n  if (permutation != null) {\n    toDispose.push(reshaped);\n    const perm = backend_util.getUndoAxesPermutation(permutation);\n    result = transpose({inputs: {x: result}, backend, attrs: {perm}});\n  }\n\n  toDispose.forEach(t => backend.disposeIntermediateTensorInfo(t));\n  return result;\n}\n\nexport const unsortedSegmentSumConfig: KernelConfig = {\n  kernelName: UnsortedSegmentSum,\n  backendName: 'webgl',\n  kernelFunc: unsortedSegmentSum as {} as KernelFunc\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {KernelConfig, registerKernel} from '@tensorflow/tfjs-core';\n\nimport {_fusedMatMulConfig} from './kernels/_FusedMatMul';\nimport {absConfig} from './kernels/Abs';\nimport {acosConfig} from './kernels/Acos';\nimport {acoshConfig} from './kernels/Acosh';\nimport {addConfig} from './kernels/Add';\nimport {addNConfig} from './kernels/AddN';\nimport {allConfig} from './kernels/All';\nimport {anyConfig} from './kernels/Any';\nimport {argMaxConfig} from './kernels/ArgMax';\nimport {argMinConfig} from './kernels/ArgMin';\nimport {asinConfig} from './kernels/Asin';\nimport {asinhConfig} from './kernels/Asinh';\nimport {atanConfig} from './kernels/Atan';\nimport {atan2Config} from './kernels/Atan2';\nimport {atanhConfig} from './kernels/Atanh';\nimport {avgPoolConfig} from './kernels/AvgPool';\nimport {avgPool3DConfig} from './kernels/AvgPool3D';\nimport {avgPoolGrad3DConfig} from './kernels/AvgPool3DGrad';\nimport {avgPoolGradConfig} from './kernels/AvgPoolGrad';\nimport {batchMatMulConfig} from './kernels/BatchMatMul';\nimport {batchNormConfig} from './kernels/BatchNorm';\nimport {batchToSpaceNDConfig} from './kernels/BatchToSpaceND';\nimport {bincountConfig} from './kernels/Bincount';\nimport {castConfig} from './kernels/Cast';\nimport {ceilConfig} from './kernels/Ceil';\nimport {clipByValueConfig} from './kernels/ClipByValue';\nimport {complexConfig} from './kernels/Complex';\nimport {complexAbsConfig} from './kernels/ComplexAbs';\nimport {concatConfig} from './kernels/Concat';\nimport {conv2DConfig} from './kernels/Conv2D';\nimport {conv2DBackpropFilterConfig} from './kernels/Conv2DBackpropFilter';\nimport {conv2DBackpropInputConfig} from './kernels/Conv2DBackpropInput';\nimport {conv3DConfig} from './kernels/Conv3D';\nimport {conv3DBackpropFilterV2Config} from './kernels/Conv3DBackpropFilterV2';\nimport {conv3DBackpropInputConfig} from './kernels/Conv3DBackpropInputV2';\nimport {cosConfig} from './kernels/Cos';\nimport {coshConfig} from './kernels/Cosh';\nimport {cropAndResizeConfig} from './kernels/CropAndResize';\nimport {cumsumConfig} from './kernels/Cumsum';\nimport {denseBincountConfig} from './kernels/DenseBincount';\nimport {depthToSpaceConfig} from './kernels/DepthToSpace';\nimport {depthwiseConv2dNativeConfig} from './kernels/DepthwiseConv2dNative';\nimport {depthwiseConv2dNativeBackpropFilterConfig} from './kernels/DepthwiseConv2dNativeBackpropFilter';\nimport {depthwiseConv2dNativeBackpropInputConfig} from './kernels/DepthwiseConv2dNativeBackpropInput';\nimport {diagConfig} from './kernels/Diag';\nimport {dilation2DConfig} from './kernels/Dilation2D';\nimport {einsumConfig} from './kernels/Einsum';\nimport {eluConfig} from './kernels/Elu';\nimport {eluGradConfig} from './kernels/EluGrad';\nimport {equalConfig} from './kernels/Equal';\nimport {erfConfig} from './kernels/Erf';\nimport {expConfig} from './kernels/Exp';\nimport {expandDimsConfig} from './kernels/ExpandDims';\nimport {expm1Config} from './kernels/Expm1';\nimport {fftConfig} from './kernels/FFT';\nimport {fillConfig} from './kernels/Fill';\nimport {flipLeftRightConfig} from './kernels/FlipLeftRight';\nimport {floorConfig} from './kernels/Floor';\nimport {floorDivConfig} from './kernels/FloorDiv';\nimport {fromPixelsConfig} from './kernels/FromPixels';\nimport {fusedConv2DConfig} from './kernels/FusedConv2D';\nimport {fusedDepthwiseConv2DConfig} from './kernels/FusedDepthwiseConv2D';\nimport {gatherNdConfig} from './kernels/GatherNd';\nimport {gatherV2Config} from './kernels/GatherV2';\nimport {greaterConfig} from './kernels/Greater';\nimport {greaterEqualConfig} from './kernels/GreaterEqual';\nimport {identityConfig} from './kernels/Identity';\nimport {ifftConfig} from './kernels/IFFT';\nimport {imagConfig} from './kernels/Imag';\nimport {isFiniteConfig} from './kernels/IsFinite';\nimport {isInfConfig} from './kernels/IsInf';\nimport {isNaNConfig} from './kernels/IsNaN';\nimport {leakyReluConfig} from './kernels/LeakyRelu';\nimport {lessConfig} from './kernels/Less';\nimport {lessEqualConfig} from './kernels/LessEqual';\nimport {linSpaceConfig} from './kernels/LinSpace';\nimport {logConfig} from './kernels/Log';\nimport {log1pConfig} from './kernels/Log1p';\nimport {logicalAndConfig} from './kernels/LogicalAnd';\nimport {logicalNotConfig} from './kernels/LogicalNot';\nimport {logicalOrConfig} from './kernels/LogicalOr';\nimport {LRNConfig} from './kernels/LRN';\nimport {LRNGradConfig} from './kernels/LRNGrad';\nimport {maxConfig} from './kernels/Max';\nimport {maximumConfig} from './kernels/Maximum';\nimport {maxPoolConfig} from './kernels/MaxPool';\nimport {maxPool3DConfig} from './kernels/MaxPool3D';\nimport {maxPoolGrad3DConfig} from './kernels/MaxPool3DGrad';\nimport {maxPoolGradConfig} from './kernels/MaxPoolGrad';\nimport {maxPoolWithArgmaxConfig} from './kernels/MaxPoolWithArgmax';\nimport {meanConfig} from './kernels/Mean';\nimport {minConfig} from './kernels/Min';\nimport {minimumConfig} from './kernels/Minimum';\nimport {mirrorPadConfig} from './kernels/MirrorPad';\nimport {modConfig} from './kernels/Mod';\nimport {multinomialConfig} from './kernels/Multinomial';\nimport {multiplyConfig} from './kernels/Multiply';\nimport {negConfig} from './kernels/Neg';\nimport {nonMaxSuppressionV3Config} from './kernels/NonMaxSuppressionV3';\nimport {nonMaxSuppressionV4Config} from './kernels/NonMaxSuppressionV4';\nimport {nonMaxSuppressionV5Config} from './kernels/NonMaxSuppressionV5';\nimport {notEqualConfig} from './kernels/NotEqual';\nimport {oneHotConfig} from './kernels/OneHot';\nimport {onesLikeConfig} from './kernels/OnesLike';\nimport {packConfig} from './kernels/Pack';\nimport {padV2Config} from './kernels/PadV2';\nimport {powConfig} from './kernels/Pow';\nimport {preluConfig} from './kernels/Prelu';\nimport {prodConfig} from './kernels/Prod';\nimport {rangeConfig} from './kernels/Range';\nimport {realConfig} from './kernels/Real';\nimport {realDivConfig} from './kernels/RealDiv';\nimport {reciprocalConfig} from './kernels/Reciprocal';\nimport {reluConfig} from './kernels/Relu';\nimport {relu6Config} from './kernels/Relu6';\nimport {reshapeConfig} from './kernels/Reshape';\nimport {resizeBilinearConfig} from './kernels/ResizeBilinear';\nimport {resizeBilinearGradConfig} from './kernels/ResizeBilinearGrad';\nimport {resizeNearestNeighborConfig} from './kernels/ResizeNearestNeighbor';\nimport {resizeNearestNeighborGradConfig} from './kernels/ResizeNearestNeighborGrad';\nimport {reverseConfig} from './kernels/Reverse';\nimport {rotateWithOffsetConfig} from './kernels/RotateWithOffset';\nimport {roundConfig} from './kernels/Round';\nimport {rsqrtConfig} from './kernels/Rsqrt';\nimport {scatterNdConfig} from './kernels/ScatterNd';\nimport {selectConfig} from './kernels/Select';\nimport {seluConfig} from './kernels/Selu';\nimport {sigmoidConfig} from './kernels/Sigmoid';\nimport {signConfig} from './kernels/Sign';\nimport {sinConfig} from './kernels/Sin';\nimport {sinhConfig} from './kernels/Sinh';\nimport {sliceConfig} from './kernels/Slice';\nimport {softmaxConfig} from './kernels/Softmax';\nimport {softplusConfig} from './kernels/Softplus';\nimport {spaceToBatchNDConfig} from './kernels/SpaceToBatchND';\nimport {sparseFillEmptyRowsConfig} from './kernels/SparseFillEmptyRows';\nimport {sparseReshapeConfig} from './kernels/SparseReshape';\nimport {sparseSegmentMeanConfig} from './kernels/SparseSegmentMean';\nimport {sparseSegmentSumConfig} from './kernels/SparseSegmentSum';\nimport {sparseToDenseConfig} from './kernels/SparseToDense';\nimport {splitVConfig} from './kernels/SplitV';\nimport {sqrtConfig} from './kernels/Sqrt';\nimport {squareConfig} from './kernels/Square';\nimport {squaredDifferenceConfig} from './kernels/SquaredDifference';\nimport {stepConfig} from './kernels/Step';\nimport {stridedSliceConfig} from './kernels/StridedSlice';\nimport {stringNGramsConfig} from './kernels/StringNGrams';\nimport {stringSplitConfig} from './kernels/StringSplit';\nimport {stringToHashBucketFastConfig} from './kernels/StringToHashBucketFast';\nimport {subConfig} from './kernels/Sub';\nimport {sumConfig} from './kernels/Sum';\nimport {tanConfig} from './kernels/Tan';\nimport {tanhConfig} from './kernels/Tanh';\nimport {tileConfig} from './kernels/Tile';\nimport {topKConfig} from './kernels/TopK';\nimport {transformConfig} from './kernels/Transform';\nimport {transposeConfig} from './kernels/Transpose';\nimport {uniqueConfig} from './kernels/Unique';\nimport {unpackConfig} from './kernels/Unpack';\nimport {unsortedSegmentSumConfig} from './kernels/UnsortedSegmentSum';\nimport {zerosLikeConfig} from './kernels/ZerosLike';\n\n// List all kernel configs here\nconst kernelConfigs: KernelConfig[] = [\n  LRNConfig,\n  LRNGradConfig,\n  _fusedMatMulConfig,\n  absConfig,\n  acosConfig,\n  acoshConfig,\n  addConfig,\n  addNConfig,\n  allConfig,\n  anyConfig,\n  argMaxConfig,\n  argMinConfig,\n  asinConfig,\n  asinhConfig,\n  atan2Config,\n  atanConfig,\n  atanhConfig,\n  avgPool3DConfig,\n  avgPoolConfig,\n  avgPoolGrad3DConfig,\n  avgPoolGradConfig,\n  batchMatMulConfig,\n  batchNormConfig,\n  batchToSpaceNDConfig,\n  bincountConfig,\n  castConfig,\n  ceilConfig,\n  clipByValueConfig,\n  complexAbsConfig,\n  complexConfig,\n  concatConfig,\n  conv2DBackpropFilterConfig,\n  conv2DBackpropInputConfig,\n  conv2DConfig,\n  conv3DBackpropFilterV2Config,\n  conv3DBackpropInputConfig,\n  conv3DConfig,\n  cosConfig,\n  coshConfig,\n  cropAndResizeConfig,\n  cumsumConfig,\n  denseBincountConfig,\n  depthToSpaceConfig,\n  depthwiseConv2dNativeBackpropFilterConfig,\n  depthwiseConv2dNativeBackpropInputConfig,\n  depthwiseConv2dNativeConfig,\n  diagConfig,\n  dilation2DConfig,\n  einsumConfig,\n  eluConfig,\n  eluGradConfig,\n  equalConfig,\n  erfConfig,\n  expConfig,\n  expandDimsConfig,\n  expm1Config,\n  fftConfig,\n  fillConfig,\n  flipLeftRightConfig,\n  floorConfig,\n  floorDivConfig,\n  fromPixelsConfig,\n  fusedConv2DConfig,\n  fusedDepthwiseConv2DConfig,\n  gatherNdConfig,\n  gatherV2Config,\n  greaterConfig,\n  greaterEqualConfig,\n  identityConfig,\n  ifftConfig,\n  imagConfig,\n  isFiniteConfig,\n  isInfConfig,\n  isNaNConfig,\n  leakyReluConfig,\n  lessConfig,\n  lessEqualConfig,\n  linSpaceConfig,\n  log1pConfig,\n  logConfig,\n  logicalAndConfig,\n  logicalNotConfig,\n  logicalOrConfig,\n  maxConfig,\n  maxPool3DConfig,\n  maxPoolConfig,\n  maxPoolGrad3DConfig,\n  maxPoolGradConfig,\n  maxPoolWithArgmaxConfig,\n  maximumConfig,\n  meanConfig,\n  minConfig,\n  minimumConfig,\n  mirrorPadConfig,\n  modConfig,\n  multinomialConfig,\n  multiplyConfig,\n  negConfig,\n  nonMaxSuppressionV3Config,\n  nonMaxSuppressionV4Config,\n  nonMaxSuppressionV5Config,\n  notEqualConfig,\n  oneHotConfig,\n  onesLikeConfig,\n  packConfig,\n  padV2Config,\n  powConfig,\n  preluConfig,\n  prodConfig,\n  rangeConfig,\n  realConfig,\n  realDivConfig,\n  reciprocalConfig,\n  relu6Config,\n  reluConfig,\n  reshapeConfig,\n  resizeBilinearConfig,\n  resizeBilinearGradConfig,\n  resizeNearestNeighborConfig,\n  resizeNearestNeighborGradConfig,\n  reverseConfig,\n  rotateWithOffsetConfig,\n  roundConfig,\n  rsqrtConfig,\n  scatterNdConfig,\n  selectConfig,\n  seluConfig,\n  sigmoidConfig,\n  signConfig,\n  sinConfig,\n  sinhConfig,\n  sliceConfig,\n  softmaxConfig,\n  softplusConfig,\n  spaceToBatchNDConfig,\n  sparseFillEmptyRowsConfig,\n  sparseReshapeConfig,\n  sparseSegmentMeanConfig,\n  sparseSegmentSumConfig,\n  sparseToDenseConfig,\n  splitVConfig,\n  sqrtConfig,\n  squareConfig,\n  squaredDifferenceConfig,\n  stepConfig,\n  stridedSliceConfig,\n  stringNGramsConfig,\n  stringSplitConfig,\n  stringToHashBucketFastConfig,\n  subConfig,\n  sumConfig,\n  tanConfig,\n  tanhConfig,\n  tileConfig,\n  topKConfig,\n  transformConfig,\n  transposeConfig,\n  uniqueConfig,\n  unpackConfig,\n  unsortedSegmentSumConfig,\n  zerosLikeConfig\n];\n\nfor (const kernelConfig of kernelConfigs) {\n  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\ No newline at end of file
diff --git a/thirdparty/tfjs/tf-converter.js b/thirdparty/tfjs/tf-converter.js
new file mode 100644
index 0000000..795805a
--- /dev/null
+++ b/thirdparty/tfjs/tf-converter.js
@@ -0,0 +1,8058 @@
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('@tensorflow/tfjs-core'), require('@tensorflow/tfjs-core/dist/ops/ops_for_converter')) :
+    typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs-core', '@tensorflow/tfjs-core/dist/ops/ops_for_converter'], factory) :
+    (global = global || self, factory(global.tf = global.tf || {}, global.tf, global.tf));
+}(this, (function (exports, tfc, tfOps) { 'use strict';
+
+    /*! *****************************************************************************
+    Copyright (c) Microsoft Corporation.
+
+    Permission to use, copy, modify, and/or distribute this software for any
+    purpose with or without fee is hereby granted.
+
+    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+    REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
+    AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+    INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
+    LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
+    OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
+    PERFORMANCE OF THIS SOFTWARE.
+    ***************************************************************************** */
+
+    var __assign = function() {
+        __assign = Object.assign || function __assign(t) {
+            for (var s, i = 1, n = arguments.length; i < n; i++) {
+                s = arguments[i];
+                for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
+            }
+            return t;
+        };
+        return __assign.apply(this, arguments);
+    };
+
+    function __awaiter(thisArg, _arguments, P, generator) {
+        function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
+        return new (P || (P = Promise))(function (resolve, reject) {
+            function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+            function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+            function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
+            step((generator = generator.apply(thisArg, _arguments || [])).next());
+        });
+    }
+
+    function __generator(thisArg, body) {
+        var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+        return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+        function verb(n) { return function (v) { return step([n, v]); }; }
+        function step(op) {
+            if (f) throw new TypeError("Generator is already executing.");
+            while (_) try {
+                if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+                if (y = 0, t) op = [op[0] & 2, t.value];
+                switch (op[0]) {
+                    case 0: case 1: t = op; break;
+                    case 4: _.label++; return { value: op[1], done: false };
+                    case 5: _.label++; y = op[1]; op = [0]; continue;
+                    case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                    default:
+                        if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                        if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                        if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                        if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                        if (t[2]) _.ops.pop();
+                        _.trys.pop(); continue;
+                }
+                op = body.call(thisArg, _);
+            } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+            if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+        }
+    }
+
+    function __read(o, n) {
+        var m = typeof Symbol === "function" && o[Symbol.iterator];
+        if (!m) return o;
+        var i = m.call(o), r, ar = [], e;
+        try {
+            while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);
+        }
+        catch (error) { e = { error: error }; }
+        finally {
+            try {
+                if (r && !r.done && (m = i["return"])) m.call(i);
+            }
+            finally { if (e) throw e.error; }
+        }
+        return ar;
+    }
+
+    function __spread() {
+        for (var ar = [], i = 0; i < arguments.length; i++)
+            ar = ar.concat(__read(arguments[i]));
+        return ar;
+    }
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     *
+     * =============================================================================
+     */
+    /** DataType enum. */
+    var DataType;
+    (function (DataType) {
+        DataType[DataType["DT_INVALID"] = 0] = "DT_INVALID";
+        DataType[DataType["DT_FLOAT"] = 1] = "DT_FLOAT";
+        DataType[DataType["DT_DOUBLE"] = 2] = "DT_DOUBLE";
+        DataType[DataType["DT_INT32"] = 3] = "DT_INT32";
+        DataType[DataType["DT_UINT8"] = 4] = "DT_UINT8";
+        DataType[DataType["DT_INT16"] = 5] = "DT_INT16";
+        DataType[DataType["DT_INT8"] = 6] = "DT_INT8";
+        DataType[DataType["DT_STRING"] = 7] = "DT_STRING";
+        DataType[DataType["DT_COMPLEX64"] = 8] = "DT_COMPLEX64";
+        DataType[DataType["DT_INT64"] = 9] = "DT_INT64";
+        DataType[DataType["DT_BOOL"] = 10] = "DT_BOOL";
+        DataType[DataType["DT_QINT8"] = 11] = "DT_QINT8";
+        DataType[DataType["DT_QUINT8"] = 12] = "DT_QUINT8";
+        DataType[DataType["DT_QINT32"] = 13] = "DT_QINT32";
+        DataType[DataType["DT_BFLOAT16"] = 14] = "DT_BFLOAT16";
+        DataType[DataType["DT_FLOAT_REF"] = 101] = "DT_FLOAT_REF";
+        DataType[DataType["DT_DOUBLE_REF"] = 102] = "DT_DOUBLE_REF";
+        DataType[DataType["DT_INT32_REF"] = 103] = "DT_INT32_REF";
+        DataType[DataType["DT_UINT8_REF"] = 104] = "DT_UINT8_REF";
+        DataType[DataType["DT_INT16_REF"] = 105] = "DT_INT16_REF";
+        DataType[DataType["DT_INT8_REF"] = 106] = "DT_INT8_REF";
+        DataType[DataType["DT_STRING_REF"] = 107] = "DT_STRING_REF";
+        DataType[DataType["DT_COMPLEX64_REF"] = 108] = "DT_COMPLEX64_REF";
+        DataType[DataType["DT_INT64_REF"] = 109] = "DT_INT64_REF";
+        DataType[DataType["DT_BOOL_REF"] = 110] = "DT_BOOL_REF";
+        DataType[DataType["DT_QINT8_REF"] = 111] = "DT_QINT8_REF";
+        DataType[DataType["DT_QUINT8_REF"] = 112] = "DT_QUINT8_REF";
+        DataType[DataType["DT_QINT32_REF"] = 113] = "DT_QINT32_REF";
+        DataType[DataType["DT_BFLOAT16_REF"] = 114] = "DT_BFLOAT16_REF";
+    })(DataType || (DataType = {}));
+    var SaverDef;
+    (function (SaverDef) {
+        /** CheckpointFormatVersion enum. */
+        var CheckpointFormatVersion;
+        (function (CheckpointFormatVersion) {
+            CheckpointFormatVersion[CheckpointFormatVersion["LEGACY"] = 0] = "LEGACY";
+            CheckpointFormatVersion[CheckpointFormatVersion["V1"] = 1] = "V1";
+            CheckpointFormatVersion[CheckpointFormatVersion["V2"] = 2] = "V2";
+        })(CheckpointFormatVersion = SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {}));
+    })(SaverDef || (SaverDef = {}));
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var CUSTOM_OPS = {};
+    /**
+     * Register an Op for graph model executor. This allow you to register
+     * TensorFlow custom op or override existing op.
+     *
+     * Here is an example of registering a new MatMul Op.
+     * ```js
+     * const customMatmul = (node) =>
+     *    tf.matMul(
+     *        node.inputs[0], node.inputs[1],
+     *        node.attrs['transpose_a'], node.attrs['transpose_b']);
+     *
+     * tf.registerOp('MatMul', customMatmul);
+     * ```
+     * The inputs and attrs of the node object is based on the TensorFlow op
+     * registry.
+     *
+     * @param name The Tensorflow Op name.
+     * @param opFunc An op function which is called with the current graph node
+     * during execution and needs to return a tensor or a list of tensors. The node
+     * has the following attributes:
+     *    - attr: A map from attribute name to its value
+     *    - inputs: A list of input tensors
+     *
+     * @doc {heading: 'Models', subheading: 'Op Registry'}
+     */
+    function registerOp(name, opFunc) {
+        var opMapper = {
+            tfOpName: name,
+            category: 'custom',
+            inputs: [],
+            attrs: [],
+            customExecutor: opFunc
+        };
+        CUSTOM_OPS[name] = opMapper;
+    }
+    /**
+     * Retrieve the OpMapper object for the registered op.
+     *
+     * @param name The Tensorflow Op name.
+     *
+     * @doc {heading: 'Models', subheading: 'Op Registry'}
+     */
+    function getRegisteredOp(name) {
+        return CUSTOM_OPS[name];
+    }
+    /**
+     * Deregister the Op for graph model executor.
+     *
+     * @param name The Tensorflow Op name.
+     *
+     * @doc {heading: 'Models', subheading: 'Op Registry'}
+     */
+    function deregisterOp(name) {
+        delete CUSTOM_OPS[name];
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function getParamValue(paramName, node, tensorMap, context, resourceManager) {
+        var inputParam = node.inputParams[paramName];
+        if (inputParam && inputParam.inputIndexStart !== undefined) {
+            var start = inputParam.inputIndexStart;
+            var end = inputParam.inputIndexEnd === 0 ?
+                undefined :
+                (inputParam.inputIndexEnd === undefined ? start + 1 :
+                    inputParam.inputIndexEnd);
+            if (inputParam.type === 'tensor') {
+                return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context, resourceManager);
+            }
+            if (inputParam.type === 'tensors') {
+                var inputs = node.inputNames.slice(start, end);
+                return inputs.map(function (name) { return getTensor(name, tensorMap, context, resourceManager); });
+            }
+            var tensor = getTensor(node.inputNames.slice(start)[0], tensorMap, context, resourceManager);
+            var data = tensor.dataSync();
+            return inputParam.type === 'number' ?
+                data[0] :
+                tfc.util.toNestedArray(tensor.shape, data);
+        }
+        var attrParam = node.attrParams[paramName];
+        return attrParam && attrParam.value;
+    }
+    /**
+     * Retrieve the tensor from tensorsMap based on input name.
+     * @param name Node input name
+     * @param tensorsMap Tensors map keyed by the node
+     * @param context contains tensors and information for running the current node.
+     * @param resourceManager Optional. Contains global resources of the model.
+     */
+    function getTensor(name, tensorsMap, context, resourceManager) {
+        var _a = __read(parseNodeName(name), 2), nodeName = _a[0], index = _a[1];
+        if (resourceManager != null) {
+            var tensor = resourceManager.getHashTableHandleByName(nodeName);
+            if (tensor != null) {
+                return tensor;
+            }
+        }
+        var contextId = context.currentContextIds.find(function (contextId) {
+            return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];
+        });
+        return contextId !== undefined ?
+            tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] :
+            undefined;
+    }
+    /**
+     * Retrieve the tensors based on input name for current context.
+     * @param name Node input name
+     * @param tensorsMap Tensors map keyed by the node
+     */
+    function getTensorsForCurrentContenxt(name, tensorsMap, context) {
+        return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];
+    }
+    /**
+     * Returns the node name, outputName and index from the Node input name.
+     * @param inputName The input name of the node, in format of
+     * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is
+     * default to 0.
+     * If the input name contains output name i.e. StringSplit:indices:0, it will
+     * return ['StringSplit', 0, 'indices'].
+     */
+    function getNodeNameAndIndex(inputName, context) {
+        var _a = __read(parseNodeName(inputName), 3), nodeName = _a[0], index = _a[1], outputName = _a[2];
+        return [
+            getNodeNameWithContextId(nodeName, context && context.currentContextId),
+            index, outputName
+        ];
+    }
+    function getNodeNameWithContextId(name, contextId) {
+        return !!contextId ? name + "-" + contextId : name;
+    }
+    function parseNodeName(name) {
+        var parts = name.split(':');
+        if (parts.length === 1) {
+            return [name, 0, undefined];
+        }
+        var nodeName = parts[0];
+        var outputName = parts.length === 3 ? parts[1] : undefined;
+        var index = Number(parts[parts.length - 1]);
+        return [nodeName, index, outputName];
+    }
+    function getPadding(node, tensorMap, context) {
+        var pad = getParamValue('pad', node, tensorMap, context);
+        if (pad === 'explicit') {
+            // This is 1d array, we need to convert it to 2d array
+            pad = getParamValue('explicitPaddings', node, tensorMap, context);
+            var explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];
+            for (var i = 0; i < 4; i++) {
+                explicitPadding[i][0] = pad[i * 2];
+                explicitPadding[i][1] = pad[i * 2 + 1];
+            }
+            return explicitPadding;
+        }
+        return pad;
+    }
+    /**
+     *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to
+     *  avoid disposal. This is important for TensorArray and TensorList ops, since
+     *  internally they use a tensor as the id for TensorArray and TensorList, and
+     * to simplify lookup, they also use Tensor.id as the key to the internal map.
+     * These id tensors have been marked as kept in the backend, we need avoid clone
+     * them in order to create new Tensor.id.
+     * @param tensor
+     */
+    function cloneTensor(tensor) {
+        return tensor.kept ? tensor : tfc.clone(tensor);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json = [
+        {
+            'tfOpName': 'Add',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'AddV2',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'AddN',
+            'category': 'arithmetic',
+            'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]
+        },
+        {
+            'tfOpName': 'BiasAdd',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'Sub',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'RealDiv',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Div',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'DivNoNan',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'FloorDiv',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Mul',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Maximum',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' }
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Minimum',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' }
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Pow',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'SquaredDifference',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Mod',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'FloorMod',
+            'category': 'arithmetic',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [{
+                    'tfName': 'T',
+                    'name': 'dtype',
+                    'type': 'dtype',
+                    'notSupported': true
+                }]
+        }
+    ];
+
+    var arithmetic = {
+        __proto__: null,
+        json: json
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$1 = [
+        {
+            'tfOpName': 'Abs',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Acos',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Asin',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Atan',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Atan2',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'y', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Ceil',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'ClipByValue',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'clipValueMin', 'type': 'number' },
+                { 'start': 2, 'name': 'clipValueMax', 'type': 'number' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Complex',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'real', 'type': 'tensor' },
+                { 'start': 1, 'name': 'imag', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'ComplexAbs',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Cos',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Cosh',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Elu',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Exp',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Floor',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Log',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Imag',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {
+                    'tfName': 'Tout',
+                    'name': 'outputType',
+                    'type': 'dtype',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'Neg',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Real',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {
+                    'tfName': 'Tout',
+                    'name': 'outputType',
+                    'type': 'dtype',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'Prelu',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'alpha', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Relu',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Relu6',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Selu',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Sigmoid',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Sin',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Sinh',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Sqrt',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Rsqrt',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Square',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Tan',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Tanh',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Sign',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Round',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Expm1',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Log1p',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Reciprocal',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Softplus',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Asinh',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Acosh',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Atanh',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Erf',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Prod',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axes', 'type': 'number[]' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'keep_dims',
+                    'name': 'keepDims',
+                    'type': 'bool',
+                    'notSupported': true
+                },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'LeakyRelu',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'alpha',
+                    'name': 'alpha',
+                    'type': 'number',
+                    'defaultValue': 0.2
+                },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'IsNan',
+            'category': 'basic_math',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [{
+                    'tfName': 'T',
+                    'name': 'dtype',
+                    'type': 'dtype',
+                    'notSupported': true
+                }]
+        }
+    ];
+
+    var basicMath = {
+        __proto__: null,
+        json: json$1
+    };
+
+    var json$2 = [
+        {
+            'tfOpName': 'EmptyTensorList',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'elementShape', 'type': 'shape' },
+                { 'start': 1, 'name': 'maxNumElements', 'type': 'number' },
+            ],
+            'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'LoopCond',
+            'category': 'control',
+            'inputs': [{ 'start': 0, 'name': 'pred', 'type': 'tensor' }]
+        },
+        {
+            'tfOpName': 'Switch',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'data', 'type': 'tensor' },
+                { 'start': 1, 'name': 'pred', 'type': 'tensor' }
+            ]
+        },
+        {
+            'tfOpName': 'Merge',
+            'category': 'control',
+            'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]
+        },
+        {
+            'tfOpName': 'Enter',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensor', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },
+                { 'tfName': 'frame_name', 'name': 'frameName', 'type': 'string' },
+                { 'tfName': 'is_constant', 'name': 'isConstant', 'type': 'bool' }
+            ]
+        },
+        {
+            'tfOpName': 'Exit',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensor', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'NextIteration',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensor', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'TensorArrayV3',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'size', 'type': 'number' },
+            ],
+            'attrs': [
+                { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },
+                { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },
+                { 'tfName': 'dynamic_size', 'name': 'dynamicSize', 'type': 'bool' },
+                { 'tfName': 'clear_after_read', 'name': 'clearAfterRead', 'type': 'bool' },
+                {
+                    'tfName': 'identical_element_shapes',
+                    'name': 'identicalElementShapes',
+                    'type': 'bool'
+                },
+                { 'tfName': 'tensor_array_name', 'name': 'name', 'type': 'string' }
+            ]
+        },
+        {
+            'tfOpName': 'TensorArrayWriteV3',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'index', 'type': 'number' },
+                { 'start': 2, 'name': 'tensor', 'type': 'tensor' },
+                { 'start': 3, 'name': 'flowIn', 'type': 'number' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'TensorArrayReadV3',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'index', 'type': 'number' },
+                { 'start': 2, 'name': 'flowIn', 'type': 'number' },
+            ],
+            'attrs': [{
+                    'tfName': 'dtype',
+                    'name': 'dtype',
+                    'type': 'dtype',
+                    'notSupported': true
+                }]
+        },
+        {
+            'tfOpName': 'TensorArrayGatherV3',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'indices', 'type': 'number[]' },
+                { 'start': 2, 'name': 'flowIn', 'type': 'number' },
+            ],
+            'attrs': [
+                { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },
+                { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }
+            ]
+        },
+        {
+            'tfOpName': 'TensorArrayScatterV3',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'indices', 'type': 'number[]' },
+                { 'start': 2, 'name': 'tensor', 'type': 'tensor' },
+                { 'start': 3, 'name': 'flowIn', 'type': 'number' },
+            ],
+            'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'TensorArrayConcatV3',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'flowIn', 'type': 'number' },
+            ],
+            'attrs': [
+                { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, {
+                    'tfName': 'element_shape_except0',
+                    'name': 'elementShapeExcept0',
+                    'type': 'shape',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'TensorArraySplitV3',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'tensor', 'type': 'tensor' },
+                { 'start': 2, 'name': 'lengths', 'type': 'number[]' },
+                { 'start': 3, 'name': 'flowIn', 'type': 'number' },
+            ],
+            'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'TensorArraySizeV3',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'flowIn', 'type': 'number' }
+            ]
+        },
+        {
+            'tfOpName': 'TensorArrayCloseV3',
+            'category': 'control',
+            'inputs': [{ 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }]
+        },
+        {
+            'tfOpName': 'StatelessIf',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'cond', 'type': 'tensor' },
+                { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }
+            ],
+            'attrs': [
+                { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },
+                { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }
+            ]
+        },
+        {
+            'tfOpName': 'If',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'cond', 'type': 'tensor' },
+                { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }
+            ],
+            'attrs': [
+                { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },
+                { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }
+            ]
+        },
+        {
+            'tfOpName': 'StatelessWhile',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },
+            ],
+            'attrs': [
+                { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },
+                { 'tfName': 'body', 'name': 'body', 'type': 'func' }
+            ]
+        },
+        {
+            'tfOpName': 'While',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },
+            ],
+            'attrs': [
+                { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },
+                { 'tfName': 'body', 'name': 'body', 'type': 'func' }
+            ]
+        },
+        {
+            'tfOpName': 'TensorListScatter',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensor', 'type': 'tensor' },
+                { 'start': 1, 'name': 'indices', 'type': 'number[]' },
+                { 'start': 2, 'name': 'elementShape', 'type': 'shape' }
+            ],
+            'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'TensorListScatterV2',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensor', 'type': 'tensor' },
+                { 'start': 1, 'name': 'indices', 'type': 'number[]' },
+                { 'start': 2, 'name': 'elementShape', 'type': 'shape' },
+                { 'start': 3, 'name': 'numElements', 'type': 'number' },
+            ],
+            'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'TensorListGather',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'indices', 'type': 'number[]' },
+                { 'start': 2, 'name': 'elementShape', 'type': 'shape' },
+            ],
+            'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'TensorListGetItem',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'index', 'type': 'number' },
+                { 'start': 2, 'name': 'elementShape', 'type': 'shape' },
+            ],
+            'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'TensorListSetItem',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'index', 'type': 'number' },
+                { 'start': 2, 'name': 'tensor', 'type': 'tensor' },
+            ],
+            'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'TensorListReserve',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'elementShape', 'type': 'shape' },
+                { 'start': 1, 'name': 'numElements', 'type': 'number' },
+            ],
+            'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'TensorListFromTensor',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensor', 'type': 'tensor' },
+                { 'start': 1, 'name': 'elementShape', 'type': 'shape' }
+            ],
+            'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'TensorListStack',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'elementShape', 'type': 'shape' },
+            ],
+            'attrs': [
+                { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' },
+                { 'tfName': 'num_elements', 'name': 'numElements', 'type': 'dtype' }
+            ]
+        },
+        {
+            'tfOpName': 'TensorListSplit',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensor', 'type': 'tensor' },
+                { 'start': 1, 'name': 'elementShape', 'type': 'shape' },
+                { 'start': 2, 'name': 'lengths', 'type': 'number[]' },
+            ],
+            'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'TensorListConcat',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },
+                { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }
+            ]
+        },
+        {
+            'tfOpName': 'TensorListPopBack',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'elementShape', 'type': 'shape' },
+            ],
+            'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'TensorListPushBack',
+            'category': 'control',
+            'inputs': [
+                { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },
+                { 'start': 1, 'name': 'tensor', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }
+            ]
+        }
+    ];
+
+    var control = {
+        __proto__: null,
+        json: json$2
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$3 = [
+        {
+            'tfOpName': 'AvgPool',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'notSupported': true
+                },
+                { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'MaxPool',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'notSupported': true
+                },
+                { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, {
+                    'tfName': 'explicit_paddings',
+                    'name': 'explicitPaddings',
+                    'type': 'number[]',
+                    'defaultValue': [],
+                    'notSupported': true
+                },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'MaxPoolWithArgmax',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },
+                { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, {
+                    'tfName': 'include_batch_in_index',
+                    'name': 'includeBatchInIndex',
+                    'type': 'bool'
+                },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'AvgPool3D',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'notSupported': true
+                },
+                { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'MaxPool3D',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'notSupported': true
+                },
+                { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Conv1D',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'filter', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'stride', 'name': 'stride', 'type': 'number' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'defaultValue': 'NWC'
+                },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {
+                    'tfName': 'dilation',
+                    'name': 'dilation',
+                    'type': 'number',
+                    'defaultValue': 1
+                }
+            ]
+        },
+        {
+            'tfOpName': 'Conv2D',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'filter', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },
+                { 'tfName': 'useCudnnOnGpu', 'name': 'useCudnnOnGpu', 'type': 'bool' }, {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'defaultValue': 'NHWC'
+                },
+                {
+                    'tfName': 'explicit_paddings',
+                    'name': 'explicitPaddings',
+                    'type': 'number[]',
+                    'defaultValue': []
+                },
+                { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }
+            ]
+        },
+        {
+            'tfOpName': '_FusedConv2D',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'filter', 'type': 'tensor' },
+                { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },
+            ],
+            'attrs': [
+                { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {
+                    'tfName': 'explicit_paddings',
+                    'name': 'explicitPaddings',
+                    'type': 'number[]',
+                    'defaultValue': []
+                },
+                {
+                    'tfName': 'use_cudnn_on_gpu',
+                    'name': 'useCudnnOnGpu',
+                    'type': 'bool',
+                    'defaultValue': true
+                },
+                {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'defaultValue': 'NHWC'
+                },
+                {
+                    'tfName': 'dilations',
+                    'name': 'dilations',
+                    'type': 'number[]',
+                    'defaultValue': [1, 1, 1, 1]
+                },
+                {
+                    'tfName': 'fused_ops',
+                    'name': 'fusedOps',
+                    'type': 'string[]',
+                    'defaultValue': []
+                },
+                {
+                    'tfName': 'epsilon',
+                    'name': 'epsilon',
+                    'type': 'number',
+                    'defaultValue': 0.0001
+                },
+                {
+                    'tfName': 'leakyrelu_alpha',
+                    'name': 'leakyreluAlpha',
+                    'type': 'number'
+                }
+            ]
+        },
+        {
+            'tfOpName': 'Conv2DBackpropInput',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 2, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'filter', 'type': 'tensor' },
+                { 'start': 0, 'name': 'outputShape', 'type': 'number[]' },
+            ],
+            'attrs': [
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'notSupported': true
+                },
+                {
+                    'tfName': 'explicit_paddings',
+                    'name': 'explicitPaddings',
+                    'type': 'number[]',
+                    'defaultValue': []
+                },
+                {
+                    'tfName': 'dilations',
+                    'name': 'dilations',
+                    'type': 'number[]',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'DepthwiseConv2d',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'input', 'type': 'tensor' },
+                { 'start': 1, 'name': 'filter', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'defaultValue': 'NHWC'
+                },
+                {
+                    'tfName': 'explicit_paddings',
+                    'name': 'explicitPaddings',
+                    'type': 'number[]',
+                    'defaultValue': []
+                },
+                { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }
+            ]
+        },
+        {
+            'tfOpName': 'DepthwiseConv2dNative',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'input', 'type': 'tensor' },
+                { 'start': 1, 'name': 'filter', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'defaultValue': 'NHWC'
+                },
+                {
+                    'tfName': 'explicit_paddings',
+                    'name': 'explicitPaddings',
+                    'type': 'number[]',
+                    'defaultValue': []
+                },
+                { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }
+            ]
+        },
+        {
+            'tfOpName': 'FusedDepthwiseConv2dNative',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'filter', 'type': 'tensor' },
+                { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },
+            ],
+            'attrs': [
+                { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'defaultValue': 'NHWC'
+                },
+                {
+                    'tfName': 'dilations',
+                    'name': 'dilations',
+                    'type': 'number[]',
+                    'defaultValue': [1, 1, 1, 1]
+                },
+                {
+                    'tfName': 'fused_ops',
+                    'name': 'fusedOps',
+                    'type': 'string[]',
+                    'defaultValue': []
+                },
+                {
+                    'tfName': 'explicit_paddings',
+                    'name': 'explicitPaddings',
+                    'type': 'number[]',
+                    'defaultValue': []
+                }
+            ]
+        },
+        {
+            'tfOpName': 'Conv3D',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'filter', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'defaultValue': 'NHWC'
+                },
+                { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }
+            ],
+        },
+        {
+            'tfOpName': 'Dilation2D',
+            'category': 'convolution',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'filter', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },
+                { 'tfName': 'rates', 'name': 'dilations', 'type': 'number[]' },
+                { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }
+            ]
+        }
+    ];
+
+    var convolution = {
+        __proto__: null,
+        json: json$3
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$4 = [
+        {
+            'tfOpName': 'Fill',
+            'category': 'creation',
+            'inputs': [
+                { 'start': 0, 'name': 'shape', 'type': 'number[]' },
+                { 'start': 1, 'name': 'value', 'type': 'number' },
+            ],
+            'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'LinSpace',
+            'category': 'creation',
+            'inputs': [
+                { 'start': 0, 'name': 'start', 'type': 'number' },
+                { 'start': 1, 'name': 'stop', 'type': 'number' },
+                { 'start': 2, 'name': 'num', 'type': 'number' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'OneHot',
+            'category': 'creation',
+            'inputs': [
+                { 'start': 0, 'name': 'indices', 'type': 'tensor' },
+                { 'start': 1, 'name': 'depth', 'type': 'number' },
+                { 'start': 2, 'name': 'onValue', 'type': 'number', 'defaultValue': 1 },
+                { 'start': 3, 'name': 'offValue', 'type': 'number', 'defaultValue': 0 },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'axis',
+                    'name': 'axis',
+                    'type': 'number',
+                    'notSupported': true
+                },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Ones',
+            'category': 'creation',
+            'inputs': [
+                { 'start': 0, 'name': 'shape', 'type': 'number[]' },
+            ],
+            'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'OnesLike',
+            'category': 'creation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [{ 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'RandomUniform',
+            'category': 'creation',
+            'inputs': [
+                { 'start': 0, 'name': 'shape', 'type': 'number[]' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'minval',
+                    'name': 'minval',
+                    'type': 'number',
+                    'defaultValue': 0
+                },
+                {
+                    'tfName': 'maxval',
+                    'name': 'maxval',
+                    'type': 'number',
+                    'defaultValue': 1
+                },
+                { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },
+                { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, {
+                    'tfName': 'seed2',
+                    'name': 'seed2',
+                    'type': 'number',
+                    'defaultValue': 0,
+                    'notSupported': true
+                },
+                { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Range',
+            'category': 'creation',
+            'inputs': [
+                { 'start': 0, 'name': 'start', 'type': 'number' },
+                { 'start': 1, 'name': 'stop', 'type': 'number' },
+                { 'start': 2, 'name': 'step', 'type': 'number', 'defaultValue': 0 },
+            ],
+            'attrs': [{ 'tfName': 'Tidx', 'name': 'dtype', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'TruncatedNormal',
+            'category': 'creation',
+            'inputs': [
+                { 'start': 0, 'name': 'shape', 'type': 'number[]' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'means',
+                    'name': 'mean',
+                    'type': 'number',
+                    'defaultValue': 0.0
+                },
+                {
+                    'tfName': 'stddev',
+                    'name': 'stdDev',
+                    'type': 'number',
+                    'defaultValue': 1.0
+                },
+                { 'tfName': 'seed', 'name': 'seed', 'type': 'number' }, {
+                    'tfName': 'seed2',
+                    'name': 'seed2',
+                    'type': 'number',
+                    'defaultValue': 0,
+                    'notSupported': true
+                },
+                { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },
+                { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Zeros',
+            'category': 'creation',
+            'inputs': [
+                { 'start': 0, 'name': 'shape', 'type': 'number[]' },
+            ],
+            'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'ZerosLike',
+            'category': 'creation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]
+        },
+        {
+            'tfOpName': 'Multinomial',
+            'category': 'creation',
+            'inputs': [
+                { 'start': 0, 'name': 'logits', 'type': 'tensor' },
+                { 'start': 1, 'name': 'numSamples', 'type': 'number' },
+            ],
+            'attrs': [
+                { 'tfName': 'seed', 'name': 'seed', 'type': 'number' },
+                { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number' },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' },
+                { 'tfName': 'output_dtype', 'name': 'output_dtype', 'type': 'dtype' }
+            ]
+        }
+    ];
+
+    var creation = {
+        __proto__: null,
+        json: json$4
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$5 = [
+        {
+            'tfOpName': 'NonMaxSuppressionV2',
+            'category': 'dynamic',
+            'inputs': [
+                { 'start': 0, 'name': 'boxes', 'type': 'tensor' },
+                { 'start': 1, 'name': 'scores', 'type': 'tensor' },
+                { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },
+                { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }
+            ]
+        },
+        {
+            'tfOpName': 'NonMaxSuppressionV3',
+            'category': 'dynamic',
+            'inputs': [
+                { 'start': 0, 'name': 'boxes', 'type': 'tensor' },
+                { 'start': 1, 'name': 'scores', 'type': 'tensor' },
+                { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },
+                { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },
+                { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }
+            ]
+        },
+        {
+            'tfOpName': 'NonMaxSuppressionV4',
+            'category': 'dynamic',
+            'inputs': [
+                { 'start': 0, 'name': 'boxes', 'type': 'tensor' },
+                { 'start': 1, 'name': 'scores', 'type': 'tensor' },
+                { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },
+                { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },
+                { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {
+                    'tfName': 'T_threshold',
+                    'name': 'threshold',
+                    'type': 'dtype',
+                    'notSupported': true
+                },
+                {
+                    'tfName': 'pad_to_max_output_size',
+                    'name': 'padToMaxOutputSize',
+                    'type': 'bool'
+                }
+            ]
+        },
+        {
+            'tfOpName': 'NonMaxSuppressionV5',
+            'category': 'dynamic',
+            'inputs': [
+                { 'start': 0, 'name': 'boxes', 'type': 'tensor' },
+                { 'start': 1, 'name': 'scores', 'type': 'tensor' },
+                { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },
+                { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },
+                { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' },
+                { 'start': 5, 'name': 'softNmsSigma', 'type': 'number' }
+            ]
+        },
+        {
+            'tfOpName': 'Where',
+            'category': 'dynamic',
+            'inputs': [
+                { 'start': 0, 'name': 'condition', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'ListDiff',
+            'category': 'dynamic',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'y', 'type': 'tensor' },
+            ],
+            'attrs': [{
+                    'tfName': 'T',
+                    'name': 'dtype',
+                    'type': 'dtype',
+                    'notSupported': true
+                }]
+        }
+    ];
+
+    var dynamic = {
+        __proto__: null,
+        json: json$5
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$6 = [
+        {
+            'tfOpName': 'TopKV2',
+            'category': 'evaluation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'k', 'type': 'number' },
+            ],
+            'attrs': [{ 'tfName': 'sorted', 'name': 'sorted', 'type': 'bool' }]
+        },
+        {
+            'tfOpName': 'Unique',
+            'category': 'evaluation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+        },
+        {
+            'tfOpName': 'UniqueV2',
+            'category': 'evaluation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number' },
+            ],
+        },
+    ];
+
+    var evaluation = {
+        __proto__: null,
+        json: json$6
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$7 = [
+        {
+            'tfOpName': 'PlaceholderWithDefault',
+            'category': 'graph',
+            'inputs': [
+                { 'start': 0, 'name': 'default', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },
+                { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }
+            ]
+        },
+        {
+            'tfOpName': 'Placeholder',
+            'category': 'graph',
+            'attrs': [
+                { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },
+                { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }
+            ]
+        },
+        { 'tfOpName': 'Const', 'category': 'graph' }, {
+            'tfOpName': 'Identity',
+            'category': 'graph',
+            'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]
+        },
+        {
+            'tfOpName': 'IdentityN',
+            'category': 'graph',
+            'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]
+        },
+        {
+            'tfOpName': 'Snapshot',
+            'category': 'graph',
+            'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]
+        },
+        {
+            'tfOpName': 'Rank',
+            'category': 'graph',
+            'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]
+        },
+        {
+            'tfOpName': 'Size',
+            'category': 'graph',
+            'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]
+        },
+        {
+            'tfOpName': 'Shape',
+            'category': 'graph',
+            'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]
+        },
+        {
+            'tfOpName': 'ShapeN',
+            'category': 'graph',
+            'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]
+        },
+        {
+            'tfOpName': 'Print',
+            'category': 'graph',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'data', 'type': 'tensors' },
+            ],
+            'attrs': [
+                { 'tfName': 'message', 'name': 'message', 'type': 'string' }, {
+                    'tfName': 'first_n',
+                    'name': 'firstN',
+                    'type': 'number',
+                    'notSupported': true
+                },
+                {
+                    'tfName': 'summarize',
+                    'name': 'summarize',
+                    'type': 'number',
+                    'defaultValue': 3
+                }
+            ]
+        },
+        { 'tfOpName': 'NoOp', 'category': 'graph', 'inputs': [] }, {
+            'tfOpName': 'StopGradient',
+            'category': 'graph',
+            'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]
+        },
+        {
+            'tfOpName': 'FakeQuantWithMinMaxVars',
+            'category': 'graph',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'min', 'name': 'min', 'type': 'number' },
+                { 'tfName': 'max', 'name': 'max', 'type': 'number' }
+            ]
+        }
+    ];
+
+    var graph = {
+        __proto__: null,
+        json: json$7
+    };
+
+    var json$8 = [
+        {
+            'tfOpName': 'HashTable',
+            'category': 'hash_table',
+            'inputs': [],
+            'attrs': [
+                { 'tfName': 'shared_name', 'name': 'sharedName', 'type': 'string' },
+                {
+                    'tfName': 'use_node_name_sharing',
+                    'name': 'useNodeNameSharing',
+                    'type': 'bool'
+                },
+                { 'tfName': 'key_dtype', 'name': 'keyDType', 'type': 'dtype' },
+                { 'tfName': 'value_dtype', 'name': 'valueDType', 'type': 'dtype' },
+            ]
+        },
+        {
+            'tfOpName': 'HashTableV2',
+            'category': 'hash_table',
+            'inputs': [],
+            'attrs': [
+                { 'tfName': 'shared_name', 'name': 'sharedName', 'type': 'string' },
+                {
+                    'tfName': 'use_node_name_sharing',
+                    'name': 'useNodeNameSharing',
+                    'type': 'bool'
+                },
+                { 'tfName': 'key_dtype', 'name': 'keyDType', 'type': 'dtype' },
+                { 'tfName': 'value_dtype', 'name': 'valueDType', 'type': 'dtype' },
+            ]
+        },
+        {
+            'tfOpName': 'LookupTableImport',
+            'category': 'hash_table',
+            'inputs': [
+                { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },
+                { 'start': 1, 'name': 'keys', 'type': 'tensor' },
+                { 'start': 2, 'name': 'values', 'type': 'tensor' }
+            ],
+            'attrs': [
+                { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {
+                    'tfName': 'Tout',
+                    'name': 'tOut',
+                    'type': 'dtype',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'LookupTableImportV2',
+            'category': 'hash_table',
+            'inputs': [
+                { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },
+                { 'start': 1, 'name': 'keys', 'type': 'tensor' },
+                { 'start': 2, 'name': 'values', 'type': 'tensor' }
+            ],
+            'attrs': [
+                { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {
+                    'tfName': 'Tout',
+                    'name': 'tOut',
+                    'type': 'dtype',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'LookupTableFind',
+            'category': 'hash_table',
+            'inputs': [
+                { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },
+                { 'start': 1, 'name': 'keys', 'type': 'tensor' },
+                { 'start': 2, 'name': 'defaultValue', 'type': 'tensor' }
+            ],
+            'attrs': [
+                { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {
+                    'tfName': 'Tout',
+                    'name': 'tOut',
+                    'type': 'dtype',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'LookupTableFindV2',
+            'category': 'hash_table',
+            'inputs': [
+                { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },
+                { 'start': 1, 'name': 'keys', 'type': 'tensor' },
+                { 'start': 2, 'name': 'defaultValue', 'type': 'tensor' }
+            ],
+            'attrs': [
+                { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {
+                    'tfName': 'Tout',
+                    'name': 'tOut',
+                    'type': 'dtype',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'LookupTableSize',
+            'category': 'hash_table',
+            'inputs': [
+                { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' }
+            ]
+        },
+        {
+            'tfOpName': 'LookupTableSizeV2',
+            'category': 'hash_table',
+            'inputs': [
+                { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' }
+            ]
+        }
+    ];
+
+    var hashTable = {
+        __proto__: null,
+        json: json$8
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$9 = [
+        {
+            'tfOpName': 'ResizeBilinear',
+            'category': 'image',
+            'inputs': [
+                { 'start': 0, 'name': 'images', 'type': 'tensor' },
+                { 'start': 1, 'name': 'size', 'type': 'number[]' },
+            ],
+            'attrs': [
+                { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' }, {
+                    'tfName': 'half_pixel_centers',
+                    'name': 'halfPixelCenters',
+                    'type': 'bool'
+                },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'ResizeNearestNeighbor',
+            'category': 'image',
+            'inputs': [
+                { 'start': 0, 'name': 'images', 'type': 'tensor' },
+                { 'start': 1, 'name': 'size', 'type': 'number[]' },
+            ],
+            'attrs': [
+                { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' }, {
+                    'tfName': 'half_pixel_centers',
+                    'name': 'halfPixelCenters',
+                    'type': 'bool'
+                },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'CropAndResize',
+            'category': 'image',
+            'inputs': [
+                { 'start': 0, 'name': 'image', 'type': 'tensor' },
+                { 'start': 1, 'name': 'boxes', 'type': 'tensor' },
+                { 'start': 2, 'name': 'boxInd', 'type': 'tensor' },
+                { 'start': 3, 'name': 'cropSize', 'type': 'number[]' },
+            ],
+            'attrs': [
+                { 'tfName': 'method', 'name': 'method', 'type': 'string' }, {
+                    'tfName': 'extrapolation_value',
+                    'name': 'extrapolationValue',
+                    'type': 'number'
+                }
+            ]
+        }
+    ];
+
+    var image = {
+        __proto__: null,
+        json: json$9
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$a = [
+        {
+            'tfOpName': 'Equal',
+            'category': 'logical',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'NotEqual',
+            'category': 'logical',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Greater',
+            'category': 'logical',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'GreaterEqual',
+            'category': 'logical',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Less',
+            'category': 'logical',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'LessEqual',
+            'category': 'logical',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'LogicalAnd',
+            'category': 'logical',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'LogicalNot',
+            'category': 'logical',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'LogicalOr',
+            'category': 'logical',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Select',
+            'category': 'logical',
+            'inputs': [
+                { 'start': 0, 'name': 'condition', 'type': 'tensor' },
+                { 'start': 1, 'name': 'a', 'type': 'tensor' },
+                { 'start': 2, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'SelectV2',
+            'category': 'logical',
+            'inputs': [
+                { 'start': 0, 'name': 'condition', 'type': 'tensor' },
+                { 'start': 1, 'name': 'a', 'type': 'tensor' },
+                { 'start': 2, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [{
+                    'tfName': 'T',
+                    'name': 'dtype',
+                    'type': 'dtype',
+                    'notSupported': true
+                }]
+        }
+    ];
+
+    var logical = {
+        __proto__: null,
+        json: json$a
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$b = [
+        {
+            'tfOpName': '_FusedMatMul',
+            'category': 'matrices',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+                { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },
+            ],
+            'attrs': [
+                { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, {
+                    'tfName': 'fused_ops',
+                    'name': 'fusedOps',
+                    'type': 'string[]',
+                    'defaultValue': []
+                },
+                {
+                    'tfName': 'epsilon',
+                    'name': 'epsilon',
+                    'type': 'number',
+                    'defaultValue': 0.0001
+                },
+                {
+                    'tfName': 'transpose_a',
+                    'name': 'transposeA',
+                    'type': 'bool',
+                    'defaultValue': false
+                },
+                {
+                    'tfName': 'transpose_b',
+                    'name': 'transposeB',
+                    'type': 'bool',
+                    'defaultValue': false
+                },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'MatMul',
+            'category': 'matrices',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'transpose_a',
+                    'name': 'transposeA',
+                    'type': 'bool',
+                    'defaultValue': false
+                },
+                {
+                    'tfName': 'transpose_b',
+                    'name': 'transposeB',
+                    'type': 'bool',
+                    'defaultValue': false
+                },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'BatchMatMul',
+            'category': 'matrices',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'adj_x',
+                    'name': 'transposeA',
+                    'type': 'bool',
+                    'defaultValue': false
+                },
+                {
+                    'tfName': 'adj_y',
+                    'name': 'transposeB',
+                    'type': 'bool',
+                    'defaultValue': false
+                },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'BatchMatMulV2',
+            'category': 'matrices',
+            'inputs': [
+                { 'start': 0, 'name': 'a', 'type': 'tensor' },
+                { 'start': 1, 'name': 'b', 'type': 'tensor' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'adj_x',
+                    'name': 'transposeA',
+                    'type': 'bool',
+                    'defaultValue': false
+                },
+                {
+                    'tfName': 'adj_y',
+                    'name': 'transposeB',
+                    'type': 'bool',
+                    'defaultValue': false
+                },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Transpose',
+            'category': 'matrices',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'perm', 'type': 'number[]' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'Einsum',
+            'category': 'matrices',
+            'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }],
+            'attrs': [
+                { 'tfName': 'equation', 'name': 'equation', 'type': 'string' },
+                { 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 },
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }
+            ]
+        }
+    ];
+
+    var matrices = {
+        __proto__: null,
+        json: json$b
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$c = [
+        {
+            'tfOpName': 'FusedBatchNorm',
+            'category': 'normalization',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'scale', 'type': 'tensor' },
+                { 'start': 2, 'name': 'offset', 'type': 'tensor' },
+                { 'start': 3, 'name': 'mean', 'type': 'tensor' },
+                { 'start': 4, 'name': 'variance', 'type': 'tensor' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'epsilon',
+                    'name': 'epsilon',
+                    'type': 'number',
+                    'defaultValue': 0.001
+                },
+                {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'FusedBatchNormV2',
+            'category': 'normalization',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'scale', 'type': 'tensor' },
+                { 'start': 2, 'name': 'offset', 'type': 'tensor' },
+                { 'start': 3, 'name': 'mean', 'type': 'tensor' },
+                { 'start': 4, 'name': 'variance', 'type': 'tensor' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'epsilon',
+                    'name': 'epsilon',
+                    'type': 'number',
+                    'defaultValue': 0.001
+                },
+                {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'FusedBatchNormV3',
+            'category': 'normalization',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'scale', 'type': 'tensor' },
+                { 'start': 2, 'name': 'offset', 'type': 'tensor' },
+                { 'start': 3, 'name': 'mean', 'type': 'tensor' },
+                { 'start': 4, 'name': 'variance', 'type': 'tensor' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'epsilon',
+                    'name': 'epsilon',
+                    'type': 'number',
+                    'defaultValue': 0.001
+                },
+                {
+                    'tfName': 'data_format',
+                    'name': 'dataFormat',
+                    'type': 'string',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'LRN',
+            'category': 'normalization',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'depth_radius',
+                    'name': 'radius',
+                    'type': 'number',
+                    'defaultValue': 5
+                },
+                { 'tfName': 'bias', 'name': 'bias', 'type': 'number', 'defaultValue': 1.0 },
+                {
+                    'tfName': 'alpha',
+                    'name': 'alpha',
+                    'type': 'number',
+                    'defaultValue': 1.0
+                },
+                {
+                    'tfName': 'beta',
+                    'name': 'beta',
+                    'type': 'number',
+                    'defaultValue': 0.5
+                }
+            ]
+        },
+        {
+            'tfOpName': 'Softmax',
+            'category': 'normalization',
+            'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]
+        },
+        {
+            'tfOpName': 'LogSoftmax',
+            'category': 'normalization',
+            'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]
+        },
+        {
+            'tfOpName': 'SparseToDense',
+            'category': 'normalization',
+            'inputs': [
+                { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },
+                { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },
+                { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },
+                { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },
+            ],
+            'attrs': [{
+                    'tfName': 'validate_indices',
+                    'name': 'validateIndices',
+                    'type': 'bool',
+                    'defaultValue': true,
+                    'notSupported': true
+                }]
+        }
+    ];
+
+    var normalization = {
+        __proto__: null,
+        json: json$c
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$d = [
+        {
+            'tfOpName': 'Bincount',
+            'category': 'reduction',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'size', 'type': 'number' },
+                { 'start': 2, 'name': 'weights', 'type': 'tensor' }
+            ]
+        },
+        {
+            'tfOpName': 'DenseBincount',
+            'category': 'reduction',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'size', 'type': 'number' },
+                { 'start': 2, 'name': 'weights', 'type': 'tensor' }
+            ],
+            'attrs': [{ 'tfName': 'binary_output', 'name': 'binaryOutput', 'type': 'bool' }]
+        },
+        {
+            'tfOpName': 'Max',
+            'category': 'reduction',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number[]' },
+            ],
+            'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]
+        },
+        {
+            'tfOpName': 'Mean',
+            'category': 'reduction',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number[]' },
+            ],
+            'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]
+        },
+        {
+            'tfOpName': 'Min',
+            'category': 'reduction',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number[]' },
+            ],
+            'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]
+        },
+        {
+            'tfOpName': 'Sum',
+            'category': 'reduction',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number[]' },
+            ],
+            'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]
+        },
+        {
+            'tfOpName': 'All',
+            'category': 'reduction',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number[]' },
+            ],
+            'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]
+        },
+        {
+            'tfOpName': 'Any',
+            'category': 'reduction',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number[]' },
+            ],
+            'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]
+        },
+        {
+            'tfOpName': 'ArgMax',
+            'category': 'reduction',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number' }
+            ]
+        },
+        {
+            'tfOpName': 'ArgMin',
+            'category': 'reduction',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number' }
+            ]
+        },
+        {
+            'tfOpName': 'Prod',
+            'category': 'reduction',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number[]' },
+            ],
+            'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]
+        },
+        {
+            'tfOpName': 'Cumsum',
+            'category': 'reduction',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number' },
+            ],
+            'attrs': [
+                { 'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool' },
+                { 'tfName': 'reverse', 'name': 'reverse', 'type': 'bool' }
+            ]
+        }
+    ];
+
+    var reduction = {
+        __proto__: null,
+        json: json$d
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$e = [
+        {
+            'tfOpName': 'ConcatV2',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'end': -1, 'name': 'tensors', 'type': 'tensors' },
+                { 'start': -1, 'name': 'axis', 'type': 'number' }
+            ],
+            'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]
+        },
+        {
+            'tfOpName': 'Concat',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 1, 'end': 0, 'name': 'tensors', 'type': 'tensors' },
+                { 'start': 0, 'name': 'axis', 'type': 'number' }
+            ],
+            'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]
+        },
+        {
+            'tfOpName': 'GatherV2',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'indices', 'type': 'tensor' },
+                { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }
+            ],
+            'attrs': [{
+                    'tfName': 'batch_dims',
+                    'name': 'batchDims',
+                    'type': 'number',
+                    'defaultValue': 0
+                }]
+        },
+        {
+            'tfOpName': 'Gather',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'indices', 'type': 'tensor' },
+            ],
+            'attrs': [{
+                    'tfName': 'validate_indices',
+                    'name': 'validateIndices',
+                    'type': 'bool',
+                    'notSupported': true
+                }]
+        },
+        {
+            'tfOpName': 'Reverse',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'dims', 'type': 'bool[]' }
+            ]
+        },
+        {
+            'tfOpName': 'ReverseV2',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number[]' }
+            ]
+        },
+        {
+            'tfOpName': 'Slice',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'begin', 'type': 'number[]' },
+                { 'start': 2, 'name': 'size', 'type': 'number[]' }
+            ]
+        },
+        {
+            'tfOpName': 'StridedSlice',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'begin', 'type': 'number[]' },
+                { 'start': 2, 'name': 'end', 'type': 'number[]' },
+                { 'start': 3, 'name': 'strides', 'type': 'number[]' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'begin_mask',
+                    'name': 'beginMask',
+                    'type': 'number',
+                    'defaultValue': 0
+                },
+                {
+                    'tfName': 'end_mask',
+                    'name': 'endMask',
+                    'type': 'number',
+                    'defaultValue': 0
+                },
+                {
+                    'tfName': 'new_axis_mask',
+                    'name': 'newAxisMask',
+                    'type': 'number',
+                    'defaultValue': 0
+                },
+                {
+                    'tfName': 'ellipsis_mask',
+                    'name': 'ellipsisMask',
+                    'type': 'number',
+                    'defaultValue': 0
+                },
+                {
+                    'tfName': 'shrink_axis_mask',
+                    'name': 'shrinkAxisMask',
+                    'type': 'number',
+                    'defaultValue': 0
+                }
+            ]
+        },
+        {
+            'tfOpName': 'Pack',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' },
+            ],
+            'attrs': [
+                { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }
+            ]
+        },
+        {
+            'tfOpName': 'Unpack',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'tensor', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {
+                    'tfName': 'num',
+                    'name': 'num',
+                    'type': 'number',
+                    'defaultValue': 0,
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'Tile',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'reps', 'type': 'number[]' }
+            ]
+        },
+        {
+            'tfOpName': 'Split',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'axis', 'type': 'number', 'defaultValue': 0 },
+                { 'start': 1, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [{
+                    'tfName': 'num_split',
+                    'name': 'numOrSizeSplits',
+                    'type': 'number',
+                    'defaultValue': 1
+                }]
+        },
+        {
+            'tfOpName': 'SplitV',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'numOrSizeSplits', 'type': 'number[]' },
+                { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }
+            ]
+        },
+        {
+            'tfOpName': 'ScatterNd',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'indices', 'type': 'tensor' },
+                { 'start': 1, 'name': 'values', 'type': 'tensor' },
+                { 'start': 2, 'name': 'shape', 'type': 'number[]' }
+            ]
+        },
+        {
+            'tfOpName': 'GatherNd',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'indices', 'type': 'tensor' }
+            ]
+        },
+        {
+            'tfOpName': 'SparseToDense',
+            'category': 'slice_join',
+            'inputs': [
+                { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },
+                { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },
+                { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },
+                { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },
+            ],
+            'attrs': [{
+                    'tfName': 'validate_indices',
+                    'name': 'validateIndices',
+                    'type': 'bool',
+                    'defaultValue': false,
+                    'notSupported': true
+                }]
+        }
+    ];
+
+    var sliceJoin = {
+        __proto__: null,
+        json: json$e
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$f = [
+        {
+            'tfOpName': 'SparseFillEmptyRows',
+            'category': 'sparse',
+            'inputs': [
+                { 'start': 0, 'name': 'indices', 'type': 'tensor' },
+                { 'start': 1, 'name': 'values', 'type': 'tensor' },
+                { 'start': 2, 'name': 'denseShape', 'type': 'tensor' },
+                { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },
+            ]
+        },
+        {
+            'tfOpName': 'SparseReshape',
+            'category': 'sparse',
+            'inputs': [
+                { 'start': 0, 'name': 'inputIndices', 'type': 'tensor' },
+                { 'start': 1, 'name': 'inputShape', 'type': 'tensor' },
+                { 'start': 2, 'name': 'newShape', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }
+            ]
+        },
+        {
+            'tfOpName': 'SparseSegmentMean',
+            'category': 'sparse',
+            'inputs': [
+                { 'start': 0, 'name': 'data', 'type': 'tensor' },
+                { 'start': 1, 'name': 'indices', 'type': 'tensor' },
+                { 'start': 2, 'name': 'segmentIds', 'type': 'tensor' },
+            ]
+        },
+        {
+            'tfOpName': 'SparseSegmentSum',
+            'category': 'sparse',
+            'inputs': [
+                { 'start': 0, 'name': 'data', 'type': 'tensor' },
+                { 'start': 1, 'name': 'indices', 'type': 'tensor' },
+                { 'start': 2, 'name': 'segmentIds', 'type': 'tensor' },
+            ]
+        }
+    ];
+
+    var sparse = {
+        __proto__: null,
+        json: json$f
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$g = [
+        {
+            'tfOpName': 'FFT',
+            'category': 'spectral',
+            'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]
+        },
+        {
+            'tfOpName': 'IFFT',
+            'category': 'spectral',
+            'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]
+        },
+        {
+            'tfOpName': 'RFFT',
+            'category': 'spectral',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' }, {
+                    'start': 1,
+                    'name': 'fft_length',
+                    'type': 'number',
+                    'notSupported': true
+                }
+            ]
+        },
+        {
+            'tfOpName': 'IRFFT',
+            'category': 'spectral',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' }, {
+                    'start': 1,
+                    'name': 'fft_length',
+                    'type': 'number',
+                    'notSupported': true
+                }
+            ]
+        }
+    ];
+
+    var spectral = {
+        __proto__: null,
+        json: json$g
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$h = [
+        {
+            'tfOpName': 'StringNGrams',
+            'category': 'string',
+            'inputs': [
+                { 'start': 0, 'name': 'data', 'type': 'tensor' },
+                { 'start': 1, 'name': 'dataSplits', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'separator', 'name': 'separator', 'type': 'string' },
+                { 'tfName': 'ngram_widths', 'name': 'nGramWidths', 'type': 'number[]' },
+                { 'tfName': 'left_pad', 'name': 'leftPad', 'type': 'string' },
+                { 'tfName': 'right_pad', 'name': 'rightPad', 'type': 'string' },
+                { 'tfName': 'pad_width', 'name': 'padWidth', 'type': 'number' }, {
+                    'tfName': 'preserve_short_sequences',
+                    'name': 'preserveShortSequences',
+                    'type': 'bool'
+                }
+            ],
+            'outputs': ['ngrams', 'ngrams_splits']
+        },
+        {
+            'tfOpName': 'StringSplit',
+            'category': 'string',
+            'inputs': [
+                { 'start': 0, 'name': 'input', 'type': 'tensor' },
+                { 'start': 1, 'name': 'delimiter', 'type': 'tensor' },
+            ],
+            'attrs': [{ 'tfName': 'skip_empty', 'name': 'skipEmpty', 'type': 'bool' }],
+            'outputs': ['indices', 'values', 'shape']
+        },
+        {
+            'tfOpName': 'StringToHashBucketFast',
+            'category': 'string',
+            'inputs': [
+                { 'start': 0, 'name': 'input', 'type': 'tensor' },
+            ],
+            'attrs': [{ 'tfName': 'num_buckets', 'name': 'numBuckets', 'type': 'number' }]
+        }
+    ];
+
+    var string = {
+        __proto__: null,
+        json: json$h
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$i = [
+        {
+            'tfOpName': 'Cast',
+            'category': 'transformation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                {
+                    'tfName': 'SrcT',
+                    'name': 'sdtype',
+                    'type': 'dtype',
+                    'notSupported': true
+                },
+                { 'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype' }
+            ]
+        },
+        {
+            'tfOpName': 'ExpandDims',
+            'category': 'transformation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'axis', 'type': 'number' }
+            ]
+        },
+        {
+            'tfOpName': 'MirrorPad',
+            'category': 'transformation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'padding', 'type': 'number[]' },
+            ],
+            'attrs': [{ 'tfName': 'mode', 'name': 'mode', 'type': 'string' }]
+        },
+        {
+            'tfOpName': 'Pad',
+            'category': 'transformation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'padding', 'type': 'number[]' },
+            ],
+            'attrs': [{
+                    'tfName': 'constant_value',
+                    'name': 'constantValue',
+                    'type': 'number',
+                    'defaultValue': 0
+                }]
+        },
+        {
+            'tfOpName': 'PadV2',
+            'category': 'transformation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'padding', 'type': 'number[]' }, {
+                    'start': 2,
+                    'name': 'constantValue',
+                    'type': 'number',
+                    'defaultValue': 0
+                }
+            ]
+        },
+        {
+            'tfOpName': 'Reshape',
+            'category': 'transformation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'shape', 'type': 'number[]' }
+            ]
+        },
+        {
+            'tfOpName': 'Squeeze',
+            'category': 'transformation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [{
+                    'tfName': 'axis',
+                    'tfDeprecatedName': 'squeeze_dims',
+                    'name': 'axis',
+                    'type': 'number[]'
+                }]
+        },
+        {
+            'tfOpName': 'SpaceToBatchND',
+            'category': 'transformation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },
+                { 'start': 2, 'name': 'paddings', 'type': 'number[]' }
+            ]
+        },
+        {
+            'tfOpName': 'BatchToSpaceND',
+            'category': 'transformation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },
+                { 'start': 2, 'name': 'crops', 'type': 'number[]' }
+            ]
+        },
+        {
+            'tfOpName': 'DepthToSpace',
+            'category': 'transformation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+            ],
+            'attrs': [
+                { 'tfName': 'block_size', 'name': 'blockSize', 'type': 'number' },
+                { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string' }
+            ]
+        },
+        {
+            'tfOpName': 'BroadcastTo',
+            'category': 'transformation',
+            'inputs': [
+                { 'start': 0, 'name': 'x', 'type': 'tensor' },
+                { 'start': 1, 'name': 'shape', 'type': 'number[]' },
+            ],
+            'attrs': []
+        }
+    ];
+
+    var transformation = {
+        __proto__: null,
+        json: json$i
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var OperationMapper = /** @class */ (function () {
+        // Loads the op mapping from the JSON file.
+        function OperationMapper() {
+            var ops = [
+                arithmetic, basicMath, control, convolution, creation, dynamic,
+                evaluation, graph, hashTable, image, logical, matrices, normalization,
+                reduction, sliceJoin, sparse, spectral, string, transformation
+            ];
+            var mappersJson = [].concat.apply([], __spread(ops.map(function (op) { return op.json; })));
+            this.opMappers = mappersJson.reduce(function (map, mapper) {
+                map[mapper.tfOpName] = mapper;
+                return map;
+            }, {});
+        }
+        Object.defineProperty(OperationMapper, "Instance", {
+            // Singleton instance for the mapper
+            get: function () {
+                return this._instance || (this._instance = new this());
+            },
+            enumerable: true,
+            configurable: true
+        });
+        // Converts the model inference graph from Tensorflow GraphDef to local
+        // representation for TensorFlow.js API
+        OperationMapper.prototype.transformGraph = function (graph, signature) {
+            var _this = this;
+            if (signature === void 0) { signature = {}; }
+            var tfNodes = graph.node;
+            var placeholders = [];
+            var weights = [];
+            var initNodes = [];
+            var nodes = tfNodes.reduce(function (map, node) {
+                map[node.name] = _this.mapNode(node);
+                if (node.op.startsWith('Placeholder')) {
+                    placeholders.push(map[node.name]);
+                }
+                else if (node.op === 'Const') {
+                    weights.push(map[node.name]);
+                }
+                else if (node.input == null || node.input.length === 0) {
+                    initNodes.push(map[node.name]);
+                }
+                return map;
+            }, {});
+            var inputs = [];
+            var outputs = [];
+            var inputNodeNameToKey = {};
+            var outputNodeNameToKey = {};
+            if (signature != null) {
+                inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);
+                outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);
+            }
+            var allNodes = Object.keys(nodes);
+            allNodes.forEach(function (key) {
+                var node = nodes[key];
+                node.inputNames.forEach(function (name, index) {
+                    var _a = __read(getNodeNameAndIndex(name), 3), nodeName = _a[0], outputName = _a[2];
+                    var inputNode = nodes[nodeName];
+                    if (inputNode.outputs != null) {
+                        var outputIndex = inputNode.outputs.indexOf(outputName);
+                        if (outputIndex !== -1) {
+                            var inputName = nodeName + ":" + outputIndex;
+                            // update the input name to use the mapped output index directly.
+                            node.inputNames[index] = inputName;
+                        }
+                    }
+                    node.inputs.push(inputNode);
+                    inputNode.children.push(node);
+                });
+            });
+            // if signature has not outputs set, add any node that does not have
+            // outputs.
+            if (Object.keys(outputNodeNameToKey).length === 0) {
+                allNodes.forEach(function (key) {
+                    var node = nodes[key];
+                    if (node.children.length === 0) {
+                        outputs.push(node);
+                    }
+                });
+            }
+            else {
+                Object.keys(outputNodeNameToKey).forEach(function (name) {
+                    var _a = __read(getNodeNameAndIndex(name), 1), nodeName = _a[0];
+                    var node = nodes[nodeName];
+                    if (node != null) {
+                        node.signatureKey = outputNodeNameToKey[name];
+                        outputs.push(node);
+                    }
+                });
+            }
+            if (Object.keys(inputNodeNameToKey).length > 0) {
+                Object.keys(inputNodeNameToKey).forEach(function (name) {
+                    var _a = __read(getNodeNameAndIndex(name), 1), nodeName = _a[0];
+                    var node = nodes[nodeName];
+                    if (node) {
+                        node.signatureKey = inputNodeNameToKey[name];
+                        inputs.push(node);
+                    }
+                });
+            }
+            else {
+                inputs = placeholders;
+            }
+            var functions = {};
+            if (graph.library != null && graph.library.function != null) {
+                functions = graph.library.function.reduce(function (functions, func) {
+                    functions[func.signature.name] = _this.mapFunction(func);
+                    return functions;
+                }, {});
+            }
+            var result = { nodes: nodes, inputs: inputs, outputs: outputs, weights: weights, placeholders: placeholders, signature: signature, functions: functions };
+            if (initNodes.length > 0) {
+                result.initNodes = initNodes;
+            }
+            return result;
+        };
+        OperationMapper.prototype.mapSignatureEntries = function (entries) {
+            return Object.keys(entries || {})
+                .reduce(function (prev, curr) {
+                prev[entries[curr].name] = curr;
+                return prev;
+            }, {});
+        };
+        OperationMapper.prototype.mapNode = function (node) {
+            // Unsupported ops will cause an error at run-time (not parse time), since
+            // they may not be used by the actual execution subgraph.
+            var mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};
+            if (node.attr == null) {
+                node.attr = {};
+            }
+            var newNode = {
+                name: node.name,
+                op: node.op,
+                category: mapper.category,
+                inputNames: (node.input ||
+                    []).map(function (input) { return input.startsWith('^') ? input.substr(1) : input; }),
+                inputs: [],
+                children: [],
+                inputParams: {},
+                attrParams: {},
+                rawAttrs: node.attr,
+                outputs: mapper.outputs
+            };
+            if (mapper.inputs != null) {
+                newNode.inputParams =
+                    mapper.inputs.reduce(function (map, param) {
+                        map[param.name] = {
+                            type: param.type,
+                            inputIndexStart: param.start,
+                            inputIndexEnd: param.end
+                        };
+                        return map;
+                    }, {});
+            }
+            if (mapper.attrs != null) {
+                newNode.attrParams =
+                    mapper.attrs.reduce(function (map, param) {
+                        var type = param.type;
+                        var value = undefined;
+                        switch (param.type) {
+                            case 'string':
+                                value = getStringParam(node.attr, param.tfName, param.defaultValue);
+                                if (value === undefined && !!param.tfDeprecatedName) {
+                                    value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+                                }
+                                break;
+                            case 'string[]':
+                                value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);
+                                if (value === undefined && !!param.tfDeprecatedName) {
+                                    value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+                                }
+                                break;
+                            case 'number':
+                                value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0));
+                                if (value === undefined && !!param.tfDeprecatedName) {
+                                    value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+                                }
+                                break;
+                            case 'number[]':
+                                value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);
+                                if (value === undefined && !!param.tfDeprecatedName) {
+                                    value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+                                }
+                                break;
+                            case 'bool':
+                                value = getBoolParam(node.attr, param.tfName, param.defaultValue);
+                                if (value === undefined && !!param.tfDeprecatedName) {
+                                    value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+                                }
+                                break;
+                            case 'bool[]':
+                                value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);
+                                if (value === undefined && !!param.tfDeprecatedName) {
+                                    value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+                                }
+                                break;
+                            case 'shape':
+                                value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);
+                                if (value === undefined && !!param.tfDeprecatedName) {
+                                    value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+                                }
+                                break;
+                            case 'shape[]':
+                                value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);
+                                if (value === undefined && !!param.tfDeprecatedName) {
+                                    value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+                                }
+                                break;
+                            case 'dtype':
+                                value = getDtypeParam(node.attr, param.tfName, param.defaultValue);
+                                if (value === undefined && !!param.tfDeprecatedName) {
+                                    value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+                                }
+                                break;
+                            case 'dtype[]':
+                                value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);
+                                if (value === undefined && !!param.tfDeprecatedName) {
+                                    value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+                                }
+                                break;
+                            case 'func':
+                                value = getFuncParam(node.attr, param.tfName, param.defaultValue);
+                                if (value === undefined && !!param.tfDeprecatedName) {
+                                    value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+                                }
+                                break;
+                            case 'tensor':
+                            case 'tensors':
+                                break;
+                            default:
+                                throw new Error("Unsupported param type: " + param.type + " for op: " + node.op);
+                        }
+                        map[param.name] = { value: value, type: type };
+                        return map;
+                    }, {});
+            }
+            return newNode;
+        };
+        // map the TFunctionDef to TFJS graph object
+        OperationMapper.prototype.mapFunction = function (functionDef) {
+            var _this = this;
+            var tfNodes = functionDef.nodeDef;
+            var placeholders = [];
+            var weights = [];
+            var nodes = {};
+            if (tfNodes != null) {
+                nodes = tfNodes.reduce(function (map, node) {
+                    map[node.name] = _this.mapNode(node);
+                    if (node.op === 'Const') {
+                        weights.push(map[node.name]);
+                    }
+                    return map;
+                }, {});
+            }
+            var inputs = [];
+            var outputs = [];
+            functionDef.signature.inputArg.forEach(function (arg) {
+                var _a = __read(getNodeNameAndIndex(arg.name), 1), nodeName = _a[0];
+                var node = {
+                    name: nodeName,
+                    op: 'Placeholder',
+                    inputs: [],
+                    inputNames: [],
+                    category: 'graph',
+                    inputParams: {},
+                    attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } },
+                    children: []
+                };
+                node.signatureKey = arg.name;
+                inputs.push(node);
+                nodes[nodeName] = node;
+            });
+            var allNodes = Object.keys(nodes);
+            allNodes.forEach(function (key) {
+                var node = nodes[key];
+                node.inputNames.forEach(function (name, index) {
+                    var _a = __read(getNodeNameAndIndex(name), 3), nodeName = _a[0], outputName = _a[2];
+                    var inputNode = nodes[nodeName];
+                    if (inputNode.outputs != null) {
+                        var outputIndex = inputNode.outputs.indexOf(outputName);
+                        if (outputIndex !== -1) {
+                            var inputName = nodeName + ":" + outputIndex;
+                            // update the input name to use the mapped output index directly.
+                            node.inputNames[index] = inputName;
+                        }
+                    }
+                    node.inputs.push(inputNode);
+                    inputNode.children.push(node);
+                });
+            });
+            var returnNodeMap = functionDef.ret;
+            functionDef.signature.outputArg.forEach(function (output) {
+                var _a = __read(getNodeNameAndIndex(returnNodeMap[output.name]), 2), nodeName = _a[0], index = _a[1];
+                var node = nodes[nodeName];
+                if (node != null) {
+                    node.defaultOutput = index;
+                    outputs.push(node);
+                }
+            });
+            var signature = this.mapArgsToSignature(functionDef);
+            return { nodes: nodes, inputs: inputs, outputs: outputs, weights: weights, placeholders: placeholders, signature: signature };
+        };
+        OperationMapper.prototype.mapArgsToSignature = function (functionDef) {
+            var _this = this;
+            return {
+                methodName: functionDef.signature.name,
+                inputs: functionDef.signature.inputArg.reduce(function (map, arg) {
+                    map[arg.name] = _this.mapArgToTensorInfo(arg);
+                    return map;
+                }, {}),
+                outputs: functionDef.signature.outputArg.reduce(function (map, arg) {
+                    map[arg.name] = _this.mapArgToTensorInfo(arg, functionDef.ret);
+                    return map;
+                }, {}),
+            };
+        };
+        OperationMapper.prototype.mapArgToTensorInfo = function (arg, nameMap) {
+            var name = arg.name;
+            if (nameMap != null) {
+                name = nameMap[name];
+            }
+            return { name: name, dtype: arg.type };
+        };
+        return OperationMapper;
+    }());
+    function decodeBase64(text) {
+        var global = tfc.env().global;
+        if (typeof global.atob !== 'undefined') {
+            return global.atob(text);
+        }
+        else if (typeof Buffer !== 'undefined') {
+            return new Buffer(text, 'base64').toString();
+        }
+        else {
+            throw new Error('Unable to decode base64 in this environment. ' +
+                'Missing built-in atob() or Buffer()');
+        }
+    }
+    function parseStringParam(s, keepCase) {
+        var value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);
+        return keepCase ? value : value.toLowerCase();
+    }
+    function getStringParam(attrs, name, def, keepCase) {
+        if (keepCase === void 0) { keepCase = false; }
+        var param = attrs[name];
+        if (param != null) {
+            return parseStringParam(param.s, keepCase);
+        }
+        return def;
+    }
+    function getBoolParam(attrs, name, def) {
+        var param = attrs[name];
+        return param ? param.b : def;
+    }
+    function getNumberParam(attrs, name, def) {
+        var param = attrs[name] || {};
+        var value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);
+        return (typeof value === 'number') ? value : parseInt(value, 10);
+    }
+    function parseDtypeParam(value) {
+        if (typeof (value) === 'string') {
+            // tslint:disable-next-line:no-any
+            value = DataType[value];
+        }
+        switch (value) {
+            case DataType.DT_FLOAT:
+                return 'float32';
+            case DataType.DT_INT32:
+            case DataType.DT_INT64:
+            case DataType.DT_INT8:
+            case DataType.DT_UINT8:
+                return 'int32';
+            case DataType.DT_BOOL:
+                return 'bool';
+            case DataType.DT_DOUBLE:
+                return 'float32';
+            case DataType.DT_STRING:
+                return 'string';
+            default:
+                // Unknown dtype error will happen at runtime (instead of parse time),
+                // since these nodes might not be used by the actual subgraph execution.
+                return null;
+        }
+    }
+    function getFuncParam(attrs, name, def) {
+        var param = attrs[name];
+        if (param && param.func) {
+            return param.func.name;
+        }
+        return def;
+    }
+    function getDtypeParam(attrs, name, def) {
+        var param = attrs[name];
+        if (param && param.type) {
+            return parseDtypeParam(param.type);
+        }
+        return def;
+    }
+    function getDtypeArrayParam(attrs, name, def) {
+        var param = attrs[name];
+        if (param && param.list && param.list.type) {
+            return param.list.type.map(function (v) { return parseDtypeParam(v); });
+        }
+        return def;
+    }
+    function parseTensorShapeParam(shape) {
+        if (shape.unknownRank) {
+            return undefined;
+        }
+        if (shape.dim != null) {
+            return shape.dim.map(function (dim) {
+                return (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10);
+            });
+        }
+        return [];
+    }
+    function getTensorShapeParam(attrs, name, def) {
+        var param = attrs[name];
+        if (param && param.shape) {
+            return parseTensorShapeParam(param.shape);
+        }
+        return def;
+    }
+    function getNumericArrayParam(attrs, name, def) {
+        var param = attrs[name];
+        if (param) {
+            return ((param.list.f && param.list.f.length ? param.list.f :
+                param.list.i) ||
+                [])
+                .map(function (v) { return (typeof v === 'number') ? v : parseInt(v, 10); });
+        }
+        return def;
+    }
+    function getStringArrayParam(attrs, name, def, keepCase) {
+        if (keepCase === void 0) { keepCase = false; }
+        var param = attrs[name];
+        if (param && param.list && param.list.s) {
+            return param.list.s.map(function (v) {
+                return parseStringParam(v, keepCase);
+            });
+        }
+        return def;
+    }
+    function getTensorShapeArrayParam(attrs, name, def) {
+        var param = attrs[name];
+        if (param && param.list && param.list.shape) {
+            return param.list.shape.map(function (v) {
+                return parseTensorShapeParam(v);
+            });
+        }
+        return def;
+    }
+    function getBoolArrayParam(attrs, name, def) {
+        var param = attrs[name];
+        if (param && param.list && param.list.b) {
+            return param.list.b;
+        }
+        return def;
+    }
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Helper class for lookup inputs and params for nodes in the model graph.
+     */
+    var NodeValueImpl = /** @class */ (function () {
+        function NodeValueImpl(node, tensorMap, context) {
+            var _this = this;
+            this.node = node;
+            this.tensorMap = tensorMap;
+            this.context = context;
+            this.inputs = [];
+            this.attrs = {};
+            this.inputs = node.inputNames.map(function (name) { return _this.getInput(name); });
+            if (node.rawAttrs != null) {
+                this.attrs = Object.keys(node.rawAttrs)
+                    .reduce(function (attrs, key) {
+                    attrs[key] = _this.getAttr(key);
+                    return attrs;
+                }, {});
+            }
+        }
+        /**
+         * Return the value of the attribute or input param.
+         * @param name String: name of attribute or input param.
+         */
+        NodeValueImpl.prototype.getInput = function (name) {
+            return getTensor(name, this.tensorMap, this.context);
+        };
+        /**
+         * Return the value of the attribute or input param.
+         * @param name String: name of attribute or input param.
+         */
+        NodeValueImpl.prototype.getAttr = function (name, defaultValue) {
+            var value = this.node.rawAttrs[name];
+            if (value.tensor != null) {
+                return getTensor(name, this.tensorMap, this.context);
+            }
+            if (value.i != null || value.f != null) {
+                return getNumberParam(this.node.rawAttrs, name, defaultValue);
+            }
+            if (value.s != null) {
+                return getStringParam(this.node.rawAttrs, name, defaultValue);
+            }
+            if (value.b != null) {
+                return getBoolParam(this.node.rawAttrs, name, defaultValue);
+            }
+            if (value.shape != null) {
+                return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);
+            }
+            if (value.type != null) {
+                return getDtypeParam(this.node.rawAttrs, name, defaultValue);
+            }
+            if (value.list != null) {
+                if (value.list.i != null || value.list.f != null) {
+                    return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);
+                }
+                if (value.list.s != null) {
+                    return getStringArrayParam(this.node.rawAttrs, name, defaultValue);
+                }
+                if (value.list.shape != null) {
+                    return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);
+                }
+                if (value.list.b != null) {
+                    return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);
+                }
+                if (value.list.type != null) {
+                    return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);
+                }
+            }
+            return defaultValue;
+        };
+        return NodeValueImpl;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'BiasAdd':
+            case 'AddV2':
+            case 'Add': {
+                return [tfOps.add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'AddN': {
+                return [tfOps.addN(getParamValue('tensors', node, tensorMap, context))];
+            }
+            case 'FloorMod':
+            case 'Mod':
+                return [tfOps.mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            case 'Mul':
+                return [tfOps.mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            case 'RealDiv':
+            case 'Div': {
+                return [tfOps.div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'DivNoNan': {
+                return [tfOps.divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'FloorDiv': {
+                return [tfOps.floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Sub': {
+                return [tfOps.sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Minimum': {
+                return [tfOps.minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Maximum': {
+                return [tfOps.maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Pow': {
+                return [tfOps.pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'SquaredDifference': {
+                return [tfOps.squaredDifference(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$1 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'Abs':
+            case 'ComplexAbs':
+                return [tfOps.abs(getParamValue('x', node, tensorMap, context))];
+            case 'Acos':
+                return [tfOps.acos(getParamValue('x', node, tensorMap, context))];
+            case 'Acosh':
+                return [tfOps.acosh(getParamValue('x', node, tensorMap, context))];
+            case 'Asin':
+                return [tfOps.asin(getParamValue('x', node, tensorMap, context))];
+            case 'Asinh':
+                return [tfOps.asinh(getParamValue('x', node, tensorMap, context))];
+            case 'Atan':
+                return [tfOps.atan(getParamValue('x', node, tensorMap, context))];
+            case 'Atan2':
+                return [tfOps.atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];
+            case 'Atanh':
+                return [tfOps.atanh(getParamValue('x', node, tensorMap, context))];
+            case 'Ceil':
+                return [tfOps.ceil(getParamValue('x', node, tensorMap, context))];
+            case 'Complex':
+                return [tfOps.complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))];
+            case 'Cos':
+                return [tfOps.cos(getParamValue('x', node, tensorMap, context))];
+            case 'Cosh':
+                return [tfOps.cosh(getParamValue('x', node, tensorMap, context))];
+            case 'Elu':
+                return [tfOps.elu(getParamValue('x', node, tensorMap, context))];
+            case 'Erf':
+                return [tfOps.erf(getParamValue('x', node, tensorMap, context))];
+            case 'Exp':
+                return [tfOps.exp(getParamValue('x', node, tensorMap, context))];
+            case 'Expm1': {
+                return [tfOps.expm1(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Floor':
+                return [tfOps.floor(getParamValue('x', node, tensorMap, context))];
+            case 'Log':
+                return [tfOps.log(getParamValue('x', node, tensorMap, context))];
+            case 'Log1p': {
+                return [tfOps.log1p(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Imag':
+                return [tfOps.imag(getParamValue('x', node, tensorMap, context))];
+            case 'Neg':
+                return [tfOps.neg(getParamValue('x', node, tensorMap, context))];
+            case 'Reciprocal': {
+                return [tfOps.reciprocal(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Real':
+                return [tfOps.real(getParamValue('x', node, tensorMap, context))];
+            case 'Relu':
+                return [tfOps.relu(getParamValue('x', node, tensorMap, context))];
+            case 'Round': {
+                return [tfOps.round(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Selu':
+                return [tfOps.selu(getParamValue('x', node, tensorMap, context))];
+            case 'Sigmoid':
+                return [tfOps.sigmoid(getParamValue('x', node, tensorMap, context))];
+            case 'Sin':
+                return [tfOps.sin(getParamValue('x', node, tensorMap, context))];
+            case 'Sign': {
+                return [tfOps.sign(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Sinh': {
+                return [tfOps.sinh(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Softplus': {
+                return [tfOps.softplus(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Sqrt': {
+                return [tfOps.sqrt(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Square': {
+                return [tfOps.square(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Tanh': {
+                return [tfOps.tanh(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Tan':
+                return [tfOps.tan(getParamValue('x', node, tensorMap, context))];
+            case 'ClipByValue':
+                return [tfOps.clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))];
+            case 'Relu6':
+                return [tfOps.relu6(getParamValue('x', node, tensorMap, context))];
+            case 'Rsqrt':
+                return [tfOps.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];
+            case 'Prod':
+                return [tfOps.prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))];
+            case 'LeakyRelu':
+                return [tfOps.leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];
+            case 'Prelu':
+                return [tfOps.prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];
+            case 'IsNan':
+                return [tfOps.isNaN(getTensor(node.inputNames[0], tensorMap, context))];
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Used by TensorList and TensorArray to verify if elementShape matches, support
+     * negative value as the dim shape.
+     * @param shapeA
+     * @param shapeB
+     * @param errorMessagePrefix
+     */
+    function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix) {
+        if (errorMessagePrefix === void 0) { errorMessagePrefix = ''; }
+        // constant shape means unknown rank
+        if (typeof shapeA === 'number' || typeof shapeB === 'number') {
+            return;
+        }
+        tfc.util.assert(shapeA.length === shapeB.length, function () { return errorMessagePrefix + (" Shapes " + shapeA + " and " + shapeB + " must match"); });
+        for (var i = 0; i < shapeA.length; i++) {
+            var dim0 = shapeA[i];
+            var dim1 = shapeB[i];
+            tfc.util.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, function () {
+                return errorMessagePrefix + (" Shapes " + shapeA + " and " + shapeB + " must match");
+            });
+        }
+    }
+    function fullDefinedShape(elementShape) {
+        if (typeof elementShape === 'number' || elementShape.some(function (dim) { return dim < 0; })) {
+            return false;
+        }
+        return true;
+    }
+    /**
+     * Generate the output element shape from the list elementShape, list tensors
+     * and input param.
+     * @param listElementShape
+     * @param tensors
+     * @param elementShape
+     */
+    function inferElementShape(listElementShape, tensors, elementShape) {
+        var partialShape = mergeElementShape(listElementShape, elementShape);
+        var notfullDefinedShape = !fullDefinedShape(partialShape);
+        if (notfullDefinedShape && tensors.length === 0) {
+            throw new Error("Tried to calculate elements of an empty list" +
+                (" with non-fully-defined elementShape: " + partialShape));
+        }
+        if (notfullDefinedShape) {
+            tensors.forEach(function (tensor) {
+                partialShape = mergeElementShape(tensor.shape, partialShape);
+            });
+        }
+        if (!fullDefinedShape(partialShape)) {
+            throw new Error("Non-fully-defined elementShape: " + partialShape);
+        }
+        return partialShape;
+    }
+    function mergeElementShape(elementShapeA, elementShapeB) {
+        if (typeof elementShapeA === 'number') {
+            return elementShapeB;
+        }
+        if (typeof elementShapeB === 'number') {
+            return elementShapeA;
+        }
+        if (elementShapeA.length !== elementShapeB.length) {
+            throw new Error("Incompatible ranks during merge: " + elementShapeA + " vs. " + elementShapeB);
+        }
+        var result = [];
+        for (var i = 0; i < elementShapeA.length; ++i) {
+            var dim0 = elementShapeA[i];
+            var dim1 = elementShapeB[i];
+            if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {
+                throw new Error("Incompatible shape during merge: " + elementShapeA + " vs. " + elementShapeB);
+            }
+            result[i] = dim0 >= 0 ? dim0 : dim1;
+        }
+        return result;
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * The TensorArray object keeps an array of Tensors.  It
+     * allows reading from the array and writing to the array.
+     */
+    var TensorArray = /** @class */ (function () {
+        function TensorArray(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {
+            this.name = name;
+            this.dtype = dtype;
+            this.maxSize = maxSize;
+            this.elementShape = elementShape;
+            this.identicalElementShapes = identicalElementShapes;
+            this.dynamicSize = dynamicSize;
+            this.clearAfterRead = clearAfterRead;
+            this.tensors = [];
+            this.closed_ = false;
+            this.idTensor = tfc.scalar(0);
+            tfc.keep(this.idTensor);
+        }
+        Object.defineProperty(TensorArray.prototype, "id", {
+            get: function () {
+                return this.idTensor.id;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TensorArray.prototype, "closed", {
+            get: function () {
+                return this.closed_;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        /**
+         * Dispose the tensors and idTensor and mark the TensoryArray as closed.
+         */
+        TensorArray.prototype.clearAndClose = function (keepIds) {
+            this.tensors.forEach(function (tensor) {
+                if (keepIds == null || !keepIds.has(tensor.tensor.id)) {
+                    tensor.tensor.dispose();
+                }
+            });
+            this.tensors = [];
+            this.closed_ = true;
+            this.idTensor.dispose();
+        };
+        TensorArray.prototype.size = function () {
+            return this.tensors.length;
+        };
+        /**
+         * Read the value at location index in the TensorArray.
+         * @param index Number the index to read from.
+         */
+        TensorArray.prototype.read = function (index) {
+            if (this.closed_) {
+                throw new Error("TensorArray " + this.name + " has already been closed.");
+            }
+            if (index < 0 || index >= this.size()) {
+                throw new Error("Tried to read from index " + index + ", but array size is: " + this.size());
+            }
+            var tensorWithState = this.tensors[index];
+            if (tensorWithState.cleared) {
+                throw new Error("TensorArray " + this.name + ": Could not read index " + index + " twice because it was cleared after a previous read " +
+                    "(perhaps try setting clear_after_read = false?).");
+            }
+            if (this.clearAfterRead) {
+                tensorWithState.cleared = true;
+            }
+            tensorWithState.read = true;
+            return tensorWithState.tensor;
+        };
+        /**
+         * Helper method to read multiple tensors from the specified indices.
+         */
+        TensorArray.prototype.readMany = function (indices) {
+            var _this = this;
+            return indices.map(function (index) { return _this.read(index); });
+        };
+        /**
+         * Write value into the index of the TensorArray.
+         * @param index number the index to write to.
+         * @param tensor
+         */
+        TensorArray.prototype.write = function (index, tensor) {
+            if (this.closed_) {
+                throw new Error("TensorArray " + this.name + " has already been closed.");
+            }
+            if (index < 0 || !this.dynamicSize && index >= this.maxSize) {
+                throw new Error("Tried to write to index " + index + ", but array is not resizeable and size is: " + this.maxSize);
+            }
+            var t = this.tensors[index] || {};
+            if (tensor.dtype !== this.dtype) {
+                throw new Error("TensorArray " + this.name + ": Could not write to TensorArray index " + index + ",\n          because the value dtype is " + tensor.dtype + ", but TensorArray dtype is " + this.dtype + ".");
+            }
+            // Set the shape for the first time write to unknow shape tensor array
+            if (this.size() === 0 &&
+                (this.elementShape == null || this.elementShape.length === 0)) {
+                this.elementShape = tensor.shape;
+            }
+            assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, "TensorArray " + this.name + ": Could not write to TensorArray index " + index + ".");
+            if (t.read) {
+                throw new Error("TensorArray " + this.name + ": Could not write to TensorArray index " + index + ", because it has already been read.");
+            }
+            if (t.written) {
+                throw new Error("TensorArray " + this.name + ": Could not write to TensorArray index " + index + ", because it has already been written.");
+            }
+            t.tensor = tensor;
+            tfc.keep(tensor);
+            t.written = true;
+            this.tensors[index] = t;
+        };
+        /**
+         * Helper method to write multiple tensors to the specified indices.
+         */
+        TensorArray.prototype.writeMany = function (indices, tensors) {
+            var _this = this;
+            if (indices.length !== tensors.length) {
+                throw new Error("TensorArray " + this.name + ": could not write multiple tensors," +
+                    ("because the index size: " + indices.length + " is not the same as tensors size: " + tensors.length + "."));
+            }
+            indices.forEach(function (i, index) { return _this.write(i, tensors[index]); });
+        };
+        /**
+         * Return selected values in the TensorArray as a packed Tensor. All of
+         * selected values must have been written and their shapes must all match.
+         * @param [indices] number[] Optional. Taking values in [0, max_value). If the
+         *    TensorArray is not dynamic, max_value=size(). If not specified returns
+         *    all tensors in the original order.
+         * @param [dtype]
+         */
+        TensorArray.prototype.gather = function (indices, dtype) {
+            if (!!dtype && dtype !== this.dtype) {
+                throw new Error("TensorArray dtype is " + this.dtype + " but gather requested dtype " + dtype);
+            }
+            if (!indices) {
+                indices = [];
+                for (var i = 0; i < this.size(); i++) {
+                    indices.push(i);
+                }
+            }
+            else {
+                indices = indices.slice(0, this.size());
+            }
+            if (indices.length === 0) {
+                return tfc.tensor([], [0].concat(this.elementShape));
+            }
+            // Read all the PersistentTensors into a vector to keep track of
+            // their memory.
+            var tensors = this.readMany(indices);
+            assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');
+            return tfc.stack(tensors, 0);
+        };
+        /**
+         * Return the values in the TensorArray as a concatenated Tensor.
+         */
+        TensorArray.prototype.concat = function (dtype) {
+            if (!!dtype && dtype !== this.dtype) {
+                throw new Error("TensorArray dtype is " + this.dtype + " but concat requested dtype " + dtype);
+            }
+            if (this.size() === 0) {
+                return tfc.tensor([], [0].concat(this.elementShape));
+            }
+            var indices = [];
+            for (var i = 0; i < this.size(); i++) {
+                indices.push(i);
+            }
+            // Collect all the tensors from the tensors array.
+            var tensors = this.readMany(indices);
+            assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, "TensorArray shape mismatch: tensor array shape (" + this.elementShape + ") vs first tensor shape (" + tensors[0].shape + ")");
+            return tfc.concat(tensors, 0);
+        };
+        /**
+         * Scatter the values of a Tensor in specific indices of a TensorArray.
+         * @param indices nummber[] values in [0, max_value). If the
+         *    TensorArray is not dynamic, max_value=size().
+         * @param tensor Tensor input tensor.
+         */
+        TensorArray.prototype.scatter = function (indices, tensor) {
+            if (tensor.dtype !== this.dtype) {
+                throw new Error("TensorArray dtype is " + this.dtype + " but tensor has dtype " + tensor.dtype);
+            }
+            if (indices.length !== tensor.shape[0]) {
+                throw new Error("Expected len(indices) == tensor.shape[0], but saw: " + indices.length + " vs. " + tensor.shape[0]);
+            }
+            var maxIndex = Math.max.apply(Math, __spread(indices));
+            if (!this.dynamicSize && maxIndex >= this.maxSize) {
+                throw new Error("Max index must be < array size (" + maxIndex + "  vs. " + this.maxSize + ")");
+            }
+            this.writeMany(indices, tfc.unstack(tensor, 0));
+        };
+        /**
+         * Split the values of a Tensor into the TensorArray.
+         * @param length number[] with the lengths to use when splitting value along
+         *    its first dimension.
+         * @param tensor Tensor, the tensor to split.
+         */
+        TensorArray.prototype.split = function (length, tensor) {
+            var _this = this;
+            if (tensor.dtype !== this.dtype) {
+                throw new Error("TensorArray dtype is " + this.dtype + " but tensor has dtype " + tensor.dtype);
+            }
+            var totalLength = 0;
+            var cumulativeLengths = length.map(function (len) {
+                totalLength += len;
+                return totalLength;
+            });
+            if (totalLength !== tensor.shape[0]) {
+                throw new Error("Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        " + totalLength + ", and tensor's shape is: " + tensor.shape);
+            }
+            if (!this.dynamicSize && length.length !== this.maxSize) {
+                throw new Error("TensorArray's size is not equal to the size of lengths (" + this.maxSize + " vs. " + length.length + "), " +
+                    'and the TensorArray is not marked as dynamically resizeable');
+            }
+            var elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;
+            var tensors = [];
+            tfc.tidy(function () {
+                tensor = tfc.reshape(tensor, [1, totalLength, elementPerRow]);
+                for (var i = 0; i < length.length; ++i) {
+                    var previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];
+                    var indices_1 = [0, previousLength, 0];
+                    var sizes = [1, length[i], elementPerRow];
+                    tensors[i] = tfc.reshape(tfc.slice(tensor, indices_1, sizes), _this.elementShape);
+                }
+                return tensors;
+            });
+            var indices = [];
+            for (var i = 0; i < length.length; i++) {
+                indices[i] = i;
+            }
+            this.writeMany(indices, tensors);
+        };
+        return TensorArray;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * TensorList stores a container of `tf.Tensor` objects, which are accessible
+     * via tensors field.
+     *
+     * In order to get a copy of the underlying list, use the copy method:
+     * ```
+     *    TensorList b = a.copy();
+     *    b.tensors().pushBack(t);  // This does not modify a.tensors().
+     * ```
+     *
+     * Note that this is not a deep copy: the memory locations of the underlying
+     * tensors will still point to the same locations of the corresponding tensors
+     * in the original.
+     */
+    var TensorList = /** @class */ (function () {
+        /**
+         *
+         * @param tensors list of tensors
+         * @param elementShape shape of each tensor, this can be a single number (any
+         * shape is allowed) or partial shape (dim = -1).
+         * @param elementDtype data type of each tensor
+         * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1
+         *   meaning that the size of `tensors` is unbounded.
+         */
+        function TensorList(tensors, elementShape, elementDtype, maxNumElements) {
+            if (maxNumElements === void 0) { maxNumElements = -1; }
+            this.tensors = tensors;
+            this.elementShape = elementShape;
+            this.elementDtype = elementDtype;
+            if (tensors != null) {
+                tensors.forEach(function (tensor) {
+                    if (elementDtype !== tensor.dtype) {
+                        throw new Error("Invalid data types; op elements " + elementDtype + ", but list elements " + tensor.dtype);
+                    }
+                    assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: ');
+                    tfc.keep(tensor);
+                });
+            }
+            this.idTensor = tfc.scalar(0);
+            this.maxNumElements = maxNumElements;
+            tfc.keep(this.idTensor);
+        }
+        Object.defineProperty(TensorList.prototype, "id", {
+            get: function () {
+                return this.idTensor.id;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        /**
+         * Get a new TensorList containing a copy of the underlying tensor container.
+         */
+        TensorList.prototype.copy = function () {
+            return new TensorList(__spread(this.tensors), this.elementShape, this.elementDtype);
+        };
+        /**
+         * Dispose the tensors and idTensor and clear the tensor list.
+         */
+        TensorList.prototype.clearAndClose = function (keepIds) {
+            this.tensors.forEach(function (tensor) {
+                if (keepIds == null || !keepIds.has(tensor.id)) {
+                    tensor.dispose();
+                }
+            });
+            this.tensors.length = 0;
+            this.idTensor.dispose();
+        };
+        /**
+         * The size of the tensors in the tensor list.
+         */
+        TensorList.prototype.size = function () {
+            return this.tensors.length;
+        };
+        /**
+         * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)
+         * tf.Tensor.
+         * @param elementShape shape of each tensor
+         * @param elementDtype data type of each tensor
+         * @param numElements the number of elements to stack
+         */
+        TensorList.prototype.stack = function (elementShape, elementDtype, numElements) {
+            var _this = this;
+            if (numElements === void 0) { numElements = -1; }
+            if (elementDtype !== this.elementDtype) {
+                throw new Error("Invalid data types; op elements " + elementDtype + ", but list elements " + this.elementDtype);
+            }
+            if (numElements !== -1 && this.tensors.length !== numElements) {
+                throw new Error("Operation expected a list with " + numElements + " elements but got a list with " + this.tensors.length + " elements.");
+            }
+            assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: ');
+            var outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+            return tfc.tidy(function () {
+                var reshapedTensors = _this.tensors.map(function (tensor) { return tfc.reshape(tensor, outputElementShape); });
+                return tfc.stack(reshapedTensors, 0);
+            });
+        };
+        /**
+         * Pop a tensor from the end of the list.
+         * @param elementShape shape of the tensor
+         * @param elementDtype data type of the tensor
+         */
+        TensorList.prototype.popBack = function (elementShape, elementDtype) {
+            if (elementDtype !== this.elementDtype) {
+                throw new Error("Invalid data types; op elements " + elementDtype + ", but list elements " + this.elementDtype);
+            }
+            if (this.size() === 0) {
+                throw new Error('Trying to pop from an empty list.');
+            }
+            var outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+            var tensor = this.tensors.pop();
+            assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: ');
+            return tfc.reshape(tensor, outputElementShape);
+        };
+        /**
+         * Push a tensor to the end of the list.
+         * @param tensor Tensor to be pushed.
+         */
+        TensorList.prototype.pushBack = function (tensor) {
+            if (tensor.dtype !== this.elementDtype) {
+                throw new Error("Invalid data types; op elements " + tensor.dtype + ", but list elements " + this.elementDtype);
+            }
+            assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: ');
+            if (this.maxNumElements === this.size()) {
+                throw new Error("Trying to push element into a full list.");
+            }
+            tfc.keep(tensor);
+            this.tensors.push(tensor);
+        };
+        /**
+         * Update the size of the list.
+         * @param size the new size of the list.
+         */
+        TensorList.prototype.resize = function (size) {
+            if (size < 0) {
+                throw new Error("TensorListResize expects size to be non-negative. Got: " + size);
+            }
+            if (this.maxNumElements !== -1 && size > this.maxNumElements) {
+                throw new Error("TensorListResize input size " + size + " is greater maxNumElement " + this.maxNumElements + ".");
+            }
+            this.tensors.length = size;
+        };
+        /**
+         * Retrieve the element at the provided index
+         * @param elementShape shape of the tensor
+         * @param elementDtype dtype of the tensor
+         * @param elementIndex index of the tensor
+         */
+        TensorList.prototype.getItem = function (elementIndex, elementShape, elementDtype) {
+            if (elementDtype !== this.elementDtype) {
+                throw new Error("Invalid data types; op elements " + elementDtype + ", but list elements " + this.elementDtype);
+            }
+            if (elementIndex < 0 || elementIndex > this.tensors.length) {
+                throw new Error("Trying to access element " + elementIndex + " in a list with " + this.tensors.length + " elements.");
+            }
+            if (this.tensors[elementIndex] == null) {
+                throw new Error("element at index " + elementIndex + " is null.");
+            }
+            assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: ');
+            var outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+            return tfc.reshape(this.tensors[elementIndex], outputElementShape);
+        };
+        /**
+         * Set the tensor at the index
+         * @param elementIndex index of the tensor
+         * @param tensor the tensor to be inserted into the list
+         */
+        TensorList.prototype.setItem = function (elementIndex, tensor) {
+            if (tensor.dtype !== this.elementDtype) {
+                throw new Error("Invalid data types; op elements " + tensor.dtype + ", but list elements " + this.elementDtype);
+            }
+            if (elementIndex < 0 ||
+                this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {
+                throw new Error("Trying to set element " + elementIndex + " in a list with max " + this.maxNumElements + " elements.");
+            }
+            assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: ');
+            tfc.keep(tensor);
+            this.tensors[elementIndex] = tensor;
+        };
+        /**
+         * Return selected values in the TensorList as a stacked Tensor. All of
+         * selected values must have been written and their shapes must all match.
+         * @param indices indices of tensors to gather
+         * @param elementDtype output tensor dtype
+         * @param elementShape output tensor element shape
+         */
+        TensorList.prototype.gather = function (indices, elementDtype, elementShape) {
+            var _this = this;
+            if (elementDtype !== this.elementDtype) {
+                throw new Error("Invalid data types; op elements " + elementDtype + ", but list elements " + this.elementDtype);
+            }
+            assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');
+            // When indices is greater than the size of the list, indices beyond the
+            // size of the list are ignored.
+            indices = indices.slice(0, this.size());
+            var outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+            if (indices.length === 0) {
+                return tfc.tensor([], [0].concat(outputElementShape));
+            }
+            return tfc.tidy(function () {
+                var tensors = indices.map(function (i) { return tfc.reshape(_this.tensors[i], outputElementShape); });
+                return tfc.stack(tensors, 0);
+            });
+        };
+        /**
+         * Return the values in the TensorList as a concatenated Tensor.
+         * @param elementDtype output tensor dtype
+         * @param elementShape output tensor element shape
+         */
+        TensorList.prototype.concat = function (elementDtype, elementShape) {
+            var _this = this;
+            if (!!elementDtype && elementDtype !== this.elementDtype) {
+                throw new Error("TensorList dtype is " + this.elementDtype + " but concat requested dtype " + elementDtype);
+            }
+            assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');
+            var outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+            if (this.size() === 0) {
+                return tfc.tensor([], [0].concat(outputElementShape));
+            }
+            return tfc.tidy(function () {
+                var tensors = _this.tensors.map(function (t) { return tfc.reshape(t, outputElementShape); });
+                return tfc.concat(tensors, 0);
+            });
+        };
+        return TensorList;
+    }());
+    /**
+     * Creates a TensorList which, when stacked, has the value of tensor.
+     * @param tensor from tensor
+     * @param elementShape output tensor element shape
+     */
+    function fromTensor(tensor, elementShape, elementDtype) {
+        var dtype = tensor.dtype;
+        if (tensor.shape.length < 1) {
+            throw new Error("Tensor must be at least a vector, but saw shape: " + tensor.shape);
+        }
+        if (tensor.dtype !== elementDtype) {
+            throw new Error("Invalid data types; op elements " + tensor.dtype + ", but list elements " + elementDtype);
+        }
+        var tensorElementShape = tensor.shape.slice(1);
+        assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, 'TensorList shape mismatch: ');
+        var tensorList = tfc.unstack(tensor);
+        return new TensorList(tensorList, elementShape, dtype);
+    }
+    /**
+     * Return a TensorList of the given size with empty elements.
+     * @param elementShape the shape of the future elements of the list
+     * @param elementDtype the desired type of elements in the list
+     * @param numElements the number of elements to reserve
+     */
+    function reserve(elementShape, elementDtype, numElements) {
+        return new TensorList([], elementShape, elementDtype, numElements);
+    }
+    /**
+     * Put tensors at specific indices of a stacked tensor into a TensorList.
+     * @param indices list of indices on how to scatter the tensor.
+     * @param tensor input tensor.
+     * @param elementShape the shape of the future elements of the list
+     * @param numElements the number of elements to scatter
+     */
+    function scatter(tensor, indices, elementShape, numElements) {
+        if (indices.length !== tensor.shape[0]) {
+            throw new Error("Expected len(indices) == tensor.shape[0], but saw: " + indices.length + " vs. " + tensor.shape[0]);
+        }
+        var maxIndex = Math.max.apply(Math, __spread(indices));
+        if (numElements != null && numElements !== -1 && maxIndex >= numElements) {
+            throw new Error("Max index must be < array size (" + maxIndex + "  vs. " + numElements + ")");
+        }
+        var list = new TensorList([], elementShape, tensor.dtype, numElements);
+        var tensors = tfc.unstack(tensor, 0);
+        indices.forEach(function (value, index) {
+            list.setItem(value, tensors[index]);
+        });
+        return list;
+    }
+    /**
+     * Split the values of a Tensor into a TensorList.
+     * @param length the lengths to use when splitting value along
+     *    its first dimension.
+     * @param tensor the tensor to split.
+     * @param elementShape the shape of the future elements of the list
+     */
+    function split(tensor, length, elementShape) {
+        var totalLength = 0;
+        var cumulativeLengths = length.map(function (len) {
+            totalLength += len;
+            return totalLength;
+        });
+        if (totalLength !== tensor.shape[0]) {
+            throw new Error("Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        " + totalLength + ", and tensor's shape is: " + tensor.shape);
+        }
+        var shapeWithoutFirstDim = tensor.shape.slice(1);
+        var outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape);
+        var elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;
+        var tensors = tfc.tidy(function () {
+            var tensors = [];
+            tensor = tfc.reshape(tensor, [1, totalLength, elementPerRow]);
+            for (var i = 0; i < length.length; ++i) {
+                var previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];
+                var indices = [0, previousLength, 0];
+                var sizes = [1, length[i], elementPerRow];
+                tensors[i] = tfc.reshape(tfc.slice(tensor, indices, sizes), outputElementShape);
+            }
+            tensor.dispose();
+            return tensors;
+        });
+        var list = new TensorList([], elementShape, tensor.dtype, length.length);
+        for (var i = 0; i < tensors.length; i++) {
+            list.setItem(i, tensors[i]);
+        }
+        return list;
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var _this = undefined;
+    var executeOp$2 = function (node, tensorMap, context) { return __awaiter(_this, void 0, void 0, function () {
+        var _a, thenFunc, elseFunc, cond, args, condValue, bodyFunc, condFunc, args, condResult, argIds_1, condValue, result, _loop_1, pred, pred, data, inputName, data, frameId, data, data, data, size, dtype, elementShape, dynamicSize, clearAfterRead, identicalElementShapes, name_1, tensorArray, id, index, writeTensor, writeTensorArray, readId, readIndex, readTensorArray, gatherId, gatherIndices, gatherDtype, gatherTensorArray, scatterId, scatterIndices, scatterTensor, scatterTensorArray, concatId, concatTensorArray, concatDtype, splitId, splitTensor, lengths, splitTensorArray, sizeId, sizeTensorArray, closeId, closeTensorArray, idTensor, index, writeTensor, tensorList, idTensor, readIndex, elementShape, elementDType, tensorList, scatterIndices, scatterTensor, elementShape, numElements, tensorList, elementShape, elementDtype, numElementsParam, numElements, tensorList, gatherId, gatherIndices, elementShape, elementDtype, tensorList, idTensor, elementShape, elementDtype, numElements, tensorList, tensor, elementShape, elementDtype, tensorList, concatId, tensorList, concatDtype, elementShape, idTensor, writeTensor, tensorList, idTensor, elementShape, elementDType, tensorList, splitTensor, elementShape, lengths, tensorList;
+        return __generator(this, function (_b) {
+            switch (_b.label) {
+                case 0:
+                    _a = node.op;
+                    switch (_a) {
+                        case 'If': return [3 /*break*/, 1];
+                        case 'StatelessIf': return [3 /*break*/, 1];
+                        case 'While': return [3 /*break*/, 3];
+                        case 'StatelessWhile': return [3 /*break*/, 3];
+                        case 'LoopCond': return [3 /*break*/, 9];
+                        case 'Switch': return [3 /*break*/, 10];
+                        case 'Merge': return [3 /*break*/, 12];
+                        case 'Enter': return [3 /*break*/, 13];
+                        case 'Exit': return [3 /*break*/, 14];
+                        case 'NextIteration': return [3 /*break*/, 15];
+                        case 'TensorArrayV3': return [3 /*break*/, 16];
+                        case 'TensorArrayWriteV3': return [3 /*break*/, 17];
+                        case 'TensorArrayReadV3': return [3 /*break*/, 18];
+                        case 'TensorArrayGatherV3': return [3 /*break*/, 19];
+                        case 'TensorArrayScatterV3': return [3 /*break*/, 20];
+                        case 'TensorArrayConcatV3': return [3 /*break*/, 21];
+                        case 'TensorArraySplitV3': return [3 /*break*/, 22];
+                        case 'TensorArraySizeV3': return [3 /*break*/, 23];
+                        case 'TensorArrayCloseV3': return [3 /*break*/, 24];
+                        case 'TensorListSetItem': return [3 /*break*/, 25];
+                        case 'TensorListGetItem': return [3 /*break*/, 26];
+                        case 'TensorListScatterV2': return [3 /*break*/, 27];
+                        case 'TensorListScatter': return [3 /*break*/, 27];
+                        case 'TensorListReserve': return [3 /*break*/, 28];
+                        case 'EmptyTensorList': return [3 /*break*/, 28];
+                        case 'TensorListGather': return [3 /*break*/, 29];
+                        case 'TensorListStack': return [3 /*break*/, 30];
+                        case 'TensorListFromTensor': return [3 /*break*/, 31];
+                        case 'TensorListConcat': return [3 /*break*/, 32];
+                        case 'TensorListPushBack': return [3 /*break*/, 33];
+                        case 'TensorListPopBack': return [3 /*break*/, 34];
+                        case 'TensorListSplit': return [3 /*break*/, 35];
+                    }
+                    return [3 /*break*/, 36];
+                case 1:
+                    thenFunc = getParamValue('thenBranch', node, tensorMap, context);
+                    elseFunc = getParamValue('elseBranch', node, tensorMap, context);
+                    cond = getParamValue('cond', node, tensorMap, context);
+                    args = getParamValue('args', node, tensorMap, context);
+                    return [4 /*yield*/, cond.data()];
+                case 2:
+                    condValue = _b.sent();
+                    if (condValue[0]) {
+                        return [2 /*return*/, context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap)];
+                    }
+                    else {
+                        return [2 /*return*/, context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap)];
+                    }
+                case 3:
+                    bodyFunc = getParamValue('body', node, tensorMap, context);
+                    condFunc = getParamValue('cond', node, tensorMap, context);
+                    args = getParamValue('args', node, tensorMap, context);
+                    return [4 /*yield*/, context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap)];
+                case 4:
+                    condResult = (_b.sent());
+                    argIds_1 = args.map(function (tensor) { return tensor.id; });
+                    return [4 /*yield*/, condResult[0].data()];
+                case 5:
+                    condValue = _b.sent();
+                    // Dispose the intermediate tensors for condition function
+                    condResult.forEach(function (tensor) {
+                        if (!tensor.kept && argIds_1.indexOf(tensor.id) === -1) {
+                            tensor.dispose();
+                        }
+                    });
+                    result = args;
+                    _loop_1 = function () {
+                        var origResult, resultIds, condResult_1;
+                        return __generator(this, function (_a) {
+                            switch (_a.label) {
+                                case 0:
+                                    origResult = result;
+                                    return [4 /*yield*/, context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap)];
+                                case 1:
+                                    // Execution the body of the loop
+                                    result = _a.sent();
+                                    resultIds = result.map(function (tensor) { return tensor.id; });
+                                    // Dispose the intermediate tensor for body function that is not global
+                                    // kept, not input/output of the body function
+                                    origResult.forEach(function (tensor) {
+                                        if (!tensor.kept && argIds_1.indexOf(tensor.id) === -1 &&
+                                            resultIds.indexOf(tensor.id) === -1) {
+                                            tensor.dispose();
+                                        }
+                                    });
+                                    return [4 /*yield*/, context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap)];
+                                case 2:
+                                    condResult_1 = (_a.sent());
+                                    return [4 /*yield*/, condResult_1[0].data()];
+                                case 3:
+                                    condValue = _a.sent();
+                                    // Dispose the intermediate tensors for condition function
+                                    condResult_1.forEach(function (tensor) {
+                                        if (!tensor.kept && argIds_1.indexOf(tensor.id) === -1 &&
+                                            resultIds.indexOf(tensor.id) === -1) {
+                                            tensor.dispose();
+                                        }
+                                    });
+                                    return [2 /*return*/];
+                            }
+                        });
+                    };
+                    _b.label = 6;
+                case 6:
+                    if (!condValue[0]) return [3 /*break*/, 8];
+                    return [5 /*yield**/, _loop_1()];
+                case 7:
+                    _b.sent();
+                    return [3 /*break*/, 6];
+                case 8: return [2 /*return*/, result];
+                case 9:
+                    {
+                        pred = getParamValue('pred', node, tensorMap, context);
+                        return [2 /*return*/, [cloneTensor(pred)]];
+                    }
+                case 10:
+                    pred = getParamValue('pred', node, tensorMap, context);
+                    data = getParamValue('data', node, tensorMap, context);
+                    if (!data.kept) {
+                        data = cloneTensor(data);
+                    }
+                    return [4 /*yield*/, pred.data()];
+                case 11: 
+                // Outputs nodes :0 => false, :1 => true
+                return [2 /*return*/, (_b.sent())[0] ? [undefined, data] : [data, undefined]];
+                case 12:
+                    {
+                        inputName = node.inputNames.find(function (name) { return getTensor(name, tensorMap, context) !== undefined; });
+                        if (inputName) {
+                            data = getTensor(inputName, tensorMap, context);
+                            return [2 /*return*/, [cloneTensor(data)]];
+                        }
+                        return [2 /*return*/, undefined];
+                    }
+                case 13:
+                    {
+                        frameId = getParamValue('frameName', node, tensorMap, context);
+                        data = getParamValue('tensor', node, tensorMap, context);
+                        context.enterFrame(frameId);
+                        return [2 /*return*/, [cloneTensor(data)]];
+                    }
+                case 14:
+                    {
+                        data = getParamValue('tensor', node, tensorMap, context);
+                        context.exitFrame();
+                        return [2 /*return*/, [cloneTensor(data)]];
+                    }
+                case 15:
+                    {
+                        data = getParamValue('tensor', node, tensorMap, context);
+                        context.nextIteration();
+                        return [2 /*return*/, [cloneTensor(data)]];
+                    }
+                case 16:
+                    {
+                        size = getParamValue('size', node, tensorMap, context);
+                        dtype = getParamValue('dtype', node, tensorMap, context);
+                        elementShape = getParamValue('elementShape', node, tensorMap, context);
+                        dynamicSize = getParamValue('dynamicSize', node, tensorMap, context);
+                        clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context);
+                        identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context);
+                        name_1 = getParamValue('name', node, tensorMap, context);
+                        tensorArray = new TensorArray(name_1, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);
+                        context.addTensorArray(tensorArray);
+                        return [2 /*return*/, [tensorArray.idTensor, tfc.scalar(1.0)]];
+                    }
+                case 17:
+                    {
+                        id = getParamValue('tensorArrayId', node, tensorMap, context);
+                        index = getParamValue('index', node, tensorMap, context);
+                        writeTensor = getParamValue('tensor', node, tensorMap, context);
+                        writeTensorArray = context.getTensorArray(id.id);
+                        writeTensorArray.write(index, writeTensor);
+                        return [2 /*return*/, [writeTensorArray.idTensor]];
+                    }
+                case 18:
+                    {
+                        readId = getParamValue('tensorArrayId', node, tensorMap, context);
+                        readIndex = getParamValue('index', node, tensorMap, context);
+                        readTensorArray = context.getTensorArray(readId.id);
+                        return [2 /*return*/, [readTensorArray.read(readIndex)]];
+                    }
+                case 19:
+                    {
+                        gatherId = getParamValue('tensorArrayId', node, tensorMap, context);
+                        gatherIndices = getParamValue('indices', node, tensorMap, context);
+                        gatherDtype = getParamValue('dtype', node, tensorMap, context);
+                        gatherTensorArray = context.getTensorArray(gatherId.id);
+                        return [2 /*return*/, [gatherTensorArray.gather(gatherIndices, gatherDtype)]];
+                    }
+                case 20:
+                    {
+                        scatterId = getParamValue('tensorArrayId', node, tensorMap, context);
+                        scatterIndices = getParamValue('indices', node, tensorMap, context);
+                        scatterTensor = getParamValue('tensor', node, tensorMap, context);
+                        scatterTensorArray = context.getTensorArray(scatterId.id);
+                        scatterTensorArray.scatter(scatterIndices, scatterTensor);
+                        return [2 /*return*/, [scatterTensorArray.idTensor]];
+                    }
+                case 21:
+                    {
+                        concatId = getParamValue('tensorArrayId', node, tensorMap, context);
+                        concatTensorArray = context.getTensorArray(concatId.id);
+                        concatDtype = getParamValue('dtype', node, tensorMap, context);
+                        return [2 /*return*/, [concatTensorArray.concat(concatDtype)]];
+                    }
+                case 22:
+                    {
+                        splitId = getParamValue('tensorArrayId', node, tensorMap, context);
+                        splitTensor = getParamValue('tensor', node, tensorMap, context);
+                        lengths = getParamValue('lengths', node, tensorMap, context);
+                        splitTensorArray = context.getTensorArray(splitId.id);
+                        splitTensorArray.split(lengths, splitTensor);
+                        return [2 /*return*/, [splitTensorArray.idTensor]];
+                    }
+                case 23:
+                    {
+                        sizeId = getParamValue('tensorArrayId', node, tensorMap, context);
+                        sizeTensorArray = context.getTensorArray(sizeId.id);
+                        return [2 /*return*/, [tfc.scalar(sizeTensorArray.size(), 'int32')]];
+                    }
+                case 24:
+                    {
+                        closeId = getParamValue('tensorArrayId', node, tensorMap, context);
+                        closeTensorArray = context.getTensorArray(closeId.id);
+                        closeTensorArray.clearAndClose();
+                        return [2 /*return*/, [closeTensorArray.idTensor]];
+                    }
+                case 25:
+                    {
+                        idTensor = getParamValue('tensorListId', node, tensorMap, context);
+                        index = getParamValue('index', node, tensorMap, context);
+                        writeTensor = getParamValue('tensor', node, tensorMap, context);
+                        tensorList = context.getTensorList(idTensor.id);
+                        tensorList.setItem(index, writeTensor);
+                        return [2 /*return*/, [tensorList.idTensor]];
+                    }
+                case 26:
+                    {
+                        idTensor = getParamValue('tensorListId', node, tensorMap, context);
+                        readIndex = getParamValue('index', node, tensorMap, context);
+                        elementShape = getParamValue('elementShape', node, tensorMap, context);
+                        elementDType = getParamValue('elementDType', node, tensorMap, context);
+                        tensorList = context.getTensorList(idTensor.id);
+                        return [2 /*return*/, [tensorList.getItem(readIndex, elementShape, elementDType)]];
+                    }
+                case 27:
+                    {
+                        scatterIndices = getParamValue('indices', node, tensorMap, context);
+                        scatterTensor = getParamValue('tensor', node, tensorMap, context);
+                        elementShape = getParamValue('elementShape', node, tensorMap, context);
+                        numElements = getParamValue('numElements', node, tensorMap, context);
+                        tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);
+                        context.addTensorList(tensorList);
+                        return [2 /*return*/, [tensorList.idTensor]];
+                    }
+                case 28:
+                    {
+                        elementShape = getParamValue('elementShape', node, tensorMap, context);
+                        elementDtype = getParamValue('elementDType', node, tensorMap, context);
+                        numElementsParam = void 0;
+                        if (node.op === 'TensorListReserve') {
+                            numElementsParam = 'numElements';
+                        }
+                        else {
+                            numElementsParam = 'maxNumElements';
+                        }
+                        numElements = getParamValue(numElementsParam, node, tensorMap, context);
+                        tensorList = reserve(elementShape, elementDtype, numElements);
+                        context.addTensorList(tensorList);
+                        return [2 /*return*/, [tensorList.idTensor]];
+                    }
+                case 29:
+                    {
+                        gatherId = getParamValue('tensorListId', node, tensorMap, context);
+                        gatherIndices = getParamValue('indices', node, tensorMap, context);
+                        elementShape = getParamValue('elementShape', node, tensorMap, context);
+                        elementDtype = getParamValue('elementDType', node, tensorMap, context);
+                        tensorList = context.getTensorList(gatherId.id);
+                        return [2 /*return*/, [tensorList.gather(gatherIndices, elementDtype, elementShape)]];
+                    }
+                case 30:
+                    {
+                        idTensor = getParamValue('tensorListId', node, tensorMap, context);
+                        elementShape = getParamValue('elementShape', node, tensorMap, context);
+                        elementDtype = getParamValue('elementDType', node, tensorMap, context);
+                        numElements = getParamValue('numElements', node, tensorMap, context);
+                        tensorList = context.getTensorList(idTensor.id);
+                        return [2 /*return*/, [tensorList.stack(elementShape, elementDtype, numElements)]];
+                    }
+                case 31:
+                    {
+                        tensor = getParamValue('tensor', node, tensorMap, context);
+                        elementShape = getParamValue('elementShape', node, tensorMap, context);
+                        elementDtype = getParamValue('elementDType', node, tensorMap, context);
+                        tensorList = fromTensor(tensor, elementShape, elementDtype);
+                        context.addTensorList(tensorList);
+                        return [2 /*return*/, [tensorList.idTensor]];
+                    }
+                case 32:
+                    {
+                        concatId = getParamValue('tensorListId', node, tensorMap, context);
+                        tensorList = context.getTensorList(concatId.id);
+                        concatDtype = getParamValue('dtype', node, tensorMap, context);
+                        elementShape = getParamValue('elementShape', node, tensorMap, context);
+                        return [2 /*return*/, [tensorList.concat(concatDtype, elementShape)]];
+                    }
+                case 33:
+                    {
+                        idTensor = getParamValue('tensorListId', node, tensorMap, context);
+                        writeTensor = getParamValue('tensor', node, tensorMap, context);
+                        tensorList = context.getTensorList(idTensor.id);
+                        tensorList.pushBack(writeTensor);
+                        return [2 /*return*/, [tensorList.idTensor]];
+                    }
+                case 34:
+                    {
+                        idTensor = getParamValue('tensorListId', node, tensorMap, context);
+                        elementShape = getParamValue('elementShape', node, tensorMap, context);
+                        elementDType = getParamValue('elementDType', node, tensorMap, context);
+                        tensorList = context.getTensorList(idTensor.id);
+                        return [2 /*return*/, [tensorList.popBack(elementShape, elementDType)]];
+                    }
+                case 35:
+                    {
+                        splitTensor = getParamValue('tensor', node, tensorMap, context);
+                        elementShape = getParamValue('elementShape', node, tensorMap, context);
+                        lengths = getParamValue('lengths', node, tensorMap, context);
+                        tensorList = split(splitTensor, lengths, elementShape);
+                        context.addTensorList(tensorList);
+                        return [2 /*return*/, [tensorList.idTensor]];
+                    }
+                case 36: throw TypeError("Node type " + node.op + " is not implemented");
+            }
+        });
+    }); };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function fusedConvAndDepthWiseParams(node, tensorMap, context) {
+        var _a = __read(getParamValue('fusedOps', node, tensorMap, context), 2), extraOp = _a[0], activationFunc = _a[1];
+        var isBiasAdd = extraOp === 'biasadd';
+        var noBiasAdd = !isBiasAdd;
+        var isPrelu = activationFunc === 'prelu';
+        var isBatchNorm = extraOp === 'fusedbatchnorm';
+        var numArgs = getParamValue('numArgs', node, tensorMap, context);
+        if (isBiasAdd) {
+            if (isPrelu && numArgs !== 2) {
+                throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +
+                    'must have two extra arguments: bias and alpha.');
+            }
+            if (!isPrelu && isBiasAdd && numArgs !== 1) {
+                throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +
+                    'one extra argument: bias.');
+            }
+        }
+        if (isBatchNorm) {
+            throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported');
+        }
+        var stride = getParamValue('strides', node, tensorMap, context);
+        var pad = getPadding(node, tensorMap, context);
+        var dataFormat = getParamValue('dataFormat', node, tensorMap, context)
+            .toUpperCase();
+        var dilations = getParamValue('dilations', node, tensorMap, context);
+        var _b = __read(getParamValue('args', node, tensorMap, context), 2), biasArg = _b[0], preluArg = _b[1];
+        if (noBiasAdd) {
+            preluArg = biasArg;
+            biasArg = undefined;
+        }
+        var leakyreluAlpha = getParamValue('leakyreluAlpha', node, tensorMap, context);
+        return {
+            stride: stride,
+            pad: pad,
+            dataFormat: dataFormat,
+            dilations: dilations,
+            biasArg: biasArg,
+            preluArg: preluArg,
+            activationFunc: activationFunc,
+            leakyreluAlpha: leakyreluAlpha
+        };
+    }
+    var executeOp$3 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'Conv1D': {
+                var stride = getParamValue('stride', node, tensorMap, context);
+                var pad = getParamValue('pad', node, tensorMap, context);
+                var dataFormat = getParamValue('dataFormat', node, tensorMap, context)
+                    .toUpperCase();
+                var dilation = getParamValue('dilation', node, tensorMap, context);
+                return [tfOps.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)];
+            }
+            case 'Conv2D': {
+                var stride = getParamValue('strides', node, tensorMap, context);
+                var pad = getPadding(node, tensorMap, context);
+                var dataFormat = getParamValue('dataFormat', node, tensorMap, context)
+                    .toUpperCase();
+                var dilations = getParamValue('dilations', node, tensorMap, context);
+                return [tfOps.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];
+            }
+            case '_FusedConv2D': {
+                var _a = fusedConvAndDepthWiseParams(node, tensorMap, context), stride = _a.stride, pad = _a.pad, dataFormat = _a.dataFormat, dilations = _a.dilations, biasArg = _a.biasArg, preluArg = _a.preluArg, activationFunc = _a.activationFunc, leakyreluAlpha = _a.leakyreluAlpha;
+                return [tfOps.fused.conv2d({
+                        x: getParamValue('x', node, tensorMap, context),
+                        filter: getParamValue('filter', node, tensorMap, context),
+                        strides: [stride[1], stride[2]],
+                        pad: pad,
+                        dataFormat: dataFormat,
+                        dilations: [dilations[1], dilations[2]],
+                        bias: biasArg,
+                        activation: activationFunc,
+                        preluActivationWeights: preluArg,
+                        leakyreluAlpha: leakyreluAlpha
+                    })];
+            }
+            case 'FusedDepthwiseConv2dNative': {
+                var _b = fusedConvAndDepthWiseParams(node, tensorMap, context), stride = _b.stride, pad = _b.pad, dataFormat = _b.dataFormat, dilations = _b.dilations, biasArg = _b.biasArg, preluArg = _b.preluArg, activationFunc = _b.activationFunc, leakyreluAlpha = _b.leakyreluAlpha;
+                return [tfOps.fused.depthwiseConv2d({
+                        x: getParamValue('x', node, tensorMap, context),
+                        filter: getParamValue('filter', node, tensorMap, context),
+                        strides: [stride[1], stride[2]],
+                        pad: pad,
+                        dataFormat: dataFormat,
+                        dilations: [dilations[1], dilations[2]],
+                        bias: biasArg,
+                        activation: activationFunc,
+                        preluActivationWeights: preluArg,
+                        leakyreluAlpha: leakyreluAlpha
+                    })];
+            }
+            case 'Conv2DBackpropInput':
+            case 'Conv2dTranspose': {
+                var shape = getParamValue('outputShape', node, tensorMap, context);
+                var stride = getParamValue('strides', node, tensorMap, context);
+                var pad = getPadding(node, tensorMap, context);
+                return [tfOps.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)];
+            }
+            case 'DepthwiseConv2dNative':
+            case 'DepthwiseConv2d': {
+                var stride = getParamValue('strides', node, tensorMap, context);
+                var pad = getPadding(node, tensorMap, context);
+                var dilations = getParamValue('dilations', node, tensorMap, context);
+                var dataFormat = getParamValue('dataFormat', node, tensorMap, context)
+                    .toUpperCase();
+                return [tfOps.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];
+            }
+            case 'Conv3D': {
+                var stride = getParamValue('strides', node, tensorMap, context);
+                var pad = getParamValue('pad', node, tensorMap, context);
+                var dataFormat = getParamValue('dataFormat', node, tensorMap, context)
+                    .toUpperCase();
+                var dilations = getParamValue('dilations', node, tensorMap, context);
+                return [tfOps.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])];
+            }
+            case 'AvgPool': {
+                var stride = getParamValue('strides', node, tensorMap, context);
+                var pad = getParamValue('pad', node, tensorMap, context);
+                var kernelSize = getParamValue('kernelSize', node, tensorMap, context);
+                return [tfOps.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];
+            }
+            case 'MaxPool': {
+                var stride = getParamValue('strides', node, tensorMap, context);
+                var pad = getParamValue('pad', node, tensorMap, context);
+                var kernelSize = getParamValue('kernelSize', node, tensorMap, context);
+                return [tfOps.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];
+            }
+            case 'MaxPoolWithArgmax': {
+                var stride = getParamValue('strides', node, tensorMap, context);
+                var pad = getParamValue('pad', node, tensorMap, context);
+                var kernelSize = getParamValue('kernelSize', node, tensorMap, context);
+                var includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context);
+                var _c = tfOps.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex), result = _c.result, indexes = _c.indexes;
+                return [result, indexes];
+            }
+            case 'AvgPool3D': {
+                var stride = getParamValue('strides', node, tensorMap, context);
+                var pad = getParamValue('pad', node, tensorMap, context);
+                var kernelSize = getParamValue('kernelSize', node, tensorMap, context);
+                return [tfOps.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];
+            }
+            case 'MaxPool3D': {
+                var stride = getParamValue('strides', node, tensorMap, context);
+                var pad = getParamValue('pad', node, tensorMap, context);
+                var kernelSize = getParamValue('kernelSize', node, tensorMap, context);
+                return [tfOps.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];
+            }
+            case 'Dilation2D': {
+                var strides = getParamValue('strides', node, tensorMap, context);
+                var pad = getParamValue('pad', node, tensorMap, context);
+                var dilations = getParamValue('dilations', node, tensorMap, context);
+                // strides: [1, stride_height, stride_width, 1].
+                var strideHeight = strides[1];
+                var strideWidth = strides[2];
+                // dilations: [1, dilation_height, dilation_width, 1].
+                var dilationHeight = dilations[1];
+                var dilationWidth = dilations[2];
+                return [tfOps.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$4 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'Fill': {
+                var shape = getParamValue('shape', node, tensorMap, context);
+                var dtype = getParamValue('dtype', node, tensorMap, context);
+                var value = getParamValue('value', node, tensorMap, context);
+                return [tfOps.fill(shape, value, dtype)];
+            }
+            case 'LinSpace': {
+                var start = getParamValue('start', node, tensorMap, context);
+                var stop_1 = getParamValue('stop', node, tensorMap, context);
+                var num = getParamValue('num', node, tensorMap, context);
+                return [tfOps.linspace(start, stop_1, num)];
+            }
+            case 'Multinomial': {
+                var logits = getParamValue('logits', node, tensorMap, context);
+                var numSamples = getParamValue('numSamples', node, tensorMap, context);
+                var seed = getParamValue('seed', node, tensorMap, context);
+                return [tfOps.multinomial(logits, numSamples, seed)];
+            }
+            case 'OneHot': {
+                var indices = getParamValue('indices', node, tensorMap, context);
+                var depth = getParamValue('depth', node, tensorMap, context);
+                var onValue = getParamValue('onValue', node, tensorMap, context);
+                var offValue = getParamValue('offValue', node, tensorMap, context);
+                return [tfOps.oneHot(indices, depth, onValue, offValue)];
+            }
+            case 'Ones': {
+                return [tfOps.ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];
+            }
+            case 'OnesLike': {
+                return [tfOps.onesLike(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'RandomUniform': {
+                return [tfOps.randomUniform(
+                    // tslint:disable-next-line:no-any
+                    getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];
+            }
+            case 'Range': {
+                var start = getParamValue('start', node, tensorMap, context);
+                var stop_2 = getParamValue('stop', node, tensorMap, context);
+                var step = getParamValue('step', node, tensorMap, context);
+                return [tfOps.range(start, stop_2, step, getParamValue('dtype', node, tensorMap, context))];
+            }
+            case 'TruncatedNormal': {
+                var shape = getParamValue('shape', node, tensorMap, context);
+                var mean = getParamValue('mean', node, tensorMap, context);
+                var stdDev = getParamValue('stdDev', node, tensorMap, context);
+                var seed = getParamValue('seed', node, tensorMap, context);
+                return [tfOps.truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)];
+            }
+            case 'Zeros': {
+                return [tfOps.zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];
+            }
+            case 'ZerosLike': {
+                return [tfOps.zerosLike(getParamValue('x', node, tensorMap, context))];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var _this$1 = undefined;
+    function nmsParams(node, tensorMap, context) {
+        var boxes = getParamValue('boxes', node, tensorMap, context);
+        var scores = getParamValue('scores', node, tensorMap, context);
+        var maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context);
+        var iouThreshold = getParamValue('iouThreshold', node, tensorMap, context);
+        var scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context);
+        var softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context);
+        return {
+            boxes: boxes,
+            scores: scores,
+            maxOutputSize: maxOutputSize,
+            iouThreshold: iouThreshold,
+            scoreThreshold: scoreThreshold,
+            softNmsSigma: softNmsSigma
+        };
+    }
+    var executeOp$5 = function (node, tensorMap, context) { return __awaiter(_this$1, void 0, void 0, function () {
+        var _a, _b, boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, result, _c, boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize, result, _d, boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, condition, result;
+        return __generator(this, function (_e) {
+            switch (_e.label) {
+                case 0:
+                    _a = node.op;
+                    switch (_a) {
+                        case 'NonMaxSuppressionV5': return [3 /*break*/, 1];
+                        case 'NonMaxSuppressionV4': return [3 /*break*/, 3];
+                        case 'NonMaxSuppressionV3': return [3 /*break*/, 5];
+                        case 'NonMaxSuppressionV2': return [3 /*break*/, 5];
+                        case 'Where': return [3 /*break*/, 7];
+                        case 'ListDiff': return [3 /*break*/, 9];
+                    }
+                    return [3 /*break*/, 10];
+                case 1:
+                    _b = nmsParams(node, tensorMap, context), boxes = _b.boxes, scores = _b.scores, maxOutputSize = _b.maxOutputSize, iouThreshold = _b.iouThreshold, scoreThreshold = _b.scoreThreshold, softNmsSigma = _b.softNmsSigma;
+                    return [4 /*yield*/, tfOps.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma)];
+                case 2:
+                    result = _e.sent();
+                    return [2 /*return*/, [result.selectedIndices, result.selectedScores]];
+                case 3:
+                    _c = nmsParams(node, tensorMap, context), boxes = _c.boxes, scores = _c.scores, maxOutputSize = _c.maxOutputSize, iouThreshold = _c.iouThreshold, scoreThreshold = _c.scoreThreshold;
+                    padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context);
+                    return [4 /*yield*/, tfOps.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize)];
+                case 4:
+                    result = _e.sent();
+                    return [2 /*return*/, [result.selectedIndices, result.validOutputs]];
+                case 5:
+                    _d = nmsParams(node, tensorMap, context), boxes = _d.boxes, scores = _d.scores, maxOutputSize = _d.maxOutputSize, iouThreshold = _d.iouThreshold, scoreThreshold = _d.scoreThreshold;
+                    return [4 /*yield*/, tfOps.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];
+                case 6: return [2 /*return*/, [_e.sent()]];
+                case 7:
+                    condition = tfOps.cast(getParamValue('condition', node, tensorMap, context), 'bool');
+                    return [4 /*yield*/, tfOps.whereAsync(condition)];
+                case 8:
+                    result = [_e.sent()];
+                    condition.dispose();
+                    return [2 /*return*/, result];
+                case 9:
+                    {
+                        return [2 /*return*/, tfOps.setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];
+                    }
+                case 10: throw TypeError("Node type " + node.op + " is not implemented");
+            }
+        });
+    }); };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$6 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'TopKV2': {
+                var x = getParamValue('x', node, tensorMap, context);
+                var k = getParamValue('k', node, tensorMap, context);
+                var sorted = getParamValue('sorted', node, tensorMap, context);
+                var result = tfOps.topk(x, k, sorted);
+                return [result.values, result.indices];
+            }
+            case 'Unique': {
+                var x = getParamValue('x', node, tensorMap, context);
+                var result = tfOps.unique(x);
+                return [result.values, result.indices];
+            }
+            case 'UniqueV2': {
+                var x = getParamValue('x', node, tensorMap, context);
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var result = tfOps.unique(x, axis);
+                return [result.values, result.indices];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$7 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'Const': {
+                return tensorMap[node.name];
+            }
+            case 'PlaceholderWithDefault':
+                var def = getParamValue('default', node, tensorMap, context);
+                return [getTensor(node.name, tensorMap, context) || def];
+            case 'Placeholder':
+                return [getTensor(node.name, tensorMap, context)];
+            case 'Identity':
+            case 'StopGradient':
+            case 'FakeQuantWithMinMaxVars': { // This op is currently ignored.
+                var data_1 = getParamValue('x', node, tensorMap, context);
+                return [cloneTensor(data_1)];
+            }
+            case 'IdentityN':
+                return getParamValue('x', node, tensorMap, context)
+                    .map(function (t) { return cloneTensor(t); });
+            case 'Snapshot':
+                var snapshot = getParamValue('x', node, tensorMap, context);
+                return [cloneTensor(snapshot)];
+            case 'Shape':
+                return [tfOps.tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')];
+            case 'ShapeN':
+                return getParamValue('x', node, tensorMap, context)
+                    .map(function (t) { return tfOps.tensor1d(t.shape); });
+            case 'Size':
+                return [tfOps.scalar(getParamValue('x', node, tensorMap, context).size, 'int32')];
+            case 'Rank':
+                return [tfOps.scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')];
+            case 'NoOp':
+                return [tfOps.scalar(1)];
+            case 'Print':
+                var input = getParamValue('x', node, tensorMap, context);
+                var data = getParamValue('data', node, tensorMap, context);
+                var message = getParamValue('message', node, tensorMap, context);
+                var summarize = getParamValue('summarize', node, tensorMap, context);
+                console.warn('The graph has a tf.print() operation,' +
+                    'usually used for debugging, which slows down performance.');
+                console.log(message);
+                for (var i = 0; i < data.length; i++) {
+                    console.log(Array.prototype.slice.call(data[i].dataSync())
+                        .slice(0, summarize));
+                }
+                return [input];
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * Hashtable contains a set of tensors, which can be accessed by key.
+     */
+    var HashTable = /** @class */ (function () {
+        /**
+         * Constructor of HashTable. Creates a hash table.
+         *
+         * @param keyDType `dtype` of the table keys.
+         * @param valueDType `dtype` of the table values.
+         */
+        function HashTable(keyDType, valueDType) {
+            this.keyDType = keyDType;
+            this.valueDType = valueDType;
+            this.handle = tfc.scalar(0);
+            // tslint:disable-next-line: no-any
+            this.tensorMap = new Map();
+            tfc.keep(this.handle);
+        }
+        Object.defineProperty(HashTable.prototype, "id", {
+            get: function () {
+                return this.handle.id;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        /**
+         * Dispose the tensors and handle and clear the hashtable.
+         */
+        HashTable.prototype.clearAndClose = function () {
+            this.tensorMap.forEach(function (value) { return value.dispose(); });
+            this.tensorMap.clear();
+            this.handle.dispose();
+        };
+        /**
+         * The number of items in the hash table.
+         */
+        HashTable.prototype.size = function () {
+            return this.tensorMap.size;
+        };
+        /**
+         * The number of items in the hash table as a rank-0 tensor.
+         */
+        HashTable.prototype.tensorSize = function () {
+            return tfOps.scalar(this.size(), 'int32');
+        };
+        /**
+         * Replaces the contents of the table with the specified keys and values.
+         * @param keys Keys to store in the hashtable.
+         * @param values Values to store in the hashtable.
+         */
+        HashTable.prototype.import = function (keys, values) {
+            return __awaiter(this, void 0, void 0, function () {
+                var $keys;
+                var _this = this;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            this.checkKeyAndValueTensor(keys, values);
+                            return [4 /*yield*/, keys.data()];
+                        case 1:
+                            $keys = _a.sent();
+                            // Clear the hashTable before inserting new values.
+                            this.tensorMap.forEach(function (value) { return value.dispose(); });
+                            this.tensorMap.clear();
+                            return [2 /*return*/, tfc.tidy(function () {
+                                    var $values = tfc.unstack(values);
+                                    var keysLength = $keys.length;
+                                    var valuesLength = $values.length;
+                                    tfc.util.assert(keysLength === valuesLength, function () { return "The number of elements doesn't match, keys has " +
+                                        (keysLength + " elements, the values has " + valuesLength + " ") +
+                                        "elements."; });
+                                    for (var i = 0; i < keysLength; i++) {
+                                        var key = $keys[i];
+                                        var value = $values[i];
+                                        tfc.keep(value);
+                                        _this.tensorMap.set(key, value);
+                                    }
+                                    return _this.handle;
+                                })];
+                    }
+                });
+            });
+        };
+        /**
+         * Looks up keys in a hash table, outputs the corresponding values.
+         *
+         * Performs batch lookups, for every element in the key tensor, `find`
+         * stacks the corresponding value into the return tensor.
+         *
+         * If an element is not present in the table, the given `defaultValue` is
+         * used.
+         *
+         * @param keys Keys to look up. Must have the same type as the keys of the
+         *     table.
+         * @param defaultValue The scalar `defaultValue` is the value output for keys
+         *     not present in the table. It must also be of the same type as the
+         *     table values.
+         */
+        HashTable.prototype.find = function (keys, defaultValue) {
+            return __awaiter(this, void 0, void 0, function () {
+                var $keys;
+                var _this = this;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            this.checkKeyAndValueTensor(keys, defaultValue);
+                            return [4 /*yield*/, keys.data()];
+                        case 1:
+                            $keys = _a.sent();
+                            return [2 /*return*/, tfc.tidy(function () {
+                                    var result = [];
+                                    for (var i = 0; i < $keys.length; i++) {
+                                        var key = $keys[i];
+                                        var value = _this.findWithDefault(key, defaultValue);
+                                        result.push(value);
+                                    }
+                                    return tfc.stack(result);
+                                })];
+                    }
+                });
+            });
+        };
+        // tslint:disable-next-line: no-any
+        HashTable.prototype.findWithDefault = function (key, defaultValue) {
+            var result = this.tensorMap.get(key);
+            return result != null ? result : defaultValue;
+        };
+        HashTable.prototype.checkKeyAndValueTensor = function (key, value) {
+            if (key.dtype !== this.keyDType) {
+                throw new Error("Expect key dtype " + this.keyDType + ", but got " +
+                    ("" + key.dtype));
+            }
+            if (value.dtype !== this.valueDType) {
+                throw new Error("Expect value dtype " + this.valueDType + ", but got " +
+                    ("" + value.dtype));
+            }
+        };
+        return HashTable;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var _this$2 = undefined;
+    var executeOp$8 = function (node, tensorMap, context, resourceManager) { return __awaiter(_this$2, void 0, void 0, function () {
+        var _a, keyDType, valueDType, hashTable, handle, keys, values, hashTable, handle, keys, defaultValue, hashTable, handle, hashTable;
+        return __generator(this, function (_b) {
+            switch (_b.label) {
+                case 0:
+                    _a = node.op;
+                    switch (_a) {
+                        case 'HashTable': return [3 /*break*/, 1];
+                        case 'HashTableV2': return [3 /*break*/, 1];
+                        case 'LookupTableImport': return [3 /*break*/, 2];
+                        case 'LookupTableImportV2': return [3 /*break*/, 2];
+                        case 'LookupTableFind': return [3 /*break*/, 4];
+                        case 'LookupTableFindV2': return [3 /*break*/, 4];
+                        case 'LookupTableSize': return [3 /*break*/, 6];
+                        case 'LookupTableSizeV2': return [3 /*break*/, 6];
+                    }
+                    return [3 /*break*/, 7];
+                case 1:
+                    {
+                        keyDType = getParamValue('keyDType', node, tensorMap, context);
+                        valueDType = getParamValue('valueDType', node, tensorMap, context);
+                        hashTable = new HashTable(keyDType, valueDType);
+                        resourceManager.addHashTable(node.name, hashTable);
+                        return [2 /*return*/, [hashTable.handle]];
+                    }
+                case 2:
+                    handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager);
+                    keys = getParamValue('keys', node, tensorMap, context);
+                    values = getParamValue('values', node, tensorMap, context);
+                    hashTable = resourceManager.getHashTableById(handle.id);
+                    return [4 /*yield*/, hashTable.import(keys, values)];
+                case 3: return [2 /*return*/, [_b.sent()]];
+                case 4:
+                    handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager);
+                    keys = getParamValue('keys', node, tensorMap, context);
+                    defaultValue = getParamValue('defaultValue', node, tensorMap, context);
+                    hashTable = resourceManager.getHashTableById(handle.id);
+                    return [4 /*yield*/, hashTable.find(keys, defaultValue)];
+                case 5: return [2 /*return*/, [_b.sent()]];
+                case 6:
+                    {
+                        handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager);
+                        hashTable = resourceManager.getHashTableById(handle.id);
+                        return [2 /*return*/, [hashTable.tensorSize()]];
+                    }
+                case 7: throw TypeError("Node type " + node.op + " is not implemented");
+            }
+        });
+    }); };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$9 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'ResizeBilinear': {
+                var images = getParamValue('images', node, tensorMap, context);
+                var size = getParamValue('size', node, tensorMap, context);
+                var alignCorners = getParamValue('alignCorners', node, tensorMap, context);
+                var halfPixelCenters = getParamValue('halfPixelCenters', node, tensorMap, context);
+                return [tfOps.image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)];
+            }
+            case 'ResizeNearestNeighbor': {
+                var images = getParamValue('images', node, tensorMap, context);
+                var size = getParamValue('size', node, tensorMap, context);
+                var alignCorners = getParamValue('alignCorners', node, tensorMap, context);
+                var halfPixelCenters = getParamValue('halfPixelCenters', node, tensorMap, context);
+                return [tfOps.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)];
+            }
+            case 'CropAndResize': {
+                var image = getParamValue('image', node, tensorMap, context);
+                var boxes = getParamValue('boxes', node, tensorMap, context);
+                var boxInd = getParamValue('boxInd', node, tensorMap, context);
+                var cropSize = getParamValue('cropSize', node, tensorMap, context);
+                var method = getParamValue('method', node, tensorMap, context);
+                var extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context);
+                return [tfOps.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$a = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'Equal': {
+                return [tfOps.equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'NotEqual': {
+                return [tfOps.notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Greater': {
+                return [tfOps.greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'GreaterEqual': {
+                return [tfOps.greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Less': {
+                return [tfOps.less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'LessEqual': {
+                return [tfOps.lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'LogicalAnd': {
+                return [tfOps.logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'LogicalNot': {
+                return [tfOps.logicalNot(getParamValue('a', node, tensorMap, context))];
+            }
+            case 'LogicalOr': {
+                return [tfOps.logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Select':
+            case 'SelectV2': {
+                return [tfOps.where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$b = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'BatchMatMul':
+            case 'BatchMatMulV2':
+            case 'MatMul':
+                return [tfOps.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];
+            case 'Einsum':
+                return [tfOps.einsum.apply(tfOps, __spread([getParamValue('equation', node, tensorMap, context)], getParamValue('tensors', node, tensorMap, context)))];
+            case 'Transpose':
+                return [tfOps.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))];
+            case '_FusedMatMul':
+                var _a = __read(getParamValue('fusedOps', node, tensorMap, context), 2), extraOp = _a[0], activationFunc = _a[1];
+                var isBiasAdd = extraOp === 'biasadd';
+                var isPrelu = activationFunc === 'prelu';
+                var numArgs = getParamValue('numArgs', node, tensorMap, context);
+                var leakyreluAlpha = getParamValue('leakyreluAlpha', node, tensorMap, context);
+                if (isBiasAdd) {
+                    if (isPrelu && numArgs !== 2) {
+                        throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' +
+                            'extra arguments: bias and alpha.');
+                    }
+                    if (!isPrelu && numArgs !== 1) {
+                        throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.');
+                    }
+                }
+                var _b = __read(getParamValue('args', node, tensorMap, context), 2), biasArg = _b[0], preluArg = _b[1];
+                return [tfOps.fused.matMul({
+                        a: getParamValue('a', node, tensorMap, context),
+                        b: getParamValue('b', node, tensorMap, context),
+                        transposeA: getParamValue('transposeA', node, tensorMap, context),
+                        transposeB: getParamValue('transposeB', node, tensorMap, context),
+                        bias: biasArg,
+                        activation: activationFunc,
+                        preluActivationWeights: preluArg,
+                        leakyreluAlpha: leakyreluAlpha
+                    })];
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$c = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'FusedBatchNorm':
+            case 'FusedBatchNormV2': {
+                return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];
+            }
+            case 'FusedBatchNormV3': {
+                return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];
+            }
+            case 'LRN': {
+                return [tfOps.localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))];
+            }
+            case 'Softmax': {
+                return [tfOps.softmax(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'LogSoftmax': {
+                return [tfOps.logSoftmax(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'SparseToDense': {
+                return [tfOps.sparseToDense(getParamValue('sparseIndices', node, tensorMap, context), getParamValue('outputShape', node, tensorMap, context), getParamValue('sparseValues', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context))];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$d = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'Max': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var keepDims = getParamValue('keepDims', node, tensorMap, context);
+                return [tfOps.max(getParamValue('x', node, tensorMap, context), axis, keepDims)];
+            }
+            case 'Mean': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var keepDims = getParamValue('keepDims', node, tensorMap, context);
+                return [tfOps.mean(getParamValue('x', node, tensorMap, context), axis, keepDims)];
+            }
+            case 'Min': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var keepDims = getParamValue('keepDims', node, tensorMap, context);
+                return [tfOps.min(getParamValue('x', node, tensorMap, context), axis, keepDims)];
+            }
+            case 'Sum': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var keepDims = getParamValue('keepDims', node, tensorMap, context);
+                return [tfOps.sum(getParamValue('x', node, tensorMap, context), axis, keepDims)];
+            }
+            case 'All': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var keepDims = getParamValue('keepDims', node, tensorMap, context);
+                return [tfOps.all(getParamValue('x', node, tensorMap, context), axis, keepDims)];
+            }
+            case 'Any': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var keepDims = getParamValue('keepDims', node, tensorMap, context);
+                return [tfOps.any(getParamValue('x', node, tensorMap, context), axis, keepDims)];
+            }
+            case 'ArgMax': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                return [tfOps.argMax(getParamValue('x', node, tensorMap, context), axis)];
+            }
+            case 'ArgMin': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                return [tfOps.argMin(getParamValue('x', node, tensorMap, context), axis)];
+            }
+            case 'Prod': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var keepDims = getParamValue('keepDims', node, tensorMap, context);
+                return [tfOps.prod(getParamValue('x', node, tensorMap, context), axis, keepDims)];
+            }
+            case 'Cumsum': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var exclusive = getParamValue('exclusive', node, tensorMap, context);
+                var reverse = getParamValue('reverse', node, tensorMap, context);
+                return [tfOps.cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)];
+            }
+            case 'Bincount':
+                var x = getParamValue('x', node, tensorMap, context);
+                var weights = getParamValue('weights', node, tensorMap, context);
+                var size = getParamValue('size', node, tensorMap, context);
+                return [tfOps.bincount(x, weights, size)];
+            case 'DenseBincount': {
+                var x_1 = getParamValue('x', node, tensorMap, context);
+                var weights_1 = getParamValue('weights', node, tensorMap, context);
+                var size_1 = getParamValue('size', node, tensorMap, context);
+                var binaryOutput = getParamValue('binaryOutput', node, tensorMap, context);
+                return [tfOps.denseBincount(x_1, weights_1, size_1, binaryOutput)];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$e = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'ConcatV2':
+            case 'Concat': {
+                var n = getParamValue('n', node, tensorMap, context);
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var inputs = getParamValue('tensors', node, tensorMap, context);
+                inputs = inputs.slice(0, n);
+                return [tfOps.concat(inputs, axis)];
+            }
+            case 'Gather': {
+                var input = getParamValue('x', node, tensorMap, context);
+                var indices = getParamValue('indices', node, tensorMap, context);
+                return [tfOps.gather(input, tfOps.cast(indices, 'int32'), 0)];
+            }
+            case 'GatherV2': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var batchDims = getParamValue('batchDims', node, tensorMap, context);
+                var input = getParamValue('x', node, tensorMap, context);
+                var indices = getParamValue('indices', node, tensorMap, context);
+                return [tfOps.gather(input, tfOps.cast(indices, 'int32'), axis, batchDims)];
+            }
+            case 'Reverse': {
+                var dims = getParamValue('dims', node, tensorMap, context);
+                var axis = [];
+                for (var i = 0; i < dims.length; i++) {
+                    if (dims[i]) {
+                        axis.push(i);
+                    }
+                }
+                var input = getParamValue('x', node, tensorMap, context);
+                return [tfOps.reverse(input, axis)];
+            }
+            case 'ReverseV2': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var input = getParamValue('x', node, tensorMap, context);
+                return [tfOps.reverse(input, axis)];
+            }
+            case 'Slice': {
+                // tslint:disable-next-line:no-any
+                var begin = getParamValue('begin', node, tensorMap, context);
+                // tslint:disable-next-line:no-any
+                var size = getParamValue('size', node, tensorMap, context);
+                return [tfOps.slice(getParamValue('x', node, tensorMap, context), begin, size)];
+            }
+            case 'StridedSlice': {
+                var begin = getParamValue('begin', node, tensorMap, context);
+                var end = getParamValue('end', node, tensorMap, context);
+                var strides = getParamValue('strides', node, tensorMap, context);
+                var beginMask = getParamValue('beginMask', node, tensorMap, context);
+                var endMask = getParamValue('endMask', node, tensorMap, context);
+                var ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context);
+                var newAxisMask = getParamValue('newAxisMask', node, tensorMap, context);
+                var shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context);
+                var tensor = getParamValue('x', node, tensorMap, context);
+                return [tfOps.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];
+            }
+            case 'Pack': {
+                return tfc.tidy(function () {
+                    var axis = getParamValue('axis', node, tensorMap, context);
+                    var tensors = getParamValue('tensors', node, tensorMap, context);
+                    // Reshape the tensors to the first tensor's shape if they don't
+                    // match.
+                    var shape = tensors[0].shape;
+                    var squeezedShape = tfOps.squeeze(tensors[0]).shape;
+                    var mapped = tensors.map(function (tensor) {
+                        var sameShape = tfc.util.arraysEqual(tensor.shape, shape);
+                        if (!sameShape &&
+                            !tfc.util.arraysEqual(tfOps.squeeze(tensor).shape, squeezedShape)) {
+                            throw new Error('the input tensors shape does not match');
+                        }
+                        return sameShape ? tensor : tfOps.reshape(tensor, shape);
+                    });
+                    return [tfOps.stack(mapped, axis)];
+                });
+            }
+            case 'Unpack': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var tensor = getParamValue('tensor', node, tensorMap, context);
+                return tfOps.unstack(tensor, axis);
+            }
+            case 'Tile': {
+                var reps = getParamValue('reps', node, tensorMap, context);
+                return [tfOps.tile(getParamValue('x', node, tensorMap, context), reps)];
+            }
+            case 'Split':
+            case 'SplitV': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context);
+                var tensor = getParamValue('x', node, tensorMap, context);
+                return tfOps.split(tensor, numOrSizeSplits, axis);
+            }
+            case 'ScatterNd': {
+                var indices = getParamValue('indices', node, tensorMap, context);
+                var values = getParamValue('values', node, tensorMap, context);
+                var shape = getParamValue('shape', node, tensorMap, context);
+                return [tfOps.scatterND(indices, values, shape)];
+            }
+            case 'GatherNd': {
+                var x = getParamValue('x', node, tensorMap, context);
+                var indices = getParamValue('indices', node, tensorMap, context);
+                return [tfOps.gatherND(x, indices)];
+            }
+            case 'SparseToDense': {
+                var indices = getParamValue('sparseIndices', node, tensorMap, context);
+                var shape = getParamValue('outputShape', node, tensorMap, context);
+                var sparseValues = getParamValue('sparseValues', node, tensorMap, context);
+                var defaultValue = getParamValue('defaultValue', node, tensorMap, context);
+                return [tfOps.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ?
+                        defaultValue :
+                        tfOps.cast(defaultValue, sparseValues.dtype))];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$f = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'SparseFillEmptyRows': {
+                var _a = tfOps.sparse.sparseFillEmptyRows(getParamValue('indices', node, tensorMap, context), getParamValue('values', node, tensorMap, context), getParamValue('denseShape', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context)), outputIndices = _a.outputIndices, outputValues = _a.outputValues, emptyRowIndicator = _a.emptyRowIndicator, reverseIndexMap = _a.reverseIndexMap;
+                return [
+                    outputIndices, outputValues, emptyRowIndicator, reverseIndexMap
+                ];
+            }
+            case 'SparseReshape': {
+                var _b = tfOps.sparse.sparseReshape(getParamValue('inputIndices', node, tensorMap, context), getParamValue('inputShape', node, tensorMap, context), getParamValue('newShape', node, tensorMap, context)), outputIndices = _b.outputIndices, outputShape = _b.outputShape;
+                return [outputIndices, outputShape];
+            }
+            case 'SparseSegmentMean': {
+                var outputData = tfOps.sparse.sparseSegmentMean(getParamValue('data', node, tensorMap, context), getParamValue('indices', node, tensorMap, context), getParamValue('segmentIds', node, tensorMap, context));
+                return [outputData];
+            }
+            case 'SparseSegmentSum': {
+                var outputData = tfOps.sparse.sparseSegmentSum(getParamValue('data', node, tensorMap, context), getParamValue('indices', node, tensorMap, context), getParamValue('segmentIds', node, tensorMap, context));
+                return [outputData];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$g = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'FFT': {
+                return [tfOps.fft(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'IFFT': {
+                return [tfOps.ifft(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'RFFT': {
+                return [tfOps.rfft(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'IRFFT': {
+                return [tfOps.irfft(getParamValue('x', node, tensorMap, context))];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$h = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'StringNGrams': {
+                var _a = tfOps.string.stringNGrams(getParamValue('data', node, tensorMap, context), getParamValue('dataSplits', node, tensorMap, context), getParamValue('separator', node, tensorMap, context), getParamValue('nGramWidths', node, tensorMap, context), getParamValue('leftPad', node, tensorMap, context), getParamValue('rightPad', node, tensorMap, context), getParamValue('padWidth', node, tensorMap, context), getParamValue('preserveShortSequences', node, tensorMap, context)), nGrams = _a.nGrams, nGramsSplits = _a.nGramsSplits;
+                return [nGrams, nGramsSplits];
+            }
+            case 'StringSplit': {
+                var _b = tfOps.string.stringSplit(getParamValue('input', node, tensorMap, context), getParamValue('delimiter', node, tensorMap, context), getParamValue('skipEmpty', node, tensorMap, context)), indices = _b.indices, values = _b.values, shape = _b.shape;
+                return [indices, values, shape];
+            }
+            case 'StringToHashBucketFast': {
+                var output = tfOps.string.stringToHashBucketFast(getParamValue('input', node, tensorMap, context), getParamValue('numBuckets', node, tensorMap, context));
+                return [output];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var executeOp$i = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'Cast': {
+                return [tfOps.cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];
+            }
+            case 'ExpandDims': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                return [tfOps.expandDims(getParamValue('x', node, tensorMap, context), axis)];
+            }
+            case 'Squeeze': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                return [tfOps.squeeze(getParamValue('x', node, tensorMap, context), axis)];
+            }
+            case 'Reshape': {
+                return [tfOps.reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];
+            }
+            case 'MirrorPad': {
+                return [tfOps.mirrorPad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('mode', node, tensorMap, context))];
+            }
+            case 'PadV2':
+            case 'Pad': {
+                return [tfOps.pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))];
+            }
+            case 'SpaceToBatchND': {
+                var blockShape = getParamValue('blockShape', node, tensorMap, context);
+                var paddings = getParamValue('paddings', node, tensorMap, context);
+                return [tfOps.spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)];
+            }
+            case 'BatchToSpaceND': {
+                var blockShape = getParamValue('blockShape', node, tensorMap, context);
+                var crops = getParamValue('crops', node, tensorMap, context);
+                return [tfOps.batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)];
+            }
+            case 'DepthToSpace': {
+                var blockSize = getParamValue('blockSize', node, tensorMap, context);
+                var dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase();
+                return [tfOps.depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)];
+            }
+            case 'BroadcastTo': {
+                return [tfOps.broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Executes the op defined by the node object.
+     * @param node
+     * @param tensorMap contains tensors for executed nodes and weights
+     * @param context contains tensors and information for running the current node.
+     * @param resourceManager Optional. Contains global resources of the model.
+     */
+    function executeOp$j(node, tensorMap, context, resourceManager) {
+        var value = (function (node, tensorMap, context) {
+            switch (node.category) {
+                case 'arithmetic':
+                    return tfc.tidy(function () { return executeOp(node, tensorMap, context); });
+                case 'basic_math':
+                    return tfc.tidy(function () { return executeOp$1(node, tensorMap, context); });
+                case 'control':
+                    return executeOp$2(node, tensorMap, context);
+                case 'convolution':
+                    return tfc.tidy(function () { return executeOp$3(node, tensorMap, context); });
+                case 'creation':
+                    return tfc.tidy(function () { return executeOp$4(node, tensorMap, context); });
+                case 'dynamic':
+                    return executeOp$5(node, tensorMap, context);
+                case 'evaluation':
+                    return tfc.tidy(function () { return executeOp$6(node, tensorMap, context); });
+                case 'image':
+                    return tfc.tidy(function () { return executeOp$9(node, tensorMap, context); });
+                case 'graph':
+                    return tfc.tidy(function () { return executeOp$7(node, tensorMap, context); });
+                case 'logical':
+                    return tfc.tidy(function () { return executeOp$a(node, tensorMap, context); });
+                case 'matrices':
+                    return tfc.tidy(function () { return executeOp$b(node, tensorMap, context); });
+                case 'normalization':
+                    return tfc.tidy(function () { return executeOp$c(node, tensorMap, context); });
+                case 'reduction':
+                    return tfc.tidy(function () { return executeOp$d(node, tensorMap, context); });
+                case 'slice_join':
+                    return tfc.tidy(function () { return executeOp$e(node, tensorMap, context); });
+                case 'sparse':
+                    return tfc.tidy(function () { return executeOp$f(node, tensorMap, context); });
+                case 'spectral':
+                    return tfc.tidy(function () { return executeOp$g(node, tensorMap, context); });
+                case 'string':
+                    return tfc.tidy(function () { return executeOp$h(node, tensorMap, context); });
+                case 'transformation':
+                    return tfc.tidy(function () { return executeOp$i(node, tensorMap, context); });
+                case 'hash_table':
+                    return executeOp$8(node, tensorMap, context, resourceManager);
+                case 'custom':
+                    var opMapper = getRegisteredOp(node.op);
+                    if (opMapper && opMapper.customExecutor) {
+                        return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));
+                    }
+                    else {
+                        throw TypeError("Custom op " + node.op + " is not registered.");
+                    }
+                default:
+                    throw TypeError("Unknown op '" + node.op + "'. File an issue at " +
+                        "https://github.com/tensorflow/tfjs/issues so we can add it" +
+                        ", or register a custom execution with tf.registerOp()");
+            }
+        })(node, tensorMap, context);
+        if (tfc.util.isPromise(value)) {
+            return value.then(function (data) { return [].concat(data); });
+        }
+        return [].concat(value);
+    }
+
+    /**
+     * ExecutionContext captures the runtime environment of the node. It keeps
+     * track of the current frame and iteration for the control flow ops.
+     *
+     * For example, typical Dynamic RNN model may contain loops, for which
+     * TensorFlow will generate graphs with Enter/Exit nodes to control the
+     * current execution frame, and NextIteration Nodes for iteration id increment.
+     * For model with branch logic, TensorFLow will generate Switch/Merge ops.
+     */
+    var ExecutionContext = /** @class */ (function () {
+        function ExecutionContext(weightMap, tensorArrayMap, tensorListMap, functionMap) {
+            if (weightMap === void 0) { weightMap = {}; }
+            if (tensorArrayMap === void 0) { tensorArrayMap = {}; }
+            if (tensorListMap === void 0) { tensorListMap = {}; }
+            if (functionMap === void 0) { functionMap = {}; }
+            this.weightMap = weightMap;
+            this.tensorArrayMap = tensorArrayMap;
+            this.tensorListMap = tensorListMap;
+            this.functionMap = functionMap;
+            this.rootContext = { id: 0, frameName: '', iterationId: 0 };
+            this.contexts = [this.rootContext];
+            this.lastId = 0;
+            this.generateCurrentContextIds();
+        }
+        ExecutionContext.prototype.newFrame = function (id, frameName) {
+            return { id: id, frameName: frameName, iterationId: 0 };
+        };
+        Object.defineProperty(ExecutionContext.prototype, "currentContext", {
+            get: function () {
+                return this.contexts;
+            },
+            /**
+             * Set the current context
+             * @param contexts: ExecutionContextInfo[] the current path of execution
+             * frames
+             */
+            set: function (contexts) {
+                if (this.contexts !== contexts) {
+                    this.contexts = contexts;
+                    this.generateCurrentContextIds();
+                }
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ExecutionContext.prototype, "currentContextId", {
+            /**
+             * Returns the current context in string format.
+             */
+            get: function () {
+                return this._currentContextIds[0];
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ExecutionContext.prototype, "currentContextIds", {
+            /**
+             * Returns the current context and all parent contexts in string format.
+             * This allow access to the nodes in the current and parent frames.
+             */
+            get: function () {
+                return this._currentContextIds;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        ExecutionContext.prototype.generateCurrentContextIds = function () {
+            var names = [];
+            for (var i = 0; i < this.contexts.length - 1; i++) {
+                var contexts = this.contexts.slice(0, this.contexts.length - i);
+                names.push(this.contextIdforContexts(contexts));
+            }
+            names.push('');
+            this._currentContextIds = names;
+        };
+        ExecutionContext.prototype.contextIdforContexts = function (contexts) {
+            return contexts ?
+                contexts
+                    .map(function (context) { return (context.id === 0 && context.iterationId === 0) ?
+                    '' :
+                    context.frameName + "-" + context.iterationId; })
+                    .join('/') :
+                '';
+        };
+        /**
+         * Enter a new frame, a new context is pushed on the current context list.
+         * @param frameId new frame id
+         */
+        ExecutionContext.prototype.enterFrame = function (frameId) {
+            if (this.contexts) {
+                this.lastId++;
+                this.contexts = this.contexts.slice();
+                this.contexts.push(this.newFrame(this.lastId, frameId));
+                this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));
+            }
+        };
+        /**
+         * Exit the current frame, the last context is removed from the current
+         * context list.
+         */
+        ExecutionContext.prototype.exitFrame = function () {
+            if (this.contexts && this.contexts.length > 1) {
+                this.contexts = this.contexts.slice();
+                this.contexts.splice(-1);
+                this.currentContextIds.shift();
+            }
+            else {
+                throw new Error('Cannot exit frame, the context is empty');
+            }
+        };
+        /**
+         * Enter the next iteration of a loop, the iteration id of last context is
+         * increased.
+         */
+        ExecutionContext.prototype.nextIteration = function () {
+            if (this.contexts && this.contexts.length > 0) {
+                this.contexts = this.contexts.slice();
+                this.lastId++;
+                var context = Object.assign({}, this.contexts[this.contexts.length - 1]);
+                context.iterationId += 1;
+                context.id = this.lastId;
+                this.contexts.splice(-1, 1, context);
+                this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));
+            }
+            else {
+                throw new Error('Cannot increase frame iteration, the context is empty');
+            }
+        };
+        ExecutionContext.prototype.getWeight = function (name) {
+            return this.weightMap[name];
+        };
+        ExecutionContext.prototype.addTensorArray = function (tensorArray) {
+            this.tensorArrayMap[tensorArray.id] = tensorArray;
+        };
+        ExecutionContext.prototype.getTensorArray = function (id) {
+            return this.tensorArrayMap[id];
+        };
+        ExecutionContext.prototype.addTensorList = function (tensorList) {
+            this.tensorListMap[tensorList.id] = tensorList;
+        };
+        ExecutionContext.prototype.getTensorList = function (id) {
+            return this.tensorListMap[id];
+        };
+        ExecutionContext.prototype.dispose = function (keepIds) {
+            for (var key in this.tensorArrayMap) {
+                this.tensorArrayMap[key].clearAndClose(keepIds);
+            }
+            for (var key in this.tensorListMap) {
+                this.tensorListMap[key].clearAndClose(keepIds);
+            }
+        };
+        return ExecutionContext;
+    }());
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Given graph inputs and desired outputs, find the minimal set of nodes
+     * to execute in order to compute the outputs. In addition return other useful
+     * info such:
+     * - Missing inputs needed to compute the output.
+     * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).
+     * - Alternative inputs in order to avoid async (dynamic op) execution.
+     */
+    function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {
+        var usedNodes = new Set();
+        var missingInputs = [];
+        var dynamicNode = null;
+        var syncInputs = null;
+        // Start with the outputs, going backwards and find all the nodes that are
+        // needed to compute those outputs.
+        var seen = new Set();
+        var inputNodeNames = Object.keys(inputs).map(function (name) { return parseNodeName(name)[0]; });
+        var initNodeNames = [];
+        if (initNodes != null) {
+            initNodeNames = initNodes.map(function (node) { return parseNodeName(node.name)[0]; });
+        }
+        var frontier = __spread(outputs);
+        while (frontier.length > 0) {
+            var node = frontier.pop();
+            if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {
+                if (dynamicNode == null) {
+                    dynamicNode = node;
+                    syncInputs = dynamicNode.children.map(function (child) { return child.name; })
+                        .filter(function (name) { return usedNodes.has(name); });
+                }
+            }
+            usedNodes.add(node.name);
+            // Weights are dead end since we already have their values.
+            if (weightMap[node.name] != null) {
+                continue;
+            }
+            // This node is a dead end since it's one of the user-provided inputs.
+            if (inputNodeNames.indexOf(node.name) !== -1) {
+                continue;
+            }
+            // This node is a dead end since it doesn't have any inputs.
+            if (initNodeNames.indexOf(node.name) !== -1) {
+                continue;
+            }
+            if (node.inputs.length === 0) {
+                missingInputs.push(node.name);
+                continue;
+            }
+            node.inputs.forEach(function (input) {
+                // Don't add to the frontier if it is already there.
+                if (seen.has(input.name)) {
+                    return;
+                }
+                seen.add(input.name);
+                frontier.push(input);
+            });
+        }
+        return { inputs: inputs, outputs: outputs, usedNodes: usedNodes, missingInputs: missingInputs, dynamicNode: dynamicNode, syncInputs: syncInputs };
+    }
+    /**
+     * Given the execution info, return a list of nodes in topological order that
+     * need to be executed to compute the output.
+     */
+    function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {
+        var usedNodes = executionInfo.usedNodes, inputs = executionInfo.inputs;
+        var frontier = [];
+        var inputNodes = Object.keys(inputs)
+            .map(function (name) { return parseNodeName(name)[0]; })
+            .map(function (name) { return graph.nodes[name]; });
+        var initNodes = graph.initNodes;
+        inputNodes.forEach(function (input) {
+            if (usedNodes.has(input.name)) {
+                frontier.push(input);
+            }
+        });
+        graph.weights.forEach(function (weight) {
+            if (usedNodes.has(weight.name)) {
+                frontier.push(weight);
+            }
+        });
+        if (initNodes != null) {
+            initNodes.forEach(function (node) {
+                if (usedNodes.has(node.name)) {
+                    frontier.push(node);
+                }
+            });
+        }
+        var seen = new Set();
+        var orderedNodes = [];
+        while (frontier.length > 0) {
+            var node = frontier.pop();
+            seen.add(node.name);
+            if (!weightMap[node.name]) {
+                orderedNodes.push(node);
+            }
+            node.children.forEach(function (child) {
+                if (!seen.has(child.name) && usedNodes.has(child.name) &&
+                    child.inputs.every(function (input) { return seen.has(input.name); })) {
+                    frontier.push(child);
+                }
+            });
+        }
+        return orderedNodes;
+    }
+    var CONTROL_FLOW_OPS = [
+        'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',
+        'StatelessWhile', 'if', 'While'
+    ];
+    var DYNAMIC_SHAPE_OPS = [
+        'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'
+    ];
+    var HASH_TABLE_OPS = [
+        'HashTable', 'HashTableV2', 'LookupTableImport', 'LookupTableImportV2',
+        'LookupTableFind', 'LookupTableFindV2', 'LookupTableSize', 'LookupTableSizeV2'
+    ];
+    function isControlFlow(node) {
+        return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;
+    }
+    function isDynamicShape(node) {
+        return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;
+    }
+    function isHashTable(node) {
+        return HASH_TABLE_OPS.indexOf(node.op) >= 0;
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var GraphExecutor = /** @class */ (function () {
+        /**
+         *
+         * @param graph Graph the model or function graph to be executed.
+         * @param parent When building function exector you need to set the parent
+         * executor. Since the weights and function executor maps are set at parant
+         * level, that function executor can access the function maps and weight maps
+         * through the parent.
+         */
+        function GraphExecutor(graph, parent) {
+            var _this = this;
+            this.graph = graph;
+            this.parent = parent;
+            this.compiledMap = new Map();
+            this._weightMap = {};
+            this.SEPERATOR = ',';
+            this._functions = {};
+            this._functionExecutorMap = {};
+            this._outputs = graph.outputs;
+            this._inputs = graph.inputs;
+            this._initNodes = graph.initNodes;
+            this._signature = graph.signature;
+            this._functions = graph.functions;
+            // create sub-graph executors
+            if (graph.functions != null) {
+                Object.keys(graph.functions).forEach(function (name) {
+                    _this._functionExecutorMap[name] =
+                        new GraphExecutor(graph.functions[name], _this);
+                });
+            }
+        }
+        Object.defineProperty(GraphExecutor.prototype, "weightIds", {
+            get: function () {
+                return this.parent ? this.parent.weightIds : this._weightIds;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphExecutor.prototype, "functionExecutorMap", {
+            get: function () {
+                return this.parent ? this.parent.functionExecutorMap :
+                    this._functionExecutorMap;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphExecutor.prototype, "weightMap", {
+            get: function () {
+                return this.parent ? this.parent.weightMap : this._weightMap;
+            },
+            set: function (weightMap) {
+                var weightIds = Object.keys(weightMap).map(function (key) { return weightMap[key].map(function (tensor) { return tensor.id; }); });
+                this._weightIds = [].concat.apply([], __spread(weightIds));
+                this._weightMap = weightMap;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphExecutor.prototype, "resourceManager", {
+            /**
+             * Set `ResourceManager` shared by executors of a model.
+             * @param resourceManager: `ResourceManager` of the `GraphModel`.
+             */
+            set: function (resourceManager) {
+                this._resourceManager = resourceManager;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphExecutor.prototype, "inputs", {
+            get: function () {
+                return this._inputs.map(function (node) {
+                    return {
+                        name: node.name,
+                        shape: node.attrParams['shape'] ?
+                            node.attrParams['shape'].value :
+                            undefined,
+                        dtype: node.attrParams['dtype'] ?
+                            node.attrParams['dtype'].value :
+                            undefined
+                    };
+                });
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphExecutor.prototype, "outputs", {
+            get: function () {
+                return this._outputs.map(function (node) {
+                    return {
+                        name: node.name,
+                        shape: node.attrParams['shape'] ?
+                            node.attrParams['shape'].value :
+                            undefined,
+                        dtype: node.attrParams['dtype'] ?
+                            node.attrParams['dtype'].value :
+                            undefined
+                    };
+                });
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphExecutor.prototype, "inputNodes", {
+            get: function () {
+                return this._inputs.map(function (node) { return node.signatureKey || node.name; });
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphExecutor.prototype, "outputNodes", {
+            get: function () {
+                return this._outputs.map(function (node) {
+                    var name = node.signatureKey || node.name;
+                    return node.defaultOutput ? (name + ":" + node.defaultOutput) : name;
+                });
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphExecutor.prototype, "functions", {
+            get: function () {
+                var _this = this;
+                return Object.keys(this._functions).reduce(function (map, key) {
+                    map[key] = _this._functions[key].signature;
+                    return map;
+                }, {});
+            },
+            enumerable: true,
+            configurable: true
+        });
+        GraphExecutor.prototype.getCompilationKey = function (inputs, outputs) {
+            var sortedInputs = inputs.map(function (node) { return node.name; }).sort();
+            var sortedOutputs = outputs.map(function (node) { return node.name; }).sort();
+            return sortedInputs.join(this.SEPERATOR) + '--' +
+                sortedOutputs.join(this.SEPERATOR);
+        };
+        /**
+         * Compiles the inference graph and returns the minimal set of nodes that are
+         * required for execution, in the correct execution order.
+         */
+        GraphExecutor.prototype.compile = function (inputs, outputs) {
+            var executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);
+            var missingInputs = executionInfo.missingInputs, dynamicNode = executionInfo.dynamicNode, syncInputs = executionInfo.syncInputs;
+            if (dynamicNode != null) {
+                throw new Error("This execution contains the node '" + dynamicNode.name + "', which has " +
+                    ("the dynamic op '" + dynamicNode.op + "'. Please use ") +
+                    "model.executeAsync() instead. Alternatively, to avoid the " +
+                    ("dynamic ops, specify the inputs [" + syncInputs + "]"));
+            }
+            if (missingInputs.length > 0) {
+                var outNames = outputs.map(function (n) { return n.name; });
+                var inNames = Object.keys(inputs);
+                throw new Error("Cannot compute the outputs [" + outNames + "] from the provided inputs " +
+                    ("[" + inNames + "]. Missing the following inputs: [" + missingInputs + "]"));
+            }
+            return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);
+        };
+        /**
+         * Executes the inference for given input tensors.
+         * @param inputs Tensor map for the model inputs, keyed by the input node
+         * names.
+         * @param outputs Optional. output node name from the Tensorflow model, if
+         * no outputs are specified, the default outputs of the model would be used.
+         * You can inspect intermediate nodes of the model by adding them to the
+         * outputs array.
+         */
+        GraphExecutor.prototype.execute = function (inputs, outputs) {
+            var _this = this;
+            inputs = this.mapInputs(inputs);
+            var names = Object.keys(inputs).sort();
+            this.checkInputs(inputs);
+            this.checkInputShapeAndType(inputs);
+            outputs = this.mapOutputs(outputs);
+            this.checkOutputs(outputs);
+            var inputNodes = names.map(function (name) { return _this.graph.nodes[parseNodeName(name)[0]]; });
+            var outputNodeNames = outputs.map(function (name) { return parseNodeName(name)[0]; });
+            var outputNodes = outputNodeNames.map(function (name) { return _this.graph.nodes[name]; });
+            // If no outputs are specified, then use the default outputs of the model.
+            if (outputNodes.length === 0) {
+                outputNodes = this._outputs;
+            }
+            var compilationKey = this.getCompilationKey(inputNodes, outputNodes);
+            // Do nothing if the compiled graph cache contains the input.
+            var orderedNodes = this.compiledMap.get(compilationKey);
+            if (orderedNodes == null) {
+                orderedNodes = this.compile(inputs, outputNodes);
+                this.compiledMap.set(compilationKey, orderedNodes);
+            }
+            var tensorArrayMap = {};
+            var tensorListMap = {};
+            return tfc.tidy(function () {
+                var context = new ExecutionContext(_this.weightMap, tensorArrayMap, tensorListMap, _this.functionExecutorMap);
+                var tensorsMap = __assign({}, _this.weightMap);
+                Object.keys(inputs).forEach(function (name) {
+                    var _a = __read(parseNodeName(name), 2), nodeName = _a[0], index = _a[1];
+                    var tensors = [];
+                    tensors[index] = inputs[name];
+                    tensorsMap[nodeName] = tensors;
+                });
+                var tensorsToKeep = _this.getFrozenTensorIds(tensorsMap);
+                var intermediateTensorConsumerCount = {};
+                for (var i = 0; i < orderedNodes.length; i++) {
+                    var node = orderedNodes[i];
+                    if (!tensorsMap[node.name]) {
+                        var tensors = executeOp$j(node, tensorsMap, context, _this._resourceManager);
+                        if (tfc.util.isPromise(tensors)) {
+                            throw new Error("The execution of the op '" + node.op + "' returned a promise. " +
+                                "Please use model.executeAsync() instead.");
+                        }
+                        tensorsMap[node.name] = tensors;
+                        _this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);
+                    }
+                }
+                // dispose the context for the root executor
+                if (_this.parent == null) {
+                    context.dispose(tensorsToKeep);
+                }
+                return outputs.map(function (name) { return getTensor(name, tensorsMap, context); });
+            });
+        };
+        GraphExecutor.prototype.getFrozenTensorIds = function (tensorMap) {
+            var ids = [].concat.apply([], Object.keys(tensorMap)
+                .map(function (key) { return tensorMap[key]; })
+                .map(function (tensors) { return tensors.map(function (tensor) { return tensor.id; }); }));
+            return new Set(ids);
+        };
+        GraphExecutor.prototype.checkTensorForDisposal = function (nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {
+            // Skip output nodes and any control flow nodes, since its dependency is
+            // tricky to track correctly.
+            if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {
+                return;
+            }
+            tensorMap[nodeName].forEach(function (tensor) {
+                if (tensor != null) {
+                    intermediateTensorConsumerCount[tensor.id] =
+                        (intermediateTensorConsumerCount[tensor.id] || 0) +
+                            node.children.length;
+                }
+            });
+            node.inputs.forEach(function (input) {
+                // Skip any control flow nodes, since its dependency is tricky to track
+                // correctly.
+                if (input.category !== 'control') {
+                    var tensors = getTensorsForCurrentContenxt(input.name, tensorMap, context);
+                    if (tensors != null) {
+                        tensors.forEach(function (tensor) {
+                            if (tensor && !tensor.kept && !tensorsToKeep.has(tensor.id)) {
+                                var count = intermediateTensorConsumerCount[tensor.id];
+                                if (count === 1) {
+                                    tensor.dispose();
+                                    delete intermediateTensorConsumerCount[tensor.id];
+                                }
+                                else if (count != null) {
+                                    // only intermediate nodes has count set, inputs and weights are
+                                    // not.
+                                    intermediateTensorConsumerCount[tensor.id]--;
+                                }
+                            }
+                        });
+                    }
+                }
+            });
+        };
+        /**
+         * Executes the inference for given input tensors in Async fashion.
+         * @param inputs Tensor map for the model inputs, keyed by the input node
+         * names.
+         * @param outputs output node name from the Tensorflow model, if no outputs
+         * are specified, the default outputs of the model would be used. You can
+         * inspect intermediate nodes of the model by adding them to the outputs
+         * array.
+         */
+        GraphExecutor.prototype.executeAsync = function (inputs, outputs) {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    return [2 /*return*/, this._executeAsync(inputs, outputs)];
+                });
+            });
+        };
+        /**
+         * Executes the inference for given input tensors in Async fashion.
+         * @param inputs Tensor map for the model inputs, keyed by the input node
+         * names.
+         * @param outputs Optional. output node name from the Tensorflow model,
+         * if no outputs are specified, the default outputs of the model would be
+         * used. You can inspect intermediate nodes of the model by adding them to the
+         * outputs array.
+         * @param isFunctionExecution Optional. Flag for executing a function.
+         * @param tensorArrayMap Optional, global TensorArray map by id. Used for
+         * function execution.
+         * @param tensorArrayMap Optinal global TensorList map by id. Used for
+         * function execution.
+         */
+        GraphExecutor.prototype._executeAsync = function (inputs, outputs, isFunctionExecution, tensorArrayMap, tensorListMap) {
+            if (isFunctionExecution === void 0) { isFunctionExecution = false; }
+            if (tensorArrayMap === void 0) { tensorArrayMap = {}; }
+            if (tensorListMap === void 0) { tensorListMap = {}; }
+            return __awaiter(this, void 0, void 0, function () {
+                var context, tensorMap, results, outputIds, inputIds, keepIds;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            if (!isFunctionExecution) {
+                                inputs = this.mapInputs(inputs);
+                                this.checkInputs(inputs);
+                                this.checkInputShapeAndType(inputs);
+                                outputs = this.mapOutputs(outputs);
+                                this.checkOutputs(outputs);
+                            }
+                            context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);
+                            return [4 /*yield*/, this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution)];
+                        case 1:
+                            tensorMap = _a.sent();
+                            results = outputs.map(function (name) { return getTensor(name, tensorMap, context); });
+                            outputIds = results.map(function (t) { return t.id; });
+                            inputIds = Object.keys(inputs).map(function (name) { return inputs[name].id; });
+                            keepIds = new Set(__spread(outputIds, inputIds, this.weightIds));
+                            Object.keys(tensorMap).forEach(function (key) {
+                                var tensorArray = tensorMap[key];
+                                tensorArray.forEach(function (tensor) {
+                                    if (tensor && !tensor.kept && !tensor.isDisposed &&
+                                        !keepIds.has(tensor.id)) {
+                                        tensor.dispose();
+                                    }
+                                });
+                            });
+                            // dispose the context for the root executor
+                            if (this.parent == null) {
+                                context.dispose(keepIds);
+                            }
+                            return [2 /*return*/, results];
+                    }
+                });
+            });
+        };
+        GraphExecutor.prototype.executeFunctionAsync = function (inputs, tensorArrayMap, tensorListMap) {
+            return __awaiter(this, void 0, void 0, function () {
+                var mappedInputs;
+                var _this = this;
+                return __generator(this, function (_a) {
+                    mappedInputs = inputs.reduce(function (map, tensor, index) {
+                        map[_this.inputs[index].name] = tensor;
+                        return map;
+                    }, {});
+                    return [2 /*return*/, this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap)];
+                });
+            });
+        };
+        /**
+         * When there are control flow nodes in the graph, the graph execution use
+         * ExecutionContext to keep track of the frames and loop iterators.
+         * @param inputs placeholder tensors for the graph.
+         * @param context the execution context object for current execution.
+         * @param outputNames Optional. output node name from the Tensorflow model,
+         * if no outputs are specified, the default outputs of the model would be
+         * used. You can inspect intermediate nodes of the model by adding them to the
+         * outputs array.
+         * @param isFunctionExecution Flag for executing a function.
+         */
+        GraphExecutor.prototype.executeWithControlFlow = function (inputs, context, outputNames, isFunctionExecution) {
+            return __awaiter(this, void 0, void 0, function () {
+                var names, inputNodes, outputNodeNames, outputNodes, _a, usedNodes, missingInputs, dynamicNode, syncInputs, stack, tensorsMap, intermediateTensorConsumerCount, tensorsToKeep, added, promises, missingOutputs, alternativeMsg;
+                var _this = this;
+                return __generator(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            names = Object.keys(inputs);
+                            inputNodes = names.map(function (name) { return _this.graph.nodes[parseNodeName(name)[0]]; });
+                            outputNodeNames = outputNames.map(function (name) { return parseNodeName(name)[0]; });
+                            outputNodes = outputNodeNames.map(function (name) { return _this.graph.nodes[name]; });
+                            // If no outputs are specified, then use the default outputs of the model.
+                            if (outputNodes.length === 0) {
+                                outputNodes = this._outputs;
+                            }
+                            _a = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes), usedNodes = _a.usedNodes, missingInputs = _a.missingInputs, dynamicNode = _a.dynamicNode, syncInputs = _a.syncInputs;
+                            stack = __spread(inputNodes, this.graph.weights, (this._initNodes || [])).map(function (node) {
+                                return { node: node, contexts: context.currentContext };
+                            });
+                            tensorsMap = __assign({}, this.weightMap);
+                            Object.keys(inputs).forEach(function (name) {
+                                var _a = __read(parseNodeName(name), 2), nodeName = _a[0], index = _a[1];
+                                var tensors = [];
+                                tensors[index] = inputs[name];
+                                tensorsMap[nodeName] = tensors;
+                            });
+                            intermediateTensorConsumerCount = {};
+                            tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
+                            added = {};
+                            _b.label = 1;
+                        case 1:
+                            if (!(stack.length > 0)) return [3 /*break*/, 3];
+                            promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);
+                            return [4 /*yield*/, Promise.all(promises)];
+                        case 2:
+                            _b.sent();
+                            return [3 /*break*/, 1];
+                        case 3:
+                            if (dynamicNode == null && !isFunctionExecution) {
+                                console.warn("This model execution did not contain any nodes with control flow " +
+                                    "or dynamic output shapes. You can use model.execute() instead.");
+                            }
+                            missingOutputs = outputNodes
+                                .filter(function (node) { return !isControlFlow(node) &&
+                                !getTensor(node.name, tensorsMap, context); })
+                                .map(function (node) { return node.name; });
+                            if (missingOutputs.length > 0) {
+                                alternativeMsg = '';
+                                if (dynamicNode != null) {
+                                    alternativeMsg =
+                                        "Alternatively, to avoid the dynamic ops, use model.execute() " +
+                                            ("and specify the inputs [" + syncInputs + "]");
+                                }
+                                throw new Error("Cannot compute the outputs [" + missingOutputs + "] from the provided " +
+                                    ("inputs [" + names + "]. Consider providing the following inputs: ") +
+                                    ("[" + missingInputs + "]. " + alternativeMsg));
+                            }
+                            return [2 /*return*/, tensorsMap];
+                    }
+                });
+            });
+        };
+        GraphExecutor.prototype.processStack = function (inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {
+            var _this = this;
+            var promises = [];
+            var _loop_1 = function () {
+                var _a, _b;
+                var item = stack.pop();
+                context.currentContext = item.contexts;
+                var nodeName = '';
+                // The tensor of the Enter op with isConstant set should be set
+                // in the parent scope, so it will be available as constant for the
+                // whole loop.
+                if (item.node.op === 'Enter' &&
+                    getParamValue('isConstant', item.node, tensorMap, context)) {
+                    _a = __read(getNodeNameAndIndex(item.node.name, context), 1), nodeName = _a[0];
+                }
+                // only process nodes that are not in the tensorMap yet, this include
+                // inputNodes and internal initNodes.
+                if (tensorMap[item.node.name] == null) {
+                    var tensors = executeOp$j(item.node, tensorMap, context, this_1._resourceManager);
+                    if (!nodeName) {
+                        _b = __read(getNodeNameAndIndex(item.node.name, context), 1), nodeName = _b[0];
+                    }
+                    var currentContext_1 = context.currentContext;
+                    if (tfc.util.isPromise(tensors)) {
+                        promises.push(tensors.then(function (t) {
+                            tensorMap[nodeName] = t;
+                            context.currentContext = currentContext_1;
+                            _this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);
+                            _this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);
+                            return t;
+                        }));
+                    }
+                    else {
+                        tensorMap[nodeName] = tensors;
+                        this_1.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);
+                        this_1.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);
+                    }
+                }
+                else {
+                    this_1.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);
+                }
+            };
+            var this_1 = this;
+            while (stack.length > 0) {
+                _loop_1();
+            }
+            return promises;
+        };
+        GraphExecutor.prototype.processChildNodes = function (node, stack, context, tensorMap, added, usedNodes) {
+            node.children.forEach(function (childNode) {
+                var _a = __read(getNodeNameAndIndex(childNode.name, context), 1), nodeName = _a[0];
+                if (added[nodeName] || !usedNodes.has(childNode.name)) {
+                    return;
+                }
+                // Merge op can be pushed if any of its inputs has value.
+                if (childNode.op === 'Merge') {
+                    if (childNode.inputNames.some(function (name) {
+                        return !!getTensor(name, tensorMap, context);
+                    })) {
+                        added[nodeName] = true;
+                        stack.push({ contexts: context.currentContext, node: childNode });
+                    }
+                }
+                else // Otherwise all inputs must to have value.
+                 if (childNode.inputNames.every(function (name) {
+                    return !!getTensor(name, tensorMap, context);
+                })) {
+                    added[nodeName] = true;
+                    stack.push({ contexts: context.currentContext, node: childNode });
+                }
+            });
+        };
+        /**
+         * Releases the memory used by the weight tensors.
+         */
+        GraphExecutor.prototype.dispose = function () {
+            var _this = this;
+            Object.keys(this.weightMap)
+                .forEach(function (key) { return _this.weightMap[key].forEach(function (tensor) { return tensor.dispose(); }); });
+        };
+        GraphExecutor.prototype.checkInputShapeAndType = function (inputs) {
+            var _this = this;
+            Object.keys(inputs).forEach(function (name) {
+                var input = inputs[name];
+                var _a = __read(parseNodeName(name), 1), nodeName = _a[0];
+                var node = _this.graph.nodes[nodeName];
+                if (node.attrParams['shape'] && node.attrParams['shape'].value) {
+                    var shape_1 = node.attrParams['shape'].value;
+                    var match = shape_1.length === input.shape.length &&
+                        input.shape.every(function (dim, index) { return shape_1[index] === -1 || shape_1[index] === dim; });
+                    tfc.util.assert(match, function () { return "The shape of dict['" + node.name + "'] provided in " +
+                        ("model.execute(dict) must be [" + shape_1 + "], but was ") +
+                        ("[" + input.shape + "]"); });
+                }
+                if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {
+                    tfc.util.assert(input.dtype === node.attrParams['dtype'].value, function () { return "The dtype of dict['" + node.name + "'] provided in " +
+                        "model.execute(dict) must be " +
+                        (node.attrParams['dtype'].value + ", but was " + input.dtype); });
+                }
+            });
+        };
+        GraphExecutor.prototype.mapInputs = function (inputs) {
+            var result = {};
+            for (var inputName in inputs) {
+                if (this._signature != null && this._signature.inputs != null &&
+                    this._signature.inputs[inputName] != null) {
+                    var tensor = this._signature.inputs[inputName];
+                    result[tensor.name] = inputs[inputName];
+                }
+                else {
+                    result[inputName] = inputs[inputName];
+                }
+            }
+            return result;
+        };
+        GraphExecutor.prototype.checkInputs = function (inputs) {
+            var _this = this;
+            var notInGraph = Object.keys(inputs).filter(function (name) {
+                var _a = __read(parseNodeName(name), 1), nodeName = _a[0];
+                return _this.graph.nodes[nodeName] == null;
+            });
+            if (notInGraph.length > 0) {
+                throw new Error("The dict provided in model.execute(dict) has " +
+                    ("keys: [" + notInGraph + "] that are not part of graph"));
+            }
+        };
+        GraphExecutor.prototype.mapOutputs = function (outputs) {
+            var _this = this;
+            return outputs.map(function (name) {
+                if (_this._signature != null && _this._signature.outputs != null &&
+                    _this._signature.outputs[name] != null) {
+                    var tensor = _this._signature.outputs[name];
+                    return tensor.name;
+                }
+                return name;
+            }, {});
+        };
+        GraphExecutor.prototype.checkOutputs = function (outputs) {
+            var _this = this;
+            outputs.forEach(function (name) {
+                var _a = __read(parseNodeName(name), 1), normalizedName = _a[0];
+                if (!_this.graph.nodes[normalizedName]) {
+                    throw new Error("The output '" + name + "' is not found in the graph");
+                }
+            });
+        };
+        return GraphExecutor;
+    }());
+
+    /**
+     * Contains global resources of a model.
+     */
+    var ResourceManager = /** @class */ (function () {
+        function ResourceManager(hashTableNameToHandle, hashTableMap) {
+            if (hashTableNameToHandle === void 0) { hashTableNameToHandle = {}; }
+            if (hashTableMap === void 0) { hashTableMap = {}; }
+            this.hashTableNameToHandle = hashTableNameToHandle;
+            this.hashTableMap = hashTableMap;
+        }
+        /**
+         * Register a `HashTable` in the resource manager.
+         *
+         * The `HashTable` can be retrieved by `resourceManager.getHashTableById`,
+         * where id is the table handle tensor's id.
+         *
+         * @param name Op node name that creates the `HashTable`.
+         * @param hashTable The `HashTable` to be added to resource manager.
+         */
+        ResourceManager.prototype.addHashTable = function (name, hashTable) {
+            this.hashTableNameToHandle[name] = hashTable.handle;
+            this.hashTableMap[hashTable.id] = hashTable;
+        };
+        /**
+         * Get the table handle by node name.
+         * @param name Op node name that creates the `HashTable`. This name is also
+         *     used in the inputs list of lookup and import `HashTable` ops.
+         */
+        ResourceManager.prototype.getHashTableHandleByName = function (name) {
+            return this.hashTableNameToHandle[name];
+        };
+        /**
+         * Get the actual `HashTable` by its handle tensor's id.
+         * @param id The id of the handle tensor.
+         */
+        ResourceManager.prototype.getHashTableById = function (id) {
+            return this.hashTableMap[id];
+        };
+        /**
+         * Dispose `ResourceManager`, including its hashTables and tensors in them.
+         */
+        ResourceManager.prototype.dispose = function () {
+            for (var key in this.hashTableMap) {
+                this.hashTableMap[key].clearAndClose();
+                delete this.hashTableMap[key];
+            }
+            for (var name_1 in this.hashTableNameToHandle) {
+                this.hashTableNameToHandle[name_1].dispose();
+                delete this.hashTableNameToHandle[name_1];
+            }
+        };
+        return ResourceManager;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var TFHUB_SEARCH_PARAM = '?tfjs-format=file';
+    var DEFAULT_MODEL_NAME = 'model.json';
+    /**
+     * A `tf.GraphModel` is a directed, acyclic graph built from a
+     * SavedModel GraphDef and allows inference execution.
+     *
+     * A `tf.GraphModel` can only be created by loading from a model converted from
+     * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using
+     * the command line converter tool and loaded via `tf.loadGraphModel`.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    var GraphModel = /** @class */ (function () {
+        /**
+         * @param modelUrl url for the model, or an `io.IOHandler`.
+         * @param weightManifestUrl url for the weight file generated by
+         * scripts/convert.py script.
+         * @param requestOption options for Request, which allows to send credentials
+         * and custom headers.
+         * @param onProgress Optional, progress callback function, fired periodically
+         * before the load is completed.
+         */
+        function GraphModel(modelUrl, loadOptions) {
+            if (loadOptions === void 0) { loadOptions = {}; }
+            this.modelUrl = modelUrl;
+            this.loadOptions = loadOptions;
+            this.version = 'n/a';
+            if (loadOptions == null) {
+                this.loadOptions = {};
+            }
+            this.resourceManager = new ResourceManager();
+        }
+        Object.defineProperty(GraphModel.prototype, "modelVersion", {
+            // Returns the version information for the tensorflow model GraphDef.
+            get: function () {
+                return this.version;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphModel.prototype, "inputNodes", {
+            get: function () {
+                return this.executor.inputNodes;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphModel.prototype, "outputNodes", {
+            get: function () {
+                return this.executor.outputNodes;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphModel.prototype, "inputs", {
+            get: function () {
+                return this.executor.inputs;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphModel.prototype, "outputs", {
+            get: function () {
+                return this.executor.outputs;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphModel.prototype, "weights", {
+            get: function () {
+                return this.executor.weightMap;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphModel.prototype, "metadata", {
+            get: function () {
+                return this.artifacts.userDefinedMetadata;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(GraphModel.prototype, "modelSignature", {
+            get: function () {
+                return this.signature;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        GraphModel.prototype.findIOHandler = function () {
+            var path = this.modelUrl;
+            if (path.load != null) {
+                // Path is an IO Handler.
+                this.handler = path;
+            }
+            else if (this.loadOptions.requestInit != null) {
+                this.handler = tfc.io.browserHTTPRequest(path, this.loadOptions);
+            }
+            else {
+                var handlers = tfc.io.getLoadHandlers(path, this.loadOptions);
+                if (handlers.length === 0) {
+                    // For backward compatibility: if no load handler can be found,
+                    // assume it is a relative http path.
+                    handlers.push(tfc.io.browserHTTPRequest(path, this.loadOptions));
+                }
+                else if (handlers.length > 1) {
+                    throw new Error("Found more than one (" + handlers.length + ") load handlers for " +
+                        ("URL '" + [path] + "'"));
+                }
+                this.handler = handlers[0];
+            }
+        };
+        /**
+         * Loads the model and weight files, construct the in memory weight map and
+         * compile the inference graph.
+         */
+        GraphModel.prototype.load = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var artifacts;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            this.findIOHandler();
+                            if (this.handler.load == null) {
+                                throw new Error('Cannot proceed with model loading because the IOHandler provided ' +
+                                    'does not have the `load` method implemented.');
+                            }
+                            return [4 /*yield*/, this.handler.load()];
+                        case 1:
+                            artifacts = _a.sent();
+                            return [2 /*return*/, this.loadSync(artifacts)];
+                    }
+                });
+            });
+        };
+        /**
+         * Synchronously construct the in memory weight map and
+         * compile the inference graph. Also initialize hashtable if any.
+         *
+         * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+         */
+        GraphModel.prototype.loadSync = function (artifacts) {
+            this.artifacts = artifacts;
+            var graph = this.artifacts.modelTopology;
+            var signature;
+            if (this.artifacts.userDefinedMetadata != null &&
+                this.artifacts.userDefinedMetadata.signature != null) {
+                signature = // tslint:disable-next-line:no-any
+                    this.artifacts.userDefinedMetadata.signature;
+            }
+            else {
+                signature = this.artifacts.signature;
+            }
+            this.signature = signature;
+            this.version = graph.versions.producer + "." + graph.versions.minConsumer;
+            var weightMap = tfc.io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
+            this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, this.signature));
+            this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);
+            // Attach a model-level resourceManager to each executor to share resources,
+            // such as `HashTable`.
+            this.executor.resourceManager = this.resourceManager;
+            if (artifacts.modelInitializer != null &&
+                artifacts.modelInitializer.node != null) {
+                var initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);
+                this.initializer = new GraphExecutor(initializer);
+                this.initializer.weightMap = this.executor.weightMap;
+                // Attach a model-level resourceManager to the initializer, the
+                // hashTables created from when executing the initializer will be stored
+                // in the resourceManager.
+                this.initializer.resourceManager = this.resourceManager;
+                this.initializer.executeAsync({}, []);
+            }
+            return true;
+        };
+        /**
+         * Save the configuration and/or weights of the GraphModel.
+         *
+         * An `IOHandler` is an object that has a `save` method of the proper
+         * signature defined. The `save` method manages the storing or
+         * transmission of serialized data ("artifacts") that represent the
+         * model's topology and weights onto or via a specific medium, such as
+         * file downloads, local storage, IndexedDB in the web browser and HTTP
+         * requests to a server. TensorFlow.js provides `IOHandler`
+         * implementations for a number of frequently used saving mediums, such as
+         * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`
+         * for more details.
+         *
+         * This method also allows you to refer to certain types of `IOHandler`s
+         * as URL-like string shortcuts, such as 'localstorage://' and
+         * 'indexeddb://'.
+         *
+         * Example 1: Save `model`'s topology and weights to browser [local
+         * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
+         * then load it back.
+         *
+         * ```js
+         * const modelUrl =
+         *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
+         * const model = await tf.loadGraphModel(modelUrl);
+         * const zeros = tf.zeros([1, 224, 224, 3]);
+         * model.predict(zeros).print();
+         *
+         * const saveResults = await model.save('localstorage://my-model-1');
+         *
+         * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
+         * console.log('Prediction from loaded model:');
+         * model.predict(zeros).print();
+         * ```
+         *
+         * @param handlerOrURL An instance of `IOHandler` or a URL-like,
+         * scheme-based string shortcut for `IOHandler`.
+         * @param config Options for saving the model.
+         * @returns A `Promise` of `SaveResult`, which summarizes the result of
+         * the saving, such as byte sizes of the saved artifacts for the model's
+         *   topology and weight values.
+         *
+         * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+         */
+        GraphModel.prototype.save = function (handlerOrURL, config) {
+            return __awaiter(this, void 0, void 0, function () {
+                var handlers;
+                return __generator(this, function (_a) {
+                    if (typeof handlerOrURL === 'string') {
+                        handlers = tfc.io.getSaveHandlers(handlerOrURL);
+                        if (handlers.length === 0) {
+                            throw new Error("Cannot find any save handlers for URL '" + handlerOrURL + "'");
+                        }
+                        else if (handlers.length > 1) {
+                            throw new Error("Found more than one (" + handlers.length + ") save handlers for " +
+                                ("URL '" + handlerOrURL + "'"));
+                        }
+                        handlerOrURL = handlers[0];
+                    }
+                    if (handlerOrURL.save == null) {
+                        throw new Error('GraphModel.save() cannot proceed because the IOHandler ' +
+                            'provided does not have the `save` attribute defined.');
+                    }
+                    return [2 /*return*/, handlerOrURL.save(this.artifacts)];
+                });
+            });
+        };
+        /**
+         * Execute the inference for the input tensors.
+         *
+         * @param input The input tensors, when there is single input for the model,
+         * inputs param should be a `tf.Tensor`. For models with mutliple inputs,
+         * inputs params should be in either `tf.Tensor`[] if the input order is
+         * fixed, or otherwise NamedTensorMap format.
+         *
+         * For model with multiple inputs, we recommend you use NamedTensorMap as the
+         * input type, if you use `tf.Tensor`[], the order of the array needs to
+         * follow the
+         * order of inputNodes array. @see {@link GraphModel.inputNodes}
+         *
+         * You can also feed any intermediate nodes using the NamedTensorMap as the
+         * input type. For example, given the graph
+         *    InputNode => Intermediate => OutputNode,
+         * you can execute the subgraph Intermediate => OutputNode by calling
+         *    model.execute('IntermediateNode' : tf.tensor(...));
+         *
+         * This is useful for models that uses tf.dynamic_rnn, where the intermediate
+         * state needs to be fed manually.
+         *
+         * For batch inference execution, the tensors for each input need to be
+         * concatenated together. For example with mobilenet, the required input shape
+         * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+         * If we are provide a batched data of 100 images, the input tensor should be
+         * in the shape of [100, 244, 244, 3].
+         *
+         * @param config Prediction configuration for specifying the batch size and
+         * output node names. Currently the batch size option is ignored for graph
+         * model.
+         *
+         * @returns Inference result tensors. The output would be single `tf.Tensor`
+         * if model has single output node, otherwise Tensor[] or NamedTensorMap[]
+         * will be returned for model with multiple outputs.
+         *
+         * @doc {heading: 'Models', subheading: 'Classes'}
+         */
+        GraphModel.prototype.predict = function (inputs, config) {
+            return this.execute(inputs, this.outputNodes);
+        };
+        GraphModel.prototype.normalizeInputs = function (inputs) {
+            if (!(inputs instanceof tfc.Tensor) && !Array.isArray(inputs)) {
+                // The input is already a NamedTensorMap.
+                return inputs;
+            }
+            inputs = Array.isArray(inputs) ? inputs : [inputs];
+            if (inputs.length !== this.inputNodes.length) {
+                throw new Error('Input tensor count mismatch,' +
+                    ("the graph model has " + this.inputNodes.length + " placeholders, ") +
+                    ("while there are " + inputs.length + " input tensors."));
+            }
+            return this.inputNodes.reduce(function (map, inputName, i) {
+                map[inputName] = inputs[i];
+                return map;
+            }, {});
+        };
+        GraphModel.prototype.normalizeOutputs = function (outputs) {
+            outputs = outputs || this.outputNodes;
+            return !Array.isArray(outputs) ? [outputs] : outputs;
+        };
+        /**
+         * Executes inference for the model for given input tensors.
+         * @param inputs tensor, tensor array or tensor map of the inputs for the
+         * model, keyed by the input node names.
+         * @param outputs output node name from the Tensorflow model, if no
+         * outputs are specified, the default outputs of the model would be used.
+         * You can inspect intermediate nodes of the model by adding them to the
+         * outputs array.
+         *
+         * @returns A single tensor if provided with a single output or no outputs
+         * are provided and there is only one default output, otherwise return a
+         * tensor array. The order of the tensor array is the same as the outputs
+         * if provided, otherwise the order of outputNodes attribute of the model.
+         *
+         * @doc {heading: 'Models', subheading: 'Classes'}
+         */
+        GraphModel.prototype.execute = function (inputs, outputs) {
+            inputs = this.normalizeInputs(inputs);
+            outputs = this.normalizeOutputs(outputs);
+            var result = this.executor.execute(inputs, outputs);
+            return result.length > 1 ? result : result[0];
+        };
+        /**
+         * Executes inference for the model for given input tensors in async
+         * fashion, use this method when your model contains control flow ops.
+         * @param inputs tensor, tensor array or tensor map of the inputs for the
+         * model, keyed by the input node names.
+         * @param outputs output node name from the Tensorflow model, if no outputs
+         * are specified, the default outputs of the model would be used. You can
+         * inspect intermediate nodes of the model by adding them to the outputs
+         * array.
+         *
+         * @returns A Promise of single tensor if provided with a single output or
+         * no outputs are provided and there is only one default output, otherwise
+         * return a tensor map.
+         *
+         * @doc {heading: 'Models', subheading: 'Classes'}
+         */
+        GraphModel.prototype.executeAsync = function (inputs, outputs) {
+            return __awaiter(this, void 0, void 0, function () {
+                var result;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            inputs = this.normalizeInputs(inputs);
+                            outputs = this.normalizeOutputs(outputs);
+                            return [4 /*yield*/, this.executor.executeAsync(inputs, outputs)];
+                        case 1:
+                            result = _a.sent();
+                            return [2 /*return*/, result.length > 1 ? result : result[0]];
+                    }
+                });
+            });
+        };
+        GraphModel.prototype.convertTensorMapToTensorsMap = function (map) {
+            return Object.keys(map).reduce(function (newMap, key) {
+                newMap[key] = [map[key]];
+                return newMap;
+            }, {});
+        };
+        /**
+         * Releases the memory used by the weight tensors and resourceManager.
+         *
+         * @doc {heading: 'Models', subheading: 'Classes'}
+         */
+        GraphModel.prototype.dispose = function () {
+            this.executor.dispose();
+            if (this.initializer) {
+                this.initializer.dispose();
+            }
+            this.resourceManager.dispose();
+        };
+        return GraphModel;
+    }());
+    /**
+     * Load a graph model given a URL to the model definition.
+     *
+     * Example of loading MobileNetV2 from a URL and making a prediction with a
+     * zeros input:
+     *
+     * ```js
+     * const modelUrl =
+     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
+     * const model = await tf.loadGraphModel(modelUrl);
+     * const zeros = tf.zeros([1, 224, 224, 3]);
+     * model.predict(zeros).print();
+     * ```
+     *
+     * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with
+     * a zeros input:
+     *
+     * ```js
+     * const modelUrl =
+     *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';
+     * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});
+     * const zeros = tf.zeros([1, 224, 224, 3]);
+     * model.predict(zeros).print();
+     * ```
+     * @param modelUrl The url or an `io.IOHandler` that loads the model.
+     * @param options Options for the HTTP request, which allows to send credentials
+     *    and custom headers.
+     *
+     * @doc {heading: 'Models', subheading: 'Loading'}
+     */
+    function loadGraphModel(modelUrl, options) {
+        if (options === void 0) { options = {}; }
+        return __awaiter(this, void 0, void 0, function () {
+            var model;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        if (modelUrl == null) {
+                            throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' +
+                                'or an IOHandler that loads the model');
+                        }
+                        if (options == null) {
+                            options = {};
+                        }
+                        if (options.fromTFHub) {
+                            if (modelUrl.load == null) {
+                                if (!modelUrl.endsWith('/')) {
+                                    modelUrl = modelUrl + '/';
+                                }
+                                modelUrl = "" + modelUrl + DEFAULT_MODEL_NAME + TFHUB_SEARCH_PARAM;
+                            }
+                        }
+                        model = new GraphModel(modelUrl, options);
+                        return [4 /*yield*/, model.load()];
+                    case 1:
+                        _a.sent();
+                        return [2 /*return*/, model];
+                }
+            });
+        });
+    }
+
+    /** @license See the LICENSE file. */
+    // This code is auto-generated, do not modify this file!
+    var version = '3.7.0';
+
+    exports.GraphModel = GraphModel;
+    exports.deregisterOp = deregisterOp;
+    exports.loadGraphModel = loadGraphModel;
+    exports.registerOp = registerOp;
+    exports.version_converter = version;
+
+    Object.defineProperty(exports, '__esModule', { value: true });
+
+})));
+//# sourceMappingURL=tf-converter.js.map
diff --git a/thirdparty/tfjs/tf-converter.js.map b/thirdparty/tfjs/tf-converter.js.map
new file mode 100644
index 0000000..54dbe33
--- /dev/null
+++ b/thirdparty/tfjs/tf-converter.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tf-converter.js","sources":["../src/data/compiled_api.ts","../src/operations/custom_op/register.ts","../src/operations/executors/utils.ts","../src/operations/op_list/arithmetic.ts","../src/operations/op_list/basic_math.ts","../src/operations/op_list/control.ts","../src/operations/op_list/convolution.ts","../src/operations/op_list/creation.ts","../src/operations/op_list/dynamic.ts","../src/operations/op_list/evaluation.ts","../src/operations/op_list/graph.ts","../src/operations/op_list/hash_table.ts","../src/operations/op_list/image.ts","../src/operations/op_list/logical.ts","../src/operations/op_list/matrices.ts","../src/operations/op_list/normalization.ts","../src/operations/op_list/reduction.ts","../src/operations/op_list/slice_join.ts","../src/operations/op_list/sparse.ts","../src/operations/op_list/spectral.ts","../src/operations/op_list/string.ts","../src/operations/op_list/transformation.ts","../src/operations/operation_mapper.ts","../src/operations/custom_op/node_value_impl.ts","../src/operations/executors/arithmetic_executor.ts","../src/operations/executors/basic_math_executor.ts","../src/executor/tensor_utils.ts","../src/executor/tensor_array.ts","../src/executor/tensor_list.ts","../src/operations/executors/control_executor.ts","../src/operations/executors/convolution_executor.ts","../src/operations/executors/creation_executor.ts","../src/operations/executors/dynamic_executor.ts","../src/operations/executors/evaluation_executor.ts","../src/operations/executors/graph_executor.ts","../src/executor/hash_table.ts","../src/operations/executors/hash_table_executor.ts","../src/operations/executors/image_executor.ts","../src/operations/executors/logical_executor.ts","../src/operations/executors/matrices_executor.ts","../src/operations/executors/normalization_executor.ts","../src/operations/executors/reduction_executor.ts","../src/operations/executors/slice_join_executor.ts","../src/operations/executors/sparse_executor.ts","../src/operations/executors/spectral_executor.ts","../src/operations/executors/string_executor.ts","../src/operations/executors/transformation_executor.ts","../src/operations/operation_executor.ts","../src/executor/execution_context.ts","../src/executor/model_analysis.ts","../src/executor/graph_executor.ts","../src/executor/resource_manager.ts","../src/executor/graph_model.ts","../src/version.ts"],"sourcesContent":["/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n\n/* tslint:disable */\n\n/** Properties of an Any. */\nexport declare interface IAny {\n  /** Any typeUrl */\n  typeUrl?: (string|null);\n\n  /** Any value */\n  value?: (Uint8Array|null);\n}\n\n/** DataType enum. */\nexport enum DataType {\n  'DT_INVALID' = 0,\n  'DT_FLOAT' = 1,\n  'DT_DOUBLE' = 2,\n  'DT_INT32' = 3,\n  'DT_UINT8' = 4,\n  'DT_INT16' = 5,\n  'DT_INT8' = 6,\n  'DT_STRING' = 7,\n  'DT_COMPLEX64' = 8,\n  'DT_INT64' = 9,\n  'DT_BOOL' = 10,\n  'DT_QINT8' = 11,\n  'DT_QUINT8' = 12,\n  'DT_QINT32' = 13,\n  'DT_BFLOAT16' = 14,\n  'DT_FLOAT_REF' = 101,\n  'DT_DOUBLE_REF' = 102,\n  'DT_INT32_REF' = 103,\n  'DT_UINT8_REF' = 104,\n  'DT_INT16_REF' = 105,\n  'DT_INT8_REF' = 106,\n  'DT_STRING_REF' = 107,\n  'DT_COMPLEX64_REF' = 108,\n  'DT_INT64_REF' = 109,\n  'DT_BOOL_REF' = 110,\n  'DT_QINT8_REF' = 111,\n  'DT_QUINT8_REF' = 112,\n  'DT_QINT32_REF' = 113,\n  'DT_BFLOAT16_REF' = 114\n}\n\n/** Properties of a TensorShape. */\nexport declare interface ITensorShape {\n  /** TensorShape dim */\n  dim?: (TensorShape.IDim[]|null);\n\n  /** TensorShape unknownRank */\n  unknownRank?: (boolean|null);\n}\n\nexport namespace TensorShape {\n  /** Properties of a Dim. */\n  export declare interface IDim {\n    /** Dim size */\n    size?: (number|string|null);\n\n    /** Dim name */\n    name?: (string|null);\n  }\n}\n\n/** Properties of a Tensor. */\nexport declare interface ITensor {\n  /** Tensor dtype */\n  dtype?: (DataType|null);\n\n  /** Tensor tensorShape */\n  tensorShape?: (ITensorShape|null);\n\n  /** Tensor versionNumber */\n  versionNumber?: (number|null);\n\n  /** Tensor tensorContent */\n  tensorContent?: (Uint8Array|null);\n\n  /** Tensor floatVal */\n  floatVal?: (number[]|null);\n\n  /** Tensor doubleVal */\n  doubleVal?: (number[]|null);\n\n  /** Tensor intVal */\n  intVal?: (number[]|null);\n\n  /** Tensor stringVal */\n  stringVal?: (Uint8Array[]|null);\n\n  /** Tensor scomplexVal */\n  scomplexVal?: (number[]|null);\n\n  /** Tensor int64Val */\n  int64Val?: ((number | string)[]|null);\n\n  /** Tensor boolVal */\n  boolVal?: (boolean[]|null);\n\n  /** Tensor uint32Val */\n  uint32Val?: (number[]|null);\n\n  /** Tensor uint64Val */\n  uint64Val?: ((number | string)[]|null);\n}\n\n/** Properties of an AttrValue. */\nexport declare interface IAttrValue {\n  /** AttrValue list */\n  list?: (AttrValue.IListValue|null);\n\n  /** AttrValue s */\n  s?: (string|null);\n\n  /** AttrValue i */\n  i?: (number|string|null);\n\n  /** AttrValue f */\n  f?: (number|null);\n\n  /** AttrValue b */\n  b?: (boolean|null);\n\n  /** AttrValue type */\n  type?: (DataType|null);\n\n  /** AttrValue shape */\n  shape?: (ITensorShape|null);\n\n  /** AttrValue tensor */\n  tensor?: (ITensor|null);\n\n  /** AttrValue placeholder */\n  placeholder?: (string|null);\n\n  /** AttrValue func */\n  func?: (INameAttrList|null);\n}\n\nexport namespace AttrValue {\n  /** Properties of a ListValue. */\n  export declare interface IListValue {\n    /** ListValue s */\n    s?: (string[]|null);\n\n    /** ListValue i */\n    i?: ((number | string)[]|null);\n\n    /** ListValue f */\n    f?: (number[]|null);\n\n    /** ListValue b */\n    b?: (boolean[]|null);\n\n    /** ListValue type */\n    type?: (DataType[]|null);\n\n    /** ListValue shape */\n    shape?: (ITensorShape[]|null);\n\n    /** ListValue tensor */\n    tensor?: (ITensor[]|null);\n\n    /** ListValue func */\n    func?: (INameAttrList[]|null);\n  }\n}\n\n/** Properties of a NameAttrList. */\nexport declare interface INameAttrList {\n  /** NameAttrList name */\n  name?: (string|null);\n\n  /** NameAttrList attr */\n  attr?: ({[k: string]: IAttrValue}|null);\n}\n\n/** Properties of a NodeDef. */\nexport declare interface INodeDef {\n  /** NodeDef name */\n  name?: (string|null);\n\n  /** NodeDef op */\n  op?: (string|null);\n\n  /** NodeDef input */\n  input?: (string[]|null);\n\n  /** NodeDef device */\n  device?: (string|null);\n\n  /** NodeDef attr */\n  attr?: ({[k: string]: IAttrValue}|null);\n}\n\n/** Properties of a VersionDef. */\nexport declare interface IVersionDef {\n  /** VersionDef producer */\n  producer?: (number|null);\n\n  /** VersionDef minConsumer */\n  minConsumer?: (number|null);\n\n  /** VersionDef badConsumers */\n  badConsumers?: (number[]|null);\n}\n\n/** Properties of a GraphDef. */\nexport declare interface IGraphDef {\n  /** GraphDef node */\n  node?: (INodeDef[]|null);\n\n  /** GraphDef versions */\n  versions?: (IVersionDef|null);\n\n  /** GraphDef library */\n  library?: (IFunctionDefLibrary|null);\n}\n\n/** Properties of a CollectionDef. */\nexport declare interface ICollectionDef {\n  /** CollectionDef nodeList */\n  nodeList?: (CollectionDef.INodeList|null);\n\n  /** CollectionDef bytesList */\n  bytesList?: (CollectionDef.IBytesList|null);\n\n  /** CollectionDef int64List */\n  int64List?: (CollectionDef.IInt64List|null);\n\n  /** CollectionDef floatList */\n  floatList?: (CollectionDef.IFloatList|null);\n\n  /** CollectionDef anyList */\n  anyList?: (CollectionDef.IAnyList|null);\n}\n\nexport namespace CollectionDef {\n  /** Properties of a NodeList. */\n  export declare interface INodeList {\n    /** NodeList value */\n    value?: (string[]|null);\n  }\n\n  /** Properties of a BytesList. */\n  export declare interface IBytesList {\n    /** BytesList value */\n    value?: (Uint8Array[]|null);\n  }\n\n  /** Properties of an Int64List. */\n  export declare interface IInt64List {\n    /** Int64List value */\n    value?: ((number | string)[]|null);\n  }\n\n  /** Properties of a FloatList. */\n  export declare interface IFloatList {\n    /** FloatList value */\n    value?: (number[]|null);\n  }\n\n  /** Properties of an AnyList. */\n  export declare interface IAnyList {\n    /** AnyList value */\n    value?: (IAny[]|null);\n  }\n}\n\n/** Properties of a SaverDef. */\nexport declare interface ISaverDef {\n  /** SaverDef filenameTensorName */\n  filenameTensorName?: (string|null);\n\n  /** SaverDef saveTensorName */\n  saveTensorName?: (string|null);\n\n  /** SaverDef restoreOpName */\n  restoreOpName?: (string|null);\n\n  /** SaverDef maxToKeep */\n  maxToKeep?: (number|null);\n\n  /** SaverDef sharded */\n  sharded?: (boolean|null);\n\n  /** SaverDef keepCheckpointEveryNHours */\n  keepCheckpointEveryNHours?: (number|null);\n\n  /** SaverDef version */\n  version?: (SaverDef.CheckpointFormatVersion|null);\n}\n\nexport namespace SaverDef {\n  /** CheckpointFormatVersion enum. */\n  export enum CheckpointFormatVersion {'LEGACY' = 0, 'V1' = 1, 'V2' = 2}\n}\n\n/** Properties of a TensorInfo. */\nexport declare interface ITensorInfo {\n  /** TensorInfo name */\n  name?: (string|null);\n\n  /** TensorInfo cooSparse */\n  cooSparse?: (TensorInfo.ICooSparse|null);\n\n  /** TensorInfo dtype */\n  dtype?: (DataType|null);\n\n  /** TensorInfo tensorShape */\n  tensorShape?: (ITensorShape|null);\n}\n\nexport namespace TensorInfo {\n  /** Properties of a CooSparse. */\n  export declare interface ICooSparse {\n    /** CooSparse valuesTensorName */\n    valuesTensorName?: (string|null);\n\n    /** CooSparse indicesTensorName */\n    indicesTensorName?: (string|null);\n\n    /** CooSparse denseShapeTensorName */\n    denseShapeTensorName?: (string|null);\n  }\n}\n\n/** Properties of a SignatureDef. */\nexport declare interface ISignatureDef {\n  /** SignatureDef inputs */\n  inputs?: ({[k: string]: ITensorInfo}|null);\n\n  /** SignatureDef outputs */\n  outputs?: ({[k: string]: ITensorInfo}|null);\n\n  /** SignatureDef methodName */\n  methodName?: (string|null);\n}\n\n/** Properties of an AssetFileDef. */\nexport declare interface IAssetFileDef {\n  /** AssetFileDef tensorInfo */\n  tensorInfo?: (ITensorInfo|null);\n\n  /** AssetFileDef filename */\n  filename?: (string|null);\n}\n\n/** Properties of an OpDef. */\nexport declare interface IOpDef {\n  /** OpDef name */\n  name?: (string|null);\n\n  /** OpDef inputArg */\n  inputArg?: (OpDef.IArgDef[]|null);\n\n  /** OpDef outputArg */\n  outputArg?: (OpDef.IArgDef[]|null);\n\n  /** OpDef attr */\n  attr?: (OpDef.IAttrDef[]|null);\n\n  /** OpDef deprecation */\n  deprecation?: (OpDef.IOpDeprecation|null);\n\n  /** OpDef summary */\n  summary?: (string|null);\n\n  /** OpDef description */\n  description?: (string|null);\n\n  /** OpDef isCommutative */\n  isCommutative?: (boolean|null);\n\n  /** OpDef isAggregate */\n  isAggregate?: (boolean|null);\n\n  /** OpDef isStateful */\n  isStateful?: (boolean|null);\n\n  /** OpDef allowsUninitializedInput */\n  allowsUninitializedInput?: (boolean|null);\n}\n\nexport namespace OpDef {\n  /** Properties of an ArgDef. */\n  export declare interface IArgDef {\n    /** ArgDef name */\n    name?: (string|null);\n\n    /** ArgDef description */\n    description?: (string|null);\n\n    /** ArgDef type */\n    type?: (DataType|null);\n\n    /** ArgDef typeAttr */\n    typeAttr?: (string|null);\n\n    /** ArgDef numberAttr */\n    numberAttr?: (string|null);\n\n    /** ArgDef typeListAttr */\n    typeListAttr?: (string|null);\n\n    /** ArgDef isRef */\n    isRef?: (boolean|null);\n  }\n\n  /** Properties of an AttrDef. */\n  export declare interface IAttrDef {\n    /** AttrDef name */\n    name?: (string|null);\n\n    /** AttrDef type */\n    type?: (string|null);\n\n    /** AttrDef defaultValue */\n    defaultValue?: (IAttrValue|null);\n\n    /** AttrDef description */\n    description?: (string|null);\n\n    /** AttrDef hasMinimum */\n    hasMinimum?: (boolean|null);\n\n    /** AttrDef minimum */\n    minimum?: (number|string|null);\n\n    /** AttrDef allowedValues */\n    allowedValues?: (IAttrValue|null);\n  }\n\n  /** Properties of an OpDeprecation. */\n  export declare interface IOpDeprecation {\n    /** OpDeprecation version */\n    version?: (number|null);\n\n    /** OpDeprecation explanation */\n    explanation?: (string|null);\n  }\n}\n\n/** Properties of an OpList. */\nexport declare interface IOpList {\n  /** OpList op */\n  op?: (IOpDef[]|null);\n}\n\n/** Properties of a MetaGraphDef. */\nexport declare interface IMetaGraphDef {\n  /** MetaGraphDef metaInfoDef */\n  metaInfoDef?: (MetaGraphDef.IMetaInfoDef|null);\n\n  /** MetaGraphDef graphDef */\n  graphDef?: (IGraphDef|null);\n\n  /** MetaGraphDef saverDef */\n  saverDef?: (ISaverDef|null);\n\n  /** MetaGraphDef collectionDef */\n  collectionDef?: ({[k: string]: ICollectionDef}|null);\n\n  /** MetaGraphDef signatureDef */\n  signatureDef?: ({[k: string]: ISignatureDef}|null);\n\n  /** MetaGraphDef assetFileDef */\n  assetFileDef?: (IAssetFileDef[]|null);\n}\n\nexport namespace MetaGraphDef {\n  /** Properties of a MetaInfoDef. */\n  export declare interface IMetaInfoDef {\n    /** MetaInfoDef metaGraphVersion */\n    metaGraphVersion?: (string|null);\n\n    /** MetaInfoDef strippedOpList */\n    strippedOpList?: (IOpList|null);\n\n    /** MetaInfoDef anyInfo */\n    anyInfo?: (IAny|null);\n\n    /** MetaInfoDef tags */\n    tags?: (string[]|null);\n\n    /** MetaInfoDef tensorflowVersion */\n    tensorflowVersion?: (string|null);\n\n    /** MetaInfoDef tensorflowGitVersion */\n    tensorflowGitVersion?: (string|null);\n  }\n}\n\n/** Properties of a SavedModel. */\nexport declare interface ISavedModel {\n  /** SavedModel savedModelSchemaVersion */\n  savedModelSchemaVersion?: (number|string|null);\n\n  /** SavedModel metaGraphs */\n  metaGraphs?: (IMetaGraphDef[]|null);\n}\n\n/** Properties of a FunctionDefLibrary. */\nexport declare interface IFunctionDefLibrary {\n  /** FunctionDefLibrary function */\n  'function'?: (IFunctionDef[]|null);\n\n  /** FunctionDefLibrary gradient */\n  gradient?: (IGradientDef[]|null);\n}\n\n/** Properties of a FunctionDef. */\nexport declare interface IFunctionDef {\n  /** FunctionDef signature */\n  signature?: (IOpDef|null);\n\n  /** FunctionDef attr */\n  attr?: ({[k: string]: IAttrValue}|null);\n\n  /** FunctionDef nodeDef */\n  nodeDef?: (INodeDef[]|null);\n\n  /** FunctionDef ret */\n  ret?: ({[k: string]: string}|null);\n}\n\n/** Properties of a GradientDef. */\nexport declare interface IGradientDef {\n  /** GradientDef functionName */\n  functionName?: (string|null);\n\n  /** GradientDef gradientFunc */\n  gradientFunc?: (string|null);\n}\n","\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpExecutor, OpMapper} from '../types';\n\nconst CUSTOM_OPS: {[key: string]: OpMapper} = {};\n\n/**\n * Register an Op for graph model executor. This allow you to register\n * TensorFlow custom op or override existing op.\n *\n * Here is an example of registering a new MatMul Op.\n * ```js\n * const customMatmul = (node) =>\n *    tf.matMul(\n *        node.inputs[0], node.inputs[1],\n *        node.attrs['transpose_a'], node.attrs['transpose_b']);\n *\n * tf.registerOp('MatMul', customMatmul);\n * ```\n * The inputs and attrs of the node object is based on the TensorFlow op\n * registry.\n *\n * @param name The Tensorflow Op name.\n * @param opFunc An op function which is called with the current graph node\n * during execution and needs to return a tensor or a list of tensors. The node\n * has the following attributes:\n *    - attr: A map from attribute name to its value\n *    - inputs: A list of input tensors\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function registerOp(name: string, opFunc: OpExecutor) {\n  const opMapper: OpMapper = {\n    tfOpName: name,\n    category: 'custom',\n    inputs: [],\n    attrs: [],\n    customExecutor: opFunc\n  };\n\n  CUSTOM_OPS[name] = opMapper;\n}\n\n/**\n * Retrieve the OpMapper object for the registered op.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function getRegisteredOp(name: string): OpMapper {\n  return CUSTOM_OPS[name];\n}\n\n/**\n * Deregister the Op for graph model executor.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function deregisterOp(name: string) {\n  delete CUSTOM_OPS[name];\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {clone, Tensor, util} from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {ResourceManager} from '../../executor/resource_manager';\nimport {Node, ValueType} from '../types';\n\nexport function getParamValue(\n    paramName: string, node: Node, tensorMap: NamedTensorsMap,\n    context: ExecutionContext, resourceManager?: ResourceManager): ValueType {\n  const inputParam = node.inputParams[paramName];\n  if (inputParam && inputParam.inputIndexStart !== undefined) {\n    const start = inputParam.inputIndexStart;\n    const end = inputParam.inputIndexEnd === 0 ?\n        undefined :\n        (inputParam.inputIndexEnd === undefined ? start + 1 :\n                                                  inputParam.inputIndexEnd);\n    if (inputParam.type === 'tensor') {\n      return getTensor(\n          node.inputNames[inputParam.inputIndexStart], tensorMap, context,\n          resourceManager);\n    }\n    if (inputParam.type === 'tensors') {\n      const inputs = node.inputNames.slice(start, end);\n\n      return inputs.map(\n          name => getTensor(name, tensorMap, context, resourceManager));\n    }\n    const tensor = getTensor(\n        node.inputNames.slice(start)[0], tensorMap, context, resourceManager);\n    const data = tensor.dataSync();\n    return inputParam.type === 'number' ?\n        data[0] :\n        util.toNestedArray(tensor.shape, data);\n  }\n  const attrParam = node.attrParams[paramName];\n  return attrParam && attrParam.value;\n}\n\n/**\n * Retrieve the tensor from tensorsMap based on input name.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n * @param context contains tensors and information for running the current node.\n * @param resourceManager Optional. Contains global resources of the model.\n */\nexport function getTensor(\n    name: string, tensorsMap: NamedTensorsMap, context: ExecutionContext,\n    resourceManager?: ResourceManager): Tensor {\n  const [nodeName, index] = parseNodeName(name);\n\n  if (resourceManager != null) {\n    const tensor = resourceManager.getHashTableHandleByName(nodeName);\n    if (tensor != null) {\n      return tensor;\n    }\n  }\n\n  const contextId = context.currentContextIds.find(contextId => {\n    return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];\n  });\n\n  return contextId !== undefined ?\n      tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] :\n      undefined;\n}\n\n/**\n * Retrieve the tensors based on input name for current context.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensorsForCurrentContenxt(\n    name: string, tensorsMap: NamedTensorsMap,\n    context: ExecutionContext): Tensor[] {\n  return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\n\n/**\n * Returns the node name, outputName and index from the Node input name.\n * @param inputName The input name of the node, in format of\n * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is\n * default to 0.\n * If the input name contains output name i.e. StringSplit:indices:0, it will\n * return ['StringSplit', 0, 'indices'].\n */\nexport function getNodeNameAndIndex(\n    inputName: string, context?: ExecutionContext): [string, number, string] {\n  const [nodeName, index, outputName] = parseNodeName(inputName);\n\n  return [\n    getNodeNameWithContextId(nodeName, context && context.currentContextId),\n    index, outputName\n  ];\n}\n\nfunction getNodeNameWithContextId(name: string, contextId?: string): string {\n  return !!contextId ? `${name}-${contextId}` : name;\n}\n\nexport function parseNodeName(name: string): [string, number, string] {\n  const parts = name.split(':');\n  if (parts.length === 1) {\n    return [name, 0, undefined];\n  }\n\n  const nodeName = parts[0];\n  const outputName = parts.length === 3 ? parts[1] : undefined;\n  const index = Number(parts[parts.length - 1]);\n  return [nodeName, index, outputName];\n}\n\nexport function split(arr: number[], size: number) {\n  const res = [];\n  for (let i = 0; i < arr.length; i += size) {\n    res.push(arr.slice(i, i + size));\n  }\n  return res;\n}\nexport function getPadding(\n    node: Node, tensorMap: NamedTensorsMap,\n    context: ExecutionContext): ValueType {\n  let pad = getParamValue('pad', node, tensorMap, context);\n  if (pad === 'explicit') {\n    // This is 1d array, we need to convert it to 2d array\n    pad = getParamValue('explicitPaddings', node, tensorMap, context);\n    const explicitPadding: [\n      [number, number], [number, number], [number, number], [number, number]\n    ] = [[0, 0], [0, 0], [0, 0], [0, 0]];\n    for (let i = 0; i < 4; i++) {\n      explicitPadding[i][0] = (pad as number[])[i * 2];\n      explicitPadding[i][1] = (pad as number[])[i * 2 + 1];\n    }\n    return explicitPadding;\n  }\n  return pad;\n}\n\n/**\n *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to\n *  avoid disposal. This is important for TensorArray and TensorList ops, since\n *  internally they use a tensor as the id for TensorArray and TensorList, and\n * to simplify lookup, they also use Tensor.id as the key to the internal map.\n * These id tensors have been marked as kept in the backend, we need avoid clone\n * them in order to create new Tensor.id.\n * @param tensor\n */\nexport function cloneTensor(tensor: Tensor): Tensor {\n  return tensor.kept ? tensor : clone(tensor);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Add',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'AddV2',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'AddN',\n    'category': 'arithmetic',\n    'inputs': [{'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors'}]\n  },\n  {\n    'tfOpName': 'BiasAdd',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}, {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Sub',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'RealDiv',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Div',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'DivNoNan',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'FloorDiv',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Mul',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Maximum',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'}\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Minimum',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'}\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Pow',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'SquaredDifference',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Mod',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'FloorMod',\n    'category': 'arithmetic',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [{\n      'tfName': 'T',\n      'name': 'dtype',\n      'type': 'dtype',\n      'notSupported': true\n    }]\n  }\n];\n","import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Abs',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Acos',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Asin',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Atan',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Atan2',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'y', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Ceil',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'ClipByValue',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'clipValueMin', 'type': 'number'},\n      {'start': 2, 'name': 'clipValueMax', 'type': 'number'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Complex',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'real', 'type': 'tensor'},\n      {'start': 1, 'name': 'imag', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'ComplexAbs',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Cos',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Cosh',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Elu',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Exp',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Floor',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Log',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Imag',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}, {\n        'tfName': 'Tout',\n        'name': 'outputType',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Neg',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Real',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}, {\n        'tfName': 'Tout',\n        'name': 'outputType',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Prelu',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'alpha', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Relu',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Relu6',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Selu',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Sigmoid',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Sin',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Sinh',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Sqrt',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Rsqrt',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Square',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Tan',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Tanh',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Sign',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Round',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Expm1',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Log1p',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Reciprocal',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Softplus',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Asinh',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Acosh',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Atanh',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Erf',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Prod',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axes', 'type': 'number[]'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'keep_dims',\n        'name': 'keepDims',\n        'type': 'bool',\n        'notSupported': true\n      },\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'LeakyRelu',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'alpha',\n        'name': 'alpha',\n        'type': 'number',\n        'defaultValue': 0.2\n      },\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'IsNan',\n    'category': 'basic_math',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [{\n      'tfName': 'T',\n      'name': 'dtype',\n      'type': 'dtype',\n      'notSupported': true\n    }]\n  }\n];\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'EmptyTensorList',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'elementShape', 'type': 'shape'},\n      {'start': 1, 'name': 'maxNumElements', 'type': 'number'},\n    ],\n    'attrs':\n        [{'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'LoopCond',\n    'category': 'control',\n    'inputs': [{'start': 0, 'name': 'pred', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'Switch',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'data', 'type': 'tensor'},\n      {'start': 1, 'name': 'pred', 'type': 'tensor'}\n    ]\n  },\n  {\n    'tfOpName': 'Merge',\n    'category': 'control',\n    'inputs': [{'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors'}]\n  },\n  {\n    'tfOpName': 'Enter',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensor', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true},\n      {'tfName': 'frame_name', 'name': 'frameName', 'type': 'string'},\n      {'tfName': 'is_constant', 'name': 'isConstant', 'type': 'bool'}\n    ]\n  },\n  {\n    'tfOpName': 'Exit',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensor', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'NextIteration',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensor', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'TensorArrayV3',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'size', 'type': 'number'},\n    ],\n    'attrs': [\n      {'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype'},\n      {'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape'},\n      {'tfName': 'dynamic_size', 'name': 'dynamicSize', 'type': 'bool'},\n      {'tfName': 'clear_after_read', 'name': 'clearAfterRead', 'type': 'bool'},\n      {\n        'tfName': 'identical_element_shapes',\n        'name': 'identicalElementShapes',\n        'type': 'bool'\n      },\n      {'tfName': 'tensor_array_name', 'name': 'name', 'type': 'string'}\n    ]\n  },\n  {\n    'tfOpName': 'TensorArrayWriteV3',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorArrayId', 'type': 'tensor'},\n      {'start': 1, 'name': 'index', 'type': 'number'},\n      {'start': 2, 'name': 'tensor', 'type': 'tensor'},\n      {'start': 3, 'name': 'flowIn', 'type': 'number'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'TensorArrayReadV3',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorArrayId', 'type': 'tensor'},\n      {'start': 1, 'name': 'index', 'type': 'number'},\n      {'start': 2, 'name': 'flowIn', 'type': 'number'},\n    ],\n    'attrs': [{\n      'tfName': 'dtype',\n      'name': 'dtype',\n      'type': 'dtype',\n      'notSupported': true\n    }]\n  },\n  {\n    'tfOpName': 'TensorArrayGatherV3',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorArrayId', 'type': 'tensor'},\n      {'start': 1, 'name': 'indices', 'type': 'number[]'},\n      {'start': 2, 'name': 'flowIn', 'type': 'number'},\n    ],\n    'attrs': [\n      {'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype'},\n      {'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape'}\n    ]\n  },\n  {\n    'tfOpName': 'TensorArrayScatterV3',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorArrayId', 'type': 'tensor'},\n      {'start': 1, 'name': 'indices', 'type': 'number[]'},\n      {'start': 2, 'name': 'tensor', 'type': 'tensor'},\n      {'start': 3, 'name': 'flowIn', 'type': 'number'},\n    ],\n    'attrs': [{'tfName': 'T', 'name': 'dtype', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'TensorArrayConcatV3',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorArrayId', 'type': 'tensor'},\n      {'start': 1, 'name': 'flowIn', 'type': 'number'},\n    ],\n    'attrs': [\n      {'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype'}, {\n        'tfName': 'element_shape_except0',\n        'name': 'elementShapeExcept0',\n        'type': 'shape',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorArraySplitV3',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorArrayId', 'type': 'tensor'},\n      {'start': 1, 'name': 'tensor', 'type': 'tensor'},\n      {'start': 2, 'name': 'lengths', 'type': 'number[]'},\n      {'start': 3, 'name': 'flowIn', 'type': 'number'},\n    ],\n    'attrs': [{'tfName': 'T', 'name': 'dtype', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'TensorArraySizeV3',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorArrayId', 'type': 'tensor'},\n      {'start': 1, 'name': 'flowIn', 'type': 'number'}\n    ]\n  },\n  {\n    'tfOpName': 'TensorArrayCloseV3',\n    'category': 'control',\n    'inputs': [{'start': 0, 'name': 'tensorArrayId', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'StatelessIf',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'cond', 'type': 'tensor'},\n      {'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors'}\n    ],\n    'attrs': [\n      {'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func'},\n      {'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func'}\n    ]\n  },\n  {\n    'tfOpName': 'If',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'cond', 'type': 'tensor'},\n      {'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors'}\n    ],\n    'attrs': [\n      {'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func'},\n      {'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func'}\n    ]\n  },\n  {\n    'tfOpName': 'StatelessWhile',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors'},\n    ],\n    'attrs': [\n      {'tfName': 'cond', 'name': 'cond', 'type': 'func'},\n      {'tfName': 'body', 'name': 'body', 'type': 'func'}\n    ]\n  },\n  {\n    'tfOpName': 'While',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors'},\n    ],\n    'attrs': [\n      {'tfName': 'cond', 'name': 'cond', 'type': 'func'},\n      {'tfName': 'body', 'name': 'body', 'type': 'func'}\n    ]\n  },\n  {\n    'tfOpName': 'TensorListScatter',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensor', 'type': 'tensor'},\n      {'start': 1, 'name': 'indices', 'type': 'number[]'},\n      {'start': 2, 'name': 'elementShape', 'type': 'shape'}\n    ],\n    'attrs':\n        [{'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'TensorListScatterV2',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensor', 'type': 'tensor'},\n      {'start': 1, 'name': 'indices', 'type': 'number[]'},\n      {'start': 2, 'name': 'elementShape', 'type': 'shape'},\n      {'start': 3, 'name': 'numElements', 'type': 'number'},\n    ],\n    'attrs':\n        [{'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'TensorListGather',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorListId', 'type': 'tensor'},\n      {'start': 1, 'name': 'indices', 'type': 'number[]'},\n      {'start': 2, 'name': 'elementShape', 'type': 'shape'},\n    ],\n    'attrs':\n        [{'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'TensorListGetItem',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorListId', 'type': 'tensor'},\n      {'start': 1, 'name': 'index', 'type': 'number'},\n      {'start': 2, 'name': 'elementShape', 'type': 'shape'},\n    ],\n    'attrs':\n        [{'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'TensorListSetItem',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorListId', 'type': 'tensor'},\n      {'start': 1, 'name': 'index', 'type': 'number'},\n      {'start': 2, 'name': 'tensor', 'type': 'tensor'},\n    ],\n    'attrs':\n        [{'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'TensorListReserve',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'elementShape', 'type': 'shape'},\n      {'start': 1, 'name': 'numElements', 'type': 'number'},\n    ],\n    'attrs':\n        [{'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'TensorListFromTensor',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensor', 'type': 'tensor'},\n      {'start': 1, 'name': 'elementShape', 'type': 'shape'}\n    ],\n    'attrs':\n        [{'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'TensorListStack',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorListId', 'type': 'tensor'},\n      {'start': 1, 'name': 'elementShape', 'type': 'shape'},\n    ],\n    'attrs': [\n      {'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'},\n      {'tfName': 'num_elements', 'name': 'numElements', 'type': 'dtype'}\n    ]\n  },\n  {\n    'tfOpName': 'TensorListSplit',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensor', 'type': 'tensor'},\n      {'start': 1, 'name': 'elementShape', 'type': 'shape'},\n      {'start': 2, 'name': 'lengths', 'type': 'number[]'},\n    ],\n    'attrs':\n        [{'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'TensorListConcat',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorListId', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape'},\n      {'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'}\n    ]\n  },\n  {\n    'tfOpName': 'TensorListPopBack',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorListId', 'type': 'tensor'},\n      {'start': 1, 'name': 'elementShape', 'type': 'shape'},\n    ],\n    'attrs':\n        [{'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'TensorListPushBack',\n    'category': 'control',\n    'inputs': [\n      {'start': 0, 'name': 'tensorListId', 'type': 'tensor'},\n      {'start': 1, 'name': 'tensor', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype'}\n    ]\n  }\n];\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'AvgPool',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'}, {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      },\n      {'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]'},\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'MaxPool',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'}, {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      },\n      {'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]'}, {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': [],\n        'notSupported': true\n      },\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'MaxPoolWithArgmax',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'},\n      {'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]'}, {\n        'tfName': 'include_batch_in_index',\n        'name': 'includeBatchInIndex',\n        'type': 'bool'\n      },\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'AvgPool3D',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'}, {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      },\n      {'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]'},\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'MaxPool3D',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'}, {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      },\n      {'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]'},\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Conv1D',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'filter', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'stride', 'name': 'stride', 'type': 'number'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'}, {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NWC'\n      },\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}, {\n        'tfName': 'dilation',\n        'name': 'dilation',\n        'type': 'number',\n        'defaultValue': 1\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Conv2D',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'filter', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true},\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'},\n      {'tfName': 'useCudnnOnGpu', 'name': 'useCudnnOnGpu', 'type': 'bool'}, {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NHWC'\n      },\n      {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': []\n      },\n      {'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]'}\n    ]\n  },\n  {\n    'tfOpName': '_FusedConv2D',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'filter', 'type': 'tensor'},\n      {'start': 2, end: 0, 'name': 'args', 'type': 'tensors'},\n    ],\n    'attrs': [\n      {'tfName': 'num_args', 'name': 'numArgs', 'type': 'number'},\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true},\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'}, {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'use_cudnn_on_gpu',\n        'name': 'useCudnnOnGpu',\n        'type': 'bool',\n        'defaultValue': true\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NHWC'\n      },\n      {\n        'tfName': 'dilations',\n        'name': 'dilations',\n        'type': 'number[]',\n        'defaultValue': [1, 1, 1, 1]\n      },\n      {\n        'tfName': 'fused_ops',\n        'name': 'fusedOps',\n        'type': 'string[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'epsilon',\n        'name': 'epsilon',\n        'type': 'number',\n        'defaultValue': 0.0001\n      },\n      {\n        'tfName': 'leakyrelu_alpha',\n        'name': 'leakyreluAlpha',\n        'type': 'number'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Conv2DBackpropInput',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 2, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'filter', 'type': 'tensor'},\n      {'start': 0, 'name': 'outputShape', 'type': 'number[]'},\n    ],\n    'attrs': [\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'}, {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      },\n      {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'dilations',\n        'name': 'dilations',\n        'type': 'number[]',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'DepthwiseConv2d',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'input', 'type': 'tensor'},\n      {'start': 1, 'name': 'filter', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'}, {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NHWC'\n      },\n      {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': []\n      },\n      {'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]'}\n    ]\n  },\n  {\n    'tfOpName': 'DepthwiseConv2dNative',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'input', 'type': 'tensor'},\n      {'start': 1, 'name': 'filter', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'}, {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NHWC'\n      },\n      {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': []\n      },\n      {'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]'}\n    ]\n  },\n  {\n    'tfOpName': 'FusedDepthwiseConv2dNative',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'filter', 'type': 'tensor'},\n      {'start': 2, end: 0, 'name': 'args', 'type': 'tensors'},\n    ],\n    'attrs': [\n      {'tfName': 'num_args', 'name': 'numArgs', 'type': 'number'},\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true},\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'}, {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NHWC'\n      },\n      {\n        'tfName': 'dilations',\n        'name': 'dilations',\n        'type': 'number[]',\n        'defaultValue': [1, 1, 1, 1]\n      },\n      {\n        'tfName': 'fused_ops',\n        'name': 'fusedOps',\n        'type': 'string[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': []\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Conv3D',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'filter', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'}, {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NHWC'\n      },\n      {'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]'}\n    ],\n  },\n  {\n    'tfOpName': 'Dilation2D',\n    'category': 'convolution',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'filter', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'strides', 'name': 'strides', 'type': 'number[]'},\n      {'tfName': 'rates', 'name': 'dilations', 'type': 'number[]'},\n      {'tfName': 'padding', 'name': 'pad', 'type': 'string'}\n    ]\n  }\n];\n","import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Fill',\n    'category': 'creation',\n    'inputs': [\n      {'start': 0, 'name': 'shape', 'type': 'number[]'},\n      {'start': 1, 'name': 'value', 'type': 'number'},\n    ],\n    'attrs': [{'tfName': 'T', 'name': 'dtype', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'LinSpace',\n    'category': 'creation',\n    'inputs': [\n      {'start': 0, 'name': 'start', 'type': 'number'},\n      {'start': 1, 'name': 'stop', 'type': 'number'},\n      {'start': 2, 'name': 'num', 'type': 'number'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'OneHot',\n    'category': 'creation',\n    'inputs': [\n      {'start': 0, 'name': 'indices', 'type': 'tensor'},\n      {'start': 1, 'name': 'depth', 'type': 'number'},\n      {'start': 2, 'name': 'onValue', 'type': 'number', 'defaultValue': 1},\n      {'start': 3, 'name': 'offValue', 'type': 'number', 'defaultValue': 0},\n    ],\n    'attrs': [\n      {\n        'tfName': 'axis',\n        'name': 'axis',\n        'type': 'number',\n        'notSupported': true\n      },\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Ones',\n    'category': 'creation',\n    'inputs': [\n      {'start': 0, 'name': 'shape', 'type': 'number[]'},\n    ],\n    'attrs': [{'tfName': 'T', 'name': 'dtype', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'OnesLike',\n    'category': 'creation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [{'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'RandomUniform',\n    'category': 'creation',\n    'inputs': [\n      {'start': 0, 'name': 'shape', 'type': 'number[]'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'minval',\n        'name': 'minval',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'maxval',\n        'name': 'maxval',\n        'type': 'number',\n        'defaultValue': 1\n      },\n      {'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype'},\n      {'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0}, {\n        'tfName': 'seed2',\n        'name': 'seed2',\n        'type': 'number',\n        'defaultValue': 0,\n        'notSupported': true\n      },\n      {'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Range',\n    'category': 'creation',\n    'inputs': [\n      {'start': 0, 'name': 'start', 'type': 'number'},\n      {'start': 1, 'name': 'stop', 'type': 'number'},\n      {'start': 2, 'name': 'step', 'type': 'number', 'defaultValue': 0},\n    ],\n    'attrs': [{'tfName': 'Tidx', 'name': 'dtype', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'TruncatedNormal',\n    'category': 'creation',\n    'inputs': [\n      {'start': 0, 'name': 'shape', 'type': 'number[]'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'means',\n        'name': 'mean',\n        'type': 'number',\n        'defaultValue': 0.0\n      },\n      {\n        'tfName': 'stddev',\n        'name': 'stdDev',\n        'type': 'number',\n        'defaultValue': 1.0\n      },\n      {'tfName': 'seed', 'name': 'seed', 'type': 'number'}, {\n        'tfName': 'seed2',\n        'name': 'seed2',\n        'type': 'number',\n        'defaultValue': 0,\n        'notSupported': true\n      },\n      {'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype'},\n      {'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Zeros',\n    'category': 'creation',\n    'inputs': [\n      {'start': 0, 'name': 'shape', 'type': 'number[]'},\n    ],\n    'attrs': [{'tfName': 'T', 'name': 'dtype', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'ZerosLike',\n    'category': 'creation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [{'tfName': 'T', 'name': 'dtype', 'type': 'dtype'}]\n  },\n  {\n    'tfOpName': 'Multinomial',\n    'category': 'creation',\n    'inputs': [\n      {'start': 0, 'name': 'logits', 'type': 'tensor'},\n      {'start': 1, 'name': 'numSamples', 'type': 'number'},\n    ],\n    'attrs': [\n      {'tfName': 'seed', 'name': 'seed', 'type': 'number'},\n      {'tfName': 'seed2', 'name': 'seed2', 'type': 'number'},\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype'},\n      {'tfName': 'output_dtype', 'name': 'output_dtype', 'type': 'dtype'}\n    ]\n  }\n];\n","import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'NonMaxSuppressionV2',\n    'category': 'dynamic',\n    'inputs': [\n      {'start': 0, 'name': 'boxes', 'type': 'tensor'},\n      {'start': 1, 'name': 'scores', 'type': 'tensor'},\n      {'start': 2, 'name': 'maxOutputSize', 'type': 'number'},\n      {'start': 3, 'name': 'iouThreshold', 'type': 'number'}\n    ]\n  },\n  {\n    'tfOpName': 'NonMaxSuppressionV3',\n    'category': 'dynamic',\n    'inputs': [\n      {'start': 0, 'name': 'boxes', 'type': 'tensor'},\n      {'start': 1, 'name': 'scores', 'type': 'tensor'},\n      {'start': 2, 'name': 'maxOutputSize', 'type': 'number'},\n      {'start': 3, 'name': 'iouThreshold', 'type': 'number'},\n      {'start': 4, 'name': 'scoreThreshold', 'type': 'number'}\n    ]\n  },\n  {\n    'tfOpName': 'NonMaxSuppressionV4',\n    'category': 'dynamic',\n    'inputs': [\n      {'start': 0, 'name': 'boxes', 'type': 'tensor'},\n      {'start': 1, 'name': 'scores', 'type': 'tensor'},\n      {'start': 2, 'name': 'maxOutputSize', 'type': 'number'},\n      {'start': 3, 'name': 'iouThreshold', 'type': 'number'},\n      {'start': 4, 'name': 'scoreThreshold', 'type': 'number'}\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}, {\n        'tfName': 'T_threshold',\n        'name': 'threshold',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'pad_to_max_output_size',\n        'name': 'padToMaxOutputSize',\n        'type': 'bool'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'NonMaxSuppressionV5',\n    'category': 'dynamic',\n    'inputs': [\n      {'start': 0, 'name': 'boxes', 'type': 'tensor'},\n      {'start': 1, 'name': 'scores', 'type': 'tensor'},\n      {'start': 2, 'name': 'maxOutputSize', 'type': 'number'},\n      {'start': 3, 'name': 'iouThreshold', 'type': 'number'},\n      {'start': 4, 'name': 'scoreThreshold', 'type': 'number'},\n      {'start': 5, 'name': 'softNmsSigma', 'type': 'number'}\n    ]\n  },\n  {\n    'tfOpName': 'Where',\n    'category': 'dynamic',\n    'inputs': [\n      {'start': 0, 'name': 'condition', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'ListDiff',\n    'category': 'dynamic',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'y', 'type': 'tensor'},\n    ],\n    'attrs': [{\n      'tfName': 'T',\n      'name': 'dtype',\n      'type': 'dtype',\n      'notSupported': true\n    }]\n  }\n];\n","import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'TopKV2',\n    'category': 'evaluation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'k', 'type': 'number'},\n    ],\n    'attrs': [{'tfName': 'sorted', 'name': 'sorted', 'type': 'bool'}]\n  },\n  {\n    'tfOpName': 'Unique',\n    'category': 'evaluation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n  },\n  {\n    'tfOpName': 'UniqueV2',\n    'category': 'evaluation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number'},\n    ],\n  },\n];\n","import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'PlaceholderWithDefault',\n    'category': 'graph',\n    'inputs': [\n      {'start': 0, 'name': 'default', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'shape', 'name': 'shape', 'type': 'shape'},\n      {'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype'}\n    ]\n  },\n  {\n    'tfOpName': 'Placeholder',\n    'category': 'graph',\n    'attrs': [\n      {'tfName': 'shape', 'name': 'shape', 'type': 'shape'},\n      {'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype'}\n    ]\n  },\n  {'tfOpName': 'Const', 'category': 'graph'}, {\n    'tfOpName': 'Identity',\n    'category': 'graph',\n    'inputs': [{'start': 0, 'name': 'x', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'IdentityN',\n    'category': 'graph',\n    'inputs': [{'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors'}]\n  },\n  {\n    'tfOpName': 'Snapshot',\n    'category': 'graph',\n    'inputs': [{'start': 0, 'name': 'x', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'Rank',\n    'category': 'graph',\n    'inputs': [{'start': 0, 'name': 'x', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'Size',\n    'category': 'graph',\n    'inputs': [{'start': 0, 'name': 'x', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'Shape',\n    'category': 'graph',\n    'inputs': [{'start': 0, 'name': 'x', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'ShapeN',\n    'category': 'graph',\n    'inputs': [{'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors'}]\n  },\n  {\n    'tfOpName': 'Print',\n    'category': 'graph',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'data', 'type': 'tensors'},\n    ],\n    'attrs': [\n      {'tfName': 'message', 'name': 'message', 'type': 'string'}, {\n        'tfName': 'first_n',\n        'name': 'firstN',\n        'type': 'number',\n        'notSupported': true\n      },\n      {\n        'tfName': 'summarize',\n        'name': 'summarize',\n        'type': 'number',\n        'defaultValue': 3\n      }\n    ]\n  },\n  {'tfOpName': 'NoOp', 'category': 'graph', 'inputs': []}, {\n    'tfOpName': 'StopGradient',\n    'category': 'graph',\n    'inputs': [{'start': 0, 'name': 'x', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'FakeQuantWithMinMaxVars',\n    'category': 'graph',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'min', 'name': 'min', 'type': 'number'},\n      {'tfName': 'max', 'name': 'max', 'type': 'number'}\n    ]\n  }\n];\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'HashTable',\n    'category': 'hash_table',\n    'inputs': [],\n    'attrs': [\n      {'tfName': 'shared_name', 'name': 'sharedName', 'type': 'string'},\n      {\n        'tfName': 'use_node_name_sharing',\n        'name': 'useNodeNameSharing',\n        'type': 'bool'\n      },\n      {'tfName': 'key_dtype', 'name': 'keyDType', 'type': 'dtype'},\n      {'tfName': 'value_dtype', 'name': 'valueDType', 'type': 'dtype'},\n    ]\n  },\n  {\n    'tfOpName': 'HashTableV2',\n    'category': 'hash_table',\n    'inputs': [],\n    'attrs': [\n      {'tfName': 'shared_name', 'name': 'sharedName', 'type': 'string'},\n      {\n        'tfName': 'use_node_name_sharing',\n        'name': 'useNodeNameSharing',\n        'type': 'bool'\n      },\n      {'tfName': 'key_dtype', 'name': 'keyDType', 'type': 'dtype'},\n      {'tfName': 'value_dtype', 'name': 'valueDType', 'type': 'dtype'},\n    ]\n  },\n  {\n    'tfOpName': 'LookupTableImport',\n    'category': 'hash_table',\n    'inputs': [\n      {'start': 0, 'name': 'tableHandle', 'type': 'tensor'},\n      {'start': 1, 'name': 'keys', 'type': 'tensor'},\n      {'start': 2, 'name': 'values', 'type': 'tensor'}\n    ],\n    'attrs': [\n      {'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true}, {\n        'tfName': 'Tout',\n        'name': 'tOut',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LookupTableImportV2',\n    'category': 'hash_table',\n    'inputs': [\n      {'start': 0, 'name': 'tableHandle', 'type': 'tensor'},\n      {'start': 1, 'name': 'keys', 'type': 'tensor'},\n      {'start': 2, 'name': 'values', 'type': 'tensor'}\n    ],\n    'attrs': [\n      {'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true}, {\n        'tfName': 'Tout',\n        'name': 'tOut',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LookupTableFind',\n    'category': 'hash_table',\n    'inputs': [\n      {'start': 0, 'name': 'tableHandle', 'type': 'tensor'},\n      {'start': 1, 'name': 'keys', 'type': 'tensor'},\n      {'start': 2, 'name': 'defaultValue', 'type': 'tensor'}\n    ],\n    'attrs': [\n      {'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true}, {\n        'tfName': 'Tout',\n        'name': 'tOut',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LookupTableFindV2',\n    'category': 'hash_table',\n    'inputs': [\n      {'start': 0, 'name': 'tableHandle', 'type': 'tensor'},\n      {'start': 1, 'name': 'keys', 'type': 'tensor'},\n      {'start': 2, 'name': 'defaultValue', 'type': 'tensor'}\n    ],\n    'attrs': [\n      {'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true}, {\n        'tfName': 'Tout',\n        'name': 'tOut',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LookupTableSize',\n    'category': 'hash_table',\n    'inputs': [\n      {'start': 0, 'name': 'tableHandle', 'type': 'tensor'}\n    ]\n  },\n  {\n    'tfOpName': 'LookupTableSizeV2',\n    'category': 'hash_table',\n    'inputs': [\n      {'start': 0, 'name': 'tableHandle', 'type': 'tensor'}\n    ]\n  }\n];\n","import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'ResizeBilinear',\n    'category': 'image',\n    'inputs': [\n      {'start': 0, 'name': 'images', 'type': 'tensor'},\n      {'start': 1, 'name': 'size', 'type': 'number[]'},\n    ],\n    'attrs': [\n      {'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool'}, {\n        'tfName': 'half_pixel_centers',\n        'name': 'halfPixelCenters',\n        'type': 'bool'\n      },\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'ResizeNearestNeighbor',\n    'category': 'image',\n    'inputs': [\n      {'start': 0, 'name': 'images', 'type': 'tensor'},\n      {'start': 1, 'name': 'size', 'type': 'number[]'},\n    ],\n    'attrs': [\n      {'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool'}, {\n        'tfName': 'half_pixel_centers',\n        'name': 'halfPixelCenters',\n        'type': 'bool'\n      },\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'CropAndResize',\n    'category': 'image',\n    'inputs': [\n      {'start': 0, 'name': 'image', 'type': 'tensor'},\n      {'start': 1, 'name': 'boxes', 'type': 'tensor'},\n      {'start': 2, 'name': 'boxInd', 'type': 'tensor'},\n      {'start': 3, 'name': 'cropSize', 'type': 'number[]'},\n    ],\n    'attrs': [\n      {'tfName': 'method', 'name': 'method', 'type': 'string'}, {\n        'tfName': 'extrapolation_value',\n        'name': 'extrapolationValue',\n        'type': 'number'\n      }\n    ]\n  }\n];\n","import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Equal',\n    'category': 'logical',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'NotEqual',\n    'category': 'logical',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Greater',\n    'category': 'logical',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'GreaterEqual',\n    'category': 'logical',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Less',\n    'category': 'logical',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'LessEqual',\n    'category': 'logical',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'LogicalAnd',\n    'category': 'logical',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'LogicalNot',\n    'category': 'logical',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'LogicalOr',\n    'category': 'logical',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Select',\n    'category': 'logical',\n    'inputs': [\n      {'start': 0, 'name': 'condition', 'type': 'tensor'},\n      {'start': 1, 'name': 'a', 'type': 'tensor'},\n      {'start': 2, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'SelectV2',\n    'category': 'logical',\n    'inputs': [\n      {'start': 0, 'name': 'condition', 'type': 'tensor'},\n      {'start': 1, 'name': 'a', 'type': 'tensor'},\n      {'start': 2, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [{\n      'tfName': 'T',\n      'name': 'dtype',\n      'type': 'dtype',\n      'notSupported': true\n    }]\n  }\n];\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': '_FusedMatMul',\n    'category': 'matrices',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n      {'start': 2, end: 0, 'name': 'args', 'type': 'tensors'},\n    ],\n    'attrs': [\n      {'tfName': 'num_args', 'name': 'numArgs', 'type': 'number'}, {\n        'tfName': 'fused_ops',\n        'name': 'fusedOps',\n        'type': 'string[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'epsilon',\n        'name': 'epsilon',\n        'type': 'number',\n        'defaultValue': 0.0001\n      },\n      {\n        'tfName': 'transpose_a',\n        'name': 'transposeA',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {\n        'tfName': 'transpose_b',\n        'name': 'transposeB',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'MatMul',\n    'category': 'matrices',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'transpose_a',\n        'name': 'transposeA',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {\n        'tfName': 'transpose_b',\n        'name': 'transposeB',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'BatchMatMul',\n    'category': 'matrices',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'adj_x',\n        'name': 'transposeA',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {\n        'tfName': 'adj_y',\n        'name': 'transposeB',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'BatchMatMulV2',\n    'category': 'matrices',\n    'inputs': [\n      {'start': 0, 'name': 'a', 'type': 'tensor'},\n      {'start': 1, 'name': 'b', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'adj_x',\n        'name': 'transposeA',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {\n        'tfName': 'adj_y',\n        'name': 'transposeB',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Transpose',\n    'category': 'matrices',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'perm', 'type': 'number[]'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'Einsum',\n    'category': 'matrices',\n    'inputs': [{'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors'}],\n    'attrs': [\n      {'tfName': 'equation', 'name': 'equation', 'type': 'string'},\n      {'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2},\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype'}\n    ]\n  }\n];\n","import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'FusedBatchNorm',\n    'category': 'normalization',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'scale', 'type': 'tensor'},\n      {'start': 2, 'name': 'offset', 'type': 'tensor'},\n      {'start': 3, 'name': 'mean', 'type': 'tensor'},\n      {'start': 4, 'name': 'variance', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'epsilon',\n        'name': 'epsilon',\n        'type': 'number',\n        'defaultValue': 0.001\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'FusedBatchNormV2',\n    'category': 'normalization',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'scale', 'type': 'tensor'},\n      {'start': 2, 'name': 'offset', 'type': 'tensor'},\n      {'start': 3, 'name': 'mean', 'type': 'tensor'},\n      {'start': 4, 'name': 'variance', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'epsilon',\n        'name': 'epsilon',\n        'type': 'number',\n        'defaultValue': 0.001\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'FusedBatchNormV3',\n    'category': 'normalization',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'scale', 'type': 'tensor'},\n      {'start': 2, 'name': 'offset', 'type': 'tensor'},\n      {'start': 3, 'name': 'mean', 'type': 'tensor'},\n      {'start': 4, 'name': 'variance', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'epsilon',\n        'name': 'epsilon',\n        'type': 'number',\n        'defaultValue': 0.001\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LRN',\n    'category': 'normalization',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'depth_radius',\n        'name': 'radius',\n        'type': 'number',\n        'defaultValue': 5\n      },\n      {'tfName': 'bias', 'name': 'bias', 'type': 'number', 'defaultValue': 1.0},\n      {\n        'tfName': 'alpha',\n        'name': 'alpha',\n        'type': 'number',\n        'defaultValue': 1.0\n      },\n      {\n        'tfName': 'beta',\n        'name': 'beta',\n        'type': 'number',\n        'defaultValue': 0.5\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Softmax',\n    'category': 'normalization',\n    'inputs': [{'start': 0, 'name': 'x', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'LogSoftmax',\n    'category': 'normalization',\n    'inputs': [{'start': 0, 'name': 'x', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'SparseToDense',\n    'category': 'normalization',\n    'inputs': [\n      {'start': 0, 'name': 'sparseIndices', 'type': 'tensor'},\n      {'start': 1, 'name': 'outputShape', 'type': 'number[]'},\n      {'start': 2, 'name': 'sparseValues', 'type': 'tensor'},\n      {'start': 3, 'name': 'defaultValue', 'type': 'tensor'},\n    ],\n    'attrs': [{\n      'tfName': 'validate_indices',\n      'name': 'validateIndices',\n      'type': 'bool',\n      'defaultValue': true,\n      'notSupported': true\n    }]\n  }\n];\n","import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Bincount',\n    'category': 'reduction',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'size', 'type': 'number'},\n      {'start': 2, 'name': 'weights', 'type': 'tensor'}\n    ]\n  },\n  {\n    'tfOpName': 'DenseBincount',\n    'category': 'reduction',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'size', 'type': 'number'},\n      {'start': 2, 'name': 'weights', 'type': 'tensor'}\n    ],\n    'attrs':\n        [{'tfName': 'binary_output', 'name': 'binaryOutput', 'type': 'bool'}]\n  },\n  {\n    'tfOpName': 'Max',\n    'category': 'reduction',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number[]'},\n    ],\n    'attrs': [{'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool'}]\n  },\n  {\n    'tfOpName': 'Mean',\n    'category': 'reduction',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number[]'},\n    ],\n    'attrs': [{'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool'}]\n  },\n  {\n    'tfOpName': 'Min',\n    'category': 'reduction',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number[]'},\n    ],\n    'attrs': [{'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool'}]\n  },\n  {\n    'tfOpName': 'Sum',\n    'category': 'reduction',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number[]'},\n    ],\n    'attrs': [{'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool'}]\n  },\n  {\n    'tfOpName': 'All',\n    'category': 'reduction',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number[]'},\n    ],\n    'attrs': [{'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool'}]\n  },\n  {\n    'tfOpName': 'Any',\n    'category': 'reduction',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number[]'},\n    ],\n    'attrs': [{'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool'}]\n  },\n  {\n    'tfOpName': 'ArgMax',\n    'category': 'reduction',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number'}\n    ]\n  },\n  {\n    'tfOpName': 'ArgMin',\n    'category': 'reduction',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number'}\n    ]\n  },\n  {\n    'tfOpName': 'Prod',\n    'category': 'reduction',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number[]'},\n    ],\n    'attrs': [{'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool'}]\n  },\n  {\n    'tfOpName': 'Cumsum',\n    'category': 'reduction',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number'},\n    ],\n    'attrs': [\n      {'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool'},\n      {'tfName': 'reverse', 'name': 'reverse', 'type': 'bool'}\n    ]\n  }\n];\n","import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'ConcatV2',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'end': -1, 'name': 'tensors', 'type': 'tensors'},\n      {'start': -1, 'name': 'axis', 'type': 'number'}\n    ],\n    'attrs':\n        [{'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2}]\n  },\n  {\n    'tfOpName': 'Concat',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 1, 'end': 0, 'name': 'tensors', 'type': 'tensors'},\n      {'start': 0, 'name': 'axis', 'type': 'number'}\n    ],\n    'attrs': [{'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2}]\n\n  },\n  {\n    'tfOpName': 'GatherV2',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'indices', 'type': 'tensor'},\n      {'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0}\n    ],\n    'attrs': [{\n      'tfName': 'batch_dims',\n      'name': 'batchDims',\n      'type': 'number',\n      'defaultValue': 0\n    }]\n  },\n  {\n    'tfOpName': 'Gather',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'indices', 'type': 'tensor'},\n    ],\n    'attrs': [{\n      'tfName': 'validate_indices',\n      'name': 'validateIndices',\n      'type': 'bool',\n      'notSupported': true\n    }]\n  },\n  {\n    'tfOpName': 'Reverse',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'dims', 'type': 'bool[]'}\n    ]\n  },\n  {\n    'tfOpName': 'ReverseV2',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number[]'}\n    ]\n  },\n  {\n    'tfOpName': 'Slice',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'begin', 'type': 'number[]'},\n      {'start': 2, 'name': 'size', 'type': 'number[]'}\n    ]\n  },\n  {\n    'tfOpName': 'StridedSlice',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'begin', 'type': 'number[]'},\n      {'start': 2, 'name': 'end', 'type': 'number[]'},\n      {'start': 3, 'name': 'strides', 'type': 'number[]'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'begin_mask',\n        'name': 'beginMask',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'end_mask',\n        'name': 'endMask',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'new_axis_mask',\n        'name': 'newAxisMask',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'ellipsis_mask',\n        'name': 'ellipsisMask',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'shrink_axis_mask',\n        'name': 'shrinkAxisMask',\n        'type': 'number',\n        'defaultValue': 0\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Pack',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors'},\n    ],\n    'attrs': [\n      {'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0}\n    ]\n  },\n  {\n    'tfOpName': 'Unpack',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'tensor', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0}, {\n        'tfName': 'num',\n        'name': 'num',\n        'type': 'number',\n        'defaultValue': 0,\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Tile',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'reps', 'type': 'number[]'}\n    ]\n  },\n  {\n    'tfOpName': 'Split',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'axis', 'type': 'number', 'defaultValue': 0},\n      {'start': 1, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [{\n      'tfName': 'num_split',\n      'name': 'numOrSizeSplits',\n      'type': 'number',\n      'defaultValue': 1\n    }]\n  },\n  {\n    'tfOpName': 'SplitV',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'numOrSizeSplits', 'type': 'number[]'},\n      {'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0}\n    ]\n  },\n  {\n    'tfOpName': 'ScatterNd',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'indices', 'type': 'tensor'},\n      {'start': 1, 'name': 'values', 'type': 'tensor'},\n      {'start': 2, 'name': 'shape', 'type': 'number[]'}\n    ]\n  },\n  {\n    'tfOpName': 'GatherNd',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'indices', 'type': 'tensor'}\n    ]\n  },\n  {\n    'tfOpName': 'SparseToDense',\n    'category': 'slice_join',\n    'inputs': [\n      {'start': 0, 'name': 'sparseIndices', 'type': 'tensor'},\n      {'start': 1, 'name': 'outputShape', 'type': 'number[]'},\n      {'start': 2, 'name': 'sparseValues', 'type': 'tensor'},\n      {'start': 3, 'name': 'defaultValue', 'type': 'tensor'},\n    ],\n    'attrs': [{\n      'tfName': 'validate_indices',\n      'name': 'validateIndices',\n      'type': 'bool',\n      'defaultValue': false,\n      'notSupported': true\n    }]\n  }\n];\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'SparseFillEmptyRows',\n    'category': 'sparse',\n    'inputs': [\n      {'start': 0, 'name': 'indices', 'type': 'tensor'},\n      {'start': 1, 'name': 'values', 'type': 'tensor'},\n      {'start': 2, 'name': 'denseShape', 'type': 'tensor'},\n      {'start': 3, 'name': 'defaultValue', 'type': 'tensor'},\n    ]\n  },\n  {\n    'tfOpName': 'SparseReshape',\n    'category': 'sparse',\n    'inputs': [\n      {'start': 0, 'name': 'inputIndices', 'type': 'tensor'},\n      {'start': 1, 'name': 'inputShape', 'type': 'tensor'},\n      {'start': 2, 'name': 'newShape', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}\n    ]\n  },\n  {\n    'tfOpName': 'SparseSegmentMean',\n    'category': 'sparse',\n    'inputs': [\n      {'start': 0, 'name': 'data', 'type': 'tensor'},\n      {'start': 1, 'name': 'indices', 'type': 'tensor'},\n      {'start': 2, 'name': 'segmentIds', 'type': 'tensor'},\n    ]\n  },\n  {\n    'tfOpName': 'SparseSegmentSum',\n    'category': 'sparse',\n    'inputs': [\n      {'start': 0, 'name': 'data', 'type': 'tensor'},\n      {'start': 1, 'name': 'indices', 'type': 'tensor'},\n      {'start': 2, 'name': 'segmentIds', 'type': 'tensor'},\n    ]\n  }\n];\n","import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'FFT',\n    'category': 'spectral',\n    'inputs': [{'start': 0, 'name': 'x', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'IFFT',\n    'category': 'spectral',\n    'inputs': [{'start': 0, 'name': 'x', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'RFFT',\n    'category': 'spectral',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'}, {\n        'start': 1,\n        'name': 'fft_length',\n        'type': 'number',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'IRFFT',\n    'category': 'spectral',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'}, {\n        'start': 1,\n        'name': 'fft_length',\n        'type': 'number',\n        'notSupported': true\n      }\n    ]\n  }\n];\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'StringNGrams',\n    'category': 'string',\n    'inputs': [\n      {'start': 0, 'name': 'data', 'type': 'tensor'},\n      {'start': 1, 'name': 'dataSplits', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'separator', 'name': 'separator', 'type': 'string'},\n      {'tfName': 'ngram_widths', 'name': 'nGramWidths', 'type': 'number[]'},\n      {'tfName': 'left_pad', 'name': 'leftPad', 'type': 'string'},\n      {'tfName': 'right_pad', 'name': 'rightPad', 'type': 'string'},\n      {'tfName': 'pad_width', 'name': 'padWidth', 'type': 'number'}, {\n        'tfName': 'preserve_short_sequences',\n        'name': 'preserveShortSequences',\n        'type': 'bool'\n      }\n    ],\n    'outputs': ['ngrams', 'ngrams_splits']\n  },\n  {\n    'tfOpName': 'StringSplit',\n    'category': 'string',\n    'inputs': [\n      {'start': 0, 'name': 'input', 'type': 'tensor'},\n      {'start': 1, 'name': 'delimiter', 'type': 'tensor'},\n    ],\n    'attrs': [{'tfName': 'skip_empty', 'name': 'skipEmpty', 'type': 'bool'}],\n    'outputs': ['indices', 'values', 'shape']\n  },\n  {\n    'tfOpName': 'StringToHashBucketFast',\n    'category': 'string',\n    'inputs': [\n      {'start': 0, 'name': 'input', 'type': 'tensor'},\n    ],\n    'attrs': [{'tfName': 'num_buckets', 'name': 'numBuckets', 'type': 'number'}]\n  }\n];\n","import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Cast',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'SrcT',\n        'name': 'sdtype',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype'}\n    ]\n  },\n  {\n    'tfOpName': 'ExpandDims',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number'}\n    ]\n  },\n  {\n    'tfOpName': 'MirrorPad',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'padding', 'type': 'number[]'},\n    ],\n    'attrs': [{'tfName': 'mode', 'name': 'mode', 'type': 'string'}]\n  },\n  {\n    'tfOpName': 'Pad',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'padding', 'type': 'number[]'},\n    ],\n    'attrs': [{\n      'tfName': 'constant_value',\n      'name': 'constantValue',\n      'type': 'number',\n      'defaultValue': 0\n    }]\n  },\n  {\n    'tfOpName': 'PadV2',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'padding', 'type': 'number[]'}, {\n        'start': 2,\n        'name': 'constantValue',\n        'type': 'number',\n        'defaultValue': 0\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Reshape',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'shape', 'type': 'number[]'}\n    ]\n  },\n  {\n    'tfOpName': 'Squeeze',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [{\n      'tfName': 'axis',\n      'tfDeprecatedName': 'squeeze_dims',\n      'name': 'axis',\n      'type': 'number[]'\n    }]\n  },\n  {\n    'tfOpName': 'SpaceToBatchND',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'blockShape', 'type': 'number[]'},\n      {'start': 2, 'name': 'paddings', 'type': 'number[]'}\n    ]\n  },\n  {\n    'tfOpName': 'BatchToSpaceND',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'blockShape', 'type': 'number[]'},\n      {'start': 2, 'name': 'crops', 'type': 'number[]'}\n    ]\n  },\n  {\n    'tfOpName': 'DepthToSpace',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {'tfName': 'block_size', 'name': 'blockSize', 'type': 'number'},\n      {'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string'}\n    ]\n  },\n  {\n    'tfOpName': 'BroadcastTo',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'shape', 'type': 'number[]'},\n    ],\n    'attrs': []\n  }\n];\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, env} from '@tensorflow/tfjs-core';\n\nimport * as tensorflow from '../data/compiled_api';\n\nimport {getRegisteredOp} from './custom_op/register';\nimport {getNodeNameAndIndex} from './executors/utils';\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as hashTable from './op_list/hash_table';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as sparse from './op_list/sparse';\nimport * as spectral from './op_list/spectral';\nimport * as string from './op_list/string';\nimport * as transformation from './op_list/transformation';\nimport {Graph, InputParamValue, Node, OpMapper, ParamValue} from './types';\n\nexport class OperationMapper {\n  private static _instance: OperationMapper;\n\n  private opMappers: {[key: string]: OpMapper};\n\n  // Singleton instance for the mapper\n  public static get Instance() {\n    return this._instance || (this._instance = new this());\n  }\n\n  // Loads the op mapping from the JSON file.\n  private constructor() {\n    const ops = [\n      arithmetic, basicMath, control, convolution, creation, dynamic,\n      evaluation, graph, hashTable, image, logical, matrices, normalization,\n      reduction, sliceJoin, sparse, spectral, string, transformation\n    ];\n    const mappersJson: OpMapper[] = [].concat(...ops.map(op => op.json));\n\n    this.opMappers = mappersJson.reduce<{[key: string]: OpMapper}>(\n        (map, mapper: OpMapper) => {\n          map[mapper.tfOpName] = mapper;\n          return map;\n        },\n        {});\n  }\n\n  // Converts the model inference graph from Tensorflow GraphDef to local\n  // representation for TensorFlow.js API\n  transformGraph(\n      graph: tensorflow.IGraphDef,\n      signature: tensorflow.ISignatureDef = {}): Graph {\n    const tfNodes = graph.node;\n    const placeholders: Node[] = [];\n    const weights: Node[] = [];\n    const initNodes: Node[] = [];\n    const nodes = tfNodes.reduce<{[key: string]: Node}>((map, node) => {\n      map[node.name] = this.mapNode(node);\n      if (node.op.startsWith('Placeholder')) {\n        placeholders.push(map[node.name]);\n      } else if (node.op === 'Const') {\n        weights.push(map[node.name]);\n      } else if (node.input == null || node.input.length === 0) {\n        initNodes.push(map[node.name]);\n      }\n      return map;\n    }, {});\n\n    let inputs: Node[] = [];\n    const outputs: Node[] = [];\n    let inputNodeNameToKey: {[key: string]: string} = {};\n    let outputNodeNameToKey: {[key: string]: string} = {};\n    if (signature != null) {\n      inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n      outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n    }\n    const allNodes = Object.keys(nodes);\n    allNodes.forEach(key => {\n      const node = nodes[key];\n      node.inputNames.forEach((name, index) => {\n        const [nodeName, , outputName] = getNodeNameAndIndex(name);\n        const inputNode = nodes[nodeName];\n        if (inputNode.outputs != null) {\n          const outputIndex = inputNode.outputs.indexOf(outputName);\n          if (outputIndex !== -1) {\n            const inputName = `${nodeName}:${outputIndex}`;\n            // update the input name to use the mapped output index directly.\n            node.inputNames[index] = inputName;\n          }\n        }\n        node.inputs.push(inputNode);\n        inputNode.children.push(node);\n      });\n    });\n\n    // if signature has not outputs set, add any node that does not have\n    // outputs.\n    if (Object.keys(outputNodeNameToKey).length === 0) {\n      allNodes.forEach(key => {\n        const node = nodes[key];\n        if (node.children.length === 0) {\n          outputs.push(node);\n        }\n      });\n    } else {\n      Object.keys(outputNodeNameToKey).forEach(name => {\n        const [nodeName, ] = getNodeNameAndIndex(name);\n        const node = nodes[nodeName];\n        if (node != null) {\n          node.signatureKey = outputNodeNameToKey[name];\n          outputs.push(node);\n        }\n      });\n    }\n\n    if (Object.keys(inputNodeNameToKey).length > 0) {\n      Object.keys(inputNodeNameToKey).forEach(name => {\n        const [nodeName, ] = getNodeNameAndIndex(name);\n        const node = nodes[nodeName];\n        if (node) {\n          node.signatureKey = inputNodeNameToKey[name];\n          inputs.push(node);\n        }\n      });\n    } else {\n      inputs = placeholders;\n    }\n\n    let functions = {};\n    if (graph.library != null && graph.library.function != null) {\n      functions = graph.library.function.reduce((functions, func) => {\n        functions[func.signature.name] = this.mapFunction(func);\n        return functions;\n      }, {} as {[key: string]: Graph});\n    }\n\n    const result: Graph =\n        {nodes, inputs, outputs, weights, placeholders, signature, functions};\n\n    if (initNodes.length > 0) {\n      result.initNodes = initNodes;\n    }\n\n    return result;\n  }\n\n  private mapSignatureEntries(entries: {[k: string]: tensorflow.ITensorInfo}) {\n    return Object.keys(entries || {})\n        .reduce<{[key: string]: string}>((prev, curr) => {\n          prev[entries[curr].name] = curr;\n          return prev;\n        }, {});\n  }\n\n  private mapNode(node: tensorflow.INodeDef): Node {\n    // Unsupported ops will cause an error at run-time (not parse time), since\n    // they may not be used by the actual execution subgraph.\n    const mapper =\n        getRegisteredOp(node.op) || this.opMappers[node.op] || {} as OpMapper;\n    if (node.attr == null) {\n      node.attr = {};\n    }\n\n    const newNode: Node = {\n      name: node.name,\n      op: node.op,\n      category: mapper.category,\n      inputNames:\n          (node.input ||\n           []).map(input => input.startsWith('^') ? input.substr(1) : input),\n      inputs: [],\n      children: [],\n      inputParams: {},\n      attrParams: {},\n      rawAttrs: node.attr,\n      outputs: mapper.outputs\n    };\n\n    if (mapper.inputs != null) {\n      newNode.inputParams =\n          mapper.inputs.reduce<{[key: string]: InputParamValue}>(\n              (map, param) => {\n                map[param.name] = {\n                  type: param.type,\n                  inputIndexStart: param.start,\n                  inputIndexEnd: param.end\n                };\n                return map;\n              },\n              {});\n    }\n    if (mapper.attrs != null) {\n      newNode.attrParams =\n          mapper.attrs.reduce<{[key: string]: ParamValue}>((map, param) => {\n            const type = param.type;\n            let value = undefined;\n            switch (param.type) {\n              case 'string':\n                value = getStringParam(\n                    node.attr, param.tfName, param.defaultValue as string);\n\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getStringParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as string);\n                }\n                break;\n              case 'string[]':\n                value = getStringArrayParam(\n                    node.attr, param.tfName, param.defaultValue as string[]);\n\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getStringArrayParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as string[]);\n                }\n                break;\n              case 'number':\n                value = getNumberParam(\n                    node.attr, param.tfName,\n                    (param.defaultValue || 0) as number);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getNumberParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as number);\n                }\n                break;\n              case 'number[]':\n                value = getNumericArrayParam(\n                    node.attr, param.tfName, param.defaultValue as number[]);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getNumericArrayParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as number[]);\n                }\n                break;\n              case 'bool':\n                value = getBoolParam(\n                    node.attr, param.tfName, param.defaultValue as boolean);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getBoolParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as boolean);\n                }\n                break;\n              case 'bool[]':\n                value = getBoolArrayParam(\n                    node.attr, param.tfName, param.defaultValue as boolean[]);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getBoolArrayParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as boolean[]);\n                }\n                break;\n              case 'shape':\n                value = getTensorShapeParam(\n                    node.attr, param.tfName, param.defaultValue as number[]);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getTensorShapeParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as number[]);\n                }\n                break;\n              case 'shape[]':\n                value = getTensorShapeArrayParam(\n                    node.attr, param.tfName, param.defaultValue as number[][]);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getTensorShapeArrayParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as number[][]);\n                }\n                break;\n              case 'dtype':\n                value = getDtypeParam(\n                    node.attr, param.tfName, param.defaultValue as DataType);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getDtypeParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as DataType);\n                }\n                break;\n              case 'dtype[]':\n                value = getDtypeArrayParam(\n                    node.attr, param.tfName, param.defaultValue as DataType[]);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getDtypeArrayParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as DataType[]);\n                }\n                break;\n              case 'func':\n                value = getFuncParam(\n                    node.attr, param.tfName, param.defaultValue as string);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getFuncParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as string);\n                }\n                break;\n              case 'tensor':\n              case 'tensors':\n                break;\n              default:\n                throw new Error(\n                    `Unsupported param type: ${param.type} for op: ${node.op}`);\n            }\n            map[param.name] = {value, type};\n            return map;\n          }, {});\n    }\n    return newNode;\n  }\n\n  // map the TFunctionDef to TFJS graph object\n  private mapFunction(functionDef: tensorflow.IFunctionDef): Graph {\n    const tfNodes = functionDef.nodeDef;\n    const placeholders: Node[] = [];\n    const weights: Node[] = [];\n    let nodes: {[key: string]: Node} = {};\n    if (tfNodes != null) {\n      nodes = tfNodes.reduce<{[key: string]: Node}>((map, node) => {\n        map[node.name] = this.mapNode(node);\n        if (node.op === 'Const') {\n          weights.push(map[node.name]);\n        }\n        return map;\n      }, {});\n    }\n    const inputs: Node[] = [];\n    const outputs: Node[] = [];\n\n    functionDef.signature.inputArg.forEach(arg => {\n      const [nodeName, ] = getNodeNameAndIndex(arg.name);\n      const node: Node = {\n        name: nodeName,\n        op: 'Placeholder',\n        inputs: [],\n        inputNames: [],\n        category: 'graph',\n        inputParams: {},\n        attrParams: {dtype: {value: parseDtypeParam(arg.type), type: 'dtype'}},\n        children: []\n      };\n      node.signatureKey = arg.name;\n      inputs.push(node);\n      nodes[nodeName] = node;\n    });\n\n    const allNodes = Object.keys(nodes);\n    allNodes.forEach(key => {\n      const node = nodes[key];\n      node.inputNames.forEach((name, index) => {\n        const [nodeName, , outputName] = getNodeNameAndIndex(name);\n        const inputNode = nodes[nodeName];\n        if (inputNode.outputs != null) {\n          const outputIndex = inputNode.outputs.indexOf(outputName);\n          if (outputIndex !== -1) {\n            const inputName = `${nodeName}:${outputIndex}`;\n            // update the input name to use the mapped output index directly.\n            node.inputNames[index] = inputName;\n          }\n        }\n        node.inputs.push(inputNode);\n        inputNode.children.push(node);\n      });\n    });\n\n    const returnNodeMap = functionDef.ret;\n\n    functionDef.signature.outputArg.forEach(output => {\n      const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n      const node = nodes[nodeName];\n      if (node != null) {\n        node.defaultOutput = index;\n        outputs.push(node);\n      }\n    });\n\n    const signature = this.mapArgsToSignature(functionDef);\n    return {nodes, inputs, outputs, weights, placeholders, signature};\n  }\n\n  private mapArgsToSignature(functionDef: tensorflow.IFunctionDef):\n      tensorflow.ISignatureDef {\n    return {\n      methodName: functionDef.signature.name,\n      inputs: functionDef.signature.inputArg.reduce(\n          (map, arg) => {\n            map[arg.name] = this.mapArgToTensorInfo(arg);\n            return map;\n          },\n          {} as {[key: string]: tensorflow.ITensorInfo}),\n      outputs: functionDef.signature.outputArg.reduce(\n          (map, arg) => {\n            map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n            return map;\n          },\n          {} as {[key: string]: tensorflow.ITensorInfo}),\n    };\n  }\n\n  private mapArgToTensorInfo(\n      arg: tensorflow.OpDef.IArgDef,\n      nameMap?: {[key: string]: string}): tensorflow.ITensorInfo {\n    let name = arg.name;\n    if (nameMap != null) {\n      name = nameMap[name];\n    }\n    return {name, dtype: arg.type};\n  }\n}\n\nexport function decodeBase64(text: string): string {\n  const global = env().global;\n  if (typeof global.atob !== 'undefined') {\n    return global.atob(text);\n  } else if (typeof Buffer !== 'undefined') {\n    return new Buffer(text, 'base64').toString();\n  } else {\n    throw new Error(\n        'Unable to decode base64 in this environment. ' +\n        'Missing built-in atob() or Buffer()');\n  }\n}\n\nexport function parseStringParam(s: []|string, keepCase: boolean): string {\n  const value =\n      Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n  return keepCase ? value : value.toLowerCase();\n}\n\nexport function getStringParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string, def: string,\n    keepCase = false): string {\n  const param = attrs[name];\n  if (param != null) {\n    return parseStringParam(param.s, keepCase);\n  }\n  return def;\n}\n\nexport function getBoolParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: boolean): boolean {\n  const param = attrs[name];\n  return param ? param.b : def;\n}\n\nexport function getNumberParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: number): number {\n  const param = attrs[name] || {};\n  const value =\n      param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);\n  return (typeof value === 'number') ? value : parseInt(value, 10);\n}\n\nexport function parseDtypeParam(value: string|tensorflow.DataType): DataType {\n  if (typeof (value) === 'string') {\n    // tslint:disable-next-line:no-any\n    value = tensorflow.DataType[value as any];\n  }\n  switch (value) {\n    case tensorflow.DataType.DT_FLOAT:\n      return 'float32';\n    case tensorflow.DataType.DT_INT32:\n    case tensorflow.DataType.DT_INT64:\n    case tensorflow.DataType.DT_INT8:\n    case tensorflow.DataType.DT_UINT8:\n      return 'int32';\n    case tensorflow.DataType.DT_BOOL:\n      return 'bool';\n    case tensorflow.DataType.DT_DOUBLE:\n      return 'float32';\n    case tensorflow.DataType.DT_STRING:\n      return 'string';\n    default:\n      // Unknown dtype error will happen at runtime (instead of parse time),\n      // since these nodes might not be used by the actual subgraph execution.\n      return null;\n  }\n}\n\nexport function getFuncParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: string): string {\n  const param = attrs[name];\n  if (param && param.func) {\n    return param.func.name;\n  }\n  return def;\n}\n\nexport function getDtypeParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: DataType): DataType {\n  const param = attrs[name];\n  if (param && param.type) {\n    return parseDtypeParam(param.type);\n  }\n  return def;\n}\n\nexport function getDtypeArrayParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: DataType[]): DataType[] {\n  const param = attrs[name];\n  if (param && param.list && param.list.type) {\n    return param.list.type.map(v => parseDtypeParam(v));\n  }\n  return def;\n}\n\nexport function parseTensorShapeParam(shape: tensorflow.ITensorShape): number[]|\n    undefined {\n  if (shape.unknownRank) {\n    return undefined;\n  }\n  if (shape.dim != null) {\n    return shape.dim.map(\n        dim =>\n            (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));\n  }\n  return [];\n}\n\nexport function getTensorShapeParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def?: number[]): number[]|undefined {\n  const param = attrs[name];\n  if (param && param.shape) {\n    return parseTensorShapeParam(param.shape);\n  }\n  return def;\n}\n\nexport function getNumericArrayParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: number[]): number[] {\n  const param = attrs[name];\n  if (param) {\n    return ((param.list.f && param.list.f.length ? param.list.f :\n                                                   param.list.i) ||\n            [])\n        .map(v => (typeof v === 'number') ? v : parseInt(v, 10));\n  }\n  return def;\n}\n\nexport function getStringArrayParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string, def: string[],\n    keepCase = false): string[] {\n  const param = attrs[name];\n  if (param && param.list && param.list.s) {\n    return param.list.s.map((v) => {\n      return parseStringParam(v, keepCase);\n    });\n  }\n  return def;\n}\n\nexport function getTensorShapeArrayParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: number[][]): number[][] {\n  const param = attrs[name];\n  if (param && param.list && param.list.shape) {\n    return param.list.shape.map((v) => {\n      return parseTensorShapeParam(v);\n    });\n  }\n  return def;\n}\n\nexport function getBoolArrayParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: boolean[]): boolean[] {\n  const param = attrs[name];\n  if (param && param.list && param.list.b) {\n    return param.list.b;\n  }\n  return def;\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, Tensor} from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {getTensor} from '../executors/utils';\nimport {getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam} from '../operation_mapper';\nimport {GraphNode, Node, ValueType} from '../types';\n\n/**\n * Helper class for lookup inputs and params for nodes in the model graph.\n */\nexport class NodeValueImpl implements GraphNode {\n  public readonly inputs: Tensor[] = [];\n  public readonly attrs: {[key: string]: ValueType} = {};\n  constructor(\n      private node: Node, private tensorMap: NamedTensorsMap,\n      private context: ExecutionContext) {\n    this.inputs = node.inputNames.map(name => this.getInput(name));\n    if (node.rawAttrs != null) {\n      this.attrs = Object.keys(node.rawAttrs)\n                       .reduce((attrs: {[key: string]: ValueType}, key) => {\n                         attrs[key] = this.getAttr(key);\n                         return attrs;\n                       }, {});\n    }\n  }\n\n  /**\n   * Return the value of the attribute or input param.\n   * @param name String: name of attribute or input param.\n   */\n  private getInput(name: string): Tensor {\n    return getTensor(name, this.tensorMap, this.context);\n  }\n\n  /**\n   * Return the value of the attribute or input param.\n   * @param name String: name of attribute or input param.\n   */\n  private getAttr(name: string, defaultValue?: ValueType): ValueType {\n    const value = this.node.rawAttrs[name];\n    if (value.tensor != null) {\n      return getTensor(name, this.tensorMap, this.context);\n    }\n    if (value.i != null || value.f != null) {\n      return getNumberParam(this.node.rawAttrs, name, defaultValue as number);\n    }\n    if (value.s != null) {\n      return getStringParam(this.node.rawAttrs, name, defaultValue as string);\n    }\n    if (value.b != null) {\n      return getBoolParam(this.node.rawAttrs, name, defaultValue as boolean);\n    }\n    if (value.shape != null) {\n      return getTensorShapeParam(\n          this.node.rawAttrs, name, defaultValue as number[]);\n    }\n    if (value.type != null) {\n      return getDtypeParam(this.node.rawAttrs, name, defaultValue as DataType);\n    }\n    if (value.list != null) {\n      if (value.list.i != null || value.list.f != null) {\n        return getNumericArrayParam(\n            this.node.rawAttrs, name, defaultValue as number[]);\n      }\n      if (value.list.s != null) {\n        return getStringArrayParam(\n            this.node.rawAttrs, name, defaultValue as string[]);\n      }\n      if (value.list.shape != null) {\n        return getTensorShapeArrayParam(\n            this.node.rawAttrs, name, defaultValue as number[][]);\n      }\n      if (value.list.b != null) {\n        return getBoolArrayParam(\n            this.node.rawAttrs, name, defaultValue as boolean[]);\n      }\n      if (value.list.type != null) {\n        return getDtypeArrayParam(\n            this.node.rawAttrs, name, defaultValue as DataType[]);\n      }\n    }\n\n    return defaultValue;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'BiasAdd':\n        case 'AddV2':\n        case 'Add': {\n          return [tfOps.add(\n              (getParamValue('a', node, tensorMap, context) as Tensor),\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'AddN': {\n          return [tfOps.addN((\n              getParamValue('tensors', node, tensorMap, context) as Tensor[]))];\n        }\n        case 'FloorMod':\n        case 'Mod':\n          return [tfOps.mod(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        case 'Mul':\n          return [tfOps.mul(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        case 'RealDiv':\n        case 'Div': {\n          return [tfOps.div(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'DivNoNan': {\n          return [tfOps.divNoNan(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'FloorDiv': {\n          return [tfOps.floorDiv(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Sub': {\n          return [tfOps.sub(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Minimum': {\n          return [tfOps.minimum(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Maximum': {\n          return [tfOps.maximum(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Pow': {\n          return [tfOps.pow(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'SquaredDifference': {\n          return [tfOps.squaredDifference(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'arithmetic';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue, getTensor} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Abs':\n        case 'ComplexAbs':\n          return [tfOps.abs(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Acos':\n          return [tfOps.acos(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Acosh':\n          return [tfOps.acosh(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Asin':\n          return [tfOps.asin(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Asinh':\n          return [tfOps.asinh(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Atan':\n          return [tfOps.atan(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Atan2':\n          return [tfOps.atan2(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('y', node, tensorMap, context) as Tensor)];\n        case 'Atanh':\n          return [tfOps.atanh(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Ceil':\n          return [tfOps.ceil(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Complex':\n          return [tfOps.complex(\n              getParamValue('real', node, tensorMap, context) as Tensor,\n              getParamValue('imag', node, tensorMap, context) as Tensor)];\n        case 'Cos':\n          return [tfOps.cos(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Cosh':\n          return [tfOps.cosh(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Elu':\n          return [tfOps.elu(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Erf':\n          return [tfOps.erf(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Exp':\n          return [tfOps.exp(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Expm1': {\n          return [tfOps.expm1(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Floor':\n          return [tfOps.floor(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Log':\n          return [tfOps.log(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Log1p': {\n          return [tfOps.log1p(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Imag':\n          return [tfOps.imag(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n\n        case 'Neg':\n          return [tfOps.neg(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Reciprocal': {\n          return [tfOps.reciprocal(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Real':\n          return [tfOps.real(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Relu':\n          return [tfOps.relu(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Round': {\n          return [tfOps.round(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Selu':\n          return [tfOps.selu(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Sigmoid':\n          return [tfOps.sigmoid(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Sin':\n          return [tfOps.sin(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Sign': {\n          return [tfOps.sign(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Sinh': {\n          return [tfOps.sinh(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Softplus': {\n          return [tfOps.softplus(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Sqrt': {\n          return [tfOps.sqrt(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Square': {\n          return [tfOps.square(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Tanh': {\n          return [tfOps.tanh(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Tan':\n          return [tfOps.tan(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'ClipByValue':\n          return [tfOps.clipByValue(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('clipValueMin', node, tensorMap, context) as number,\n              getParamValue('clipValueMax', node, tensorMap, context) as\n                  number)];\n        case 'Relu6':\n          return [tfOps.relu6(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Rsqrt':\n          return [tfOps.rsqrt(\n              getTensor(node.inputNames[0], tensorMap, context))];\n        case 'Prod':\n          return [tfOps.prod(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('axes', node, tensorMap, context) as number[])];\n        case 'LeakyRelu':\n          return [tfOps.leakyRelu(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('alpha', node, tensorMap, context) as number)];\n        case 'Prelu':\n          return [tfOps.prelu(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('alpha', node, tensorMap, context) as Tensor)];\n        case 'IsNan':\n          return [tfOps.isNaN(\n              getTensor(node.inputNames[0], tensorMap, context))];\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'basic_math';\n","\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This differs from util.assertShapesMatch in that it allows values of\n * negative one, an undefined size of a dimensinon, in a shape to match\n * anything.\n */\n\nimport {Tensor, util} from '@tensorflow/tfjs-core';\n\n/**\n * Used by TensorList and TensorArray to verify if elementShape matches, support\n * negative value as the dim shape.\n * @param shapeA\n * @param shapeB\n * @param errorMessagePrefix\n */\nexport function assertShapesMatchAllowUndefinedSize(\n    shapeA: number|number[], shapeB: number|number[],\n    errorMessagePrefix = ''): void {\n  // constant shape means unknown rank\n  if (typeof shapeA === 'number' || typeof shapeB === 'number') {\n    return;\n  }\n  util.assert(\n      shapeA.length === shapeB.length,\n      () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n  for (let i = 0; i < shapeA.length; i++) {\n    const dim0 = shapeA[i];\n    const dim1 = shapeB[i];\n    util.assert(\n        dim0 < 0 || dim1 < 0 || dim0 === dim1,\n        () =>\n            errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n  }\n}\n\nexport function fullDefinedShape(elementShape: number|number[]): boolean {\n  if (typeof elementShape === 'number' || elementShape.some(dim => dim < 0)) {\n    return false;\n  }\n  return true;\n}\n/**\n * Generate the output element shape from the list elementShape, list tensors\n * and input param.\n * @param listElementShape\n * @param tensors\n * @param elementShape\n */\nexport function inferElementShape(\n    listElementShape: number|number[], tensors: Tensor[],\n    elementShape: number|number[]): number[] {\n  let partialShape = mergeElementShape(listElementShape, elementShape);\n  const notfullDefinedShape = !fullDefinedShape(partialShape);\n  if (notfullDefinedShape && tensors.length === 0) {\n    throw new Error(\n        `Tried to calculate elements of an empty list` +\n        ` with non-fully-defined elementShape: ${partialShape}`);\n  }\n  if (notfullDefinedShape) {\n    tensors.forEach(tensor => {\n      partialShape = mergeElementShape(tensor.shape, partialShape);\n    });\n  }\n  if (!fullDefinedShape(partialShape)) {\n    throw new Error(`Non-fully-defined elementShape: ${partialShape}`);\n  }\n  return partialShape as number[];\n}\n\nexport function mergeElementShape(\n    elementShapeA: number|number[], elementShapeB: number|number[]): number|\n    number[] {\n  if (typeof elementShapeA === 'number') {\n    return elementShapeB;\n  }\n  if (typeof elementShapeB === 'number') {\n    return elementShapeA;\n  }\n\n  if (elementShapeA.length !== elementShapeB.length) {\n    throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${\n        elementShapeB}`);\n  }\n\n  const result: number[] = [];\n  for (let i = 0; i < elementShapeA.length; ++i) {\n    const dim0 = elementShapeA[i];\n    const dim1 = elementShapeB[i];\n    if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {\n      throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${\n          elementShapeB}`);\n    }\n    result[i] = dim0 >= 0 ? dim0 : dim1;\n  }\n  return result;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {concat, DataType, keep, reshape, scalar, slice, stack, Tensor, tensor, tidy, unstack} from '@tensorflow/tfjs-core';\n\nimport {assertShapesMatchAllowUndefinedSize} from './tensor_utils';\n\nexport interface TensorWithState {\n  tensor?: Tensor;\n  written?: boolean;\n  read?: boolean;\n  cleared?: boolean;\n}\n/**\n * The TensorArray object keeps an array of Tensors.  It\n * allows reading from the array and writing to the array.\n */\nexport class TensorArray {\n  private tensors: TensorWithState[] = [];\n  private closed_ = false;\n  readonly idTensor: Tensor;\n  constructor(\n      readonly name: string, readonly dtype: DataType, private maxSize: number,\n      private elementShape: number[], readonly identicalElementShapes: boolean,\n      readonly dynamicSize: boolean, readonly clearAfterRead: boolean) {\n    this.idTensor = scalar(0);\n    keep(this.idTensor);\n  }\n\n  get id() {\n    return this.idTensor.id;\n  }\n\n  get closed() {\n    return this.closed_;\n  }\n\n  /**\n   * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n   */\n  clearAndClose(keepIds?: Set<number>) {\n    this.tensors.forEach(tensor => {\n      if (keepIds == null || !keepIds.has(tensor.tensor.id)) {\n        tensor.tensor.dispose();\n      }\n    });\n    this.tensors = [];\n    this.closed_ = true;\n    this.idTensor.dispose();\n  }\n\n  size(): number {\n    return this.tensors.length;\n  }\n\n  /**\n   * Read the value at location index in the TensorArray.\n   * @param index Number the index to read from.\n   */\n  read(index: number): Tensor {\n    if (this.closed_) {\n      throw new Error(`TensorArray ${this.name} has already been closed.`);\n    }\n\n    if (index < 0 || index >= this.size()) {\n      throw new Error(`Tried to read from index ${index}, but array size is: ${\n          this.size()}`);\n    }\n\n    const tensorWithState = this.tensors[index];\n    if (tensorWithState.cleared) {\n      throw new Error(\n          `TensorArray ${this.name}: Could not read index ${\n              index} twice because it was cleared after a previous read ` +\n          `(perhaps try setting clear_after_read = false?).`);\n    }\n\n    if (this.clearAfterRead) {\n      tensorWithState.cleared = true;\n    }\n\n    tensorWithState.read = true;\n    return tensorWithState.tensor;\n  }\n\n  /**\n   * Helper method to read multiple tensors from the specified indices.\n   */\n  readMany(indices: number[]): Tensor[] {\n    return indices.map(index => this.read(index));\n  }\n\n  /**\n   * Write value into the index of the TensorArray.\n   * @param index number the index to write to.\n   * @param tensor\n   */\n  write(index: number, tensor: Tensor) {\n    if (this.closed_) {\n      throw new Error(`TensorArray ${this.name} has already been closed.`);\n    }\n\n    if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n      throw new Error(`Tried to write to index ${\n          index}, but array is not resizeable and size is: ${this.maxSize}`);\n    }\n\n    const t = this.tensors[index] || {};\n\n    if (tensor.dtype !== this.dtype) {\n      throw new Error(`TensorArray ${\n          this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${\n          tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);\n    }\n\n    // Set the shape for the first time write to unknow shape tensor array\n    if (this.size() === 0 &&\n        (this.elementShape == null || this.elementShape.length === 0)) {\n      this.elementShape = tensor.shape;\n    }\n\n    assertShapesMatchAllowUndefinedSize(\n        this.elementShape, tensor.shape,\n        `TensorArray ${this.name}: Could not write to TensorArray index ${\n            index}.`);\n\n    if (t.read) {\n      throw new Error(\n          `TensorArray ${this.name}: Could not write to TensorArray index ${\n              index}, because it has already been read.`);\n    }\n\n    if (t.written) {\n      throw new Error(\n          `TensorArray ${this.name}: Could not write to TensorArray index ${\n              index}, because it has already been written.`);\n    }\n\n    t.tensor = tensor;\n    keep(tensor);\n    t.written = true;\n\n    this.tensors[index] = t;\n  }\n\n  /**\n   * Helper method to write multiple tensors to the specified indices.\n   */\n  writeMany(indices: number[], tensors: Tensor[]) {\n    if (indices.length !== tensors.length) {\n      throw new Error(\n          `TensorArray ${this.name}: could not write multiple tensors,` +\n          `because the index size: ${\n              indices.length} is not the same as tensors size: ${\n              tensors.length}.`);\n    }\n\n    indices.forEach((i, index) => this.write(i, tensors[index]));\n  }\n\n  /**\n   * Return selected values in the TensorArray as a packed Tensor. All of\n   * selected values must have been written and their shapes must all match.\n   * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n   *    TensorArray is not dynamic, max_value=size(). If not specified returns\n   *    all tensors in the original order.\n   * @param [dtype]\n   */\n  gather(indices?: number[], dtype?: DataType): Tensor {\n    if (!!dtype && dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${\n          this.dtype} but gather requested dtype ${dtype}`);\n    }\n\n    if (!indices) {\n      indices = [];\n      for (let i = 0; i < this.size(); i++) {\n        indices.push(i);\n      }\n    } else {\n      indices = indices.slice(0, this.size());\n    }\n\n    if (indices.length === 0) {\n      return tensor([], [0].concat(this.elementShape));\n    }\n\n    // Read all the PersistentTensors into a vector to keep track of\n    // their memory.\n    const tensors = this.readMany(indices);\n\n    assertShapesMatchAllowUndefinedSize(\n        this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');\n\n    return stack(tensors, 0);\n  }\n\n  /**\n   * Return the values in the TensorArray as a concatenated Tensor.\n   */\n  concat(dtype?: DataType): Tensor {\n    if (!!dtype && dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${\n          this.dtype} but concat requested dtype ${dtype}`);\n    }\n\n    if (this.size() === 0) {\n      return tensor([], [0].concat(this.elementShape));\n    }\n\n    const indices = [];\n    for (let i = 0; i < this.size(); i++) {\n      indices.push(i);\n    }\n    // Collect all the tensors from the tensors array.\n    const tensors = this.readMany(indices);\n\n    assertShapesMatchAllowUndefinedSize(\n        this.elementShape, tensors[0].shape,\n        `TensorArray shape mismatch: tensor array shape (${\n            this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n\n    return concat(tensors, 0);\n  }\n\n  /**\n   * Scatter the values of a Tensor in specific indices of a TensorArray.\n   * @param indices nummber[] values in [0, max_value). If the\n   *    TensorArray is not dynamic, max_value=size().\n   * @param tensor Tensor input tensor.\n   */\n  scatter(indices: number[], tensor: Tensor) {\n    if (tensor.dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${\n          this.dtype} but tensor has dtype ${tensor.dtype}`);\n    }\n\n    if (indices.length !== tensor.shape[0]) {\n      throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${\n          indices.length} vs. ${tensor.shape[0]}`);\n    }\n\n    const maxIndex = Math.max(...indices);\n\n    if (!this.dynamicSize && maxIndex >= this.maxSize) {\n      throw new Error(\n          `Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n    }\n\n    this.writeMany(indices, unstack(tensor, 0));\n  }\n\n  /**\n   * Split the values of a Tensor into the TensorArray.\n   * @param length number[] with the lengths to use when splitting value along\n   *    its first dimension.\n   * @param tensor Tensor, the tensor to split.\n   */\n  split(length: number[], tensor: Tensor) {\n    if (tensor.dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${\n          this.dtype} but tensor has dtype ${tensor.dtype}`);\n    }\n    let totalLength = 0;\n    const cumulativeLengths = length.map(len => {\n      totalLength += len;\n      return totalLength;\n    });\n\n    if (totalLength !== tensor.shape[0]) {\n      throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n    }\n\n    if (!this.dynamicSize && length.length !== this.maxSize) {\n      throw new Error(\n          `TensorArray's size is not equal to the size of lengths (${\n              this.maxSize} vs. ${length.length}), ` +\n          'and the TensorArray is not marked as dynamically resizeable');\n    }\n\n    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n    const tensors: Tensor[] = [];\n    tidy(() => {\n      tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n      for (let i = 0; i < length.length; ++i) {\n        const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n        const indices = [0, previousLength, 0];\n        const sizes = [1, length[i], elementPerRow];\n        tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);\n      }\n      return tensors;\n    });\n    const indices = [];\n    for (let i = 0; i < length.length; i++) {\n      indices[i] = i;\n    }\n    this.writeMany(indices, tensors);\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {concat, DataType, keep, reshape, scalar, slice, stack, Tensor, tensor, tidy, unstack} from '@tensorflow/tfjs-core';\n\nimport {assertShapesMatchAllowUndefinedSize, inferElementShape, mergeElementShape} from './tensor_utils';\n\n/**\n * TensorList stores a container of `tf.Tensor` objects, which are accessible\n * via tensors field.\n *\n * In order to get a copy of the underlying list, use the copy method:\n * ```\n *    TensorList b = a.copy();\n *    b.tensors().pushBack(t);  // This does not modify a.tensors().\n * ```\n *\n * Note that this is not a deep copy: the memory locations of the underlying\n * tensors will still point to the same locations of the corresponding tensors\n * in the original.\n */\n\nexport class TensorList {\n  readonly idTensor: Tensor;\n  maxNumElements: number;\n\n  get id() {\n    return this.idTensor.id;\n  }\n  /**\n   *\n   * @param tensors list of tensors\n   * @param elementShape shape of each tensor, this can be a single number (any\n   * shape is allowed) or partial shape (dim = -1).\n   * @param elementDtype data type of each tensor\n   * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n   *   meaning that the size of `tensors` is unbounded.\n   */\n  constructor(\n      readonly tensors: Tensor[], readonly elementShape: number|number[],\n      readonly elementDtype: DataType, maxNumElements = -1) {\n    if (tensors != null) {\n      tensors.forEach(tensor => {\n        if (elementDtype !== tensor.dtype) {\n          throw new Error(`Invalid data types; op elements ${\n              elementDtype}, but list elements ${tensor.dtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(\n            elementShape, tensor.shape, 'TensorList shape mismatch: ');\n\n        keep(tensor);\n      });\n    }\n    this.idTensor = scalar(0);\n    this.maxNumElements = maxNumElements;\n    keep(this.idTensor);\n  }\n\n  /**\n   * Get a new TensorList containing a copy of the underlying tensor container.\n   */\n  copy(): TensorList {\n    return new TensorList(\n        [...this.tensors], this.elementShape, this.elementDtype);\n  }\n\n  /**\n   * Dispose the tensors and idTensor and clear the tensor list.\n   */\n  clearAndClose(keepIds?: Set<number>) {\n    this.tensors.forEach(tensor => {\n      if (keepIds == null || !keepIds.has(tensor.id)) {\n        tensor.dispose();\n      }\n    });\n    this.tensors.length = 0;\n    this.idTensor.dispose();\n  }\n  /**\n   * The size of the tensors in the tensor list.\n   */\n  size() {\n    return this.tensors.length;\n  }\n\n  /**\n   * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n   * tf.Tensor.\n   * @param elementShape shape of each tensor\n   * @param elementDtype data type of each tensor\n   * @param numElements the number of elements to stack\n   */\n  stack(elementShape: number[], elementDtype: DataType, numElements = -1):\n      Tensor {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${\n          elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (numElements !== -1 && this.tensors.length !== numElements) {\n      throw new Error(`Operation expected a list with ${\n          numElements} elements but got a list with ${\n          this.tensors.length} elements.`);\n    }\n    assertShapesMatchAllowUndefinedSize(\n        elementShape, this.elementShape, 'TensorList shape mismatch: ');\n    const outputElementShape =\n        inferElementShape(this.elementShape, this.tensors, elementShape);\n    return tidy(() => {\n      const reshapedTensors =\n          this.tensors.map(tensor => reshape(tensor, outputElementShape));\n      return stack(reshapedTensors, 0);\n    });\n  }\n\n  /**\n   * Pop a tensor from the end of the list.\n   * @param elementShape shape of the tensor\n   * @param elementDtype data type of the tensor\n   */\n  popBack(elementShape: number[], elementDtype: DataType): Tensor {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${\n          elementDtype}, but list elements ${this.elementDtype}`);\n    }\n\n    if (this.size() === 0) {\n      throw new Error('Trying to pop from an empty list.');\n    }\n    const outputElementShape =\n        inferElementShape(this.elementShape, this.tensors, elementShape);\n    const tensor = this.tensors.pop();\n\n    assertShapesMatchAllowUndefinedSize(\n        tensor.shape, elementShape, 'TensorList shape mismatch: ');\n\n    return reshape(tensor, outputElementShape);\n  }\n\n  /**\n   * Push a tensor to the end of the list.\n   * @param tensor Tensor to be pushed.\n   */\n  pushBack(tensor: Tensor) {\n    if (tensor.dtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${\n          tensor.dtype}, but list elements ${this.elementDtype}`);\n    }\n\n    assertShapesMatchAllowUndefinedSize(\n        tensor.shape, this.elementShape, 'TensorList shape mismatch: ');\n\n    if (this.maxNumElements === this.size()) {\n      throw new Error(`Trying to push element into a full list.`);\n    }\n    keep(tensor);\n    this.tensors.push(tensor);\n  }\n\n  /**\n   * Update the size of the list.\n   * @param size the new size of the list.\n   */\n  resize(size: number) {\n    if (size < 0) {\n      throw new Error(\n          `TensorListResize expects size to be non-negative. Got: ${size}`);\n    }\n\n    if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n      throw new Error(`TensorListResize input size ${\n          size} is greater maxNumElement ${this.maxNumElements}.`);\n    }\n    this.tensors.length = size;\n  }\n\n  /**\n   * Retrieve the element at the provided index\n   * @param elementShape shape of the tensor\n   * @param elementDtype dtype of the tensor\n   * @param elementIndex index of the tensor\n   */\n  getItem(elementIndex: number, elementShape: number[], elementDtype: DataType):\n      Tensor {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${\n          elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (elementIndex < 0 || elementIndex > this.tensors.length) {\n      throw new Error(`Trying to access element ${\n          elementIndex} in a list with ${this.tensors.length} elements.`);\n    }\n\n    if (this.tensors[elementIndex] == null) {\n      throw new Error(`element at index ${elementIndex} is null.`);\n    }\n\n    assertShapesMatchAllowUndefinedSize(\n        this.tensors[elementIndex].shape, elementShape,\n        'TensorList shape mismatch: ');\n    const outputElementShape =\n        inferElementShape(this.elementShape, this.tensors, elementShape);\n    return reshape(this.tensors[elementIndex], outputElementShape);\n  }\n\n  /**\n   * Set the tensor at the index\n   * @param elementIndex index of the tensor\n   * @param tensor the tensor to be inserted into the list\n   */\n  setItem(elementIndex: number, tensor: Tensor) {\n    if (tensor.dtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${\n          tensor.dtype}, but list elements ${this.elementDtype}`);\n    }\n\n    if (elementIndex < 0 ||\n        this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n      throw new Error(`Trying to set element ${\n          elementIndex} in a list with max ${this.maxNumElements} elements.`);\n    }\n\n    assertShapesMatchAllowUndefinedSize(\n        this.elementShape, tensor.shape, 'TensorList shape mismatch: ');\n    keep(tensor);\n    this.tensors[elementIndex] = tensor;\n  }\n\n  /**\n   * Return selected values in the TensorList as a stacked Tensor. All of\n   * selected values must have been written and their shapes must all match.\n   * @param indices indices of tensors to gather\n   * @param elementDtype output tensor dtype\n   * @param elementShape output tensor element shape\n   */\n  gather(indices: number[], elementDtype: DataType, elementShape: number[]):\n      Tensor {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${\n          elementDtype}, but list elements ${this.elementDtype}`);\n    }\n\n    assertShapesMatchAllowUndefinedSize(\n        this.elementShape, elementShape, 'TensorList shape mismatch: ');\n\n    // When indices is greater than the size of the list, indices beyond the\n    // size of the list are ignored.\n    indices = indices.slice(0, this.size());\n    const outputElementShape =\n        inferElementShape(this.elementShape, this.tensors, elementShape);\n    if (indices.length === 0) {\n      return tensor([], [0].concat(outputElementShape));\n    }\n\n    return tidy(() => {\n      const tensors =\n          indices.map(i => reshape(this.tensors[i], outputElementShape));\n      return stack(tensors, 0);\n    });\n  }\n\n  /**\n   * Return the values in the TensorList as a concatenated Tensor.\n   * @param elementDtype output tensor dtype\n   * @param elementShape output tensor element shape\n   */\n  concat(elementDtype: DataType, elementShape: number[]): Tensor {\n    if (!!elementDtype && elementDtype !== this.elementDtype) {\n      throw new Error(`TensorList dtype is ${\n          this.elementDtype} but concat requested dtype ${elementDtype}`);\n    }\n\n    assertShapesMatchAllowUndefinedSize(\n        this.elementShape, elementShape, 'TensorList shape mismatch: ');\n    const outputElementShape =\n        inferElementShape(this.elementShape, this.tensors, elementShape);\n\n    if (this.size() === 0) {\n      return tensor([], [0].concat(outputElementShape));\n    }\n    return tidy(() => {\n      const tensors = this.tensors.map(t => reshape(t, outputElementShape));\n      return concat(tensors, 0);\n    });\n  }\n}\n\n/**\n * Creates a TensorList which, when stacked, has the value of tensor.\n * @param tensor from tensor\n * @param elementShape output tensor element shape\n */\nexport function fromTensor(\n    tensor: Tensor, elementShape: number[], elementDtype: DataType) {\n  const dtype = tensor.dtype;\n  if (tensor.shape.length < 1) {\n    throw new Error(\n        `Tensor must be at least a vector, but saw shape: ${tensor.shape}`);\n  }\n  if (tensor.dtype !== elementDtype) {\n    throw new Error(`Invalid data types; op elements ${\n        tensor.dtype}, but list elements ${elementDtype}`);\n  }\n  const tensorElementShape = tensor.shape.slice(1);\n  assertShapesMatchAllowUndefinedSize(\n      tensorElementShape, elementShape, 'TensorList shape mismatch: ');\n  const tensorList: Tensor[] = unstack(tensor);\n  return new TensorList(tensorList, elementShape, dtype);\n}\n\n/**\n * Return a TensorList of the given size with empty elements.\n * @param elementShape the shape of the future elements of the list\n * @param elementDtype the desired type of elements in the list\n * @param numElements the number of elements to reserve\n */\nexport function reserve(\n    elementShape: number[], elementDtype: DataType, numElements: number) {\n  return new TensorList([], elementShape, elementDtype, numElements);\n}\n\n/**\n * Put tensors at specific indices of a stacked tensor into a TensorList.\n * @param indices list of indices on how to scatter the tensor.\n * @param tensor input tensor.\n * @param elementShape the shape of the future elements of the list\n * @param numElements the number of elements to scatter\n */\nexport function scatter(\n    tensor: Tensor, indices: number[], elementShape: number[],\n    numElements?: number): TensorList {\n  if (indices.length !== tensor.shape[0]) {\n    throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${\n        indices.length} vs. ${tensor.shape[0]}`);\n  }\n\n  const maxIndex = Math.max(...indices);\n\n  if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n    throw new Error(\n        `Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n  }\n\n  const list = new TensorList([], elementShape, tensor.dtype, numElements);\n  const tensors = unstack(tensor, 0);\n  indices.forEach((value, index) => {\n    list.setItem(value, tensors[index]);\n  });\n  return list;\n}\n\n/**\n * Split the values of a Tensor into a TensorList.\n * @param length the lengths to use when splitting value along\n *    its first dimension.\n * @param tensor the tensor to split.\n * @param elementShape the shape of the future elements of the list\n */\nexport function split(\n    tensor: Tensor, length: number[], elementShape: number[]) {\n  let totalLength = 0;\n  const cumulativeLengths = length.map(len => {\n    totalLength += len;\n    return totalLength;\n  });\n\n  if (totalLength !== tensor.shape[0]) {\n    throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n  }\n\n  const shapeWithoutFirstDim = tensor.shape.slice(1);\n  const outputElementShape =\n      mergeElementShape(shapeWithoutFirstDim, elementShape);\n  const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n  const tensors: Tensor[] = tidy(() => {\n    const tensors = [];\n    tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n    for (let i = 0; i < length.length; ++i) {\n      const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n      const indices = [0, previousLength, 0];\n      const sizes = [1, length[i], elementPerRow];\n      tensors[i] = reshape(\n          slice(tensor, indices, sizes), outputElementShape as number[]);\n    }\n    tensor.dispose();\n    return tensors;\n  });\n\n  const list = new TensorList([], elementShape, tensor.dtype, length.length);\n\n  for (let i = 0; i < tensors.length; i++) {\n    list.setItem(i, tensors[i]);\n  }\n  return list;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, scalar, Tensor} from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {TensorArray} from '../../executor/tensor_array';\nimport {fromTensor, reserve, scatter, split} from '../../executor/tensor_list';\nimport {InternalOpAsyncExecutor, Node} from '../types';\n\nimport {cloneTensor, getParamValue, getTensor} from './utils';\n\nexport const executeOp: InternalOpAsyncExecutor = async(\n    node: Node, tensorMap: NamedTensorsMap,\n    context: ExecutionContext): Promise<Tensor[]> => {\n  switch (node.op) {\n    case 'If':\n    case 'StatelessIf': {\n      const thenFunc =\n          getParamValue('thenBranch', node, tensorMap, context) as string;\n      const elseFunc =\n          getParamValue('elseBranch', node, tensorMap, context) as string;\n      const cond = getParamValue('cond', node, tensorMap, context) as Tensor;\n      const args = getParamValue('args', node, tensorMap, context) as Tensor[];\n      const condValue = await cond.data();\n      if (condValue[0]) {\n        return context.functionMap[thenFunc].executeFunctionAsync(\n            args, context.tensorArrayMap, context.tensorListMap);\n      } else {\n        return context.functionMap[elseFunc].executeFunctionAsync(\n            args, context.tensorArrayMap, context.tensorListMap);\n      }\n    }\n    case 'While':\n    case 'StatelessWhile': {\n      const bodyFunc =\n          getParamValue('body', node, tensorMap, context) as string;\n      const condFunc =\n          getParamValue('cond', node, tensorMap, context) as string;\n      const args = getParamValue('args', node, tensorMap, context) as Tensor[];\n\n      // Calculate the condition of the loop\n      const condResult =\n          (await context.functionMap[condFunc].executeFunctionAsync(\n              args, context.tensorArrayMap, context.tensorListMap));\n      const argIds = args.map(tensor => tensor.id);\n      let condValue = await condResult[0].data();\n      // Dispose the intermediate tensors for condition function\n      condResult.forEach(tensor => {\n        if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {\n          tensor.dispose();\n        }\n      });\n\n      let result: Tensor[] = args;\n\n      while (condValue[0]) {\n        // Record the previous result for intermediate tensor tracking\n        const origResult = result;\n        // Execution the body of the loop\n        result = await context.functionMap[bodyFunc].executeFunctionAsync(\n            result, context.tensorArrayMap, context.tensorListMap);\n        const resultIds = result.map(tensor => tensor.id);\n\n        // Dispose the intermediate tensor for body function that is not global\n        // kept, not input/output of the body function\n        origResult.forEach(tensor => {\n          if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n              resultIds.indexOf(tensor.id) === -1) {\n            tensor.dispose();\n          }\n        });\n\n        // Recalcuate the condition of the loop using the latest results.\n        const condResult =\n            (await context.functionMap[condFunc].executeFunctionAsync(\n                result, context.tensorArrayMap, context.tensorListMap));\n        condValue = await condResult[0].data();\n        // Dispose the intermediate tensors for condition function\n        condResult.forEach(tensor => {\n          if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n              resultIds.indexOf(tensor.id) === -1) {\n            tensor.dispose();\n          }\n        });\n      }\n      return result;\n    }\n    case 'LoopCond': {\n      const pred = getParamValue('pred', node, tensorMap, context) as Tensor;\n      return [cloneTensor(pred)];\n    }\n    case 'Switch': {\n      const pred = getParamValue('pred', node, tensorMap, context) as Tensor;\n      let data = getParamValue('data', node, tensorMap, context) as Tensor;\n      if (!data.kept) {\n        data = cloneTensor(data);\n      }\n      // Outputs nodes :0 => false, :1 => true\n      return (await pred.data())[0] ? [undefined, data] : [data, undefined];\n    }\n    case 'Merge': {\n      const inputName = node.inputNames.find(\n          name => getTensor(name, tensorMap, context) !== undefined);\n      if (inputName) {\n        const data = getTensor(inputName, tensorMap, context);\n        return [cloneTensor(data)];\n      }\n      return undefined;\n    }\n    case 'Enter': {\n      const frameId =\n          getParamValue('frameName', node, tensorMap, context) as string;\n      const data = getParamValue('tensor', node, tensorMap, context) as Tensor;\n      context.enterFrame(frameId);\n      return [cloneTensor(data)];\n    }\n    case 'Exit': {\n      const data = getParamValue('tensor', node, tensorMap, context) as Tensor;\n      context.exitFrame();\n      return [cloneTensor(data)];\n    }\n    case 'NextIteration': {\n      const data = getParamValue('tensor', node, tensorMap, context) as Tensor;\n      context.nextIteration();\n      return [cloneTensor(data)];\n    }\n    case 'TensorArrayV3': {\n      const size = getParamValue('size', node, tensorMap, context) as number;\n      const dtype =\n          getParamValue('dtype', node, tensorMap, context) as DataType;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const dynamicSize =\n          getParamValue('dynamicSize', node, tensorMap, context) as boolean;\n      const clearAfterRead =\n          getParamValue('clearAfterRead', node, tensorMap, context) as boolean;\n      const identicalElementShapes =\n          getParamValue('identicalElementShapes', node, tensorMap, context) as\n          boolean;\n      const name = getParamValue('name', node, tensorMap, context) as string;\n      const tensorArray = new TensorArray(\n          name, dtype, size, elementShape, identicalElementShapes, dynamicSize,\n          clearAfterRead);\n      context.addTensorArray(tensorArray);\n      return [tensorArray.idTensor, scalar(1.0)];\n    }\n    case 'TensorArrayWriteV3': {\n      const id =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const index = getParamValue('index', node, tensorMap, context) as number;\n      const writeTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const writeTensorArray = context.getTensorArray(id.id);\n      writeTensorArray.write(index, writeTensor);\n      return [writeTensorArray.idTensor];\n    }\n    case 'TensorArrayReadV3': {\n      const readId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const readIndex =\n          getParamValue('index', node, tensorMap, context) as number;\n      const readTensorArray = context.getTensorArray(readId.id);\n      return [readTensorArray.read(readIndex)];\n    }\n    case 'TensorArrayGatherV3': {\n      const gatherId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const gatherIndices =\n          getParamValue('indices', node, tensorMap, context) as number[];\n      const gatherDtype =\n          getParamValue('dtype', node, tensorMap, context) as DataType;\n      const gatherTensorArray = context.getTensorArray(gatherId.id);\n      return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n    }\n    case 'TensorArrayScatterV3': {\n      const scatterId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const scatterIndices =\n          getParamValue('indices', node, tensorMap, context) as number[];\n      const scatterTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const scatterTensorArray = context.getTensorArray(scatterId.id);\n      scatterTensorArray.scatter(scatterIndices, scatterTensor);\n      return [scatterTensorArray.idTensor];\n    }\n    case 'TensorArrayConcatV3': {\n      const concatId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const concatTensorArray = context.getTensorArray(concatId.id);\n      const concatDtype =\n          getParamValue('dtype', node, tensorMap, context) as DataType;\n      return [concatTensorArray.concat(concatDtype)];\n    }\n    case 'TensorArraySplitV3': {\n      const splitId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const splitTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const lengths =\n          getParamValue('lengths', node, tensorMap, context) as number[];\n      const splitTensorArray = context.getTensorArray(splitId.id);\n      splitTensorArray.split(lengths, splitTensor);\n      return [splitTensorArray.idTensor];\n    }\n    case 'TensorArraySizeV3': {\n      const sizeId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const sizeTensorArray = context.getTensorArray(sizeId.id);\n      return [scalar(sizeTensorArray.size(), 'int32')];\n    }\n    case 'TensorArrayCloseV3': {\n      const closeId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const closeTensorArray = context.getTensorArray(closeId.id);\n      closeTensorArray.clearAndClose();\n      return [closeTensorArray.idTensor];\n    }\n    case 'TensorListSetItem': {\n      const idTensor =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const index = getParamValue('index', node, tensorMap, context) as number;\n      const writeTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const tensorList = context.getTensorList(idTensor.id);\n      tensorList.setItem(index, writeTensor);\n      return [tensorList.idTensor];\n    }\n    case 'TensorListGetItem': {\n      const idTensor =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const readIndex =\n          getParamValue('index', node, tensorMap, context) as number;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n\n      const elementDType =\n          getParamValue('elementDType', node, tensorMap, context) as DataType;\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.getItem(readIndex, elementShape, elementDType)];\n    }\n    case 'TensorListScatterV2':\n    case 'TensorListScatter': {\n      const scatterIndices =\n          getParamValue('indices', node, tensorMap, context) as number[];\n      const scatterTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const numElements =\n          getParamValue('numElements', node, tensorMap, context) as number;\n      const tensorList =\n          scatter(scatterTensor, scatterIndices, elementShape, numElements);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case 'TensorListReserve':\n    case 'EmptyTensorList': {\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const elementDtype =\n          getParamValue('elementDType', node, tensorMap, context) as DataType;\n      let numElementsParam;\n\n      if (node.op === 'TensorListReserve') {\n        numElementsParam = 'numElements';\n      } else {\n        numElementsParam = 'maxNumElements';\n      }\n\n      const numElements =\n          getParamValue(numElementsParam, node, tensorMap, context) as number;\n\n      const tensorList = reserve(elementShape, elementDtype, numElements);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case 'TensorListGather': {\n      const gatherId =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const gatherIndices =\n          getParamValue('indices', node, tensorMap, context) as number[];\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const elementDtype =\n          getParamValue('elementDType', node, tensorMap, context) as DataType;\n      const tensorList = context.getTensorList(gatherId.id);\n      return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n    }\n    case 'TensorListStack': {\n      const idTensor =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const elementDtype =\n          getParamValue('elementDType', node, tensorMap, context) as DataType;\n      const numElements =\n          getParamValue('numElements', node, tensorMap, context) as number;\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.stack(elementShape, elementDtype, numElements)];\n    }\n    case 'TensorListFromTensor': {\n      const tensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const elementDtype =\n          getParamValue('elementDType', node, tensorMap, context) as DataType;\n      const tensorList = fromTensor(tensor, elementShape, elementDtype);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case 'TensorListConcat': {\n      const concatId =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const tensorList = context.getTensorList(concatId.id);\n      const concatDtype =\n          getParamValue('dtype', node, tensorMap, context) as DataType;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      return [tensorList.concat(concatDtype, elementShape)];\n    }\n    case 'TensorListPushBack': {\n      const idTensor =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const writeTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const tensorList = context.getTensorList(idTensor.id);\n      tensorList.pushBack(writeTensor);\n      return [tensorList.idTensor];\n    }\n    case 'TensorListPopBack': {\n      const idTensor =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const elementDType =\n          getParamValue('elementDType', node, tensorMap, context) as DataType;\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.popBack(elementShape, elementDType)];\n    }\n    case 'TensorListSplit': {\n      const splitTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const lengths =\n          getParamValue('lengths', node, tensorMap, context) as number[];\n\n      const tensorList = split(splitTensor, lengths, elementShape);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\nexport const CATEGORY = 'control';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Rank, Tensor, Tensor3D, Tensor4D, Tensor5D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getPadding, getParamValue} from './utils';\n\nfunction fusedConvAndDepthWiseParams(\n    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext) {\n  const [extraOp, activationFunc] =\n      (getParamValue('fusedOps', node, tensorMap, context) as string[]);\n\n  const isBiasAdd = extraOp === 'biasadd';\n  const noBiasAdd = !isBiasAdd;\n  const isPrelu = activationFunc === 'prelu';\n  const isBatchNorm = extraOp === 'fusedbatchnorm';\n\n  const numArgs =\n      (getParamValue('numArgs', node, tensorMap, context) as number);\n  if (isBiasAdd) {\n    if (isPrelu && numArgs !== 2) {\n      throw new Error(\n          'FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n          'must have two extra arguments: bias and alpha.');\n    }\n    if (!isPrelu && isBiasAdd && numArgs !== 1) {\n      throw new Error(\n          'FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n          'one extra argument: bias.');\n    }\n  }\n  if (isBatchNorm) {\n    throw new Error(\n        'FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported');\n  }\n  const stride = getParamValue('strides', node, tensorMap, context) as number[];\n  const pad = getPadding(node, tensorMap, context);\n  const dataFormat =\n      (getParamValue('dataFormat', node, tensorMap, context) as string)\n          .toUpperCase();\n  const dilations =\n      getParamValue('dilations', node, tensorMap, context) as number[];\n  let [biasArg, preluArg] =\n      getParamValue('args', node, tensorMap, context) as Tensor[];\n  if (noBiasAdd) {\n    preluArg = biasArg;\n    biasArg = undefined;\n  }\n  const leakyreluAlpha =\n      getParamValue('leakyreluAlpha', node, tensorMap, context) as number;\n\n  return {\n    stride,\n    pad,\n    dataFormat,\n    dilations,\n    biasArg,\n    preluArg,\n    activationFunc,\n    leakyreluAlpha\n  };\n}\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Conv1D': {\n          const stride =\n              getParamValue('stride', node, tensorMap, context) as number;\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as string)\n                  .toUpperCase();\n          const dilation =\n              getParamValue('dilation', node, tensorMap, context) as number;\n          return [tfOps.conv1d(\n              getParamValue('x', node, tensorMap, context) as Tensor3D,\n              getParamValue('filter', node, tensorMap, context) as Tensor3D,\n              stride, pad as 'valid' | 'same', dataFormat as 'NWC' | 'NCW',\n              dilation)];\n        }\n        case 'Conv2D': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getPadding(node, tensorMap, context);\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as string)\n                  .toUpperCase();\n          const dilations =\n              getParamValue('dilations', node, tensorMap, context) as number[];\n          return [tfOps.conv2d(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('filter', node, tensorMap, context) as Tensor4D,\n              [stride[1], stride[2]], pad as 'valid' | 'same',\n              dataFormat as 'NHWC' | 'NCHW', [dilations[1], dilations[2]])];\n        }\n        case '_FusedConv2D': {\n          const {\n            stride,\n            pad,\n            dataFormat,\n            dilations,\n            biasArg,\n            preluArg,\n            activationFunc,\n            leakyreluAlpha\n          } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n\n          return [tfOps.fused.conv2d({\n            x: getParamValue('x', node, tensorMap, context) as Tensor3D |\n                Tensor4D,\n            filter: getParamValue('filter', node, tensorMap, context) as\n                Tensor4D,\n            strides: [stride[1], stride[2]],\n            pad: pad as 'valid' | 'same',\n            dataFormat: dataFormat as 'NHWC' | 'NCHW',\n            dilations: [dilations[1], dilations[2]],\n            bias: biasArg,\n            activation: activationFunc as tfOps.fused.Activation,\n            preluActivationWeights: preluArg,\n            leakyreluAlpha\n          })];\n        }\n\n        case 'FusedDepthwiseConv2dNative': {\n          const {\n            stride,\n            pad,\n            dataFormat,\n            dilations,\n            biasArg,\n            preluArg,\n            activationFunc,\n            leakyreluAlpha,\n          } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n\n          return [tfOps.fused.depthwiseConv2d({\n            x: getParamValue('x', node, tensorMap, context) as Tensor3D |\n                Tensor4D,\n            filter: getParamValue('filter', node, tensorMap, context) as\n                Tensor4D,\n            strides: [stride[1], stride[2]],\n            pad: pad as 'valid' | 'same',\n            dataFormat: dataFormat as 'NHWC' | 'NCHW',\n            dilations: [dilations[1], dilations[2]],\n            bias: biasArg,\n            activation: activationFunc as tfOps.fused.Activation,\n            preluActivationWeights: preluArg,\n            leakyreluAlpha\n          })];\n        }\n        case 'Conv2DBackpropInput':\n        case 'Conv2dTranspose': {\n          const shape = getParamValue(\n                            'outputShape', node, tensorMap,\n                            context) as [number, number, number] |\n              [number, number, number, number];\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getPadding(node, tensorMap, context);\n          return [tfOps.conv2dTranspose(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('filter', node, tensorMap, context) as Tensor4D,\n              shape, [stride[1], stride[2]], pad as 'valid' | 'same')];\n        }\n        case 'DepthwiseConv2dNative':\n        case 'DepthwiseConv2d': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getPadding(node, tensorMap, context);\n          const dilations =\n              getParamValue('dilations', node, tensorMap, context) as number[];\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as string)\n                  .toUpperCase();\n\n          return [tfOps.depthwiseConv2d(\n              getParamValue('input', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('filter', node, tensorMap, context) as Tensor4D,\n              [stride[1], stride[2]], pad as 'valid' | 'same',\n              dataFormat as 'NHWC' | 'NCHW', [dilations[1], dilations[2]])];\n        }\n        case 'Conv3D': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as string)\n                  .toUpperCase();\n          const dilations =\n              getParamValue('dilations', node, tensorMap, context) as number[];\n          return [tfOps.conv3d(\n              getParamValue('x', node, tensorMap, context) as Tensor4D |\n                  Tensor<Rank.R5>,\n              getParamValue('filter', node, tensorMap, context) as\n                  Tensor<Rank.R5>,\n              [stride[1], stride[2], stride[3]], pad as 'valid' | 'same',\n              dataFormat as 'NDHWC' | 'NCDHW',\n              [dilations[1], dilations[2], dilations[3]])];\n        }\n        case 'AvgPool': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n\n          return [tfOps.avgPool(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              [kernelSize[1], kernelSize[2]], [stride[1], stride[2]],\n              pad as 'valid' | 'same')];\n        }\n        case 'MaxPool': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n\n          return [tfOps.maxPool(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              [kernelSize[1], kernelSize[2]], [stride[1], stride[2]],\n              pad as 'valid' | 'same')];\n        }\n        case 'MaxPoolWithArgmax': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n          const includeBatchInIndex =\n              getParamValue('includeBatchInIndex', node, tensorMap, context) as\n              boolean;\n          const {result, indexes} = tfOps.maxPoolWithArgmax(\n              getParamValue('x', node, tensorMap, context) as Tensor4D,\n              [kernelSize[1], kernelSize[2]], [stride[1], stride[2]],\n              pad as 'valid' | 'same', includeBatchInIndex);\n          return [result, indexes];\n        }\n        case 'AvgPool3D': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n\n          return [tfOps.avgPool3d(\n              getParamValue('x', node, tensorMap, context) as Tensor5D,\n              [kernelSize[1], kernelSize[2], kernelSize[3]],\n              [stride[1], stride[2], stride[3]], pad as 'valid' | 'same')];\n        }\n\n        case 'MaxPool3D': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n\n          return [tfOps.maxPool3d(\n              getParamValue('x', node, tensorMap, context) as Tensor5D,\n              [kernelSize[1], kernelSize[2], kernelSize[3]],\n              [stride[1], stride[2], stride[3]], pad as 'valid' | 'same')];\n        }\n\n        case 'Dilation2D': {\n          const strides =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const dilations =\n              getParamValue('dilations', node, tensorMap, context) as number[];\n\n          // strides: [1, stride_height, stride_width, 1].\n          const strideHeight = strides[1];\n          const strideWidth = strides[2];\n\n          // dilations: [1, dilation_height, dilation_width, 1].\n          const dilationHeight = dilations[1];\n          const dilationWidth = dilations[2];\n\n          return [tfOps.dilation2d(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('filter', node, tensorMap, context) as Tensor3D,\n              [strideHeight, strideWidth], pad as 'valid' | 'same',\n              [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n        }\n\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'convolution';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, Tensor, Tensor1D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Fill': {\n          const shape =\n              getParamValue('shape', node, tensorMap, context) as number[];\n          const dtype =\n              getParamValue('dtype', node, tensorMap, context) as DataType;\n          const value =\n              getParamValue('value', node, tensorMap, context) as number;\n          return [tfOps.fill(shape, value, dtype)];\n        }\n        case 'LinSpace': {\n          const start =\n              getParamValue('start', node, tensorMap, context) as number;\n          const stop =\n              getParamValue('stop', node, tensorMap, context) as number;\n          const num = getParamValue('num', node, tensorMap, context) as number;\n          return [tfOps.linspace(start, stop, num)];\n        }\n        case 'Multinomial': {\n          const logits =\n              getParamValue('logits', node, tensorMap, context) as Tensor1D;\n          const numSamples =\n              getParamValue('numSamples', node, tensorMap, context) as number;\n          const seed =\n              getParamValue('seed', node, tensorMap, context) as number;\n          return [tfOps.multinomial(logits, numSamples, seed)];\n        }\n        case 'OneHot': {\n          const indices =\n              getParamValue('indices', node, tensorMap, context) as Tensor1D;\n          const depth =\n              getParamValue('depth', node, tensorMap, context) as number;\n          const onValue =\n              getParamValue('onValue', node, tensorMap, context) as number;\n          const offValue =\n              getParamValue('offValue', node, tensorMap, context) as number;\n          return [tfOps.oneHot(indices, depth, onValue, offValue)];\n        }\n        case 'Ones': {\n          return [tfOps.ones(\n              getParamValue('shape', node, tensorMap, context) as number[],\n              getParamValue('dtype', node, tensorMap, context) as DataType)];\n        }\n        case 'OnesLike': {\n          return [tfOps.onesLike(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'RandomUniform': {\n          return [tfOps.randomUniform(\n              // tslint:disable-next-line:no-any\n              getParamValue('shape', node, tensorMap, context) as any,\n              getParamValue('minval', node, tensorMap, context) as number,\n              getParamValue('maxval', node, tensorMap, context) as number,\n              getParamValue('dtype', node, tensorMap, context) as DataType)];\n        }\n        case 'Range': {\n          const start =\n              getParamValue('start', node, tensorMap, context) as number;\n          const stop =\n              getParamValue('stop', node, tensorMap, context) as number;\n          const step =\n              getParamValue('step', node, tensorMap, context) as number;\n          return [tfOps.range(\n              start, stop, step,\n              getParamValue('dtype', node, tensorMap, context) as 'float32' |\n                  'int32')];\n        }\n        case 'TruncatedNormal': {\n          const shape =\n              getParamValue('shape', node, tensorMap, context) as number[];\n          const mean =\n              getParamValue('mean', node, tensorMap, context) as number;\n          const stdDev =\n              getParamValue('stdDev', node, tensorMap, context) as number;\n          const seed =\n              getParamValue('seed', node, tensorMap, context) as number;\n          return [tfOps.truncatedNormal(\n              shape, mean, stdDev,\n              getParamValue('dtype', node, tensorMap, context) as 'float32' |\n                  'int32',\n              seed)];\n        }\n        case 'Zeros': {\n          return [tfOps.zeros(\n              getParamValue('shape', node, tensorMap, context) as number[],\n              getParamValue('dtype', node, tensorMap, context) as DataType)];\n        }\n        case 'ZerosLike': {\n          return [tfOps.zerosLike(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'creation';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor1D, Tensor2D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpAsyncExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nfunction nmsParams(\n    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext) {\n  const boxes = getParamValue('boxes', node, tensorMap, context) as Tensor;\n  const scores = getParamValue('scores', node, tensorMap, context) as Tensor;\n  const maxOutputSize =\n      getParamValue('maxOutputSize', node, tensorMap, context) as number;\n  const iouThreshold =\n      getParamValue('iouThreshold', node, tensorMap, context) as number;\n  const scoreThreshold =\n      getParamValue('scoreThreshold', node, tensorMap, context) as number;\n  const softNmsSigma =\n      getParamValue('softNmsSigma', node, tensorMap, context) as number;\n\n  return {\n    boxes,\n    scores,\n    maxOutputSize,\n    iouThreshold,\n    scoreThreshold,\n    softNmsSigma\n  };\n}\n\nexport const executeOp: InternalOpAsyncExecutor = async(\n    node: Node, tensorMap: NamedTensorsMap,\n    context: ExecutionContext): Promise<Tensor[]> => {\n  switch (node.op) {\n    case 'NonMaxSuppressionV5': {\n      const {\n        boxes,\n        scores,\n        maxOutputSize,\n        iouThreshold,\n        scoreThreshold,\n        softNmsSigma\n      } = nmsParams(node, tensorMap, context);\n\n      const result = await tfOps.image.nonMaxSuppressionWithScoreAsync(\n          boxes as Tensor2D, scores as Tensor1D, maxOutputSize, iouThreshold,\n          scoreThreshold, softNmsSigma);\n\n      return [result.selectedIndices, result.selectedScores];\n    }\n    case 'NonMaxSuppressionV4': {\n      const {boxes, scores, maxOutputSize, iouThreshold, scoreThreshold} =\n          nmsParams(node, tensorMap, context);\n\n      const padToMaxOutputSize =\n          getParamValue('padToMaxOutputSize', node, tensorMap, context) as\n          boolean;\n\n      const result = await tfOps.image.nonMaxSuppressionPaddedAsync(\n          boxes as Tensor2D, scores as Tensor1D, maxOutputSize, iouThreshold,\n          scoreThreshold, padToMaxOutputSize);\n\n      return [result.selectedIndices, result.validOutputs];\n    }\n    case 'NonMaxSuppressionV3':\n    case 'NonMaxSuppressionV2': {\n      const {boxes, scores, maxOutputSize, iouThreshold, scoreThreshold} =\n          nmsParams(node, tensorMap, context);\n\n      return [await tfOps.image.nonMaxSuppressionAsync(\n          boxes as Tensor2D, scores as Tensor1D, maxOutputSize, iouThreshold,\n          scoreThreshold)];\n    }\n    case 'Where': {\n      const condition = tfOps.cast(\n          (getParamValue('condition', node, tensorMap, context) as Tensor),\n          'bool');\n      const result = [await tfOps.whereAsync(condition)];\n      condition.dispose();\n      return result;\n    }\n    case 'ListDiff': {\n      return tfOps.setdiff1dAsync(\n          getParamValue('x', node, tensorMap, context) as Tensor,\n          getParamValue('y', node, tensorMap, context) as Tensor);\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\nexport const CATEGORY = 'dynamic';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext):\n        Tensor[] => {\n          switch (node.op) {\n            case 'TopKV2': {\n              const x = getParamValue('x', node, tensorMap, context) as Tensor;\n              const k = getParamValue('k', node, tensorMap, context) as number;\n              const sorted =\n                  getParamValue('sorted', node, tensorMap, context) as boolean;\n              const result = tfOps.topk(x, k, sorted);\n              return [result.values, result.indices];\n            }\n            case 'Unique': {\n              const x = getParamValue('x', node, tensorMap, context) as Tensor;\n              const result = tfOps.unique(x);\n              return [result.values, result.indices];\n            }\n            case 'UniqueV2': {\n              const x = getParamValue('x', node, tensorMap, context) as Tensor;\n              const axis =\n                  getParamValue('axis', node, tensorMap, context) as number;\n              const result = tfOps.unique(x, axis);\n              return [result.values, result.indices];\n            }\n            default:\n              throw TypeError(`Node type ${node.op} is not implemented`);\n          }\n        };\n\nexport const CATEGORY = 'evaluation';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {cloneTensor, getParamValue, getTensor} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Const': {\n          return tensorMap[node.name];\n        }\n        case 'PlaceholderWithDefault':\n          const def =\n              getParamValue('default', node, tensorMap, context) as Tensor;\n          return [getTensor(node.name, tensorMap, context) || def];\n        case 'Placeholder':\n          return [getTensor(node.name, tensorMap, context)];\n        case 'Identity':\n        case 'StopGradient':\n        case 'FakeQuantWithMinMaxVars': {  // This op is currently ignored.\n          const data = getParamValue('x', node, tensorMap, context) as Tensor;\n          return [cloneTensor(data)];\n        }\n        case 'IdentityN':\n          return (getParamValue('x', node, tensorMap, context) as Tensor[])\n              .map((t: Tensor) => cloneTensor(t));\n        case 'Snapshot':\n          const snapshot =\n              (getParamValue('x', node, tensorMap, context) as Tensor);\n          return [cloneTensor(snapshot)];\n        case 'Shape':\n          return [tfOps.tensor1d(\n              (getParamValue('x', node, tensorMap, context) as Tensor).shape,\n              'int32')];\n        case 'ShapeN':\n          return (getParamValue('x', node, tensorMap, context) as Tensor[])\n              .map((t: Tensor) => tfOps.tensor1d(t.shape));\n        case 'Size':\n          return [tfOps.scalar(\n              (getParamValue('x', node, tensorMap, context) as Tensor).size,\n              'int32')];\n        case 'Rank':\n          return [tfOps.scalar(\n              (getParamValue('x', node, tensorMap, context) as Tensor).rank,\n              'int32')];\n        case 'NoOp':\n          return [tfOps.scalar(1)];\n        case 'Print':\n          const input = getParamValue('x', node, tensorMap, context) as Tensor;\n          const data =\n              getParamValue('data', node, tensorMap, context) as Tensor[];\n          const message =\n              getParamValue('message', node, tensorMap, context) as string;\n          const summarize =\n              getParamValue('summarize', node, tensorMap, context) as number;\n          console.warn(\n              'The graph has a tf.print() operation,' +\n              'usually used for debugging, which slows down performance.');\n          console.log(message);\n          for (let i = 0; i < data.length; i++) {\n            console.log(Array.prototype.slice.call(data[i].dataSync())\n                            .slice(0, summarize));\n          }\n          return [input];\n\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'graph';\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {DataType, keep, scalar, stack, Tensor, tidy, unstack, util} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\n/**\n * Hashtable contains a set of tensors, which can be accessed by key.\n */\nexport class HashTable {\n  readonly handle: Tensor;\n\n  // tslint:disable-next-line: no-any\n  private tensorMap: Map<any, Tensor>;\n\n  get id() {\n    return this.handle.id;\n  }\n\n  /**\n   * Constructor of HashTable. Creates a hash table.\n   *\n   * @param keyDType `dtype` of the table keys.\n   * @param valueDType `dtype` of the table values.\n   */\n  constructor(readonly keyDType: DataType, readonly valueDType: DataType) {\n    this.handle = scalar(0);\n    // tslint:disable-next-line: no-any\n    this.tensorMap = new Map<any, Tensor>();\n\n    keep(this.handle);\n  }\n\n  /**\n   * Dispose the tensors and handle and clear the hashtable.\n   */\n  clearAndClose() {\n    this.tensorMap.forEach(value => value.dispose());\n    this.tensorMap.clear();\n    this.handle.dispose();\n  }\n\n  /**\n   * The number of items in the hash table.\n   */\n  size(): number {\n    return this.tensorMap.size;\n  }\n\n  /**\n   * The number of items in the hash table as a rank-0 tensor.\n   */\n  tensorSize(): Tensor {\n    return tfOps.scalar(this.size(), 'int32');\n  }\n\n  /**\n   * Replaces the contents of the table with the specified keys and values.\n   * @param keys Keys to store in the hashtable.\n   * @param values Values to store in the hashtable.\n   */\n  async import(keys: Tensor, values: Tensor): Promise<Tensor> {\n    this.checkKeyAndValueTensor(keys, values);\n\n    // We only store the primitive values of the keys, this allows lookup\n    // to be O(1).\n    const $keys = await keys.data();\n\n    // Clear the hashTable before inserting new values.\n    this.tensorMap.forEach(value => value.dispose());\n    this.tensorMap.clear();\n\n    return tidy(() => {\n      const $values = unstack(values);\n\n      const keysLength = $keys.length;\n      const valuesLength = $values.length;\n\n      util.assert(\n          keysLength === valuesLength,\n          () => `The number of elements doesn't match, keys has ` +\n              `${keysLength} elements, the values has ${valuesLength} ` +\n              `elements.`);\n\n      for (let i = 0; i < keysLength; i++) {\n        const key = $keys[i];\n        const value = $values[i];\n\n        keep(value);\n        this.tensorMap.set(key, value);\n      }\n\n      return this.handle;\n    });\n  }\n\n  /**\n   * Looks up keys in a hash table, outputs the corresponding values.\n   *\n   * Performs batch lookups, for every element in the key tensor, `find`\n   * stacks the corresponding value into the return tensor.\n   *\n   * If an element is not present in the table, the given `defaultValue` is\n   * used.\n   *\n   * @param keys Keys to look up. Must have the same type as the keys of the\n   *     table.\n   * @param defaultValue The scalar `defaultValue` is the value output for keys\n   *     not present in the table. It must also be of the same type as the\n   *     table values.\n   */\n  async find(keys: Tensor, defaultValue: Tensor): Promise<Tensor> {\n    this.checkKeyAndValueTensor(keys, defaultValue);\n\n    const $keys = await keys.data();\n\n    return tidy(() => {\n      const result: Tensor[] = [];\n\n      for (let i = 0; i < $keys.length; i++) {\n        const key = $keys[i];\n\n        const value = this.findWithDefault(key, defaultValue);\n        result.push(value);\n      }\n\n      return stack(result);\n    });\n  }\n\n  // tslint:disable-next-line: no-any\n  private findWithDefault(key: any, defaultValue: Tensor): Tensor {\n    const result = this.tensorMap.get(key);\n\n    return result != null ? result : defaultValue;\n  }\n\n  private checkKeyAndValueTensor(key: Tensor, value: Tensor) {\n    if (key.dtype !== this.keyDType) {\n      throw new Error(\n          `Expect key dtype ${this.keyDType}, but got ` +\n          `${key.dtype}`);\n    }\n\n    if (value.dtype !== this.valueDType) {\n      throw new Error(\n          `Expect value dtype ${this.valueDType}, but got ` +\n          `${value.dtype}`);\n    }\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, Tensor} from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {HashTable} from '../../executor/hash_table';\nimport {ResourceManager} from '../../executor/resource_manager';\nimport {InternalOpAsyncExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpAsyncExecutor = async(\n    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,\n    resourceManager: ResourceManager): Promise<Tensor[]> => {\n  switch (node.op) {\n    case 'HashTable':\n    case 'HashTableV2': {\n      const keyDType =\n          getParamValue('keyDType', node, tensorMap, context) as DataType;\n      const valueDType =\n          getParamValue('valueDType', node, tensorMap, context) as DataType;\n\n      const hashTable = new HashTable(keyDType, valueDType);\n      resourceManager.addHashTable(node.name, hashTable);\n      return [hashTable.handle];\n    }\n    case 'LookupTableImport':\n    case 'LookupTableImportV2': {\n      const handle = getParamValue(\n                         'tableHandle', node, tensorMap, context,\n                         resourceManager) as Tensor;\n      const keys = getParamValue('keys', node, tensorMap, context) as Tensor;\n      const values =\n          getParamValue('values', node, tensorMap, context) as Tensor;\n\n      const hashTable = resourceManager.getHashTableById(handle.id);\n\n      return [await hashTable.import(keys, values)];\n    }\n    case 'LookupTableFind':\n    case 'LookupTableFindV2': {\n      const handle = getParamValue(\n                         'tableHandle', node, tensorMap, context,\n                         resourceManager) as Tensor;\n      const keys = getParamValue('keys', node, tensorMap, context) as Tensor;\n      const defaultValue =\n          getParamValue('defaultValue', node, tensorMap, context) as Tensor;\n\n      const hashTable = resourceManager.getHashTableById(handle.id);\n      return [await hashTable.find(keys, defaultValue)];\n    }\n    case 'LookupTableSize':\n    case 'LookupTableSizeV2': {\n      const handle = getParamValue(\n                         'tableHandle', node, tensorMap, context,\n                         resourceManager) as Tensor;\n\n      const hashTable = resourceManager.getHashTableById(handle.id);\n      return [hashTable.tensorSize()];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\nexport const CATEGORY = 'hash_table';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'ResizeBilinear': {\n          const images =\n              getParamValue('images', node, tensorMap, context) as Tensor;\n          const size =\n              getParamValue('size', node, tensorMap, context) as number[];\n          const alignCorners =\n              getParamValue('alignCorners', node, tensorMap, context) as\n              boolean;\n          const halfPixelCenters =\n              getParamValue('halfPixelCenters', node, tensorMap, context) as\n              boolean;\n          return [tfOps.image.resizeBilinear(\n              images as Tensor3D | Tensor4D, [size[0], size[1]], alignCorners,\n              halfPixelCenters)];\n        }\n        case 'ResizeNearestNeighbor': {\n          const images =\n              getParamValue('images', node, tensorMap, context) as Tensor;\n          const size =\n              getParamValue('size', node, tensorMap, context) as number[];\n          const alignCorners =\n              getParamValue('alignCorners', node, tensorMap, context) as\n              boolean;\n          const halfPixelCenters =\n              getParamValue('halfPixelCenters', node, tensorMap, context) as\n              boolean;\n          return [tfOps.image.resizeNearestNeighbor(\n              images as Tensor3D | Tensor4D, [size[0], size[1]], alignCorners,\n              halfPixelCenters)];\n        }\n        case 'CropAndResize': {\n          const image =\n              getParamValue('image', node, tensorMap, context) as Tensor;\n          const boxes =\n              getParamValue('boxes', node, tensorMap, context) as Tensor;\n          const boxInd =\n              getParamValue('boxInd', node, tensorMap, context) as Tensor;\n          const cropSize =\n              getParamValue('cropSize', node, tensorMap, context) as number[];\n          const method =\n              getParamValue('method', node, tensorMap, context) as string;\n          const extrapolationValue =\n              getParamValue('extrapolationValue', node, tensorMap, context) as\n              number;\n          return [tfOps.image.cropAndResize(\n              image as Tensor4D, boxes as Tensor2D, boxInd as Tensor1D,\n              cropSize as [number, number], method as 'bilinear' | 'nearest',\n              extrapolationValue)];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'image';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Equal': {\n          return [tfOps.equal(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'NotEqual': {\n          return [tfOps.notEqual(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Greater': {\n          return [tfOps.greater(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'GreaterEqual': {\n          return [tfOps.greaterEqual(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Less': {\n          return [tfOps.less(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'LessEqual': {\n          return [tfOps.lessEqual(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'LogicalAnd': {\n          return [tfOps.logicalAnd(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'LogicalNot': {\n          return [tfOps.logicalNot(\n              getParamValue('a', node, tensorMap, context) as Tensor)];\n        }\n        case 'LogicalOr': {\n          return [tfOps.logicalOr(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Select':\n        case 'SelectV2': {\n          return [tfOps.where(\n              getParamValue('condition', node, tensorMap, context) as Tensor,\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'logical';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor2D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'BatchMatMul':\n        case 'BatchMatMulV2':\n        case 'MatMul':\n          return [tfOps.matMul(\n              getParamValue('a', node, tensorMap, context) as Tensor2D,\n              getParamValue('b', node, tensorMap, context) as Tensor2D,\n              getParamValue('transposeA', node, tensorMap, context) as boolean,\n              getParamValue('transposeB', node, tensorMap, context) as\n                  boolean)];\n\n        case 'Einsum':\n          return [tfOps.einsum(\n              getParamValue('equation', node, tensorMap, context) as string,\n              ...getParamValue('tensors', node, tensorMap, context) as\n                  Tensor[])];\n\n        case 'Transpose':\n          return [tfOps.transpose(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('perm', node, tensorMap, context) as number[])];\n\n        case '_FusedMatMul':\n          const [extraOp, activationFunc] =\n              (getParamValue('fusedOps', node, tensorMap, context) as string[]);\n\n          const isBiasAdd = extraOp === 'biasadd';\n          const isPrelu = activationFunc === 'prelu';\n\n          const numArgs =\n              (getParamValue('numArgs', node, tensorMap, context) as number);\n          const leakyreluAlpha =\n              getParamValue('leakyreluAlpha', node, tensorMap, context) as\n              number;\n\n          if (isBiasAdd) {\n            if (isPrelu && numArgs !== 2) {\n              throw new Error(\n                  'Fused MatMul with BiasAdd and Prelu must have two ' +\n                  'extra arguments: bias and alpha.');\n            }\n            if (!isPrelu && numArgs !== 1) {\n              throw new Error(\n                  'Fused MatMul with BiasAdd must have one extra argument: bias.');\n            }\n          }\n          const [biasArg, preluArg] =\n              getParamValue('args', node, tensorMap, context) as Tensor[];\n          return [tfOps.fused.matMul({\n            a: getParamValue('a', node, tensorMap, context) as Tensor2D,\n            b: getParamValue('b', node, tensorMap, context) as Tensor2D,\n            transposeA: getParamValue('transposeA', node, tensorMap, context) as\n                boolean,\n            transposeB: getParamValue('transposeB', node, tensorMap, context) as\n                boolean,\n            bias: biasArg,\n            activation: activationFunc as tfOps.fused.Activation,\n            preluActivationWeights: preluArg,\n            leakyreluAlpha\n          })];\n\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'matrices';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor, Tensor3D, Tensor4D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'FusedBatchNorm':\n        case 'FusedBatchNormV2': {\n          return [tfOps.batchNorm(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('mean', node, tensorMap, context) as Tensor,\n              getParamValue('variance', node, tensorMap, context) as Tensor,\n              getParamValue('offset', node, tensorMap, context) as Tensor,\n              getParamValue('scale', node, tensorMap, context) as Tensor,\n              getParamValue('epsilon', node, tensorMap, context) as number)];\n        }\n        case 'FusedBatchNormV3': {\n          return [tfOps.batchNorm(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('mean', node, tensorMap, context) as Tensor,\n              getParamValue('variance', node, tensorMap, context) as Tensor,\n              getParamValue('offset', node, tensorMap, context) as Tensor,\n              getParamValue('scale', node, tensorMap, context) as Tensor,\n              getParamValue('epsilon', node, tensorMap, context) as number)];\n        }\n        case 'LRN': {\n          return [tfOps.localResponseNormalization(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('radius', node, tensorMap, context) as number,\n              getParamValue('bias', node, tensorMap, context) as number,\n              getParamValue('alpha', node, tensorMap, context) as number,\n              getParamValue('beta', node, tensorMap, context) as number)];\n        }\n        case 'Softmax': {\n          return [tfOps.softmax(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'LogSoftmax': {\n          return [tfOps.logSoftmax(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'SparseToDense': {\n          return [tfOps.sparseToDense(\n              getParamValue('sparseIndices', node, tensorMap, context) as\n                  Tensor,\n              getParamValue('outputShape', node, tensorMap, context) as Tensor,\n              getParamValue('sparseValues', node, tensorMap, context) as\n                  number[],\n              getParamValue('defaultValue', node, tensorMap, context) as\n                  Scalar)];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'normalization';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor1D, Tensor2D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Max': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.max(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'Mean': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.mean(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'Min': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.min(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'Sum': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.sum(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'All': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.all(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'Any': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.any(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'ArgMax': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          return [tfOps.argMax(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis)];\n        }\n        case 'ArgMin': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          return [tfOps.argMin(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis)];\n        }\n        case 'Prod': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.prod(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'Cumsum': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          const exclusive =\n              getParamValue('exclusive', node, tensorMap, context) as boolean;\n          const reverse =\n              getParamValue('reverse', node, tensorMap, context) as boolean;\n          return [tfOps.cumsum(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              exclusive, reverse)];\n        }\n        case 'Bincount':\n          const x = getParamValue('x', node, tensorMap, context) as Tensor1D;\n          const weights =\n              getParamValue('weights', node, tensorMap, context) as Tensor1D;\n          const size =\n              getParamValue('size', node, tensorMap, context) as number;\n\n          return [tfOps.bincount(x, weights, size)];\n        case 'DenseBincount': {\n          const x = getParamValue('x', node, tensorMap, context) as Tensor1D |\n              Tensor2D;\n          const weights =\n              getParamValue('weights', node, tensorMap, context) as Tensor1D |\n              Tensor2D;\n          const size =\n              getParamValue('size', node, tensorMap, context) as number;\n\n          const binaryOutput =\n              getParamValue('binaryOutput', node, tensorMap, context) as\n              boolean;\n\n          return [tfOps.denseBincount(x, weights, size, binaryOutput)];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'reduction';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor, Tensor1D, tidy, util} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'ConcatV2':\n        case 'Concat': {\n          const n = getParamValue('n', node, tensorMap, context) as number;\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          let inputs =\n              getParamValue('tensors', node, tensorMap, context) as Tensor[];\n          inputs = inputs.slice(0, n);\n          return [tfOps.concat(inputs, axis)];\n        }\n        case 'Gather': {\n          const input = getParamValue('x', node, tensorMap, context) as Tensor;\n          const indices =\n              getParamValue('indices', node, tensorMap, context) as Tensor1D;\n          return [tfOps.gather(input, tfOps.cast(indices, 'int32'), 0)];\n        }\n        case 'GatherV2': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          const batchDims =\n              getParamValue('batchDims', node, tensorMap, context) as number;\n          const input = getParamValue('x', node, tensorMap, context) as Tensor;\n          const indices =\n              getParamValue('indices', node, tensorMap, context) as Tensor1D;\n          return [tfOps.gather(\n              input, tfOps.cast(indices, 'int32'), axis, batchDims)];\n        }\n        case 'Reverse': {\n          const dims =\n              getParamValue('dims', node, tensorMap, context) as boolean[];\n          const axis = [];\n          for (let i = 0; i < dims.length; i++) {\n            if (dims[i]) {\n              axis.push(i);\n            }\n          }\n          const input = getParamValue('x', node, tensorMap, context) as Tensor;\n          return [tfOps.reverse(input, axis)];\n        }\n        case 'ReverseV2': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const input = getParamValue('x', node, tensorMap, context) as Tensor;\n          return [tfOps.reverse(input, axis)];\n        }\n        case 'Slice': {\n          // tslint:disable-next-line:no-any\n          const begin = getParamValue('begin', node, tensorMap, context) as any;\n          // tslint:disable-next-line:no-any\n          const size = getParamValue('size', node, tensorMap, context) as any;\n          return [tfOps.slice(\n              getParamValue('x', node, tensorMap, context) as Tensor, begin,\n              size)];\n        }\n        case 'StridedSlice': {\n          const begin =\n              getParamValue('begin', node, tensorMap, context) as number[];\n          const end =\n              getParamValue('end', node, tensorMap, context) as number[];\n          const strides =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const beginMask =\n              getParamValue('beginMask', node, tensorMap, context) as number;\n          const endMask =\n              getParamValue('endMask', node, tensorMap, context) as number;\n          const ellipsisMask =\n              getParamValue('ellipsisMask', node, tensorMap, context) as number;\n          const newAxisMask =\n              getParamValue('newAxisMask', node, tensorMap, context) as number;\n          const shrinkAxisMask =\n              getParamValue('shrinkAxisMask', node, tensorMap, context) as\n              number;\n          const tensor = getParamValue('x', node, tensorMap, context) as Tensor;\n\n          return [tfOps.stridedSlice(\n              tensor, begin, end, strides, beginMask, endMask, ellipsisMask,\n              newAxisMask, shrinkAxisMask)];\n        }\n        case 'Pack': {\n          return tidy(() => {\n            const axis =\n                getParamValue('axis', node, tensorMap, context) as number;\n            const tensors =\n                getParamValue('tensors', node, tensorMap, context) as Tensor[];\n            // Reshape the tensors to the first tensor's shape if they don't\n            // match.\n            const shape = tensors[0].shape;\n            const squeezedShape = tfOps.squeeze(tensors[0]).shape;\n            const mapped = tensors.map(tensor => {\n              const sameShape = util.arraysEqual(tensor.shape, shape);\n              if (!sameShape &&\n                  !util.arraysEqual(\n                      tfOps.squeeze(tensor).shape, squeezedShape)) {\n                throw new Error('the input tensors shape does not match');\n              }\n              return sameShape ? tensor : tfOps.reshape(tensor, shape);\n            });\n            return [tfOps.stack(mapped, axis)];\n          });\n        }\n        case 'Unpack': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          const tensor =\n              getParamValue('tensor', node, tensorMap, context) as Tensor;\n          return tfOps.unstack(tensor, axis);\n        }\n        case 'Tile': {\n          const reps =\n              getParamValue('reps', node, tensorMap, context) as number[];\n          return [tfOps.tile(\n              getParamValue('x', node, tensorMap, context) as Tensor, reps)];\n        }\n        case 'Split':\n        case 'SplitV': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          const numOrSizeSplits =\n              getParamValue('numOrSizeSplits', node, tensorMap, context) as\n                  number |\n              number[];\n          const tensor = getParamValue('x', node, tensorMap, context) as Tensor;\n\n          return tfOps.split(tensor, numOrSizeSplits, axis);\n        }\n        case 'ScatterNd': {\n          const indices =\n              getParamValue('indices', node, tensorMap, context) as Tensor;\n          const values =\n              getParamValue('values', node, tensorMap, context) as Tensor;\n          const shape =\n              getParamValue('shape', node, tensorMap, context) as number[];\n          return [tfOps.scatterND(indices, values, shape)];\n        }\n        case 'GatherNd': {\n          const x = getParamValue('x', node, tensorMap, context) as Tensor;\n          const indices =\n              getParamValue('indices', node, tensorMap, context) as Tensor;\n          return [tfOps.gatherND(x, indices)];\n        }\n        case 'SparseToDense': {\n          const indices =\n              getParamValue('sparseIndices', node, tensorMap, context) as\n              Tensor;\n          const shape =\n              getParamValue('outputShape', node, tensorMap, context) as\n              number[];\n          const sparseValues =\n              getParamValue('sparseValues', node, tensorMap, context) as Tensor;\n          const defaultValue =\n              getParamValue('defaultValue', node, tensorMap, context) as Scalar;\n          return [tfOps.sparseToDense(\n              indices, sparseValues, shape,\n              sparseValues.dtype === defaultValue.dtype ?\n                  defaultValue :\n                  tfOps.cast(defaultValue, sparseValues.dtype))];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'slice_join';\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor, Tensor1D, Tensor2D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'SparseFillEmptyRows': {\n          const {\n            outputIndices,\n            outputValues,\n            emptyRowIndicator,\n            reverseIndexMap\n          } =\n              tfOps.sparse.sparseFillEmptyRows(\n                  getParamValue('indices', node, tensorMap, context) as\n                      Tensor2D,\n                  getParamValue('values', node, tensorMap, context) as Tensor1D,\n                  getParamValue('denseShape', node, tensorMap, context) as\n                      Tensor1D,\n                  getParamValue('defaultValue', node, tensorMap, context) as\n                      Scalar);\n          return [\n            outputIndices, outputValues, emptyRowIndicator, reverseIndexMap\n          ];\n        }\n        case 'SparseReshape': {\n          const {outputIndices, outputShape} = tfOps.sparse.sparseReshape(\n              getParamValue('inputIndices', node, tensorMap, context) as\n                  Tensor2D,\n              getParamValue('inputShape', node, tensorMap, context) as Tensor1D,\n              getParamValue('newShape', node, tensorMap, context) as Tensor1D);\n          return [outputIndices, outputShape];\n        }\n        case 'SparseSegmentMean': {\n          const outputData = tfOps.sparse.sparseSegmentMean(\n              getParamValue('data', node, tensorMap, context) as Tensor,\n              getParamValue('indices', node, tensorMap, context) as Tensor1D,\n              getParamValue('segmentIds', node, tensorMap, context) as\n                  Tensor1D);\n          return [outputData];\n        }\n        case 'SparseSegmentSum': {\n          const outputData = tfOps.sparse.sparseSegmentSum(\n              getParamValue('data', node, tensorMap, context) as Tensor,\n              getParamValue('indices', node, tensorMap, context) as Tensor1D,\n              getParamValue('segmentIds', node, tensorMap, context) as\n                  Tensor1D);\n          return [outputData];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'sparse';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext):\n        Tensor[] => {\n          switch (node.op) {\n            case 'FFT': {\n              return [tfOps.fft(\n                  getParamValue('x', node, tensorMap, context) as Tensor)];\n            }\n            case 'IFFT': {\n              return [tfOps.ifft(\n                  getParamValue('x', node, tensorMap, context) as Tensor)];\n            }\n            case 'RFFT': {\n              return [tfOps.rfft(\n                  getParamValue('x', node, tensorMap, context) as Tensor)];\n            }\n            case 'IRFFT': {\n              return [tfOps.irfft(\n                  getParamValue('x', node, tensorMap, context) as Tensor)];\n            }\n            default:\n              throw TypeError(`Node type ${node.op} is not implemented`);\n          }\n        };\n\nexport const CATEGORY = 'spectral';\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor, Tensor1D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'StringNGrams': {\n          const {nGrams, nGramsSplits} = tfOps.string.stringNGrams(\n              getParamValue('data', node, tensorMap, context) as Tensor1D,\n              getParamValue('dataSplits', node, tensorMap, context) as Tensor,\n              getParamValue('separator', node, tensorMap, context) as string,\n              getParamValue('nGramWidths', node, tensorMap, context) as\n                  number[],\n              getParamValue('leftPad', node, tensorMap, context) as string,\n              getParamValue('rightPad', node, tensorMap, context) as string,\n              getParamValue('padWidth', node, tensorMap, context) as number,\n              getParamValue(\n                  'preserveShortSequences', node, tensorMap, context) as\n                  boolean);\n          return [nGrams, nGramsSplits];\n        }\n        case 'StringSplit': {\n          const {indices, values, shape} = tfOps.string.stringSplit(\n              getParamValue('input', node, tensorMap, context) as Tensor1D,\n              getParamValue('delimiter', node, tensorMap, context) as Scalar,\n              getParamValue('skipEmpty', node, tensorMap, context) as boolean);\n          return [indices, values, shape];\n        }\n        case 'StringToHashBucketFast': {\n          const output = tfOps.string.stringToHashBucketFast(\n              getParamValue('input', node, tensorMap, context) as Tensor,\n              getParamValue('numBuckets', node, tensorMap, context) as number);\n          return [output];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'string';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor4D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Cast': {\n          return [tfOps.cast(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('dtype', node, tensorMap, context) as 'int32' |\n                  'float32' | 'bool')];\n        }\n        case 'ExpandDims': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          return [tfOps.expandDims(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis)];\n        }\n        case 'Squeeze': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          return [tfOps.squeeze(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis)];\n        }\n\n        case 'Reshape': {\n          return [tfOps.reshape(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('shape', node, tensorMap, context) as number[])];\n        }\n        case 'MirrorPad': {\n          return [tfOps.mirrorPad(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('padding', node, tensorMap, context) as\n                  Array<[number, number]>,\n              getParamValue('mode', node, tensorMap, context) as 'reflect' |\n                  'symmetric')];\n        }\n        case 'PadV2':\n        case 'Pad': {\n          return [tfOps.pad(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('padding', node, tensorMap, context) as\n                  Array<[number, number]>,\n              getParamValue('constantValue', node, tensorMap, context) as\n                  number)];\n        }\n        case 'SpaceToBatchND': {\n          const blockShape =\n              getParamValue('blockShape', node, tensorMap, context) as number[];\n          const paddings =\n              getParamValue('paddings', node, tensorMap, context) as number[][];\n          return [tfOps.spaceToBatchND(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              blockShape, paddings)];\n        }\n        case 'BatchToSpaceND': {\n          const blockShape =\n              getParamValue('blockShape', node, tensorMap, context) as number[];\n          const crops =\n              getParamValue('crops', node, tensorMap, context) as number[][];\n          return [tfOps.batchToSpaceND(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              blockShape, crops)];\n        }\n        case 'DepthToSpace': {\n          const blockSize =\n              getParamValue('blockSize', node, tensorMap, context) as number;\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as\n               string).toUpperCase() as 'NHWC' |\n              'NCHW';\n          return [tfOps.depthToSpace(\n              getParamValue('x', node, tensorMap, context) as Tensor4D,\n              blockSize, dataFormat)];\n        }\n        case 'BroadcastTo': {\n          return [tfOps.broadcastTo(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('shape', node, tensorMap, context) as number[])];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'transformation';\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tfc from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap} from '../data/types';\nimport {ExecutionContext} from '../executor/execution_context';\nimport {ResourceManager} from '../executor/resource_manager';\n\nimport {NodeValueImpl} from './custom_op/node_value_impl';\nimport {getRegisteredOp} from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as hashTable from './executors/hash_table_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as sparse from './executors/sparse_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as string from './executors/string_executor';\nimport * as transformation from './executors/transformation_executor';\nimport {Node} from './types';\n\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n * @param context contains tensors and information for running the current node.\n * @param resourceManager Optional. Contains global resources of the model.\n */\nexport function executeOp(\n    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,\n    resourceManager?: ResourceManager): tfc.Tensor[]|Promise<tfc.Tensor[]> {\n  const value =\n      ((node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext) => {\n        switch (node.category) {\n          case 'arithmetic':\n            return tfc.tidy(\n                () => arithmetic.executeOp(node, tensorMap, context));\n          case 'basic_math':\n            return tfc.tidy(\n                () => basicMath.executeOp(node, tensorMap, context));\n          case 'control':\n            return control.executeOp(node, tensorMap, context);\n          case 'convolution':\n            return tfc.tidy(\n                () => convolution.executeOp(node, tensorMap, context));\n          case 'creation':\n            return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n          case 'dynamic':\n            return dynamic.executeOp(node, tensorMap, context);\n          case 'evaluation':\n            return tfc.tidy(\n                () => evaluation.executeOp(node, tensorMap, context));\n          case 'image':\n            return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n          case 'graph':\n            return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n          case 'logical':\n            return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n          case 'matrices':\n            return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n          case 'normalization':\n            return tfc.tidy(\n                () => normalization.executeOp(node, tensorMap, context));\n          case 'reduction':\n            return tfc.tidy(\n                () => reduction.executeOp(node, tensorMap, context));\n          case 'slice_join':\n            return tfc.tidy(\n                () => sliceJoin.executeOp(node, tensorMap, context));\n          case 'sparse':\n            return tfc.tidy(() => sparse.executeOp(node, tensorMap, context));\n          case 'spectral':\n            return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n          case 'string':\n            return tfc.tidy(() => string.executeOp(node, tensorMap, context));\n          case 'transformation':\n            return tfc.tidy(\n                () => transformation.executeOp(node, tensorMap, context));\n          case 'hash_table':\n            return hashTable.executeOp(\n                node, tensorMap, context, resourceManager);\n          case 'custom':\n            const opMapper = getRegisteredOp(node.op);\n            if (opMapper && opMapper.customExecutor) {\n              return opMapper.customExecutor(\n                  new NodeValueImpl(node, tensorMap, context));\n            } else {\n              throw TypeError(`Custom op ${node.op} is not registered.`);\n            }\n          default:\n            throw TypeError(\n                `Unknown op '${node.op}'. File an issue at ` +\n                `https://github.com/tensorflow/tfjs/issues so we can add it` +\n                `, or register a custom execution with tf.registerOp()`);\n        }\n      })(node, tensorMap, context);\n  if (tfc.util.isPromise(value)) {\n    return (value as Promise<tfc.Tensor>).then((data) => [].concat(data));\n  }\n  return [].concat(value);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap, TensorArrayMap, TensorListMap} from '../data/types';\n\nimport {TensorArray} from './tensor_array';\nimport {TensorList} from './tensor_list';\nimport {FunctionExecutor} from './types';\n\nexport interface ExecutionContextInfo {\n  id: number;           // the unique id of the context info\n  frameName: string;    // The frame name of the loop, this comes from\n                        // the TensorFlow NodeDef.\n  iterationId: number;  // The iteration id of the loop\n}\n\n/**\n * ExecutionContext captures the runtime environment of the node. It keeps\n * track of the current frame and iteration for the control flow ops.\n *\n * For example, typical Dynamic RNN model may contain loops, for which\n * TensorFlow will generate graphs with Enter/Exit nodes to control the\n * current execution frame, and NextIteration Nodes for iteration id increment.\n * For model with branch logic, TensorFLow will generate Switch/Merge ops.\n */\nexport class ExecutionContext {\n  private rootContext = {id: 0, frameName: '', iterationId: 0};\n  private contexts: ExecutionContextInfo[] = [this.rootContext];\n  private lastId = 0;\n  private _currentContextIds: string[];\n\n  constructor(\n      readonly weightMap: NamedTensorsMap = {},\n      readonly tensorArrayMap: TensorArrayMap = {},\n      readonly tensorListMap: TensorListMap = {},\n      readonly functionMap: {[key: string]: FunctionExecutor} = {}) {\n    this.generateCurrentContextIds();\n  }\n\n  private newFrame(id: number, frameName: string) {\n    return {id, frameName, iterationId: 0};\n  }\n\n  /**\n   * Set the current context\n   * @param contexts: ExecutionContextInfo[] the current path of execution\n   * frames\n   */\n  set currentContext(contexts: ExecutionContextInfo[]) {\n    if (this.contexts !== contexts) {\n      this.contexts = contexts;\n      this.generateCurrentContextIds();\n    }\n  }\n\n  get currentContext(): ExecutionContextInfo[] {\n    return this.contexts;\n  }\n\n  /**\n   * Returns the current context in string format.\n   */\n  get currentContextId(): string {\n    return this._currentContextIds[0];\n  }\n\n  /**\n   * Returns the current context and all parent contexts in string format.\n   * This allow access to the nodes in the current and parent frames.\n   */\n  get currentContextIds(): string[] {\n    return this._currentContextIds;\n  }\n\n  private generateCurrentContextIds() {\n    const names = [];\n    for (let i = 0; i < this.contexts.length - 1; i++) {\n      const contexts = this.contexts.slice(0, this.contexts.length - i);\n      names.push(this.contextIdforContexts(contexts));\n    }\n    names.push('');\n    this._currentContextIds = names;\n  }\n\n  private contextIdforContexts(contexts: ExecutionContextInfo[]) {\n    return contexts ?\n        contexts\n            .map(\n                context => (context.id === 0 && context.iterationId === 0) ?\n                    '' :\n                    `${context.frameName}-${context.iterationId}`)\n            .join('/') :\n        '';\n  }\n\n  /**\n   * Enter a new frame, a new context is pushed on the current context list.\n   * @param frameId new frame id\n   */\n  enterFrame(frameId: string) {\n    if (this.contexts) {\n      this.lastId++;\n      this.contexts = this.contexts.slice();\n      this.contexts.push(this.newFrame(this.lastId, frameId));\n      this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n    }\n  }\n\n  /**\n   * Exit the current frame, the last context is removed from the current\n   * context list.\n   */\n  exitFrame() {\n    if (this.contexts && this.contexts.length > 1) {\n      this.contexts = this.contexts.slice();\n      this.contexts.splice(-1);\n      this.currentContextIds.shift();\n    } else {\n      throw new Error('Cannot exit frame, the context is empty');\n    }\n  }\n\n  /**\n   * Enter the next iteration of a loop, the iteration id of last context is\n   * increased.\n   */\n  nextIteration() {\n    if (this.contexts && this.contexts.length > 0) {\n      this.contexts = this.contexts.slice();\n      this.lastId++;\n      const context =\n          Object.assign({}, this.contexts[this.contexts.length - 1]);\n      context.iterationId += 1;\n      context.id = this.lastId;\n      this.contexts.splice(-1, 1, context);\n      this._currentContextIds.splice(\n          0, 1, this.contextIdforContexts(this.contexts));\n    } else {\n      throw new Error('Cannot increase frame iteration, the context is empty');\n    }\n  }\n\n  getWeight(name: string): Tensor[] {\n    return this.weightMap[name];\n  }\n\n  addTensorArray(tensorArray: TensorArray) {\n    this.tensorArrayMap[tensorArray.id] = tensorArray;\n  }\n\n  getTensorArray(id: number): TensorArray {\n    return this.tensorArrayMap[id];\n  }\n\n  addTensorList(tensorList: TensorList) {\n    this.tensorListMap[tensorList.id] = tensorList;\n  }\n\n  getTensorList(id: number): TensorList {\n    return this.tensorListMap[id];\n  }\n\n  dispose(keepIds: Set<number>) {\n    for (const key in this.tensorArrayMap) {\n      this.tensorArrayMap[key].clearAndClose(keepIds);\n    }\n\n    for (const key in this.tensorListMap) {\n      this.tensorListMap[key].clearAndClose(keepIds);\n    }\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {NamedTensorMap} from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap} from '../data/types';\nimport {parseNodeName} from '../operations/executors/utils';\nimport {Graph, Node} from '../operations/types';\n\nexport interface ExecutionInfo {\n  inputs: NamedTensorMap;\n  outputs: Node[];\n  usedNodes: Set<string>;\n  missingInputs: string[];\n  dynamicNode: Node;\n  syncInputs: string[];\n}\n\n/**\n * Given graph inputs and desired outputs, find the minimal set of nodes\n * to execute in order to compute the outputs. In addition return other useful\n * info such:\n * - Missing inputs needed to compute the output.\n * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).\n * - Alternative inputs in order to avoid async (dynamic op) execution.\n */\nexport function getExecutionSubgraph(\n    inputs: NamedTensorMap, outputs: Node[], weightMap: NamedTensorsMap,\n    initNodes?: Node[]): ExecutionInfo {\n  const usedNodes = new Set<string>();\n  const missingInputs: string[] = [];\n  let dynamicNode: Node = null;\n  let syncInputs: string[] = null;\n\n  // Start with the outputs, going backwards and find all the nodes that are\n  // needed to compute those outputs.\n  const seen = new Set<string>();\n  const inputNodeNames =\n      Object.keys(inputs).map(name => parseNodeName(name)[0]);\n\n  let initNodeNames: string[] = [];\n  if (initNodes != null) {\n    initNodeNames = initNodes.map(node => parseNodeName(node.name)[0]);\n  }\n\n  const frontier = [...outputs];\n  while (frontier.length > 0) {\n    const node = frontier.pop();\n    if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {\n      if (dynamicNode == null) {\n        dynamicNode = node;\n        syncInputs = dynamicNode.children.map(child => child.name)\n                         .filter(name => usedNodes.has(name));\n      }\n    }\n    usedNodes.add(node.name);\n\n    // Weights are dead end since we already have their values.\n    if (weightMap[node.name] != null) {\n      continue;\n    }\n    // This node is a dead end since it's one of the user-provided inputs.\n    if (inputNodeNames.indexOf(node.name) !== -1) {\n      continue;\n    }\n    // This node is a dead end since it doesn't have any inputs.\n    if (initNodeNames.indexOf(node.name) !== -1) {\n      continue;\n    }\n    if (node.inputs.length === 0) {\n      missingInputs.push(node.name);\n      continue;\n    }\n    node.inputs.forEach(input => {\n      // Don't add to the frontier if it is already there.\n      if (seen.has(input.name)) {\n        return;\n      }\n      seen.add(input.name);\n      frontier.push(input);\n    });\n  }\n  return {inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs};\n}\n\n/**\n * Given the execution info, return a list of nodes in topological order that\n * need to be executed to compute the output.\n */\nexport function getNodesInTopologicalOrder(\n    graph: Graph, weightMap: NamedTensorsMap,\n    executionInfo: ExecutionInfo): Node[] {\n  const {usedNodes, inputs} = executionInfo;\n  const frontier: Node[] = [];\n  const inputNodes = Object.keys(inputs)\n                         .map(name => parseNodeName(name)[0])\n                         .map(name => graph.nodes[name]);\n  const initNodes = graph.initNodes;\n\n  inputNodes.forEach(input => {\n    if (usedNodes.has(input.name)) {\n      frontier.push(input);\n    }\n  });\n  graph.weights.forEach(weight => {\n    if (usedNodes.has(weight.name)) {\n      frontier.push(weight);\n    }\n  });\n  if (initNodes != null) {\n    initNodes.forEach(node => {\n      if (usedNodes.has(node.name)) {\n        frontier.push(node);\n      }\n    });\n  }\n  const seen = new Set<string>();\n  const orderedNodes: Node[] = [];\n  while (frontier.length > 0) {\n    const node = frontier.pop();\n    seen.add(node.name);\n    if (!weightMap[node.name]) {\n      orderedNodes.push(node);\n    }\n    node.children.forEach(child => {\n      if (!seen.has(child.name) && usedNodes.has(child.name) &&\n          child.inputs.every(input => seen.has(input.name))) {\n        frontier.push(child);\n      }\n    });\n  }\n  return orderedNodes;\n}\n\nconst CONTROL_FLOW_OPS = [\n  'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',\n  'StatelessWhile', 'if', 'While'\n];\nconst DYNAMIC_SHAPE_OPS = [\n  'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'\n];\nconst HASH_TABLE_OPS = [\n  'HashTable', 'HashTableV2', 'LookupTableImport', 'LookupTableImportV2',\n  'LookupTableFind', 'LookupTableFindV2', 'LookupTableSize', 'LookupTableSizeV2'\n];\n\nexport function isControlFlow(node: Node) {\n  return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\n\nexport function isDynamicShape(node: Node) {\n  return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\n\nexport function isHashTable(node: Node) {\n  return HASH_TABLE_OPS.indexOf(node.op) >= 0;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, NamedTensorMap, Tensor, tidy, util} from '@tensorflow/tfjs-core';\n\nimport {ISignatureDef} from '../data/compiled_api';\nimport {NamedTensorsMap, TensorArrayMap, TensorInfo, TensorListMap} from '../data/types';\nimport {getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContenxt, parseNodeName} from '../operations/executors/utils';\nimport {executeOp} from '../operations/operation_executor';\nimport {Graph, Node} from '../operations/types';\n\nimport {ExecutionContext, ExecutionContextInfo} from './execution_context';\nimport {getExecutionSubgraph, getNodesInTopologicalOrder, isControlFlow} from './model_analysis';\nimport {ResourceManager} from './resource_manager';\nimport {FunctionExecutor} from './types';\n\ninterface NodeWithContexts {\n  contexts: ExecutionContextInfo[];\n  node: Node;\n}\n\nexport class GraphExecutor implements FunctionExecutor {\n  private compiledMap: Map<string, Node[]> = new Map();\n  private _weightMap: NamedTensorsMap = {};\n  private _weightIds: number[];\n  private _signature: ISignatureDef;\n  private _inputs: Node[];\n  private _outputs: Node[];\n  private _initNodes: Node[];  // Internal init nodes to start initialization.\n  private SEPERATOR = ',';\n  private _functions: {[key: string]: Graph} = {};\n  private _functionExecutorMap: {[key: string]: FunctionExecutor} = {};\n  private _resourceManager: ResourceManager;\n\n  get weightIds(): number[] {\n    return this.parent ? this.parent.weightIds : this._weightIds;\n  }\n\n  get functionExecutorMap(): {[key: string]: FunctionExecutor} {\n    return this.parent ? this.parent.functionExecutorMap :\n                         this._functionExecutorMap;\n  }\n\n  get weightMap(): NamedTensorsMap {\n    return this.parent ? this.parent.weightMap : this._weightMap;\n  }\n\n  set weightMap(weightMap: NamedTensorsMap) {\n    const weightIds = Object.keys(weightMap).map(\n        key => weightMap[key].map(tensor => tensor.id));\n    this._weightIds = [].concat(...weightIds);\n    this._weightMap = weightMap;\n  }\n\n  /**\n   * Set `ResourceManager` shared by executors of a model.\n   * @param resourceManager: `ResourceManager` of the `GraphModel`.\n   */\n  set resourceManager(resourceManager: ResourceManager) {\n    this._resourceManager = resourceManager;\n  }\n\n  get inputs(): TensorInfo[] {\n    return this._inputs.map(node => {\n      return {\n        name: node.name,\n        shape: node.attrParams['shape'] ?\n            node.attrParams['shape'].value as number[] :\n            undefined,\n        dtype: node.attrParams['dtype'] ?\n            node.attrParams['dtype'].value as DataType :\n            undefined\n      };\n    });\n  }\n\n  get outputs(): TensorInfo[] {\n    return this._outputs.map(node => {\n      return {\n        name: node.name,\n        shape: node.attrParams['shape'] ?\n            node.attrParams['shape'].value as number[] :\n            undefined,\n        dtype: node.attrParams['dtype'] ?\n            node.attrParams['dtype'].value as DataType :\n            undefined\n      };\n    });\n  }\n\n  get inputNodes(): string[] {\n    return this._inputs.map(node => node.signatureKey || node.name);\n  }\n\n  get outputNodes(): string[] {\n    return this._outputs.map((node) => {\n      const name = node.signatureKey || node.name;\n      return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;\n    });\n  }\n\n  get functions(): {[key: string]: ISignatureDef} {\n    return Object.keys(this._functions).reduce((map, key) => {\n      map[key] = this._functions[key].signature;\n      return map;\n    }, {} as {[key: string]: ISignatureDef});\n  }\n\n  /**\n   *\n   * @param graph Graph the model or function graph to be executed.\n   * @param parent When building function exector you need to set the parent\n   * executor. Since the weights and function executor maps are set at parant\n   * level, that function executor can access the function maps and weight maps\n   * through the parent.\n   */\n  constructor(private graph: Graph, private parent?: GraphExecutor) {\n    this._outputs = graph.outputs;\n    this._inputs = graph.inputs;\n    this._initNodes = graph.initNodes;\n    this._signature = graph.signature;\n    this._functions = graph.functions;\n    // create sub-graph executors\n    if (graph.functions != null) {\n      Object.keys(graph.functions).forEach(name => {\n        this._functionExecutorMap[name] =\n            new GraphExecutor(graph.functions[name], this);\n      });\n    }\n  }\n\n  private getCompilationKey(inputs: Node[], outputs: Node[]): string {\n    const sortedInputs = inputs.map(node => node.name).sort();\n    const sortedOutputs = outputs.map(node => node.name).sort();\n    return sortedInputs.join(this.SEPERATOR) + '--' +\n        sortedOutputs.join(this.SEPERATOR);\n  }\n\n  /**\n   * Compiles the inference graph and returns the minimal set of nodes that are\n   * required for execution, in the correct execution order.\n   */\n  private compile(inputs: NamedTensorMap, outputs: Node[]): Node[] {\n    const executionInfo =\n        getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n    const {missingInputs, dynamicNode, syncInputs} = executionInfo;\n    if (dynamicNode != null) {\n      throw new Error(\n          `This execution contains the node '${dynamicNode.name}', which has ` +\n          `the dynamic op '${dynamicNode.op}'. Please use ` +\n          `model.executeAsync() instead. Alternatively, to avoid the ` +\n          `dynamic ops, specify the inputs [${syncInputs}]`);\n    }\n\n    if (missingInputs.length > 0) {\n      const outNames = outputs.map(n => n.name);\n      const inNames = Object.keys(inputs);\n      throw new Error(\n          `Cannot compute the outputs [${outNames}] from the provided inputs ` +\n          `[${inNames}]. Missing the following inputs: [${missingInputs}]`);\n    }\n\n    return getNodesInTopologicalOrder(\n        this.graph, this.weightMap, executionInfo);\n  }\n\n  /**\n   * Executes the inference for given input tensors.\n   * @param inputs Tensor map for the model inputs, keyed by the input node\n   * names.\n   * @param outputs Optional. output node name from the Tensorflow model, if\n   * no outputs are specified, the default outputs of the model would be used.\n   * You can inspect intermediate nodes of the model by adding them to the\n   * outputs array.\n   */\n  execute(inputs: NamedTensorMap, outputs?: string[]): Tensor[] {\n    inputs = this.mapInputs(inputs);\n    const names = Object.keys(inputs).sort();\n    this.checkInputs(inputs);\n    this.checkInputShapeAndType(inputs);\n    outputs = this.mapOutputs(outputs);\n    this.checkOutputs(outputs);\n    const inputNodes =\n        names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n    const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);\n    let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n\n    // If no outputs are specified, then use the default outputs of the model.\n    if (outputNodes.length === 0) {\n      outputNodes = this._outputs;\n    }\n\n    const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n\n    // Do nothing if the compiled graph cache contains the input.\n    let orderedNodes = this.compiledMap.get(compilationKey);\n    if (orderedNodes == null) {\n      orderedNodes = this.compile(inputs, outputNodes);\n      this.compiledMap.set(compilationKey, orderedNodes);\n    }\n\n    const tensorArrayMap: TensorArrayMap = {};\n    const tensorListMap: TensorListMap = {};\n\n    return tidy(() => {\n      const context = new ExecutionContext(\n          this.weightMap, tensorArrayMap, tensorListMap,\n          this.functionExecutorMap);\n      const tensorsMap: NamedTensorsMap = {...this.weightMap};\n\n      Object.keys(inputs).forEach(name => {\n        const [nodeName, index] = parseNodeName(name);\n        const tensors: Tensor[] = [];\n        tensors[index] = inputs[name];\n        tensorsMap[nodeName] = tensors;\n      });\n\n      const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n      const intermediateTensorConsumerCount: {[key: number]: number} = {};\n      for (let i = 0; i < orderedNodes.length; i++) {\n        const node = orderedNodes[i];\n        if (!tensorsMap[node.name]) {\n          const tensors =\n              executeOp(node, tensorsMap, context, this._resourceManager) as\n              Tensor[];\n          if (util.isPromise(tensors)) {\n            throw new Error(\n                `The execution of the op '${node.op}' returned a promise. ` +\n                `Please use model.executeAsync() instead.`);\n          }\n          tensorsMap[node.name] = tensors;\n          this.checkTensorForDisposal(\n              node.name, node, tensorsMap, context, tensorsToKeep,\n              outputNodeNames, intermediateTensorConsumerCount);\n        }\n      }\n      // dispose the context for the root executor\n      if (this.parent == null) {\n        context.dispose(tensorsToKeep);\n      }\n      return outputs.map(name => getTensor(name, tensorsMap, context));\n    });\n  }\n\n  private getFrozenTensorIds(tensorMap: NamedTensorsMap): Set<number> {\n    const ids = [].concat.apply(\n        [],\n        Object.keys(tensorMap)\n            .map(key => tensorMap[key])\n            .map(tensors => tensors.map(tensor => tensor.id)));\n    return new Set(ids);\n  }\n  private checkTensorForDisposal(\n      nodeName: string, node: Node, tensorMap: NamedTensorsMap,\n      context: ExecutionContext, tensorsToKeep: Set<number>,\n      outputNames: string[],\n      intermediateTensorConsumerCount: {[key: string]: number}) {\n    // Skip output nodes and any control flow nodes, since its dependency is\n    // tricky to track correctly.\n    if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {\n      return;\n    }\n\n    tensorMap[nodeName].forEach(tensor => {\n      if (tensor != null) {\n        intermediateTensorConsumerCount[tensor.id] =\n            (intermediateTensorConsumerCount[tensor.id] || 0) +\n            node.children.length;\n      }\n    });\n    node.inputs.forEach(input => {\n      // Skip any control flow nodes, since its dependency is tricky to track\n      // correctly.\n      if (input.category !== 'control') {\n        const tensors =\n            getTensorsForCurrentContenxt(input.name, tensorMap, context);\n        if (tensors != null) {\n          tensors.forEach(tensor => {\n            if (tensor && !tensor.kept && !tensorsToKeep.has(tensor.id)) {\n              const count = intermediateTensorConsumerCount[tensor.id];\n              if (count === 1) {\n                tensor.dispose();\n                delete intermediateTensorConsumerCount[tensor.id];\n              } else if (count != null) {\n                // only intermediate nodes has count set, inputs and weights are\n                // not.\n                intermediateTensorConsumerCount[tensor.id]--;\n              }\n            }\n          });\n        }\n      }\n    });\n  }\n\n  /**\n   * Executes the inference for given input tensors in Async fashion.\n   * @param inputs Tensor map for the model inputs, keyed by the input node\n   * names.\n   * @param outputs output node name from the Tensorflow model, if no outputs\n   * are specified, the default outputs of the model would be used. You can\n   * inspect intermediate nodes of the model by adding them to the outputs\n   * array.\n   */\n  async executeAsync(inputs: NamedTensorMap, outputs?: string[]):\n      Promise<Tensor[]> {\n    return this._executeAsync(inputs, outputs);\n  }\n\n  /**\n   * Executes the inference for given input tensors in Async fashion.\n   * @param inputs Tensor map for the model inputs, keyed by the input node\n   * names.\n   * @param outputs Optional. output node name from the Tensorflow model,\n   * if no outputs are specified, the default outputs of the model would be\n   * used. You can inspect intermediate nodes of the model by adding them to the\n   * outputs array.\n   * @param isFunctionExecution Optional. Flag for executing a function.\n   * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n   * function execution.\n   * @param tensorArrayMap Optinal global TensorList map by id. Used for\n   * function execution.\n   */\n  private async _executeAsync(\n      inputs: NamedTensorMap, outputs?: string[], isFunctionExecution = false,\n      tensorArrayMap: TensorArrayMap = {},\n      tensorListMap: TensorListMap = {}): Promise<Tensor[]> {\n    if (!isFunctionExecution) {\n      inputs = this.mapInputs(inputs);\n      this.checkInputs(inputs);\n      this.checkInputShapeAndType(inputs);\n      outputs = this.mapOutputs(outputs);\n      this.checkOutputs(outputs);\n    }\n\n    const context = new ExecutionContext(\n        this.weightMap, tensorArrayMap, tensorListMap,\n        this.functionExecutorMap);\n\n    // Graph with control flow op requires runtime evaluation of the execution\n    // order, while without control flow the execution order is pre-determined\n    // in the compile method.\n    const tensorMap = await this.executeWithControlFlow(\n        inputs, context, outputs, isFunctionExecution);\n    const results = outputs.map(name => getTensor(name, tensorMap, context));\n\n    // dispose all the intermediate tensors\n    const outputIds = results.map(t => t.id);\n    const inputIds = Object.keys(inputs).map(name => inputs[name].id);\n    const keepIds =\n        new Set<number>([...outputIds, ...inputIds, ...this.weightIds]);\n    Object.keys(tensorMap).forEach(key => {\n      const tensorArray = tensorMap[key];\n      tensorArray.forEach(tensor => {\n        if (tensor && !tensor.kept && !tensor.isDisposed &&\n            !keepIds.has(tensor.id)) {\n          tensor.dispose();\n        }\n      });\n    });\n    // dispose the context for the root executor\n    if (this.parent == null) {\n      context.dispose(keepIds);\n    }\n\n    return results;\n  }\n\n  async executeFunctionAsync(\n      inputs: Tensor[], tensorArrayMap: TensorArrayMap,\n      tensorListMap: TensorListMap): Promise<Tensor[]> {\n    const mappedInputs = inputs.reduce((map, tensor, index) => {\n      map[this.inputs[index].name] = tensor;\n      return map;\n    }, {} as NamedTensorMap);\n\n    return this._executeAsync(\n        mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n  }\n  /**\n   * When there are control flow nodes in the graph, the graph execution use\n   * ExecutionContext to keep track of the frames and loop iterators.\n   * @param inputs placeholder tensors for the graph.\n   * @param context the execution context object for current execution.\n   * @param outputNames Optional. output node name from the Tensorflow model,\n   * if no outputs are specified, the default outputs of the model would be\n   * used. You can inspect intermediate nodes of the model by adding them to the\n   * outputs array.\n   * @param isFunctionExecution Flag for executing a function.\n   */\n  private async executeWithControlFlow(\n      inputs: NamedTensorMap, context: ExecutionContext, outputNames?: string[],\n      isFunctionExecution?: boolean): Promise<NamedTensorsMap> {\n    const names = Object.keys(inputs);\n    const inputNodes =\n        names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n    const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);\n    let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n\n    // If no outputs are specified, then use the default outputs of the model.\n    if (outputNodes.length === 0) {\n      outputNodes = this._outputs;\n    }\n\n    const {usedNodes, missingInputs, dynamicNode, syncInputs} =\n        getExecutionSubgraph(\n            inputs, outputNodes, this.weightMap, this._initNodes);\n\n    // First nodes to execute include inputNodes, weights, and initNodes.\n    const stack: NodeWithContexts[] = [\n      ...inputNodes, ...this.graph.weights, ...(this._initNodes || [])\n    ].map(node => {\n      return {node, contexts: context.currentContext};\n    });\n    const tensorsMap: NamedTensorsMap = {...this.weightMap};\n    Object.keys(inputs).forEach(name => {\n      const [nodeName, index] = parseNodeName(name);\n      const tensors: Tensor[] = [];\n      tensors[index] = inputs[name];\n      tensorsMap[nodeName] = tensors;\n    });\n    const intermediateTensorConsumerCount: {[key: number]: number} = {};\n    const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n    const added: {[key: string]: boolean} = {};\n    while (stack.length > 0) {\n      const promises = this.processStack(\n          inputNodes, stack, context, tensorsMap, added, tensorsToKeep,\n          outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n      await Promise.all(promises);\n    }\n    if (dynamicNode == null && !isFunctionExecution) {\n      console.warn(\n          `This model execution did not contain any nodes with control flow ` +\n          `or dynamic output shapes. You can use model.execute() instead.`);\n    }\n    const missingOutputs =\n        outputNodes\n            .filter(\n                node => !isControlFlow(node) &&\n                    !getTensor(node.name, tensorsMap, context))\n            .map(node => node.name);\n    if (missingOutputs.length > 0) {\n      let alternativeMsg = '';\n      if (dynamicNode != null) {\n        alternativeMsg =\n            `Alternatively, to avoid the dynamic ops, use model.execute() ` +\n            `and specify the inputs [${syncInputs}]`;\n      }\n      throw new Error(\n          `Cannot compute the outputs [${missingOutputs}] from the provided ` +\n          `inputs [${names}]. Consider providing the following inputs: ` +\n          `[${missingInputs}]. ${alternativeMsg}`);\n    }\n    return tensorsMap;\n  }\n\n  private processStack(\n      inputNodes: Node[], stack: NodeWithContexts[], context: ExecutionContext,\n      tensorMap: NamedTensorsMap, added: {[key: string]: boolean},\n      tensorsToKeep: Set<number>, outputNames: string[],\n      intermediateTensorConsumerCount: {[key: number]: number},\n      usedNodes: Set<string>) {\n    const promises: Array<Promise<Tensor[]>> = [];\n    while (stack.length > 0) {\n      const item = stack.pop();\n      context.currentContext = item.contexts;\n      let nodeName = '';\n      // The tensor of the Enter op with isConstant set should be set\n      // in the parent scope, so it will be available as constant for the\n      // whole loop.\n      if (item.node.op === 'Enter' &&\n          getParamValue('isConstant', item.node, tensorMap, context)) {\n        [nodeName] = getNodeNameAndIndex(item.node.name, context);\n      }\n\n      // only process nodes that are not in the tensorMap yet, this include\n      // inputNodes and internal initNodes.\n      if (tensorMap[item.node.name] == null) {\n        const tensors =\n            executeOp(item.node, tensorMap, context, this._resourceManager);\n        if (!nodeName) {\n          [nodeName] = getNodeNameAndIndex(item.node.name, context);\n        }\n        const currentContext = context.currentContext;\n        if (util.isPromise(tensors)) {\n          promises.push((tensors as Promise<Tensor[]>).then(t => {\n            tensorMap[nodeName] = t;\n            context.currentContext = currentContext;\n            this.checkTensorForDisposal(\n                nodeName, item.node, tensorMap, context, tensorsToKeep,\n                outputNames, intermediateTensorConsumerCount);\n            this.processChildNodes(\n                item.node, stack, context, tensorMap, added, usedNodes);\n            return t;\n          }));\n        } else {\n          tensorMap[nodeName] = tensors as Tensor[];\n          this.checkTensorForDisposal(\n              nodeName, item.node, tensorMap, context, tensorsToKeep,\n              outputNames, intermediateTensorConsumerCount);\n          this.processChildNodes(\n              item.node, stack, context, tensorMap, added, usedNodes);\n        }\n      } else {\n        this.processChildNodes(\n            item.node, stack, context, tensorMap, added, usedNodes);\n      }\n    }\n    return promises;\n  }\n\n  private processChildNodes(\n      node: Node, stack: NodeWithContexts[], context: ExecutionContext,\n      tensorMap: NamedTensorsMap, added: {[key: string]: boolean},\n      usedNodes: Set<string>) {\n    node.children.forEach((childNode) => {\n      const [nodeName, ] = getNodeNameAndIndex(childNode.name, context);\n      if (added[nodeName] || !usedNodes.has(childNode.name)) {\n        return;\n      }\n      // Merge op can be pushed if any of its inputs has value.\n      if (childNode.op === 'Merge') {\n        if (childNode.inputNames.some(name => {\n              return !!getTensor(name, tensorMap, context);\n            })) {\n          added[nodeName] = true;\n          stack.push({contexts: context.currentContext, node: childNode});\n        }\n      } else  // Otherwise all inputs must to have value.\n          if (childNode.inputNames.every(name => {\n                return !!getTensor(name, tensorMap, context);\n              })) {\n        added[nodeName] = true;\n        stack.push({contexts: context.currentContext, node: childNode});\n      }\n    });\n  }\n\n  /**\n   * Releases the memory used by the weight tensors.\n   */\n  dispose() {\n    Object.keys(this.weightMap)\n        .forEach(\n            key => this.weightMap[key].forEach(tensor => tensor.dispose()));\n  }\n\n  private checkInputShapeAndType(inputs: NamedTensorMap) {\n    Object.keys(inputs).forEach(name => {\n      const input = inputs[name];\n      const [nodeName, ] = parseNodeName(name);\n      const node = this.graph.nodes[nodeName];\n      if (node.attrParams['shape'] && node.attrParams['shape'].value) {\n        const shape = node.attrParams['shape'].value as number[];\n        const match = shape.length === input.shape.length &&\n            input.shape.every(\n                (dim, index) => shape[index] === -1 || shape[index] === dim);\n        util.assert(\n            match,\n            () => `The shape of dict['${node.name}'] provided in ` +\n                `model.execute(dict) must be [${shape}], but was ` +\n                `[${input.shape}]`);\n      }\n      if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {\n        util.assert(\n            input.dtype === node.attrParams['dtype'].value as string,\n            () => `The dtype of dict['${node.name}'] provided in ` +\n                `model.execute(dict) must be ` +\n                `${node.attrParams['dtype'].value}, but was ${input.dtype}`);\n      }\n    });\n  }\n\n  private mapInputs(inputs: NamedTensorMap) {\n    const result: NamedTensorMap = {};\n    for (const inputName in inputs) {\n      if (this._signature != null && this._signature.inputs != null &&\n          this._signature.inputs[inputName] != null) {\n        const tensor = this._signature.inputs[inputName];\n        result[tensor.name] = inputs[inputName];\n      } else {\n        result[inputName] = inputs[inputName];\n      }\n    }\n    return result;\n  }\n\n  private checkInputs(inputs: NamedTensorMap) {\n    const notInGraph = Object.keys(inputs).filter(name => {\n      const [nodeName] = parseNodeName(name);\n      return this.graph.nodes[nodeName] == null;\n    });\n    if (notInGraph.length > 0) {\n      throw new Error(\n          `The dict provided in model.execute(dict) has ` +\n          `keys: [${notInGraph}] that are not part of graph`);\n    }\n  }\n\n  private mapOutputs(outputs: string[]) {\n    return outputs.map(name => {\n      if (this._signature != null && this._signature.outputs != null &&\n          this._signature.outputs[name] != null) {\n        const tensor = this._signature.outputs[name];\n        return tensor.name;\n      }\n      return name;\n    }, {});\n  }\n\n  private checkOutputs(outputs: string[]): void {\n    outputs.forEach(name => {\n      const [normalizedName] = parseNodeName(name);\n      if (!this.graph.nodes[normalizedName]) {\n        throw new Error(`The output '${name}' is not found in the graph`);\n      }\n    });\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {HashTableMap, NamedTensorMap} from '../data/types';\nimport {HashTable} from './hash_table';\n\n/**\n * Contains global resources of a model.\n */\nexport class ResourceManager {\n  constructor(\n      readonly hashTableNameToHandle: NamedTensorMap = {},\n      readonly hashTableMap: HashTableMap = {}) {}\n\n  /**\n   * Register a `HashTable` in the resource manager.\n   *\n   * The `HashTable` can be retrieved by `resourceManager.getHashTableById`,\n   * where id is the table handle tensor's id.\n   *\n   * @param name Op node name that creates the `HashTable`.\n   * @param hashTable The `HashTable` to be added to resource manager.\n   */\n  addHashTable(name: string, hashTable: HashTable) {\n    this.hashTableNameToHandle[name] = hashTable.handle;\n    this.hashTableMap[hashTable.id] = hashTable;\n  }\n\n  /**\n   * Get the table handle by node name.\n   * @param name Op node name that creates the `HashTable`. This name is also\n   *     used in the inputs list of lookup and import `HashTable` ops.\n   */\n  getHashTableHandleByName(name: string) {\n    return this.hashTableNameToHandle[name];\n  }\n\n  /**\n   * Get the actual `HashTable` by its handle tensor's id.\n   * @param id The id of the handle tensor.\n   */\n  getHashTableById(id: number): HashTable {\n    return this.hashTableMap[id];\n  }\n\n  /**\n   * Dispose `ResourceManager`, including its hashTables and tensors in them.\n   */\n  dispose() {\n    for (const key in this.hashTableMap) {\n      this.hashTableMap[key].clearAndClose();\n      delete this.hashTableMap[key];\n    }\n\n    for (const name in this.hashTableNameToHandle) {\n      this.hashTableNameToHandle[name].dispose();\n      delete this.hashTableNameToHandle[name];\n    }\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {InferenceModel, io, ModelPredictConfig, NamedTensorMap, Tensor} from '@tensorflow/tfjs-core';\n\nimport * as tensorflow from '../data/compiled_api';\nimport {NamedTensorsMap, TensorInfo} from '../data/types';\nimport {OperationMapper} from '../operations/operation_mapper';\n\nimport {GraphExecutor} from './graph_executor';\nimport {ResourceManager} from './resource_manager';\n\nexport const TFHUB_SEARCH_PARAM = '?tfjs-format=file';\nexport const DEFAULT_MODEL_NAME = 'model.json';\n/**\n * A `tf.GraphModel` is a directed, acyclic graph built from a\n * SavedModel GraphDef and allows inference execution.\n *\n * A `tf.GraphModel` can only be created by loading from a model converted from\n * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using\n * the command line converter tool and loaded via `tf.loadGraphModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class GraphModel implements InferenceModel {\n  private executor: GraphExecutor;\n  private version = 'n/a';\n  private handler: io.IOHandler;\n  private artifacts: io.ModelArtifacts;\n  private initializer: GraphExecutor;\n  private resourceManager: ResourceManager;\n  private signature: tensorflow.ISignatureDef;\n\n  // Returns the version information for the tensorflow model GraphDef.\n  get modelVersion(): string {\n    return this.version;\n  }\n\n  get inputNodes(): string[] {\n    return this.executor.inputNodes;\n  }\n\n  get outputNodes(): string[] {\n    return this.executor.outputNodes;\n  }\n\n  get inputs(): TensorInfo[] {\n    return this.executor.inputs;\n  }\n\n  get outputs(): TensorInfo[] {\n    return this.executor.outputs;\n  }\n\n  get weights(): NamedTensorsMap {\n    return this.executor.weightMap;\n  }\n\n  get metadata(): {} {\n    return this.artifacts.userDefinedMetadata;\n  }\n\n  get modelSignature(): {} {\n    return this.signature;\n  }\n\n  /**\n   * @param modelUrl url for the model, or an `io.IOHandler`.\n   * @param weightManifestUrl url for the weight file generated by\n   * scripts/convert.py script.\n   * @param requestOption options for Request, which allows to send credentials\n   * and custom headers.\n   * @param onProgress Optional, progress callback function, fired periodically\n   * before the load is completed.\n   */\n  constructor(\n      private modelUrl: string|io.IOHandler,\n      private loadOptions: io.LoadOptions = {}) {\n    if (loadOptions == null) {\n      this.loadOptions = {};\n    }\n    this.resourceManager = new ResourceManager();\n  }\n\n  private findIOHandler() {\n    const path = this.modelUrl;\n    if ((path as io.IOHandler).load != null) {\n      // Path is an IO Handler.\n      this.handler = path as io.IOHandler;\n    } else if (this.loadOptions.requestInit != null) {\n      this.handler = io.browserHTTPRequest(path as string, this.loadOptions);\n    } else {\n      const handlers = io.getLoadHandlers(path as string, this.loadOptions);\n      if (handlers.length === 0) {\n        // For backward compatibility: if no load handler can be found,\n        // assume it is a relative http path.\n        handlers.push(io.browserHTTPRequest(path as string, this.loadOptions));\n      } else if (handlers.length > 1) {\n        throw new Error(\n            `Found more than one (${handlers.length}) load handlers for ` +\n            `URL '${[path]}'`);\n      }\n      this.handler = handlers[0];\n    }\n  }\n\n  /**\n   * Loads the model and weight files, construct the in memory weight map and\n   * compile the inference graph.\n   */\n  async load(): Promise<boolean> {\n    this.findIOHandler();\n    if (this.handler.load == null) {\n      throw new Error(\n          'Cannot proceed with model loading because the IOHandler provided ' +\n          'does not have the `load` method implemented.');\n    }\n    const artifacts = await this.handler.load();\n\n    return this.loadSync(artifacts);\n  }\n\n  /**\n   * Synchronously construct the in memory weight map and\n   * compile the inference graph. Also initialize hashtable if any.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n   */\n  loadSync(artifacts: io.ModelArtifacts) {\n    this.artifacts = artifacts;\n    const graph = this.artifacts.modelTopology as tensorflow.IGraphDef;\n\n    let signature;\n    if (this.artifacts.userDefinedMetadata != null &&\n        this.artifacts.userDefinedMetadata.signature != null) {\n      signature =  // tslint:disable-next-line:no-any\n          (this.artifacts.userDefinedMetadata as any).signature as\n          tensorflow.ISignatureDef;\n    } else {\n      signature = this.artifacts.signature;\n    }\n    this.signature = signature;\n\n    this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n    const weightMap =\n        io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n    this.executor = new GraphExecutor(\n        OperationMapper.Instance.transformGraph(graph, this.signature));\n    this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n    // Attach a model-level resourceManager to each executor to share resources,\n    // such as `HashTable`.\n    this.executor.resourceManager = this.resourceManager;\n\n    if (artifacts.modelInitializer != null &&\n        (artifacts.modelInitializer as tensorflow.IGraphDef).node != null) {\n      const initializer =\n          OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n      this.initializer = new GraphExecutor(initializer);\n      this.initializer.weightMap = this.executor.weightMap;\n      // Attach a model-level resourceManager to the initializer, the\n      // hashTables created from when executing the initializer will be stored\n      // in the resourceManager.\n      this.initializer.resourceManager = this.resourceManager;\n      this.initializer.executeAsync({}, []);\n    }\n\n    return true;\n  }\n\n  /**\n   * Save the configuration and/or weights of the GraphModel.\n   *\n   * An `IOHandler` is an object that has a `save` method of the proper\n   * signature defined. The `save` method manages the storing or\n   * transmission of serialized data (\"artifacts\") that represent the\n   * model's topology and weights onto or via a specific medium, such as\n   * file downloads, local storage, IndexedDB in the web browser and HTTP\n   * requests to a server. TensorFlow.js provides `IOHandler`\n   * implementations for a number of frequently used saving mediums, such as\n   * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n   * for more details.\n   *\n   * This method also allows you to refer to certain types of `IOHandler`s\n   * as URL-like string shortcuts, such as 'localstorage://' and\n   * 'indexeddb://'.\n   *\n   * Example 1: Save `model`'s topology and weights to browser [local\n   * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n   * then load it back.\n   *\n   * ```js\n   * const modelUrl =\n   *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n   * const model = await tf.loadGraphModel(modelUrl);\n   * const zeros = tf.zeros([1, 224, 224, 3]);\n   * model.predict(zeros).print();\n   *\n   * const saveResults = await model.save('localstorage://my-model-1');\n   *\n   * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n   * console.log('Prediction from loaded model:');\n   * model.predict(zeros).print();\n   * ```\n   *\n   * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n   * scheme-based string shortcut for `IOHandler`.\n   * @param config Options for saving the model.\n   * @returns A `Promise` of `SaveResult`, which summarizes the result of\n   * the saving, such as byte sizes of the saved artifacts for the model's\n   *   topology and weight values.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n   */\n  async save(handlerOrURL: io.IOHandler|string, config?: io.SaveConfig):\n      Promise<io.SaveResult> {\n    if (typeof handlerOrURL === 'string') {\n      const handlers = io.getSaveHandlers(handlerOrURL);\n      if (handlers.length === 0) {\n        throw new Error(\n            `Cannot find any save handlers for URL '${handlerOrURL}'`);\n      } else if (handlers.length > 1) {\n        throw new Error(\n            `Found more than one (${handlers.length}) save handlers for ` +\n            `URL '${handlerOrURL}'`);\n      }\n      handlerOrURL = handlers[0];\n    }\n    if (handlerOrURL.save == null) {\n      throw new Error(\n          'GraphModel.save() cannot proceed because the IOHandler ' +\n          'provided does not have the `save` attribute defined.');\n    }\n\n    return handlerOrURL.save(this.artifacts);\n  }\n\n  /**\n   * Execute the inference for the input tensors.\n   *\n   * @param input The input tensors, when there is single input for the model,\n   * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n   * inputs params should be in either `tf.Tensor`[] if the input order is\n   * fixed, or otherwise NamedTensorMap format.\n   *\n   * For model with multiple inputs, we recommend you use NamedTensorMap as the\n   * input type, if you use `tf.Tensor`[], the order of the array needs to\n   * follow the\n   * order of inputNodes array. @see {@link GraphModel.inputNodes}\n   *\n   * You can also feed any intermediate nodes using the NamedTensorMap as the\n   * input type. For example, given the graph\n   *    InputNode => Intermediate => OutputNode,\n   * you can execute the subgraph Intermediate => OutputNode by calling\n   *    model.execute('IntermediateNode' : tf.tensor(...));\n   *\n   * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n   * state needs to be fed manually.\n   *\n   * For batch inference execution, the tensors for each input need to be\n   * concatenated together. For example with mobilenet, the required input shape\n   * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n   * If we are provide a batched data of 100 images, the input tensor should be\n   * in the shape of [100, 244, 244, 3].\n   *\n   * @param config Prediction configuration for specifying the batch size and\n   * output node names. Currently the batch size option is ignored for graph\n   * model.\n   *\n   * @returns Inference result tensors. The output would be single `tf.Tensor`\n   * if model has single output node, otherwise Tensor[] or NamedTensorMap[]\n   * will be returned for model with multiple outputs.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes'}\n   */\n  predict(inputs: Tensor|Tensor[]|NamedTensorMap, config?: ModelPredictConfig):\n      Tensor|Tensor[]|NamedTensorMap {\n    return this.execute(inputs, this.outputNodes);\n  }\n\n  private normalizeInputs(inputs: Tensor|Tensor[]|\n                          NamedTensorMap): NamedTensorMap {\n    if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n      // The input is already a NamedTensorMap.\n      return inputs;\n    }\n    inputs = Array.isArray(inputs) ? inputs : [inputs];\n    if (inputs.length !== this.inputNodes.length) {\n      throw new Error(\n          'Input tensor count mismatch,' +\n          `the graph model has ${this.inputNodes.length} placeholders, ` +\n          `while there are ${inputs.length} input tensors.`);\n    }\n    return this.inputNodes.reduce((map, inputName, i) => {\n      map[inputName] = (inputs as Tensor[])[i];\n      return map;\n    }, {} as NamedTensorMap);\n  }\n\n  private normalizeOutputs(outputs: string|string[]): string[] {\n    outputs = outputs || this.outputNodes;\n    return !Array.isArray(outputs) ? [outputs] : outputs;\n  }\n\n  /**\n   * Executes inference for the model for given input tensors.\n   * @param inputs tensor, tensor array or tensor map of the inputs for the\n   * model, keyed by the input node names.\n   * @param outputs output node name from the Tensorflow model, if no\n   * outputs are specified, the default outputs of the model would be used.\n   * You can inspect intermediate nodes of the model by adding them to the\n   * outputs array.\n   *\n   * @returns A single tensor if provided with a single output or no outputs\n   * are provided and there is only one default output, otherwise return a\n   * tensor array. The order of the tensor array is the same as the outputs\n   * if provided, otherwise the order of outputNodes attribute of the model.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes'}\n   */\n  execute(inputs: Tensor|Tensor[]|NamedTensorMap, outputs?: string|string[]):\n      Tensor|Tensor[] {\n    inputs = this.normalizeInputs(inputs);\n    outputs = this.normalizeOutputs(outputs);\n    const result = this.executor.execute(inputs, outputs);\n    return result.length > 1 ? result : result[0];\n  }\n  /**\n   * Executes inference for the model for given input tensors in async\n   * fashion, use this method when your model contains control flow ops.\n   * @param inputs tensor, tensor array or tensor map of the inputs for the\n   * model, keyed by the input node names.\n   * @param outputs output node name from the Tensorflow model, if no outputs\n   * are specified, the default outputs of the model would be used. You can\n   * inspect intermediate nodes of the model by adding them to the outputs\n   * array.\n   *\n   * @returns A Promise of single tensor if provided with a single output or\n   * no outputs are provided and there is only one default output, otherwise\n   * return a tensor map.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes'}\n   */\n  async executeAsync(\n      inputs: Tensor|Tensor[]|NamedTensorMap,\n      outputs?: string|string[]): Promise<Tensor|Tensor[]> {\n    inputs = this.normalizeInputs(inputs);\n    outputs = this.normalizeOutputs(outputs);\n    const result = await this.executor.executeAsync(inputs, outputs);\n    return result.length > 1 ? result : result[0];\n  }\n\n  private convertTensorMapToTensorsMap(map: NamedTensorMap): NamedTensorsMap {\n    return Object.keys(map).reduce((newMap: NamedTensorsMap, key) => {\n      newMap[key] = [map[key]];\n      return newMap;\n    }, {});\n  }\n\n  /**\n   * Releases the memory used by the weight tensors and resourceManager.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes'}\n   */\n  dispose() {\n    this.executor.dispose();\n\n    if (this.initializer) {\n      this.initializer.dispose();\n    }\n\n    this.resourceManager.dispose();\n  }\n}\n\n/**\n * Load a graph model given a URL to the model definition.\n *\n * Example of loading MobileNetV2 from a URL and making a prediction with a\n * zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n *\n * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with\n * a zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';\n * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n * @param modelUrl The url or an `io.IOHandler` that loads the model.\n * @param options Options for the HTTP request, which allows to send credentials\n *    and custom headers.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadGraphModel(\n    modelUrl: string|io.IOHandler,\n    options: io.LoadOptions = {}): Promise<GraphModel> {\n  if (modelUrl == null) {\n    throw new Error(\n        'modelUrl in loadGraphModel() cannot be null. Please provide a url ' +\n        'or an IOHandler that loads the model');\n  }\n  if (options == null) {\n    options = {};\n  }\n\n  if (options.fromTFHub) {\n    if ((modelUrl as io.IOHandler).load == null) {\n      if (!(modelUrl as string).endsWith('/')) {\n        modelUrl = (modelUrl as string) + '/';\n      }\n      modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n    }\n  }\n  const model = new GraphModel(modelUrl, options);\n  await model.load();\n  return model;\n}\n","/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '3.7.0';\nexport 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\ No newline at end of file
diff --git a/thirdparty/tfjs/tf-core.js b/thirdparty/tfjs/tf-core.js
new file mode 100644
index 0000000..5898f7b
--- /dev/null
+++ b/thirdparty/tfjs/tf-core.js
@@ -0,0 +1,27681 @@
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :
+    typeof define === 'function' && define.amd ? define(['exports'], factory) :
+    (global = global || self, factory(global.tf = global.tf || {}));
+}(this, (function (exports) { 'use strict';
+
+    /*! *****************************************************************************
+    Copyright (c) Microsoft Corporation.
+
+    Permission to use, copy, modify, and/or distribute this software for any
+    purpose with or without fee is hereby granted.
+
+    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+    REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
+    AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+    INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
+    LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
+    OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
+    PERFORMANCE OF THIS SOFTWARE.
+    ***************************************************************************** */
+    /* global Reflect, Promise */
+
+    var extendStatics = function(d, b) {
+        extendStatics = Object.setPrototypeOf ||
+            ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+            function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+        return extendStatics(d, b);
+    };
+
+    function __extends(d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    }
+
+    function __awaiter(thisArg, _arguments, P, generator) {
+        function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
+        return new (P || (P = Promise))(function (resolve, reject) {
+            function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+            function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+            function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
+            step((generator = generator.apply(thisArg, _arguments || [])).next());
+        });
+    }
+
+    function __generator(thisArg, body) {
+        var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+        return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+        function verb(n) { return function (v) { return step([n, v]); }; }
+        function step(op) {
+            if (f) throw new TypeError("Generator is already executing.");
+            while (_) try {
+                if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+                if (y = 0, t) op = [op[0] & 2, t.value];
+                switch (op[0]) {
+                    case 0: case 1: t = op; break;
+                    case 4: _.label++; return { value: op[1], done: false };
+                    case 5: _.label++; y = op[1]; op = [0]; continue;
+                    case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                    default:
+                        if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                        if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                        if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                        if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                        if (t[2]) _.ops.pop();
+                        _.trys.pop(); continue;
+                }
+                op = body.call(thisArg, _);
+            } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+            if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var EPSILON_FLOAT32 = 1e-7;
+    var EPSILON_FLOAT16 = 1e-4;
+    /** Convenient class for storing tensor-related data. */
+    var DataStorage = /** @class */ (function () {
+        function DataStorage(backend, dataMover) {
+            this.backend = backend;
+            this.dataMover = dataMover;
+            this.data = new WeakMap();
+            this.dataIdsCount = 0;
+        }
+        DataStorage.prototype.get = function (dataId) {
+            if (!this.data.has(dataId)) {
+                this.dataMover.moveData(this.backend, dataId);
+            }
+            return this.data.get(dataId);
+        };
+        DataStorage.prototype.set = function (dataId, value) {
+            this.dataIdsCount++;
+            this.data.set(dataId, value);
+        };
+        DataStorage.prototype.has = function (dataId) {
+            return this.data.has(dataId);
+        };
+        DataStorage.prototype.delete = function (dataId) {
+            this.dataIdsCount--;
+            return this.data.delete(dataId);
+        };
+        DataStorage.prototype.numDataIds = function () {
+            return this.dataIdsCount;
+        };
+        return DataStorage;
+    }());
+    /**
+     * The interface that defines the kernels that should be implemented when
+     * adding a new backend. New backends don't need to implement every one of the
+     * methods, this can be done gradually (throw an error for unimplemented
+     * methods).
+     */
+    var KernelBackend = /** @class */ (function () {
+        function KernelBackend() {
+        }
+        KernelBackend.prototype.refCount = function (dataId) {
+            return notYetImplemented('refCount');
+        };
+        KernelBackend.prototype.incRef = function (dataId) {
+            return notYetImplemented('incRef');
+        };
+        KernelBackend.prototype.timerAvailable = function () {
+            return true;
+        };
+        KernelBackend.prototype.time = function (f) {
+            return notYetImplemented('time');
+        };
+        KernelBackend.prototype.read = function (dataId) {
+            return notYetImplemented('read');
+        };
+        KernelBackend.prototype.readSync = function (dataId) {
+            return notYetImplemented('readSync');
+        };
+        KernelBackend.prototype.numDataIds = function () {
+            return notYetImplemented('numDataIds');
+        };
+        KernelBackend.prototype.disposeData = function (dataId, force) {
+            return notYetImplemented('disposeData');
+        };
+        KernelBackend.prototype.write = function (values, shape, dtype) {
+            return notYetImplemented('write');
+        };
+        KernelBackend.prototype.move = function (dataId, values, shape, dtype, refCount) {
+            return notYetImplemented('move');
+        };
+        KernelBackend.prototype.memory = function () {
+            return notYetImplemented('memory');
+        };
+        /** Returns the highest precision for floats in bits (e.g. 16 or 32) */
+        KernelBackend.prototype.floatPrecision = function () {
+            return notYetImplemented('floatPrecision');
+        };
+        /** Returns the smallest representable number.  */
+        KernelBackend.prototype.epsilon = function () {
+            return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
+        };
+        KernelBackend.prototype.dispose = function () {
+            return notYetImplemented('dispose');
+        };
+        return KernelBackend;
+    }());
+    function notYetImplemented(kernelName) {
+        throw new Error("'" + kernelName + "' not yet implemented or not found in the registry. " +
+            "This kernel may not be supported by the tfjs backend you have chosen");
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Shuffles the array in-place using Fisher-Yates algorithm.
+     *
+     * ```js
+     * const a = [1, 2, 3, 4, 5];
+     * tf.util.shuffle(a);
+     * console.log(a);
+     * ```
+     *
+     * @param array The array to shuffle in-place.
+     *
+     * @doc {heading: 'Util', namespace: 'util'}
+     */
+    // tslint:disable-next-line:no-any
+    function shuffle(array) {
+        var counter = array.length;
+        var temp = 0;
+        var index = 0;
+        // While there are elements in the array
+        while (counter > 0) {
+            // Pick a random index
+            index = (Math.random() * counter) | 0;
+            // Decrease counter by 1
+            counter--;
+            // And swap the last element with it
+            temp = array[counter];
+            array[counter] = array[index];
+            array[index] = temp;
+        }
+    }
+    /**
+     * Shuffles two arrays in-place the same way using Fisher-Yates algorithm.
+     *
+     * ```js
+     * const a = [1,2,3,4,5];
+     * const b = [11,22,33,44,55];
+     * tf.util.shuffleCombo(a, b);
+     * console.log(a, b);
+     * ```
+     *
+     * @param array The first array to shuffle in-place.
+     * @param array2 The second array to shuffle in-place with the same permutation
+     *     as the first array.
+     *
+     * @doc {heading: 'Util', namespace: 'util'}
+     */
+    function shuffleCombo(
+    // tslint:disable-next-line:no-any
+    array, 
+    // tslint:disable-next-line:no-any
+    array2) {
+        if (array.length !== array2.length) {
+            throw new Error("Array sizes must match to be shuffled together " +
+                ("First array length was " + array.length) +
+                ("Second array length was " + array2.length));
+        }
+        var counter = array.length;
+        var temp, temp2;
+        var index = 0;
+        // While there are elements in the array
+        while (counter > 0) {
+            // Pick a random index
+            index = (Math.random() * counter) | 0;
+            // Decrease counter by 1
+            counter--;
+            // And swap the last element of each array with it
+            temp = array[counter];
+            temp2 = array2[counter];
+            array[counter] = array[index];
+            array2[counter] = array2[index];
+            array[index] = temp;
+            array2[index] = temp2;
+        }
+    }
+    /** Clamps a value to a specified range. */
+    function clamp(min, x, max) {
+        return Math.max(min, Math.min(x, max));
+    }
+    function nearestLargerEven(val) {
+        return val % 2 === 0 ? val : val + 1;
+    }
+    function sum(arr) {
+        var sum = 0;
+        for (var i = 0; i < arr.length; i++) {
+            sum += arr[i];
+        }
+        return sum;
+    }
+    /**
+     * Returns a sample from a uniform [a, b) distribution.
+     *
+     * @param a The minimum support (inclusive).
+     * @param b The maximum support (exclusive).
+     * @return A pseudorandom number on the half-open interval [a,b).
+     */
+    function randUniform(a, b) {
+        var r = Math.random();
+        return (b * r) + (1 - r) * a;
+    }
+    /** Returns the squared Euclidean distance between two vectors. */
+    function distSquared(a, b) {
+        var result = 0;
+        for (var i = 0; i < a.length; i++) {
+            var diff = Number(a[i]) - Number(b[i]);
+            result += diff * diff;
+        }
+        return result;
+    }
+    /**
+     * Asserts that the expression is true. Otherwise throws an error with the
+     * provided message.
+     *
+     * ```js
+     * const x = 2;
+     * tf.util.assert(x === 2, 'x is not 2');
+     * ```
+     *
+     * @param expr The expression to assert (as a boolean).
+     * @param msg A function that returns the message to report when throwing an
+     *     error. We use a function for performance reasons.
+     *
+     * @doc {heading: 'Util', namespace: 'util'}
+     */
+    function assert(expr, msg) {
+        if (!expr) {
+            throw new Error(typeof msg === 'string' ? msg : msg());
+        }
+    }
+    function assertShapesMatch(shapeA, shapeB, errorMessagePrefix) {
+        if (errorMessagePrefix === void 0) { errorMessagePrefix = ''; }
+        assert(arraysEqual(shapeA, shapeB), function () { return errorMessagePrefix + (" Shapes " + shapeA + " and " + shapeB + " must match"); });
+    }
+    function assertNonNull(a) {
+        assert(a != null, function () { return "The input to the tensor constructor must be a non-null value."; });
+    }
+    // NOTE: We explicitly type out what T extends instead of any so that
+    // util.flatten on a nested array of number doesn't try to infer T as a
+    // number[][], causing us to explicitly type util.flatten<number>().
+    /**
+     *  Flattens an arbitrarily nested array.
+     *
+     * ```js
+     * const a = [[1, 2], [3, 4], [5, [6, [7]]]];
+     * const flat = tf.util.flatten(a);
+     * console.log(flat);
+     * ```
+     *
+     *  @param arr The nested array to flatten.
+     *  @param result The destination array which holds the elements.
+     *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults
+     *      to false.
+     *
+     * @doc {heading: 'Util', namespace: 'util'}
+     */
+    function flatten(arr, result, skipTypedArray) {
+        if (result === void 0) { result = []; }
+        if (skipTypedArray === void 0) { skipTypedArray = false; }
+        if (result == null) {
+            result = [];
+        }
+        if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
+            for (var i = 0; i < arr.length; ++i) {
+                flatten(arr[i], result, skipTypedArray);
+            }
+        }
+        else {
+            result.push(arr);
+        }
+        return result;
+    }
+    /**
+     * Returns the size (number of elements) of the tensor given its shape.
+     *
+     * ```js
+     * const shape = [3, 4, 2];
+     * const size = tf.util.sizeFromShape(shape);
+     * console.log(size);
+     * ```
+     *
+     * @doc {heading: 'Util', namespace: 'util'}
+     */
+    function sizeFromShape(shape) {
+        if (shape.length === 0) {
+            // Scalar.
+            return 1;
+        }
+        var size = shape[0];
+        for (var i = 1; i < shape.length; i++) {
+            size *= shape[i];
+        }
+        return size;
+    }
+    function isScalarShape(shape) {
+        return shape.length === 0;
+    }
+    function arraysEqual(n1, n2) {
+        if (n1 === n2) {
+            return true;
+        }
+        if (n1 == null || n2 == null) {
+            return false;
+        }
+        if (n1.length !== n2.length) {
+            return false;
+        }
+        for (var i = 0; i < n1.length; i++) {
+            if (n1[i] !== n2[i]) {
+                return false;
+            }
+        }
+        return true;
+    }
+    function isInt(a) {
+        return a % 1 === 0;
+    }
+    function tanh(x) {
+        // tslint:disable-next-line:no-any
+        if (Math.tanh != null) {
+            // tslint:disable-next-line:no-any
+            return Math.tanh(x);
+        }
+        if (x === Infinity) {
+            return 1;
+        }
+        else if (x === -Infinity) {
+            return -1;
+        }
+        else {
+            var e2x = Math.exp(2 * x);
+            return (e2x - 1) / (e2x + 1);
+        }
+    }
+    function sizeToSquarishShape(size) {
+        var width = Math.ceil(Math.sqrt(size));
+        return [width, Math.ceil(size / width)];
+    }
+    /**
+     * Creates a new array with randomized indicies to a given quantity.
+     *
+     * ```js
+     * const randomTen = tf.util.createShuffledIndices(10);
+     * console.log(randomTen);
+     * ```
+     *
+     * @param number Quantity of how many shuffled indicies to create.
+     *
+     * @doc {heading: 'Util', namespace: 'util'}
+     */
+    function createShuffledIndices(n) {
+        var shuffledIndices = new Uint32Array(n);
+        for (var i = 0; i < n; ++i) {
+            shuffledIndices[i] = i;
+        }
+        shuffle(shuffledIndices);
+        return shuffledIndices;
+    }
+    function rightPad(a, size) {
+        if (size <= a.length) {
+            return a;
+        }
+        return a + ' '.repeat(size - a.length);
+    }
+    function repeatedTry(checkFn, delayFn, maxCounter) {
+        if (delayFn === void 0) { delayFn = function (counter) { return 0; }; }
+        return new Promise(function (resolve, reject) {
+            var tryCount = 0;
+            var tryFn = function () {
+                if (checkFn()) {
+                    resolve();
+                    return;
+                }
+                tryCount++;
+                var nextBackoff = delayFn(tryCount);
+                if (maxCounter != null && tryCount >= maxCounter) {
+                    reject();
+                    return;
+                }
+                setTimeout(tryFn, nextBackoff);
+            };
+            tryFn();
+        });
+    }
+    /**
+     * Given the full size of the array and a shape that may contain -1 as the
+     * implicit dimension, returns the inferred shape where -1 is replaced.
+     * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].
+     *
+     * @param shape The shape, which may contain -1 in some dimension.
+     * @param size The full size (number of elements) of the array.
+     * @return The inferred shape where -1 is replaced with the inferred size.
+     */
+    function inferFromImplicitShape(shape, size) {
+        var shapeProd = 1;
+        var implicitIdx = -1;
+        for (var i = 0; i < shape.length; ++i) {
+            if (shape[i] >= 0) {
+                shapeProd *= shape[i];
+            }
+            else if (shape[i] === -1) {
+                if (implicitIdx !== -1) {
+                    throw Error("Shapes can only have 1 implicit size. " +
+                        ("Found -1 at dim " + implicitIdx + " and dim " + i));
+                }
+                implicitIdx = i;
+            }
+            else if (shape[i] < 0) {
+                throw Error("Shapes can not be < 0. Found " + shape[i] + " at dim " + i);
+            }
+        }
+        if (implicitIdx === -1) {
+            if (size > 0 && size !== shapeProd) {
+                throw Error("Size(" + size + ") must match the product of shape " + shape);
+            }
+            return shape;
+        }
+        if (shapeProd === 0) {
+            throw Error("Cannot infer the missing size in [" + shape + "] when " +
+                "there are 0 elements");
+        }
+        if (size % shapeProd !== 0) {
+            throw Error("The implicit shape can't be a fractional number. " +
+                ("Got " + size + " / " + shapeProd));
+        }
+        var newShape = shape.slice();
+        newShape[implicitIdx] = size / shapeProd;
+        return newShape;
+    }
+    function parseAxisParam(axis, shape) {
+        var rank = shape.length;
+        // Normalize input
+        axis = axis == null ? shape.map(function (s, i) { return i; }) : [].concat(axis);
+        // Check for valid range
+        assert(axis.every(function (ax) { return ax >= -rank && ax < rank; }), function () {
+            return "All values in axis param must be in range [-" + rank + ", " + rank + ") but " +
+                ("got axis " + axis);
+        });
+        // Check for only integers
+        assert(axis.every(function (ax) { return isInt(ax); }), function () { return "All values in axis param must be integers but " +
+            ("got axis " + axis); });
+        // Handle negative axis.
+        return axis.map(function (a) { return a < 0 ? rank + a : a; });
+    }
+    /** Reduces the shape by removing all dimensions of shape 1. */
+    function squeezeShape(shape, axis) {
+        var newShape = [];
+        var keptDims = [];
+        var isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
+        var axes = (axis == null || isEmptyArray) ?
+            null :
+            parseAxisParam(axis, shape).sort();
+        var j = 0;
+        for (var i = 0; i < shape.length; ++i) {
+            if (axes != null) {
+                if (axes[j] === i && shape[i] !== 1) {
+                    throw new Error("Can't squeeze axis " + i + " since its dim '" + shape[i] + "' is not 1");
+                }
+                if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
+                    newShape.push(shape[i]);
+                    keptDims.push(i);
+                }
+                if (axes[j] <= i) {
+                    j++;
+                }
+            }
+            if (shape[i] !== 1) {
+                newShape.push(shape[i]);
+                keptDims.push(i);
+            }
+        }
+        return { newShape: newShape, keptDims: keptDims };
+    }
+    function getTypedArrayFromDType(dtype, size) {
+        var values = null;
+        if (dtype == null || dtype === 'float32') {
+            values = new Float32Array(size);
+        }
+        else if (dtype === 'int32') {
+            values = new Int32Array(size);
+        }
+        else if (dtype === 'bool') {
+            values = new Uint8Array(size);
+        }
+        else {
+            throw new Error("Unknown data type " + dtype);
+        }
+        return values;
+    }
+    function getArrayFromDType(dtype, size) {
+        var values = null;
+        if (dtype == null || dtype === 'float32') {
+            values = new Float32Array(size);
+        }
+        else if (dtype === 'int32') {
+            values = new Int32Array(size);
+        }
+        else if (dtype === 'bool') {
+            values = new Uint8Array(size);
+        }
+        else if (dtype === 'string') {
+            values = new Array(size);
+        }
+        else {
+            throw new Error("Unknown data type " + dtype);
+        }
+        return values;
+    }
+    function checkConversionForErrors(vals, dtype) {
+        for (var i = 0; i < vals.length; i++) {
+            var num = vals[i];
+            if (isNaN(num) || !isFinite(num)) {
+                throw Error("A tensor of type " + dtype + " being uploaded contains " + num + ".");
+            }
+        }
+    }
+    /** Returns true if the dtype is valid. */
+    function isValidDtype(dtype) {
+        return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||
+            dtype === 'int32' || dtype === 'string';
+    }
+    /**
+     * Returns true if the new type can't encode the old type without loss of
+     * precision.
+     */
+    function hasEncodingLoss(oldType, newType) {
+        if (newType === 'complex64') {
+            return false;
+        }
+        if (newType === 'float32' && oldType !== 'complex64') {
+            return false;
+        }
+        if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {
+            return false;
+        }
+        if (newType === 'bool' && oldType === 'bool') {
+            return false;
+        }
+        return true;
+    }
+    function isTypedArray(a) {
+        return a instanceof Float32Array || a instanceof Int32Array ||
+            a instanceof Uint8Array;
+    }
+    function bytesPerElement(dtype) {
+        if (dtype === 'float32' || dtype === 'int32') {
+            return 4;
+        }
+        else if (dtype === 'complex64') {
+            return 8;
+        }
+        else if (dtype === 'bool') {
+            return 1;
+        }
+        else {
+            throw new Error("Unknown dtype " + dtype);
+        }
+    }
+    /**
+     * Returns the approximate number of bytes allocated in the string array - 2
+     * bytes per character. Computing the exact bytes for a native string in JS is
+     * not possible since it depends on the encoding of the html page that serves
+     * the website.
+     */
+    function bytesFromStringArray(arr) {
+        if (arr == null) {
+            return 0;
+        }
+        var bytes = 0;
+        arr.forEach(function (x) { return bytes += x.length; });
+        return bytes;
+    }
+    /** Returns true if the value is a string. */
+    function isString(value) {
+        return typeof value === 'string' || value instanceof String;
+    }
+    function isBoolean(value) {
+        return typeof value === 'boolean';
+    }
+    function isNumber(value) {
+        return typeof value === 'number';
+    }
+    function inferDtype(values) {
+        if (Array.isArray(values)) {
+            return inferDtype(values[0]);
+        }
+        if (values instanceof Float32Array) {
+            return 'float32';
+        }
+        else if (values instanceof Int32Array || values instanceof Uint8Array) {
+            return 'int32';
+        }
+        else if (isNumber(values)) {
+            return 'float32';
+        }
+        else if (isString(values)) {
+            return 'string';
+        }
+        else if (isBoolean(values)) {
+            return 'bool';
+        }
+        return 'float32';
+    }
+    function isFunction(f) {
+        return !!(f && f.constructor && f.call && f.apply);
+    }
+    function nearestDivisor(size, start) {
+        for (var i = start; i < size; ++i) {
+            if (size % i === 0) {
+                return i;
+            }
+        }
+        return size;
+    }
+    function computeStrides(shape) {
+        var rank = shape.length;
+        if (rank < 2) {
+            return [];
+        }
+        // Last dimension has implicit stride of 1, thus having D-1 (instead of D)
+        // strides.
+        var strides = new Array(rank - 1);
+        strides[rank - 2] = shape[rank - 1];
+        for (var i = rank - 3; i >= 0; --i) {
+            strides[i] = strides[i + 1] * shape[i + 1];
+        }
+        return strides;
+    }
+    function createNestedArray(offset, shape, a, isComplex) {
+        if (isComplex === void 0) { isComplex = false; }
+        var ret = new Array();
+        if (shape.length === 1) {
+            var d = shape[0] * (isComplex ? 2 : 1);
+            for (var i = 0; i < d; i++) {
+                ret[i] = a[offset + i];
+            }
+        }
+        else {
+            var d = shape[0];
+            var rest = shape.slice(1);
+            var len = rest.reduce(function (acc, c) { return acc * c; }) * (isComplex ? 2 : 1);
+            for (var i = 0; i < d; i++) {
+                ret[i] = createNestedArray(offset + i * len, rest, a, isComplex);
+            }
+        }
+        return ret;
+    }
+    // Provide a nested array of TypedArray in given shape.
+    function toNestedArray(shape, a, isComplex) {
+        if (isComplex === void 0) { isComplex = false; }
+        if (shape.length === 0) {
+            // Scalar type should return a single number.
+            return a[0];
+        }
+        var size = shape.reduce(function (acc, c) { return acc * c; }) * (isComplex ? 2 : 1);
+        if (size === 0) {
+            // A tensor with shape zero should be turned into empty list.
+            return [];
+        }
+        if (size !== a.length) {
+            throw new Error("[" + shape + "] does not match the input size " + a.length + (isComplex ? ' for a complex tensor' : '') + ".");
+        }
+        return createNestedArray(0, shape, a, isComplex);
+    }
+    function makeOnesTypedArray(size, dtype) {
+        var array = makeZerosTypedArray(size, dtype);
+        for (var i = 0; i < array.length; i++) {
+            array[i] = 1;
+        }
+        return array;
+    }
+    function makeZerosTypedArray(size, dtype) {
+        if (dtype == null || dtype === 'float32' || dtype === 'complex64') {
+            return new Float32Array(size);
+        }
+        else if (dtype === 'int32') {
+            return new Int32Array(size);
+        }
+        else if (dtype === 'bool') {
+            return new Uint8Array(size);
+        }
+        else {
+            throw new Error("Unknown data type " + dtype);
+        }
+    }
+    /**
+     * Make nested `TypedArray` filled with zeros.
+     * @param shape The shape information for the nested array.
+     * @param dtype dtype of the array element.
+     */
+    function makeZerosNestedTypedArray(shape, dtype) {
+        var size = shape.reduce(function (prev, curr) { return prev * curr; }, 1);
+        if (dtype == null || dtype === 'float32') {
+            return toNestedArray(shape, new Float32Array(size));
+        }
+        else if (dtype === 'int32') {
+            return toNestedArray(shape, new Int32Array(size));
+        }
+        else if (dtype === 'bool') {
+            return toNestedArray(shape, new Uint8Array(size));
+        }
+        else {
+            throw new Error("Unknown data type " + dtype);
+        }
+    }
+    function assertNonNegativeIntegerDimensions(shape) {
+        shape.forEach(function (dimSize) {
+            assert(Number.isInteger(dimSize) && dimSize >= 0, function () {
+                return "Tensor must have a shape comprised of positive integers but got " +
+                    ("shape [" + shape + "].");
+            });
+        });
+    }
+    /**
+     * Computes flat index for a given location (multidimentionsal index) in a
+     * Tensor/multidimensional array.
+     *
+     * @param locs Location in the tensor.
+     * @param rank Rank of the tensor.
+     * @param strides Tensor strides.
+     */
+    function locToIndex(locs, rank, strides) {
+        if (rank === 0) {
+            return 0;
+        }
+        else if (rank === 1) {
+            return locs[0];
+        }
+        var index = locs[locs.length - 1];
+        for (var i = 0; i < locs.length - 1; ++i) {
+            index += strides[i] * locs[i];
+        }
+        return index;
+    }
+    /**
+     * Computes the location (multidimensional index) in a tensor/multidimentional
+     * array for a given flat index.
+     *
+     * @param index Index in flat array.
+     * @param rank Rank of tensor.
+     * @param strides Strides of tensor.
+     */
+    function indexToLoc(index, rank, strides) {
+        if (rank === 0) {
+            return [];
+        }
+        else if (rank === 1) {
+            return [index];
+        }
+        var locs = new Array(rank);
+        for (var i = 0; i < locs.length - 1; ++i) {
+            locs[i] = Math.floor(index / strides[i]);
+            index -= locs[i] * strides[i];
+        }
+        locs[locs.length - 1] = index;
+        return locs;
+    }
+    /**
+     * This method asserts whether an object is a Promise instance.
+     * @param object
+     */
+    // tslint:disable-next-line: no-any
+    function isPromise(object) {
+        //  We chose to not use 'obj instanceOf Promise' for two reasons:
+        //  1. It only reliably works for es6 Promise, not other Promise
+        //  implementations.
+        //  2. It doesn't work with framework that uses zone.js. zone.js monkey patch
+        //  the async calls, so it is possible the obj (patched) is comparing to a
+        //  pre-patched Promise.
+        return object && object.then && typeof object.then === 'function';
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.
+    var TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';
+    /**
+     * The environment contains evaluated flags as well as the registered platform.
+     * This is always used as a global singleton and can be retrieved with
+     * `tf.env()`.
+     *
+     * @doc {heading: 'Environment'}
+     */
+    var Environment = /** @class */ (function () {
+        // tslint:disable-next-line: no-any
+        function Environment(global) {
+            this.global = global;
+            this.flags = {};
+            this.flagRegistry = {};
+            this.urlFlags = {};
+            // Jasmine spies on this in 'environment_test.ts'
+            this.getQueryParams = getQueryParams;
+            this.populateURLFlags();
+        }
+        Environment.prototype.setPlatform = function (platformName, platform) {
+            if (this.platform != null) {
+                console.warn("Platform " + this.platformName + " has already been set. " +
+                    ("Overwriting the platform with " + platform + "."));
+            }
+            this.platformName = platformName;
+            this.platform = platform;
+        };
+        Environment.prototype.registerFlag = function (flagName, evaluationFn, setHook) {
+            this.flagRegistry[flagName] = { evaluationFn: evaluationFn, setHook: setHook };
+            // Override the flag value from the URL. This has to happen here because the
+            // environment is initialized before flags get registered.
+            if (this.urlFlags[flagName] != null) {
+                var flagValue = this.urlFlags[flagName];
+                console.warn("Setting feature override from URL " + flagName + ": " + flagValue + ".");
+                this.set(flagName, flagValue);
+            }
+        };
+        Environment.prototype.getAsync = function (flagName) {
+            return __awaiter(this, void 0, void 0, function () {
+                var _a, _b;
+                return __generator(this, function (_c) {
+                    switch (_c.label) {
+                        case 0:
+                            if (flagName in this.flags) {
+                                return [2 /*return*/, this.flags[flagName]];
+                            }
+                            _a = this.flags;
+                            _b = flagName;
+                            return [4 /*yield*/, this.evaluateFlag(flagName)];
+                        case 1:
+                            _a[_b] = _c.sent();
+                            return [2 /*return*/, this.flags[flagName]];
+                    }
+                });
+            });
+        };
+        Environment.prototype.get = function (flagName) {
+            if (flagName in this.flags) {
+                return this.flags[flagName];
+            }
+            var flagValue = this.evaluateFlag(flagName);
+            if (isPromise(flagValue)) {
+                throw new Error("Flag " + flagName + " cannot be synchronously evaluated. " +
+                    "Please use getAsync() instead.");
+            }
+            this.flags[flagName] = flagValue;
+            return this.flags[flagName];
+        };
+        Environment.prototype.getNumber = function (flagName) {
+            return this.get(flagName);
+        };
+        Environment.prototype.getBool = function (flagName) {
+            return this.get(flagName);
+        };
+        Environment.prototype.getFlags = function () {
+            return this.flags;
+        };
+        Object.defineProperty(Environment.prototype, "features", {
+            // For backwards compatibility.
+            get: function () {
+                return this.flags;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Environment.prototype.set = function (flagName, value) {
+            if (this.flagRegistry[flagName] == null) {
+                throw new Error("Cannot set flag " + flagName + " as it has not been registered.");
+            }
+            this.flags[flagName] = value;
+            if (this.flagRegistry[flagName].setHook != null) {
+                this.flagRegistry[flagName].setHook(value);
+            }
+        };
+        Environment.prototype.evaluateFlag = function (flagName) {
+            if (this.flagRegistry[flagName] == null) {
+                throw new Error("Cannot evaluate flag '" + flagName + "': no evaluation function found.");
+            }
+            return this.flagRegistry[flagName].evaluationFn();
+        };
+        Environment.prototype.setFlags = function (flags) {
+            this.flags = Object.assign({}, flags);
+        };
+        Environment.prototype.reset = function () {
+            this.flags = {};
+            this.urlFlags = {};
+            this.populateURLFlags();
+        };
+        Environment.prototype.populateURLFlags = function () {
+            var _this = this;
+            if (typeof this.global === 'undefined' ||
+                typeof this.global.location === 'undefined' ||
+                typeof this.global.location.search === 'undefined') {
+                return;
+            }
+            var urlParams = this.getQueryParams(this.global.location.search);
+            if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
+                var keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');
+                keyValues.forEach(function (keyValue) {
+                    var _a = keyValue.split(':'), key = _a[0], value = _a[1];
+                    _this.urlFlags[key] = parseValue(key, value);
+                });
+            }
+        };
+        return Environment;
+    }());
+    function getQueryParams(queryString) {
+        var params = {};
+        queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, function (s) {
+            var t = [];
+            for (var _i = 1; _i < arguments.length; _i++) {
+                t[_i - 1] = arguments[_i];
+            }
+            decodeParam(params, t[0], t[1]);
+            return t.join('=');
+        });
+        return params;
+    }
+    function decodeParam(params, name, value) {
+        params[decodeURIComponent(name)] = decodeURIComponent(value || '');
+    }
+    function parseValue(flagName, value) {
+        value = value.toLowerCase();
+        if (value === 'true' || value === 'false') {
+            return value === 'true';
+        }
+        else if ("" + +value === value) {
+            return +value;
+        }
+        throw new Error("Could not parse value flag value " + value + " for flag " + flagName + ".");
+    }
+    /**
+     * Returns the current environment (a global singleton).
+     *
+     * The environment object contains the evaluated feature values as well as the
+     * active platform.
+     *
+     * @doc {heading: 'Environment'}
+     */
+    function env() {
+        return exports.ENV;
+    }
+    exports.ENV = null;
+    function setEnvironmentGlobal(environment) {
+        exports.ENV = environment;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // Note that the identifier globalNameSpace is scoped to this module, but will
+    // always resolve to the same global object regardless of how the module is
+    // resolved.
+    // tslint:disable-next-line:no-any
+    var globalNameSpace;
+    // tslint:disable-next-line:no-any
+    function getGlobalNamespace() {
+        if (globalNameSpace == null) {
+            // tslint:disable-next-line:no-any
+            var ns = void 0;
+            if (typeof (window) !== 'undefined') {
+                ns = window;
+            }
+            else if (typeof (global) !== 'undefined') {
+                ns = global;
+            }
+            else if (typeof (process) !== 'undefined') {
+                ns = process;
+            }
+            else if (typeof (self) !== 'undefined') {
+                ns = self;
+            }
+            else {
+                throw new Error('Could not find a global object');
+            }
+            globalNameSpace = ns;
+        }
+        return globalNameSpace;
+    }
+    // tslint:disable-next-line:no-any
+    function getGlobalMap() {
+        var ns = getGlobalNamespace();
+        if (ns._tfGlobals == null) {
+            ns._tfGlobals = new Map();
+        }
+        return ns._tfGlobals;
+    }
+    /**
+     * Returns a globally accessible 'singleton' object.
+     *
+     * @param key the name of the object
+     * @param init a function to initialize to initialize this object
+     *             the first time it is fetched.
+     */
+    function getGlobal(key, init) {
+        var globalMap = getGlobalMap();
+        if (globalMap.has(key)) {
+            return globalMap.get(key);
+        }
+        else {
+            var singleton = init();
+            globalMap.set(key, singleton);
+            return globalMap.get(key);
+        }
+    }
+
+    var Abs = 'Abs';
+    var Acos = 'Acos';
+    var Acosh = 'Acosh';
+    var Add = 'Add';
+    var AddN = 'AddN';
+    var All = 'All';
+    var Any = 'Any';
+    var ArgMax = 'ArgMax';
+    var ArgMin = 'ArgMin';
+    var Asin = 'Asin';
+    var Asinh = 'Asinh';
+    var Atan = 'Atan';
+    var Atanh = 'Atanh';
+    var Atan2 = 'Atan2';
+    var AvgPool = 'AvgPool';
+    var AvgPoolGrad = 'AvgPoolGrad';
+    var AvgPool3D = 'AvgPool3D';
+    var AvgPool3DGrad = 'AvgPool3DGrad';
+    var BatchMatMul = 'BatchMatMul';
+    var BatchToSpaceND = 'BatchToSpaceND';
+    var Bincount = 'Bincount';
+    var BroadcastTo = 'BroadcastTo';
+    var Cast = 'Cast';
+    var Ceil = 'Ceil';
+    var ClipByValue = 'ClipByValue';
+    var Complex = 'Complex';
+    var ComplexAbs = 'ComplexAbs';
+    var Concat = 'Concat';
+    var Conv2D = 'Conv2D';
+    var Conv2DBackpropFilter = 'Conv2DBackpropFilter';
+    var Conv2DBackpropInput = 'Conv2DBackpropInput';
+    var Conv3D = 'Conv3D';
+    var Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';
+    var Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';
+    var Cos = 'Cos';
+    var Cosh = 'Cosh';
+    var Cumsum = 'Cumsum';
+    var CropAndResize = 'CropAndResize';
+    var DenseBincount = 'DenseBincount';
+    var DepthToSpace = 'DepthToSpace';
+    var DepthwiseConv2dNative = 'DepthwiseConv2dNative';
+    var DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter';
+    var DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput';
+    var Diag = 'Diag';
+    var Dilation2D = 'Dilation2D';
+    var Dilation2DBackpropInput = 'Dilation2DBackpropInput';
+    var Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';
+    var RealDiv = 'RealDiv';
+    var Einsum = 'Einsum';
+    var Elu = 'Elu';
+    var EluGrad = 'EluGrad';
+    var Erf = 'Erf';
+    var Equal = 'Equal';
+    var Exp = 'Exp';
+    var ExpandDims = 'ExpandDims';
+    var Expm1 = 'Expm1';
+    var FFT = 'FFT';
+    var Fill = 'Fill';
+    var FlipLeftRight = 'FlipLeftRight';
+    var Floor = 'Floor';
+    var FloorDiv = 'FloorDiv';
+    var FusedBatchNorm = 'FusedBatchNorm';
+    var GatherV2 = 'GatherV2';
+    var GatherNd = 'GatherNd';
+    var Greater = 'Greater';
+    var GreaterEqual = 'GreaterEqual';
+    var Identity = 'Identity';
+    var IFFT = 'IFFT';
+    var Imag = 'Imag';
+    var IsFinite = 'IsFinite';
+    var IsInf = 'IsInf';
+    var IsNan = 'IsNan';
+    var LeakyRelu = 'LeakyRelu';
+    var Less = 'Less';
+    var LessEqual = 'LessEqual';
+    var LinSpace = 'LinSpace';
+    var Log = 'Log';
+    var Log1p = 'Log1p';
+    var LogicalAnd = 'LogicalAnd';
+    var LogicalNot = 'LogicalNot';
+    var LogicalOr = 'LogicalOr';
+    var LogSoftmax = 'LogSoftmax';
+    var LRN = 'LRN';
+    var LRNGrad = 'LRNGrad';
+    var Max = 'Max';
+    var Maximum = 'Maximum';
+    var MaxPool = 'MaxPool';
+    var MaxPoolGrad = 'MaxPoolGrad';
+    var MaxPool3D = 'MaxPool3D';
+    var MaxPool3DGrad = 'MaxPool3DGrad';
+    var MaxPoolWithArgmax = 'MaxPoolWithArgmax';
+    var Mean = 'Mean';
+    var Min = 'Min';
+    var Minimum = 'Minimum';
+    var MirrorPad = 'MirrorPad';
+    var Mod = 'Mod';
+    var Multinomial = 'Multinomial';
+    var Multiply = 'Multiply';
+    var Neg = 'Neg';
+    var NotEqual = 'NotEqual';
+    var NonMaxSuppressionV3 = 'NonMaxSuppressionV3';
+    var NonMaxSuppressionV4 = 'NonMaxSuppressionV4';
+    var NonMaxSuppressionV5 = 'NonMaxSuppressionV5';
+    var OnesLike = 'OnesLike';
+    var OneHot = 'OneHot';
+    var Pack = 'Pack';
+    var PadV2 = 'PadV2';
+    var Pool = 'Pool';
+    var Pow = 'Pow';
+    var Prelu = 'Prelu';
+    var Prod = 'Prod';
+    var Range = 'Range';
+    var Real = 'Real';
+    var Reciprocal = 'Reciprocal';
+    var Relu = 'Relu';
+    var Reshape = 'Reshape';
+    var ResizeNearestNeighbor = 'ResizeNearestNeighbor';
+    var ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';
+    var ResizeBilinear = 'ResizeBilinear';
+    var ResizeBilinearGrad = 'ResizeBilinearGrad';
+    var Relu6 = 'Relu6';
+    var Reverse = 'Reverse';
+    var Round = 'Round';
+    var Rsqrt = 'Rsqrt';
+    var ScatterNd = 'ScatterNd';
+    var Select = 'Select';
+    var Selu = 'Selu';
+    var Slice = 'Slice';
+    var Sin = 'Sin';
+    var Sinh = 'Sinh';
+    var Sign = 'Sign';
+    var Sigmoid = 'Sigmoid';
+    var Softplus = 'Softplus';
+    var Sqrt = 'Sqrt';
+    var Sum = 'Sum';
+    var SpaceToBatchND = 'SpaceToBatchND';
+    var SplitV = 'SplitV';
+    var Softmax = 'Softmax';
+    var SparseFillEmptyRows = 'SparseFillEmptyRows';
+    var SparseReshape = 'SparseReshape';
+    var SparseSegmentMean = 'SparseSegmentMean';
+    var SparseSegmentSum = 'SparseSegmentSum';
+    var SparseToDense = 'SparseToDense';
+    var SquaredDifference = 'SquaredDifference';
+    var Square = 'Square';
+    var StridedSlice = 'StridedSlice';
+    var StringNGrams = 'StringNGrams';
+    var StringSplit = 'StringSplit';
+    var StringToHashBucketFast = 'StringToHashBucketFast';
+    var Sub = 'Sub';
+    var Tan = 'Tan';
+    var Tanh = 'Tanh';
+    var Tile = 'Tile';
+    var TopK = 'TopK';
+    var Transform = 'Transform';
+    var Transpose = 'Transpose';
+    var Unique = 'Unique';
+    var Unpack = 'Unpack';
+    var UnsortedSegmentSum = 'UnsortedSegmentSum';
+    var ZerosLike = 'ZerosLike';
+    /**
+     * TensorFlow.js-only kernels
+     */
+    var Step = 'Step';
+    var FromPixels = 'FromPixels';
+    var RotateWithOffset = 'RotateWithOffset';
+    var _FusedMatMul = '_FusedMatMul';
+    var FusedConv2D = 'FusedConv2D';
+    var FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var kernelRegistry = getGlobal('kernelRegistry', function () { return new Map(); });
+    var gradRegistry = getGlobal('gradRegistry', function () { return new Map(); });
+    /**
+     * Returns the kernel function (code) associated with the provided names.
+     *
+     * @param kernelName The official name of the kernel.
+     * @param backendName The official name of the backend.
+     */
+    function getKernel(kernelName, backendName) {
+        var key = makeKey(kernelName, backendName);
+        return kernelRegistry.get(key);
+    }
+    /**
+     * Returns the registered gradient info associated with the provided kernel.
+     * @param kernelName The official TF kernel name.
+     */
+    function getGradient(kernelName) {
+        return gradRegistry.get(kernelName);
+    }
+    function getKernelsForBackend(backendName) {
+        var it = kernelRegistry.entries();
+        var result = [];
+        while (true) {
+            var _a = it.next(), done = _a.done, value = _a.value;
+            if (done) {
+                break;
+            }
+            var key = value[0], config = value[1];
+            var backend = key.split('_')[0];
+            if (backend === backendName) {
+                result.push(config);
+            }
+        }
+        return result;
+    }
+    /**
+     * Registers the function (forward pass) for the kernel in a global registry.
+     *
+     * @param config A config object with the following properties:
+     * - `kernelName` The official name of the kernel.
+     * - `backendName` The official name of the backend.
+     * - `kernelFunc` The function to run during the forward pass of the kernel.
+     * - `setupFunc` Optional. Gets called once, after the backend initializes.
+     * - `disposeFunc` Optional. Gets called once, right before the backend is
+     * disposed.
+     */
+    function registerKernel(config) {
+        var kernelName = config.kernelName, backendName = config.backendName;
+        var key = makeKey(kernelName, backendName);
+        if (kernelRegistry.has(key)) {
+            console.warn("The kernel '" + kernelName + "' for backend " +
+                ("'" + backendName + "' is already registered"));
+        }
+        kernelRegistry.set(key, config);
+    }
+    /**
+     * Registers a gradient function for a given kernel in the global registry,
+     * to be used during the back-propagation of that kernel.
+     *
+     * @param config An object with the following properties:
+     * - `kernelName` The name of the kernel that the gradient function is for.
+     * - `gradFunc` The function to run during back-propagation.
+     */
+    function registerGradient(config) {
+        var kernelName = config.kernelName;
+        if (gradRegistry.has(kernelName)) {
+            // TODO (yassogba) after 3.0 assess whether we need to keep this gated
+            // to debug mode.
+            if (env().getBool('DEBUG')) {
+                console.warn("Overriding the gradient for '" + kernelName + "'");
+            }
+        }
+        gradRegistry.set(kernelName, config);
+    }
+    /**
+     * Removes the kernel function from the registry.
+     *
+     * @param kernelName The official name of the kernel.
+     * @param backendName The official name of the backend.
+     *
+     */
+    function unregisterKernel(kernelName, backendName) {
+        var key = makeKey(kernelName, backendName);
+        if (!kernelRegistry.has(key)) {
+            throw new Error("The kernel '" + kernelName + "' for backend " +
+                ("'" + backendName + "' is not registered"));
+        }
+        kernelRegistry.delete(key);
+    }
+    /** Removes the registered gradient from the global registry. */
+    function unregisterGradient(kernelName) {
+        if (!gradRegistry.has(kernelName)) {
+            throw new Error("The gradient '" + kernelName + "' for backend is not registered");
+        }
+        gradRegistry.delete(kernelName);
+    }
+    /**
+     * Finds kernels that have already been registered to a backend and re-registers
+     * them for a new backend. Useful for registering custom backends.
+     * @param registeredBackendName Already registered backend.
+     * @param newBackendName New backend.
+     */
+    function copyRegisteredKernels(registeredBackendName, newBackendName) {
+        var kernels = getKernelsForBackend(registeredBackendName);
+        kernels.forEach(function (kernelConfig) {
+            var newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });
+            registerKernel(newKernelConfig);
+        });
+    }
+    function makeKey(kernelName, backendName) {
+        return backendName + "_" + kernelName;
+    }
+
+    var long_1 = Long;
+
+    /**
+     * wasm optimizations, to do native i64 multiplication and divide
+     */
+    var wasm = null;
+
+    try {
+      wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([
+        0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11
+      ])), {}).exports;
+    } catch (e) {
+      // no wasm support :(
+    }
+
+    /**
+     * Constructs a 64 bit two's-complement integer, given its low and high 32 bit values as *signed* integers.
+     *  See the from* functions below for more convenient ways of constructing Longs.
+     * @exports Long
+     * @class A Long class for representing a 64 bit two's-complement integer value.
+     * @param {number} low The low (signed) 32 bits of the long
+     * @param {number} high The high (signed) 32 bits of the long
+     * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
+     * @constructor
+     */
+    function Long(low, high, unsigned) {
+
+        /**
+         * The low 32 bits as a signed value.
+         * @type {number}
+         */
+        this.low = low | 0;
+
+        /**
+         * The high 32 bits as a signed value.
+         * @type {number}
+         */
+        this.high = high | 0;
+
+        /**
+         * Whether unsigned or not.
+         * @type {boolean}
+         */
+        this.unsigned = !!unsigned;
+    }
+
+    // The internal representation of a long is the two given signed, 32-bit values.
+    // We use 32-bit pieces because these are the size of integers on which
+    // Javascript performs bit-operations.  For operations like addition and
+    // multiplication, we split each number into 16 bit pieces, which can easily be
+    // multiplied within Javascript's floating-point representation without overflow
+    // or change in sign.
+    //
+    // In the algorithms below, we frequently reduce the negative case to the
+    // positive case by negating the input(s) and then post-processing the result.
+    // Note that we must ALWAYS check specially whether those values are MIN_VALUE
+    // (-2^63) because -MIN_VALUE == MIN_VALUE (since 2^63 cannot be represented as
+    // a positive number, it overflows back into a negative).  Not handling this
+    // case would often result in infinite recursion.
+    //
+    // Common constant values ZERO, ONE, NEG_ONE, etc. are defined below the from*
+    // methods on which they depend.
+
+    /**
+     * An indicator used to reliably determine if an object is a Long or not.
+     * @type {boolean}
+     * @const
+     * @private
+     */
+    Long.prototype.__isLong__;
+
+    Object.defineProperty(Long.prototype, "__isLong__", { value: true });
+
+    /**
+     * @function
+     * @param {*} obj Object
+     * @returns {boolean}
+     * @inner
+     */
+    function isLong(obj) {
+        return (obj && obj["__isLong__"]) === true;
+    }
+
+    /**
+     * Tests if the specified object is a Long.
+     * @function
+     * @param {*} obj Object
+     * @returns {boolean}
+     */
+    Long.isLong = isLong;
+
+    /**
+     * A cache of the Long representations of small integer values.
+     * @type {!Object}
+     * @inner
+     */
+    var INT_CACHE = {};
+
+    /**
+     * A cache of the Long representations of small unsigned integer values.
+     * @type {!Object}
+     * @inner
+     */
+    var UINT_CACHE = {};
+
+    /**
+     * @param {number} value
+     * @param {boolean=} unsigned
+     * @returns {!Long}
+     * @inner
+     */
+    function fromInt(value, unsigned) {
+        var obj, cachedObj, cache;
+        if (unsigned) {
+            value >>>= 0;
+            if (cache = (0 <= value && value < 256)) {
+                cachedObj = UINT_CACHE[value];
+                if (cachedObj)
+                    return cachedObj;
+            }
+            obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true);
+            if (cache)
+                UINT_CACHE[value] = obj;
+            return obj;
+        } else {
+            value |= 0;
+            if (cache = (-128 <= value && value < 128)) {
+                cachedObj = INT_CACHE[value];
+                if (cachedObj)
+                    return cachedObj;
+            }
+            obj = fromBits(value, value < 0 ? -1 : 0, false);
+            if (cache)
+                INT_CACHE[value] = obj;
+            return obj;
+        }
+    }
+
+    /**
+     * Returns a Long representing the given 32 bit integer value.
+     * @function
+     * @param {number} value The 32 bit integer in question
+     * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
+     * @returns {!Long} The corresponding Long value
+     */
+    Long.fromInt = fromInt;
+
+    /**
+     * @param {number} value
+     * @param {boolean=} unsigned
+     * @returns {!Long}
+     * @inner
+     */
+    function fromNumber(value, unsigned) {
+        if (isNaN(value))
+            return unsigned ? UZERO : ZERO;
+        if (unsigned) {
+            if (value < 0)
+                return UZERO;
+            if (value >= TWO_PWR_64_DBL)
+                return MAX_UNSIGNED_VALUE;
+        } else {
+            if (value <= -TWO_PWR_63_DBL)
+                return MIN_VALUE;
+            if (value + 1 >= TWO_PWR_63_DBL)
+                return MAX_VALUE;
+        }
+        if (value < 0)
+            return fromNumber(-value, unsigned).neg();
+        return fromBits((value % TWO_PWR_32_DBL) | 0, (value / TWO_PWR_32_DBL) | 0, unsigned);
+    }
+
+    /**
+     * Returns a Long representing the given value, provided that it is a finite number. Otherwise, zero is returned.
+     * @function
+     * @param {number} value The number in question
+     * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
+     * @returns {!Long} The corresponding Long value
+     */
+    Long.fromNumber = fromNumber;
+
+    /**
+     * @param {number} lowBits
+     * @param {number} highBits
+     * @param {boolean=} unsigned
+     * @returns {!Long}
+     * @inner
+     */
+    function fromBits(lowBits, highBits, unsigned) {
+        return new Long(lowBits, highBits, unsigned);
+    }
+
+    /**
+     * Returns a Long representing the 64 bit integer that comes by concatenating the given low and high bits. Each is
+     *  assumed to use 32 bits.
+     * @function
+     * @param {number} lowBits The low 32 bits
+     * @param {number} highBits The high 32 bits
+     * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
+     * @returns {!Long} The corresponding Long value
+     */
+    Long.fromBits = fromBits;
+
+    /**
+     * @function
+     * @param {number} base
+     * @param {number} exponent
+     * @returns {number}
+     * @inner
+     */
+    var pow_dbl = Math.pow; // Used 4 times (4*8 to 15+4)
+
+    /**
+     * @param {string} str
+     * @param {(boolean|number)=} unsigned
+     * @param {number=} radix
+     * @returns {!Long}
+     * @inner
+     */
+    function fromString(str, unsigned, radix) {
+        if (str.length === 0)
+            throw Error('empty string');
+        if (str === "NaN" || str === "Infinity" || str === "+Infinity" || str === "-Infinity")
+            return ZERO;
+        if (typeof unsigned === 'number') {
+            // For goog.math.long compatibility
+            radix = unsigned,
+            unsigned = false;
+        } else {
+            unsigned = !! unsigned;
+        }
+        radix = radix || 10;
+        if (radix < 2 || 36 < radix)
+            throw RangeError('radix');
+
+        var p;
+        if ((p = str.indexOf('-')) > 0)
+            throw Error('interior hyphen');
+        else if (p === 0) {
+            return fromString(str.substring(1), unsigned, radix).neg();
+        }
+
+        // Do several (8) digits each time through the loop, so as to
+        // minimize the calls to the very expensive emulated div.
+        var radixToPower = fromNumber(pow_dbl(radix, 8));
+
+        var result = ZERO;
+        for (var i = 0; i < str.length; i += 8) {
+            var size = Math.min(8, str.length - i),
+                value = parseInt(str.substring(i, i + size), radix);
+            if (size < 8) {
+                var power = fromNumber(pow_dbl(radix, size));
+                result = result.mul(power).add(fromNumber(value));
+            } else {
+                result = result.mul(radixToPower);
+                result = result.add(fromNumber(value));
+            }
+        }
+        result.unsigned = unsigned;
+        return result;
+    }
+
+    /**
+     * Returns a Long representation of the given string, written using the specified radix.
+     * @function
+     * @param {string} str The textual representation of the Long
+     * @param {(boolean|number)=} unsigned Whether unsigned or not, defaults to signed
+     * @param {number=} radix The radix in which the text is written (2-36), defaults to 10
+     * @returns {!Long} The corresponding Long value
+     */
+    Long.fromString = fromString;
+
+    /**
+     * @function
+     * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val
+     * @param {boolean=} unsigned
+     * @returns {!Long}
+     * @inner
+     */
+    function fromValue(val, unsigned) {
+        if (typeof val === 'number')
+            return fromNumber(val, unsigned);
+        if (typeof val === 'string')
+            return fromString(val, unsigned);
+        // Throws for non-objects, converts non-instanceof Long:
+        return fromBits(val.low, val.high, typeof unsigned === 'boolean' ? unsigned : val.unsigned);
+    }
+
+    /**
+     * Converts the specified value to a Long using the appropriate from* function for its type.
+     * @function
+     * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val Value
+     * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
+     * @returns {!Long}
+     */
+    Long.fromValue = fromValue;
+
+    // NOTE: the compiler should inline these constant values below and then remove these variables, so there should be
+    // no runtime penalty for these.
+
+    /**
+     * @type {number}
+     * @const
+     * @inner
+     */
+    var TWO_PWR_16_DBL = 1 << 16;
+
+    /**
+     * @type {number}
+     * @const
+     * @inner
+     */
+    var TWO_PWR_24_DBL = 1 << 24;
+
+    /**
+     * @type {number}
+     * @const
+     * @inner
+     */
+    var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;
+
+    /**
+     * @type {number}
+     * @const
+     * @inner
+     */
+    var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;
+
+    /**
+     * @type {number}
+     * @const
+     * @inner
+     */
+    var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;
+
+    /**
+     * @type {!Long}
+     * @const
+     * @inner
+     */
+    var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);
+
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var ZERO = fromInt(0);
+
+    /**
+     * Signed zero.
+     * @type {!Long}
+     */
+    Long.ZERO = ZERO;
+
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var UZERO = fromInt(0, true);
+
+    /**
+     * Unsigned zero.
+     * @type {!Long}
+     */
+    Long.UZERO = UZERO;
+
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var ONE = fromInt(1);
+
+    /**
+     * Signed one.
+     * @type {!Long}
+     */
+    Long.ONE = ONE;
+
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var UONE = fromInt(1, true);
+
+    /**
+     * Unsigned one.
+     * @type {!Long}
+     */
+    Long.UONE = UONE;
+
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var NEG_ONE = fromInt(-1);
+
+    /**
+     * Signed negative one.
+     * @type {!Long}
+     */
+    Long.NEG_ONE = NEG_ONE;
+
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var MAX_VALUE = fromBits(0xFFFFFFFF|0, 0x7FFFFFFF|0, false);
+
+    /**
+     * Maximum signed value.
+     * @type {!Long}
+     */
+    Long.MAX_VALUE = MAX_VALUE;
+
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var MAX_UNSIGNED_VALUE = fromBits(0xFFFFFFFF|0, 0xFFFFFFFF|0, true);
+
+    /**
+     * Maximum unsigned value.
+     * @type {!Long}
+     */
+    Long.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;
+
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var MIN_VALUE = fromBits(0, 0x80000000|0, false);
+
+    /**
+     * Minimum signed value.
+     * @type {!Long}
+     */
+    Long.MIN_VALUE = MIN_VALUE;
+
+    /**
+     * @alias Long.prototype
+     * @inner
+     */
+    var LongPrototype = Long.prototype;
+
+    /**
+     * Converts the Long to a 32 bit integer, assuming it is a 32 bit integer.
+     * @returns {number}
+     */
+    LongPrototype.toInt = function toInt() {
+        return this.unsigned ? this.low >>> 0 : this.low;
+    };
+
+    /**
+     * Converts the Long to a the nearest floating-point representation of this value (double, 53 bit mantissa).
+     * @returns {number}
+     */
+    LongPrototype.toNumber = function toNumber() {
+        if (this.unsigned)
+            return ((this.high >>> 0) * TWO_PWR_32_DBL) + (this.low >>> 0);
+        return this.high * TWO_PWR_32_DBL + (this.low >>> 0);
+    };
+
+    /**
+     * Converts the Long to a string written in the specified radix.
+     * @param {number=} radix Radix (2-36), defaults to 10
+     * @returns {string}
+     * @override
+     * @throws {RangeError} If `radix` is out of range
+     */
+    LongPrototype.toString = function toString(radix) {
+        radix = radix || 10;
+        if (radix < 2 || 36 < radix)
+            throw RangeError('radix');
+        if (this.isZero())
+            return '0';
+        if (this.isNegative()) { // Unsigned Longs are never negative
+            if (this.eq(MIN_VALUE)) {
+                // We need to change the Long value before it can be negated, so we remove
+                // the bottom-most digit in this base and then recurse to do the rest.
+                var radixLong = fromNumber(radix),
+                    div = this.div(radixLong),
+                    rem1 = div.mul(radixLong).sub(this);
+                return div.toString(radix) + rem1.toInt().toString(radix);
+            } else
+                return '-' + this.neg().toString(radix);
+        }
+
+        // Do several (6) digits each time through the loop, so as to
+        // minimize the calls to the very expensive emulated div.
+        var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned),
+            rem = this;
+        var result = '';
+        while (true) {
+            var remDiv = rem.div(radixToPower),
+                intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0,
+                digits = intval.toString(radix);
+            rem = remDiv;
+            if (rem.isZero())
+                return digits + result;
+            else {
+                while (digits.length < 6)
+                    digits = '0' + digits;
+                result = '' + digits + result;
+            }
+        }
+    };
+
+    /**
+     * Gets the high 32 bits as a signed integer.
+     * @returns {number} Signed high bits
+     */
+    LongPrototype.getHighBits = function getHighBits() {
+        return this.high;
+    };
+
+    /**
+     * Gets the high 32 bits as an unsigned integer.
+     * @returns {number} Unsigned high bits
+     */
+    LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {
+        return this.high >>> 0;
+    };
+
+    /**
+     * Gets the low 32 bits as a signed integer.
+     * @returns {number} Signed low bits
+     */
+    LongPrototype.getLowBits = function getLowBits() {
+        return this.low;
+    };
+
+    /**
+     * Gets the low 32 bits as an unsigned integer.
+     * @returns {number} Unsigned low bits
+     */
+    LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {
+        return this.low >>> 0;
+    };
+
+    /**
+     * Gets the number of bits needed to represent the absolute value of this Long.
+     * @returns {number}
+     */
+    LongPrototype.getNumBitsAbs = function getNumBitsAbs() {
+        if (this.isNegative()) // Unsigned Longs are never negative
+            return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();
+        var val = this.high != 0 ? this.high : this.low;
+        for (var bit = 31; bit > 0; bit--)
+            if ((val & (1 << bit)) != 0)
+                break;
+        return this.high != 0 ? bit + 33 : bit + 1;
+    };
+
+    /**
+     * Tests if this Long's value equals zero.
+     * @returns {boolean}
+     */
+    LongPrototype.isZero = function isZero() {
+        return this.high === 0 && this.low === 0;
+    };
+
+    /**
+     * Tests if this Long's value equals zero. This is an alias of {@link Long#isZero}.
+     * @returns {boolean}
+     */
+    LongPrototype.eqz = LongPrototype.isZero;
+
+    /**
+     * Tests if this Long's value is negative.
+     * @returns {boolean}
+     */
+    LongPrototype.isNegative = function isNegative() {
+        return !this.unsigned && this.high < 0;
+    };
+
+    /**
+     * Tests if this Long's value is positive.
+     * @returns {boolean}
+     */
+    LongPrototype.isPositive = function isPositive() {
+        return this.unsigned || this.high >= 0;
+    };
+
+    /**
+     * Tests if this Long's value is odd.
+     * @returns {boolean}
+     */
+    LongPrototype.isOdd = function isOdd() {
+        return (this.low & 1) === 1;
+    };
+
+    /**
+     * Tests if this Long's value is even.
+     * @returns {boolean}
+     */
+    LongPrototype.isEven = function isEven() {
+        return (this.low & 1) === 0;
+    };
+
+    /**
+     * Tests if this Long's value equals the specified's.
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.equals = function equals(other) {
+        if (!isLong(other))
+            other = fromValue(other);
+        if (this.unsigned !== other.unsigned && (this.high >>> 31) === 1 && (other.high >>> 31) === 1)
+            return false;
+        return this.high === other.high && this.low === other.low;
+    };
+
+    /**
+     * Tests if this Long's value equals the specified's. This is an alias of {@link Long#equals}.
+     * @function
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.eq = LongPrototype.equals;
+
+    /**
+     * Tests if this Long's value differs from the specified's.
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.notEquals = function notEquals(other) {
+        return !this.eq(/* validates */ other);
+    };
+
+    /**
+     * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}.
+     * @function
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.neq = LongPrototype.notEquals;
+
+    /**
+     * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}.
+     * @function
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.ne = LongPrototype.notEquals;
+
+    /**
+     * Tests if this Long's value is less than the specified's.
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.lessThan = function lessThan(other) {
+        return this.comp(/* validates */ other) < 0;
+    };
+
+    /**
+     * Tests if this Long's value is less than the specified's. This is an alias of {@link Long#lessThan}.
+     * @function
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.lt = LongPrototype.lessThan;
+
+    /**
+     * Tests if this Long's value is less than or equal the specified's.
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {
+        return this.comp(/* validates */ other) <= 0;
+    };
+
+    /**
+     * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}.
+     * @function
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.lte = LongPrototype.lessThanOrEqual;
+
+    /**
+     * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}.
+     * @function
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.le = LongPrototype.lessThanOrEqual;
+
+    /**
+     * Tests if this Long's value is greater than the specified's.
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.greaterThan = function greaterThan(other) {
+        return this.comp(/* validates */ other) > 0;
+    };
+
+    /**
+     * Tests if this Long's value is greater than the specified's. This is an alias of {@link Long#greaterThan}.
+     * @function
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.gt = LongPrototype.greaterThan;
+
+    /**
+     * Tests if this Long's value is greater than or equal the specified's.
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {
+        return this.comp(/* validates */ other) >= 0;
+    };
+
+    /**
+     * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}.
+     * @function
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.gte = LongPrototype.greaterThanOrEqual;
+
+    /**
+     * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}.
+     * @function
+     * @param {!Long|number|string} other Other value
+     * @returns {boolean}
+     */
+    LongPrototype.ge = LongPrototype.greaterThanOrEqual;
+
+    /**
+     * Compares this Long's value with the specified's.
+     * @param {!Long|number|string} other Other value
+     * @returns {number} 0 if they are the same, 1 if the this is greater and -1
+     *  if the given one is greater
+     */
+    LongPrototype.compare = function compare(other) {
+        if (!isLong(other))
+            other = fromValue(other);
+        if (this.eq(other))
+            return 0;
+        var thisNeg = this.isNegative(),
+            otherNeg = other.isNegative();
+        if (thisNeg && !otherNeg)
+            return -1;
+        if (!thisNeg && otherNeg)
+            return 1;
+        // At this point the sign bits are the same
+        if (!this.unsigned)
+            return this.sub(other).isNegative() ? -1 : 1;
+        // Both are positive if at least one is unsigned
+        return (other.high >>> 0) > (this.high >>> 0) || (other.high === this.high && (other.low >>> 0) > (this.low >>> 0)) ? -1 : 1;
+    };
+
+    /**
+     * Compares this Long's value with the specified's. This is an alias of {@link Long#compare}.
+     * @function
+     * @param {!Long|number|string} other Other value
+     * @returns {number} 0 if they are the same, 1 if the this is greater and -1
+     *  if the given one is greater
+     */
+    LongPrototype.comp = LongPrototype.compare;
+
+    /**
+     * Negates this Long's value.
+     * @returns {!Long} Negated Long
+     */
+    LongPrototype.negate = function negate() {
+        if (!this.unsigned && this.eq(MIN_VALUE))
+            return MIN_VALUE;
+        return this.not().add(ONE);
+    };
+
+    /**
+     * Negates this Long's value. This is an alias of {@link Long#negate}.
+     * @function
+     * @returns {!Long} Negated Long
+     */
+    LongPrototype.neg = LongPrototype.negate;
+
+    /**
+     * Returns the sum of this and the specified Long.
+     * @param {!Long|number|string} addend Addend
+     * @returns {!Long} Sum
+     */
+    LongPrototype.add = function add(addend) {
+        if (!isLong(addend))
+            addend = fromValue(addend);
+
+        // Divide each number into 4 chunks of 16 bits, and then sum the chunks.
+
+        var a48 = this.high >>> 16;
+        var a32 = this.high & 0xFFFF;
+        var a16 = this.low >>> 16;
+        var a00 = this.low & 0xFFFF;
+
+        var b48 = addend.high >>> 16;
+        var b32 = addend.high & 0xFFFF;
+        var b16 = addend.low >>> 16;
+        var b00 = addend.low & 0xFFFF;
+
+        var c48 = 0, c32 = 0, c16 = 0, c00 = 0;
+        c00 += a00 + b00;
+        c16 += c00 >>> 16;
+        c00 &= 0xFFFF;
+        c16 += a16 + b16;
+        c32 += c16 >>> 16;
+        c16 &= 0xFFFF;
+        c32 += a32 + b32;
+        c48 += c32 >>> 16;
+        c32 &= 0xFFFF;
+        c48 += a48 + b48;
+        c48 &= 0xFFFF;
+        return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned);
+    };
+
+    /**
+     * Returns the difference of this and the specified Long.
+     * @param {!Long|number|string} subtrahend Subtrahend
+     * @returns {!Long} Difference
+     */
+    LongPrototype.subtract = function subtract(subtrahend) {
+        if (!isLong(subtrahend))
+            subtrahend = fromValue(subtrahend);
+        return this.add(subtrahend.neg());
+    };
+
+    /**
+     * Returns the difference of this and the specified Long. This is an alias of {@link Long#subtract}.
+     * @function
+     * @param {!Long|number|string} subtrahend Subtrahend
+     * @returns {!Long} Difference
+     */
+    LongPrototype.sub = LongPrototype.subtract;
+
+    /**
+     * Returns the product of this and the specified Long.
+     * @param {!Long|number|string} multiplier Multiplier
+     * @returns {!Long} Product
+     */
+    LongPrototype.multiply = function multiply(multiplier) {
+        if (this.isZero())
+            return ZERO;
+        if (!isLong(multiplier))
+            multiplier = fromValue(multiplier);
+
+        // use wasm support if present
+        if (wasm) {
+            var low = wasm.mul(this.low,
+                               this.high,
+                               multiplier.low,
+                               multiplier.high);
+            return fromBits(low, wasm.get_high(), this.unsigned);
+        }
+
+        if (multiplier.isZero())
+            return ZERO;
+        if (this.eq(MIN_VALUE))
+            return multiplier.isOdd() ? MIN_VALUE : ZERO;
+        if (multiplier.eq(MIN_VALUE))
+            return this.isOdd() ? MIN_VALUE : ZERO;
+
+        if (this.isNegative()) {
+            if (multiplier.isNegative())
+                return this.neg().mul(multiplier.neg());
+            else
+                return this.neg().mul(multiplier).neg();
+        } else if (multiplier.isNegative())
+            return this.mul(multiplier.neg()).neg();
+
+        // If both longs are small, use float multiplication
+        if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24))
+            return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);
+
+        // Divide each long into 4 chunks of 16 bits, and then add up 4x4 products.
+        // We can skip products that would overflow.
+
+        var a48 = this.high >>> 16;
+        var a32 = this.high & 0xFFFF;
+        var a16 = this.low >>> 16;
+        var a00 = this.low & 0xFFFF;
+
+        var b48 = multiplier.high >>> 16;
+        var b32 = multiplier.high & 0xFFFF;
+        var b16 = multiplier.low >>> 16;
+        var b00 = multiplier.low & 0xFFFF;
+
+        var c48 = 0, c32 = 0, c16 = 0, c00 = 0;
+        c00 += a00 * b00;
+        c16 += c00 >>> 16;
+        c00 &= 0xFFFF;
+        c16 += a16 * b00;
+        c32 += c16 >>> 16;
+        c16 &= 0xFFFF;
+        c16 += a00 * b16;
+        c32 += c16 >>> 16;
+        c16 &= 0xFFFF;
+        c32 += a32 * b00;
+        c48 += c32 >>> 16;
+        c32 &= 0xFFFF;
+        c32 += a16 * b16;
+        c48 += c32 >>> 16;
+        c32 &= 0xFFFF;
+        c32 += a00 * b32;
+        c48 += c32 >>> 16;
+        c32 &= 0xFFFF;
+        c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;
+        c48 &= 0xFFFF;
+        return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned);
+    };
+
+    /**
+     * Returns the product of this and the specified Long. This is an alias of {@link Long#multiply}.
+     * @function
+     * @param {!Long|number|string} multiplier Multiplier
+     * @returns {!Long} Product
+     */
+    LongPrototype.mul = LongPrototype.multiply;
+
+    /**
+     * Returns this Long divided by the specified. The result is signed if this Long is signed or
+     *  unsigned if this Long is unsigned.
+     * @param {!Long|number|string} divisor Divisor
+     * @returns {!Long} Quotient
+     */
+    LongPrototype.divide = function divide(divisor) {
+        if (!isLong(divisor))
+            divisor = fromValue(divisor);
+        if (divisor.isZero())
+            throw Error('division by zero');
+
+        // use wasm support if present
+        if (wasm) {
+            // guard against signed division overflow: the largest
+            // negative number / -1 would be 1 larger than the largest
+            // positive number, due to two's complement.
+            if (!this.unsigned &&
+                this.high === -0x80000000 &&
+                divisor.low === -1 && divisor.high === -1) {
+                // be consistent with non-wasm code path
+                return this;
+            }
+            var low = (this.unsigned ? wasm.div_u : wasm.div_s)(
+                this.low,
+                this.high,
+                divisor.low,
+                divisor.high
+            );
+            return fromBits(low, wasm.get_high(), this.unsigned);
+        }
+
+        if (this.isZero())
+            return this.unsigned ? UZERO : ZERO;
+        var approx, rem, res;
+        if (!this.unsigned) {
+            // This section is only relevant for signed longs and is derived from the
+            // closure library as a whole.
+            if (this.eq(MIN_VALUE)) {
+                if (divisor.eq(ONE) || divisor.eq(NEG_ONE))
+                    return MIN_VALUE;  // recall that -MIN_VALUE == MIN_VALUE
+                else if (divisor.eq(MIN_VALUE))
+                    return ONE;
+                else {
+                    // At this point, we have |other| >= 2, so |this/other| < |MIN_VALUE|.
+                    var halfThis = this.shr(1);
+                    approx = halfThis.div(divisor).shl(1);
+                    if (approx.eq(ZERO)) {
+                        return divisor.isNegative() ? ONE : NEG_ONE;
+                    } else {
+                        rem = this.sub(divisor.mul(approx));
+                        res = approx.add(rem.div(divisor));
+                        return res;
+                    }
+                }
+            } else if (divisor.eq(MIN_VALUE))
+                return this.unsigned ? UZERO : ZERO;
+            if (this.isNegative()) {
+                if (divisor.isNegative())
+                    return this.neg().div(divisor.neg());
+                return this.neg().div(divisor).neg();
+            } else if (divisor.isNegative())
+                return this.div(divisor.neg()).neg();
+            res = ZERO;
+        } else {
+            // The algorithm below has not been made for unsigned longs. It's therefore
+            // required to take special care of the MSB prior to running it.
+            if (!divisor.unsigned)
+                divisor = divisor.toUnsigned();
+            if (divisor.gt(this))
+                return UZERO;
+            if (divisor.gt(this.shru(1))) // 15 >>> 1 = 7 ; with divisor = 8 ; true
+                return UONE;
+            res = UZERO;
+        }
+
+        // Repeat the following until the remainder is less than other:  find a
+        // floating-point that approximates remainder / other *from below*, add this
+        // into the result, and subtract it from the remainder.  It is critical that
+        // the approximate value is less than or equal to the real value so that the
+        // remainder never becomes negative.
+        rem = this;
+        while (rem.gte(divisor)) {
+            // Approximate the result of division. This may be a little greater or
+            // smaller than the actual value.
+            approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));
+
+            // We will tweak the approximate result by changing it in the 48-th digit or
+            // the smallest non-fractional digit, whichever is larger.
+            var log2 = Math.ceil(Math.log(approx) / Math.LN2),
+                delta = (log2 <= 48) ? 1 : pow_dbl(2, log2 - 48),
+
+            // Decrease the approximation until it is smaller than the remainder.  Note
+            // that if it is too large, the product overflows and is negative.
+                approxRes = fromNumber(approx),
+                approxRem = approxRes.mul(divisor);
+            while (approxRem.isNegative() || approxRem.gt(rem)) {
+                approx -= delta;
+                approxRes = fromNumber(approx, this.unsigned);
+                approxRem = approxRes.mul(divisor);
+            }
+
+            // We know the answer can't be zero... and actually, zero would cause
+            // infinite recursion since we would make no progress.
+            if (approxRes.isZero())
+                approxRes = ONE;
+
+            res = res.add(approxRes);
+            rem = rem.sub(approxRem);
+        }
+        return res;
+    };
+
+    /**
+     * Returns this Long divided by the specified. This is an alias of {@link Long#divide}.
+     * @function
+     * @param {!Long|number|string} divisor Divisor
+     * @returns {!Long} Quotient
+     */
+    LongPrototype.div = LongPrototype.divide;
+
+    /**
+     * Returns this Long modulo the specified.
+     * @param {!Long|number|string} divisor Divisor
+     * @returns {!Long} Remainder
+     */
+    LongPrototype.modulo = function modulo(divisor) {
+        if (!isLong(divisor))
+            divisor = fromValue(divisor);
+
+        // use wasm support if present
+        if (wasm) {
+            var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(
+                this.low,
+                this.high,
+                divisor.low,
+                divisor.high
+            );
+            return fromBits(low, wasm.get_high(), this.unsigned);
+        }
+
+        return this.sub(this.div(divisor).mul(divisor));
+    };
+
+    /**
+     * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}.
+     * @function
+     * @param {!Long|number|string} divisor Divisor
+     * @returns {!Long} Remainder
+     */
+    LongPrototype.mod = LongPrototype.modulo;
+
+    /**
+     * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}.
+     * @function
+     * @param {!Long|number|string} divisor Divisor
+     * @returns {!Long} Remainder
+     */
+    LongPrototype.rem = LongPrototype.modulo;
+
+    /**
+     * Returns the bitwise NOT of this Long.
+     * @returns {!Long}
+     */
+    LongPrototype.not = function not() {
+        return fromBits(~this.low, ~this.high, this.unsigned);
+    };
+
+    /**
+     * Returns the bitwise AND of this Long and the specified.
+     * @param {!Long|number|string} other Other Long
+     * @returns {!Long}
+     */
+    LongPrototype.and = function and(other) {
+        if (!isLong(other))
+            other = fromValue(other);
+        return fromBits(this.low & other.low, this.high & other.high, this.unsigned);
+    };
+
+    /**
+     * Returns the bitwise OR of this Long and the specified.
+     * @param {!Long|number|string} other Other Long
+     * @returns {!Long}
+     */
+    LongPrototype.or = function or(other) {
+        if (!isLong(other))
+            other = fromValue(other);
+        return fromBits(this.low | other.low, this.high | other.high, this.unsigned);
+    };
+
+    /**
+     * Returns the bitwise XOR of this Long and the given one.
+     * @param {!Long|number|string} other Other Long
+     * @returns {!Long}
+     */
+    LongPrototype.xor = function xor(other) {
+        if (!isLong(other))
+            other = fromValue(other);
+        return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);
+    };
+
+    /**
+     * Returns this Long with bits shifted to the left by the given amount.
+     * @param {number|!Long} numBits Number of bits
+     * @returns {!Long} Shifted Long
+     */
+    LongPrototype.shiftLeft = function shiftLeft(numBits) {
+        if (isLong(numBits))
+            numBits = numBits.toInt();
+        if ((numBits &= 63) === 0)
+            return this;
+        else if (numBits < 32)
+            return fromBits(this.low << numBits, (this.high << numBits) | (this.low >>> (32 - numBits)), this.unsigned);
+        else
+            return fromBits(0, this.low << (numBits - 32), this.unsigned);
+    };
+
+    /**
+     * Returns this Long with bits shifted to the left by the given amount. This is an alias of {@link Long#shiftLeft}.
+     * @function
+     * @param {number|!Long} numBits Number of bits
+     * @returns {!Long} Shifted Long
+     */
+    LongPrototype.shl = LongPrototype.shiftLeft;
+
+    /**
+     * Returns this Long with bits arithmetically shifted to the right by the given amount.
+     * @param {number|!Long} numBits Number of bits
+     * @returns {!Long} Shifted Long
+     */
+    LongPrototype.shiftRight = function shiftRight(numBits) {
+        if (isLong(numBits))
+            numBits = numBits.toInt();
+        if ((numBits &= 63) === 0)
+            return this;
+        else if (numBits < 32)
+            return fromBits((this.low >>> numBits) | (this.high << (32 - numBits)), this.high >> numBits, this.unsigned);
+        else
+            return fromBits(this.high >> (numBits - 32), this.high >= 0 ? 0 : -1, this.unsigned);
+    };
+
+    /**
+     * Returns this Long with bits arithmetically shifted to the right by the given amount. This is an alias of {@link Long#shiftRight}.
+     * @function
+     * @param {number|!Long} numBits Number of bits
+     * @returns {!Long} Shifted Long
+     */
+    LongPrototype.shr = LongPrototype.shiftRight;
+
+    /**
+     * Returns this Long with bits logically shifted to the right by the given amount.
+     * @param {number|!Long} numBits Number of bits
+     * @returns {!Long} Shifted Long
+     */
+    LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {
+        if (isLong(numBits))
+            numBits = numBits.toInt();
+        numBits &= 63;
+        if (numBits === 0)
+            return this;
+        else {
+            var high = this.high;
+            if (numBits < 32) {
+                var low = this.low;
+                return fromBits((low >>> numBits) | (high << (32 - numBits)), high >>> numBits, this.unsigned);
+            } else if (numBits === 32)
+                return fromBits(high, 0, this.unsigned);
+            else
+                return fromBits(high >>> (numBits - 32), 0, this.unsigned);
+        }
+    };
+
+    /**
+     * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}.
+     * @function
+     * @param {number|!Long} numBits Number of bits
+     * @returns {!Long} Shifted Long
+     */
+    LongPrototype.shru = LongPrototype.shiftRightUnsigned;
+
+    /**
+     * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}.
+     * @function
+     * @param {number|!Long} numBits Number of bits
+     * @returns {!Long} Shifted Long
+     */
+    LongPrototype.shr_u = LongPrototype.shiftRightUnsigned;
+
+    /**
+     * Converts this Long to signed.
+     * @returns {!Long} Signed long
+     */
+    LongPrototype.toSigned = function toSigned() {
+        if (!this.unsigned)
+            return this;
+        return fromBits(this.low, this.high, false);
+    };
+
+    /**
+     * Converts this Long to unsigned.
+     * @returns {!Long} Unsigned long
+     */
+    LongPrototype.toUnsigned = function toUnsigned() {
+        if (this.unsigned)
+            return this;
+        return fromBits(this.low, this.high, true);
+    };
+
+    /**
+     * Converts this Long to its byte representation.
+     * @param {boolean=} le Whether little or big endian, defaults to big endian
+     * @returns {!Array.<number>} Byte representation
+     */
+    LongPrototype.toBytes = function toBytes(le) {
+        return le ? this.toBytesLE() : this.toBytesBE();
+    };
+
+    /**
+     * Converts this Long to its little endian byte representation.
+     * @returns {!Array.<number>} Little endian byte representation
+     */
+    LongPrototype.toBytesLE = function toBytesLE() {
+        var hi = this.high,
+            lo = this.low;
+        return [
+            lo        & 0xff,
+            lo >>>  8 & 0xff,
+            lo >>> 16 & 0xff,
+            lo >>> 24       ,
+            hi        & 0xff,
+            hi >>>  8 & 0xff,
+            hi >>> 16 & 0xff,
+            hi >>> 24
+        ];
+    };
+
+    /**
+     * Converts this Long to its big endian byte representation.
+     * @returns {!Array.<number>} Big endian byte representation
+     */
+    LongPrototype.toBytesBE = function toBytesBE() {
+        var hi = this.high,
+            lo = this.low;
+        return [
+            hi >>> 24       ,
+            hi >>> 16 & 0xff,
+            hi >>>  8 & 0xff,
+            hi        & 0xff,
+            lo >>> 24       ,
+            lo >>> 16 & 0xff,
+            lo >>>  8 & 0xff,
+            lo        & 0xff
+        ];
+    };
+
+    /**
+     * Creates a Long from its byte representation.
+     * @param {!Array.<number>} bytes Byte representation
+     * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
+     * @param {boolean=} le Whether little or big endian, defaults to big endian
+     * @returns {Long} The corresponding Long value
+     */
+    Long.fromBytes = function fromBytes(bytes, unsigned, le) {
+        return le ? Long.fromBytesLE(bytes, unsigned) : Long.fromBytesBE(bytes, unsigned);
+    };
+
+    /**
+     * Creates a Long from its little endian byte representation.
+     * @param {!Array.<number>} bytes Little endian byte representation
+     * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
+     * @returns {Long} The corresponding Long value
+     */
+    Long.fromBytesLE = function fromBytesLE(bytes, unsigned) {
+        return new Long(
+            bytes[0]       |
+            bytes[1] <<  8 |
+            bytes[2] << 16 |
+            bytes[3] << 24,
+            bytes[4]       |
+            bytes[5] <<  8 |
+            bytes[6] << 16 |
+            bytes[7] << 24,
+            unsigned
+        );
+    };
+
+    /**
+     * Creates a Long from its big endian byte representation.
+     * @param {!Array.<number>} bytes Big endian byte representation
+     * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
+     * @returns {Long} The corresponding Long value
+     */
+    Long.fromBytesBE = function fromBytesBE(bytes, unsigned) {
+        return new Long(
+            bytes[4] << 24 |
+            bytes[5] << 16 |
+            bytes[6] <<  8 |
+            bytes[7],
+            bytes[0] << 24 |
+            bytes[1] << 16 |
+            bytes[2] <<  8 |
+            bytes[3],
+            unsigned
+        );
+    };
+
+    var LongExports = {
+        __proto__: null,
+        'default': long_1,
+        __moduleExports: long_1
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // tslint:disable-next-line
+    var Long$1 = 
+    // tslint:disable-next-line
+    long_1 || LongExports;
+    function hexToLong(hex) {
+        return Long$1.fromString(hex, true, 16);
+    }
+    // Some primes between 2^63 and 2^64 for various uses.
+    // Hex 0xc3a5c85c97cb3127
+    var k0 = hexToLong('c3a5c85c97cb3127');
+    // Hex 0xb492b66fbe98f273
+    var k1 = hexToLong('b492b66fbe98f273');
+    // Hex 0x9ae16a3b2f90404f
+    var k2 = hexToLong('9ae16a3b2f90404f');
+    function shiftMix(val) {
+        return val.xor(val.shru(47));
+    }
+    function fetch$1(s, offset, numBytes) {
+        var bytes = s.slice(offset, offset + numBytes);
+        return Long$1.fromBytes(Array.from(bytes), true, true);
+    }
+    function fetch64(s, offset) {
+        return fetch$1(s, offset, 8);
+    }
+    function fetch32(s, offset) {
+        return fetch$1(s, offset, 4);
+    }
+    function rotate64(val, shift) {
+        // Avoid shifting by 64: doing so yields an undefined result.
+        return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));
+    }
+    function hashLen16(u, v, mul) {
+        if (mul === void 0) { mul = hexToLong('9ddfea08eb382d69'); }
+        // Murmur-inspired hashing.
+        var a = u.xor(v).mul(mul);
+        a = a.xor(a.shru(47));
+        var b = v.xor(a).mul(mul);
+        b = b.xor(b.shru(47));
+        b = b.mul(mul);
+        return b;
+    }
+    // Return a 16-byte hash for 48 bytes.  Quick and dirty.
+    // Callers do best to use "random-looking" values for a and b.
+    function weakHashLen32WithSeeds(w, x, y, z, a, b) {
+        a = a.add(w);
+        b = rotate64(b.add(a).add(z), 21);
+        var c = a;
+        a = a.add(x);
+        a = a.add(y);
+        b = b.add(rotate64(a, 44));
+        return [a.add(z), b.add(c)];
+    }
+    function weakHashLen32WithSeedsStr(s, offset, a, b) {
+        return weakHashLen32WithSeeds(fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16), fetch64(s, offset + 24), a, b);
+    }
+    function hashLen0to16(s, len) {
+        if (len === void 0) { len = s.length; }
+        if (len >= 8) {
+            var mul = k2.add(len * 2);
+            var a = fetch64(s, 0).add(k2);
+            var b = fetch64(s, len - 8);
+            var c = rotate64(b, 37).mul(mul).add(a);
+            var d = rotate64(a, 25).add(b).mul(mul);
+            return hashLen16(c, d, mul);
+        }
+        if (len >= 4) {
+            var mul = k2.add(len * 2);
+            var a = fetch32(s, 0);
+            return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul);
+        }
+        if (len > 0) {
+            var a = s[0];
+            var b = s[len >> 1];
+            var c = s[len - 1];
+            var y = a + (b << 8);
+            var z = len + (c << 2);
+            return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);
+        }
+        return k2;
+    }
+    function hashLen17to32(s, len) {
+        if (len === void 0) { len = s.length; }
+        var mul = k2.add(len * 2);
+        var a = fetch64(s, 0).mul(k1);
+        var b = fetch64(s, 8);
+        var c = fetch64(s, len - 8).mul(mul);
+        var d = fetch64(s, len - 16).mul(k2);
+        return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul);
+    }
+    function hashLen33to64(s, len) {
+        if (len === void 0) { len = s.length; }
+        var mul = k2.add(len * 2);
+        var a = fetch64(s, 0).mul(k2);
+        var b = fetch64(s, 8);
+        var c = fetch64(s, len - 8).mul(mul);
+        var d = fetch64(s, len - 16).mul(k2);
+        var y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d);
+        var z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul);
+        var e = fetch64(s, 16).mul(mul);
+        var f = fetch64(s, 24);
+        var g = y.add(fetch64(s, len - 32)).mul(mul);
+        var h = z.add(fetch64(s, len - 24)).mul(mul);
+        return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul);
+    }
+    function fingerPrint64(s, len) {
+        var _a, _b;
+        if (len === void 0) { len = s.length; }
+        var seed = Long$1.fromNumber(81, true);
+        if (len <= 32) {
+            if (len <= 16) {
+                return hashLen0to16(s, len);
+            }
+            else {
+                return hashLen17to32(s, len);
+            }
+        }
+        else if (len <= 64) {
+            return hashLen33to64(s, len);
+        }
+        // For strings over 64 bytes we loop.  Internal state consists of
+        // 56 bytes: v, w, x, y, and z.
+        var x = seed;
+        var y = seed.mul(k1).add(113);
+        var z = shiftMix(y.mul(k2).add(113)).mul(k2);
+        var v = [Long$1.UZERO, Long$1.UZERO];
+        var w = [Long$1.UZERO, Long$1.UZERO];
+        x = x.mul(k2).add(fetch64(s, 0));
+        var offset = 0;
+        // Set end so that after the loop we have 1 to 64 bytes left to process.
+        var end = ((len - 1) >> 6) * 64;
+        var last64 = end + ((len - 1) & 63) - 63;
+        do {
+            x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1);
+            y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1);
+            x = x.xor(w[1]);
+            y = y.add(v[0]).add(fetch64(s, offset + 40));
+            z = rotate64(z.add(w[0]), 33).mul(k1);
+            v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0]));
+            w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));
+            _a = [x, z], z = _a[0], x = _a[1];
+            offset += 64;
+        } while (offset !== end);
+        var mul = k1.add(z.and(0xff).shl(1));
+        // Point to the last 64 bytes of input.
+        offset = last64;
+        w[0] = w[0].add((len - 1) & 63);
+        v[0] = v[0].add(w[0]);
+        w[0] = w[0].add(v[0]);
+        x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul);
+        y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul);
+        x = x.xor(w[1].mul(9));
+        y = y.add(v[0].mul(9).add(fetch64(s, offset + 40)));
+        z = rotate64(z.add(w[0]), 33).mul(mul);
+        v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul), x.add(w[0]));
+        w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));
+        _b = [x, z], z = _b[0], x = _b[1];
+        return hashLen16(hashLen16(v[0], w[0], mul).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul).add(x), mul);
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Create typed array for scalar value. Used for storing in `DataStorage`.
+     */
+    function createScalarValue(value, dtype) {
+        if (dtype === 'string') {
+            return encodeString(value);
+        }
+        return toTypedArray([value], dtype);
+    }
+    function noConversionNeeded(a, dtype) {
+        return (a instanceof Float32Array && dtype === 'float32') ||
+            (a instanceof Int32Array && dtype === 'int32') ||
+            (a instanceof Uint8Array && dtype === 'bool');
+    }
+    function toTypedArray(a, dtype) {
+        if (dtype === 'string') {
+            throw new Error('Cannot convert a string[] to a TypedArray');
+        }
+        if (Array.isArray(a)) {
+            a = flatten(a);
+        }
+        if (env().getBool('DEBUG')) {
+            checkConversionForErrors(a, dtype);
+        }
+        if (noConversionNeeded(a, dtype)) {
+            return a;
+        }
+        if (dtype == null || dtype === 'float32' || dtype === 'complex64') {
+            return new Float32Array(a);
+        }
+        else if (dtype === 'int32') {
+            return new Int32Array(a);
+        }
+        else if (dtype === 'bool') {
+            var bool = new Uint8Array(a.length);
+            for (var i = 0; i < bool.length; ++i) {
+                if (Math.round(a[i]) !== 0) {
+                    bool[i] = 1;
+                }
+            }
+            return bool;
+        }
+        else {
+            throw new Error("Unknown data type " + dtype);
+        }
+    }
+    /**
+     * Returns the current high-resolution time in milliseconds relative to an
+     * arbitrary time in the past. It works across different platforms (node.js,
+     * browsers).
+     *
+     * ```js
+     * console.log(tf.util.now());
+     * ```
+     *
+     * @doc {heading: 'Util', namespace: 'util'}
+     */
+    function now() {
+        return env().platform.now();
+    }
+    /**
+     * Returns a platform-specific implementation of
+     * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).
+     *
+     * If `fetch` is defined on the global object (`window`, `process`, etc.),
+     * `tf.util.fetch` returns that function.
+     *
+     * If not, `tf.util.fetch` returns a platform-specific solution.
+     *
+     * ```js
+     * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');
+     * // handle response
+     * ```
+     *
+     * @doc {heading: 'Util'}
+     */
+    function fetch$2(path, requestInits) {
+        return env().platform.fetch(path, requestInits);
+    }
+    /**
+     * Encodes the provided string into bytes using the provided encoding scheme.
+     *
+     * @param s The string to encode.
+     * @param encoding The encoding scheme. Defaults to utf-8.
+     *
+     * @doc {heading: 'Util'}
+     */
+    function encodeString(s, encoding) {
+        if (encoding === void 0) { encoding = 'utf-8'; }
+        encoding = encoding || 'utf-8';
+        return env().platform.encode(s, encoding);
+    }
+    /**
+     * Decodes the provided bytes into a string using the provided encoding scheme.
+     * @param bytes The bytes to decode.
+     *
+     * @param encoding The encoding scheme. Defaults to utf-8.
+     *
+     * @doc {heading: 'Util'}
+     */
+    function decodeString(bytes, encoding) {
+        if (encoding === void 0) { encoding = 'utf-8'; }
+        encoding = encoding || 'utf-8';
+        return env().platform.decode(bytes, encoding);
+    }
+
+    var util = {
+        __proto__: null,
+        createScalarValue: createScalarValue,
+        toTypedArray: toTypedArray,
+        now: now,
+        fetch: fetch$2,
+        encodeString: encodeString,
+        decodeString: decodeString,
+        shuffle: shuffle,
+        shuffleCombo: shuffleCombo,
+        clamp: clamp,
+        nearestLargerEven: nearestLargerEven,
+        sum: sum,
+        randUniform: randUniform,
+        distSquared: distSquared,
+        assert: assert,
+        assertShapesMatch: assertShapesMatch,
+        assertNonNull: assertNonNull,
+        flatten: flatten,
+        sizeFromShape: sizeFromShape,
+        isScalarShape: isScalarShape,
+        arraysEqual: arraysEqual,
+        isInt: isInt,
+        tanh: tanh,
+        sizeToSquarishShape: sizeToSquarishShape,
+        createShuffledIndices: createShuffledIndices,
+        rightPad: rightPad,
+        repeatedTry: repeatedTry,
+        inferFromImplicitShape: inferFromImplicitShape,
+        parseAxisParam: parseAxisParam,
+        squeezeShape: squeezeShape,
+        getTypedArrayFromDType: getTypedArrayFromDType,
+        getArrayFromDType: getArrayFromDType,
+        checkConversionForErrors: checkConversionForErrors,
+        isValidDtype: isValidDtype,
+        hasEncodingLoss: hasEncodingLoss,
+        isTypedArray: isTypedArray,
+        bytesPerElement: bytesPerElement,
+        bytesFromStringArray: bytesFromStringArray,
+        isString: isString,
+        isBoolean: isBoolean,
+        isNumber: isNumber,
+        inferDtype: inferDtype,
+        isFunction: isFunction,
+        nearestDivisor: nearestDivisor,
+        computeStrides: computeStrides,
+        toNestedArray: toNestedArray,
+        makeOnesTypedArray: makeOnesTypedArray,
+        makeZerosTypedArray: makeZerosTypedArray,
+        makeZerosNestedTypedArray: makeZerosNestedTypedArray,
+        assertNonNegativeIntegerDimensions: assertNonNegativeIntegerDimensions,
+        locToIndex: locToIndex,
+        indexToLoc: indexToLoc,
+        isPromise: isPromise,
+        hexToLong: hexToLong,
+        fingerPrint64: fingerPrint64
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var Profiler = /** @class */ (function () {
+        function Profiler(backendTimer, logger) {
+            this.backendTimer = backendTimer;
+            this.logger = logger;
+            if (logger == null) {
+                this.logger = new Logger();
+            }
+        }
+        Profiler.prototype.profileKernel = function (kernelName, inputs, f) {
+            var outputs;
+            var holdResultWrapperFn = function () {
+                outputs = f();
+            };
+            var timer;
+            var start = now();
+            if (this.backendTimer.timerAvailable()) {
+                timer = this.backendTimer.time(holdResultWrapperFn);
+            }
+            else {
+                holdResultWrapperFn();
+                for (var _i = 0, outputs_1 = outputs; _i < outputs_1.length; _i++) {
+                    var output = outputs_1[_i];
+                    output.dataSync();
+                }
+                timer = Promise.resolve({ kernelMs: now() - start });
+            }
+            if (env().getBool('CHECK_COMPUTATION_FOR_ERRORS')) {
+                var _loop_1 = function (i) {
+                    var output = outputs[i];
+                    // Dangling promise here because we don't want to propagate up
+                    // asynchronicity.
+                    output.data().then(function (tensorVals) {
+                        checkComputationForErrors(tensorVals, output.dtype, kernelName);
+                    });
+                };
+                for (var i = 0; i < outputs.length; i++) {
+                    _loop_1(i);
+                }
+            }
+            var kernelProfile = {
+                kernelName: kernelName,
+                outputs: outputs,
+                inputs: inputs,
+                timeMs: timer.then(function (timing) { return timing.kernelMs; }),
+                extraInfo: timer.then(function (timing) { return timing.getExtraProfileInfo != null ?
+                    timing.getExtraProfileInfo() :
+                    ''; })
+            };
+            return kernelProfile;
+        };
+        Profiler.prototype.logKernelProfile = function (kernelProfile) {
+            var _this = this;
+            var kernelName = kernelProfile.kernelName, outputs = kernelProfile.outputs, timeMs = kernelProfile.timeMs, inputs = kernelProfile.inputs, extraInfo = kernelProfile.extraInfo;
+            outputs.forEach(function (result) {
+                Promise.all([result.data(), timeMs, extraInfo]).then(function (valueContainer) {
+                    _this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);
+                });
+            });
+        };
+        return Profiler;
+    }());
+    function checkComputationForErrors(vals, dtype, kernelName) {
+        if (dtype !== 'float32') {
+            // Only floating point computations will generate NaN values
+            return false;
+        }
+        for (var i = 0; i < vals.length; i++) {
+            var num = vals[i];
+            if (isNaN(num) || !isFinite(num)) {
+                // Throwing custom exception so behavior is testable.
+                console.warn("Found " + num + " in the result of '" + kernelName + "'");
+                return true;
+            }
+        }
+        return false;
+    }
+    var Logger = /** @class */ (function () {
+        function Logger() {
+        }
+        Logger.prototype.logKernelProfile = function (name, result, vals, timeMs, inputs, extraInfo) {
+            var time = typeof timeMs === 'number' ? rightPad(timeMs + "ms", 9) :
+                timeMs['error'];
+            var paddedName = rightPad(name, 25);
+            var rank = result.rank;
+            var size = result.size;
+            var shape = rightPad(result.shape.toString(), 14);
+            var inputShapesDescription = '';
+            for (var name_1 in inputs) {
+                var input = inputs[name_1];
+                if (input != null) {
+                    // The input might be a non-tensor (e.g HTMLImageElement), in which case
+                    // we claim the output shape as input shape.
+                    var inputShape = input.shape || result.shape;
+                    var inputRank = inputShape.length;
+                    inputShapesDescription +=
+                        name_1 + ": " + inputRank + "D " + (inputRank > 0 ? inputShape : '') + " ";
+                }
+            }
+            console.log("%c" + paddedName + "\t%c" + time + "\t%c" + rank + "D " + shape + "\t%c" + size + "\t%c" + inputShapesDescription + "\t%c" + extraInfo, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');
+        };
+        return Logger;
+    }());
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes a list of TapeNodes that connect x to y, filtering everything else
+     * out and preserving the order of the original tape elements.
+     *
+     * @param tape The tape elements to filter.
+     * @param xs The input Tensors.
+     * @param y The output Tensor.
+     */
+    function getFilteredNodesXToY(tape, xs, y) {
+        // Forward pass to compute all the nodes and Tensors that are transitively a
+        // function of x.
+        var tensorsFromX = {};
+        var nodesFromX = {};
+        for (var i = 0; i < xs.length; i++) {
+            tensorsFromX[xs[i].id] = true;
+        }
+        for (var i = 0; i < tape.length; i++) {
+            var node = tape[i];
+            var nodeInputs = node.inputs;
+            for (var inputName in nodeInputs) {
+                var input = nodeInputs[inputName];
+                var anyInputFromX = false;
+                for (var j = 0; j < xs.length; j++) {
+                    if (tensorsFromX[input.id]) {
+                        node.outputs.forEach(function (output) { return tensorsFromX[output.id] = true; });
+                        anyInputFromX = true;
+                        nodesFromX[node.id] = true;
+                        break;
+                    }
+                }
+                if (anyInputFromX) {
+                    break;
+                }
+            }
+        }
+        // Backward pass to find all of the nodes and Tensors that lead to y.
+        var tensorsLeadToY = {};
+        tensorsLeadToY[y.id] = true;
+        var nodesToY = {};
+        for (var i = tape.length - 1; i >= 0; i--) {
+            var node = tape[i];
+            var nodeInputs = node.inputs;
+            // If any of the outputs lead to y, mark all of the inputs as leading to y.
+            for (var j = 0; j < node.outputs.length; j++) {
+                if (tensorsLeadToY[node.outputs[j].id]) {
+                    for (var inputName in nodeInputs) {
+                        tensorsLeadToY[nodeInputs[inputName].id] = true;
+                        nodesToY[node.id] = true;
+                    }
+                    break;
+                }
+            }
+        }
+        // Return the paths that come from x and lead to y.
+        var filteredTape = [];
+        for (var i = 0; i < tape.length; i++) {
+            var node = tape[i];
+            if (nodesFromX[node.id] && nodesToY[node.id]) {
+                // Prune the inputs from the node that aren't a function of x.
+                var prunedInputs = {};
+                for (var inputName in node.inputs) {
+                    var nodeInput = node.inputs[inputName];
+                    if (tensorsFromX[nodeInput.id]) {
+                        prunedInputs[inputName] = nodeInput;
+                    }
+                }
+                // Copy the node and overwrite inputsAndArgs to the pruned version.
+                var prunedNode = Object.assign({}, node);
+                prunedNode.inputs = prunedInputs;
+                prunedNode.outputs = node.outputs;
+                filteredTape.push(prunedNode);
+            }
+        }
+        return filteredTape;
+    }
+    /**
+     * Backpropagate gradients through the filtered TapeNodes.
+     *
+     * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map
+     * is mutated by this method.
+     * @param filteredTape The filtered TapeNodes to backprop through.
+     */
+    function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {
+        var _loop_1 = function (i) {
+            var node = filteredTape[i];
+            var dys = [];
+            node.outputs.forEach(function (o) {
+                var gradTensor = tensorAccumulatedGradientMap[o.id];
+                if (gradTensor != null) {
+                    dys.push(gradTensor);
+                }
+                else {
+                    // This particular output is not in the back-propagation subgraph, so it
+                    // does not affect the final output, thus we put null for its dy.
+                    dys.push(null);
+                }
+            });
+            if (node.gradient == null) {
+                throw new Error("Cannot compute gradient: gradient function not found " +
+                    ("for " + node.kernelName + "."));
+            }
+            // Backprop dy through this node and accumulate gradients over the inputs.
+            var inputGradients = node.gradient(dys);
+            var _loop_2 = function (inputName) {
+                if (!(inputName in inputGradients)) {
+                    throw new Error("Cannot backprop through input " + inputName + ". " +
+                        ("Available gradients found: " + Object.keys(inputGradients) + "."));
+                }
+                // Call the gradient function.
+                var dx = tidy(function () { return inputGradients[inputName](); });
+                if (dx.dtype !== 'float32') {
+                    throw new Error("Error in gradient for op " + node.kernelName + ". The gradient of input " +
+                        (inputName + " must have 'float32' dtype, but has '" + dx.dtype + "'"));
+                }
+                var x = node.inputs[inputName];
+                if (!arraysEqual(dx.shape, x.shape)) {
+                    throw new Error("Error in gradient for op " + node.kernelName + ". The gradient of input " +
+                        ("'" + inputName + "' has shape '" + dx.shape + "', which does not match ") +
+                        ("the shape of the input '" + x.shape + "'"));
+                }
+                if (tensorAccumulatedGradientMap[x.id] == null) {
+                    tensorAccumulatedGradientMap[x.id] = dx;
+                }
+                else {
+                    var curGradient = tensorAccumulatedGradientMap[x.id];
+                    tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);
+                    curGradient.dispose();
+                }
+            };
+            for (var inputName in node.inputs) {
+                _loop_2(inputName);
+            }
+        };
+        // Walk the tape backward and keep a map of Tensor to its gradient.
+        for (var i = filteredTape.length - 1; i >= 0; i--) {
+            _loop_1(i);
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // Maximum number of values before we decide to show ellipsis.
+    var FORMAT_LIMIT_NUM_VALS = 20;
+    // Number of first and last values to show when displaying a, b,...,y, z.
+    var FORMAT_NUM_FIRST_LAST_VALS = 3;
+    // Number of significant digits to show.
+    var FORMAT_NUM_SIG_DIGITS = 7;
+    function tensorToString(vals, shape, dtype, verbose) {
+        var strides = computeStrides(shape);
+        var padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
+        var rank = shape.length;
+        var valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
+        var lines = ['Tensor'];
+        if (verbose) {
+            lines.push("  dtype: " + dtype);
+            lines.push("  rank: " + rank);
+            lines.push("  shape: [" + shape + "]");
+            lines.push("  values:");
+        }
+        lines.push(valsLines.map(function (l) { return '    ' + l; }).join('\n'));
+        return lines.join('\n');
+    }
+    function computeMaxSizePerColumn(vals, shape, dtype, strides) {
+        var n = sizeFromShape(shape);
+        var numCols = strides[strides.length - 1];
+        var padPerCol = new Array(numCols).fill(0);
+        var rank = shape.length;
+        var valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;
+        if (rank > 1) {
+            for (var row = 0; row < n / numCols; row++) {
+                var offset = row * numCols;
+                for (var j = 0; j < numCols; j++) {
+                    padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
+                }
+            }
+        }
+        return padPerCol;
+    }
+    function valToString(val, pad, dtype) {
+        var valStr;
+        if (Array.isArray(val)) {
+            valStr = parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS)) + " + " +
+                (parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS)) + "j");
+        }
+        else if (isString(val)) {
+            valStr = "'" + val + "'";
+        }
+        else if (dtype === 'bool') {
+            valStr = boolNumToString(val);
+        }
+        else {
+            valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
+        }
+        return rightPad(valStr, pad);
+    }
+    function boolNumToString(v) {
+        return v === 0 ? 'false' : 'true';
+    }
+    function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast) {
+        if (isLast === void 0) { isLast = true; }
+        var storagePerElement = dtype === 'complex64' ? 2 : 1;
+        var size = shape[0];
+        var rank = shape.length;
+        if (rank === 0) {
+            if (dtype === 'complex64') {
+                var complexTuple = createComplexTuples(vals);
+                return [valToString(complexTuple[0], 0, dtype)];
+            }
+            if (dtype === 'bool') {
+                return [boolNumToString(vals[0])];
+            }
+            return [vals[0].toString()];
+        }
+        if (rank === 1) {
+            if (size > FORMAT_LIMIT_NUM_VALS) {
+                var firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
+                var firstVals = Array.from(vals.slice(0, firstValsSize));
+                var lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));
+                if (dtype === 'complex64') {
+                    firstVals = createComplexTuples(firstVals);
+                    lastVals = createComplexTuples(lastVals);
+                }
+                return [
+                    '[' +
+                        firstVals.map(function (x, i) { return valToString(x, padPerCol[i], dtype); })
+                            .join(', ') +
+                        ', ..., ' +
+                        lastVals
+                            .map(function (x, i) { return valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype); })
+                            .join(', ') +
+                        ']'
+                ];
+            }
+            var displayVals = dtype === 'complex64' ? createComplexTuples(vals) :
+                Array.from(vals);
+            return [
+                '[' +
+                    displayVals.map(function (x, i) { return valToString(x, padPerCol[i], dtype); })
+                        .join(', ') +
+                    ']'
+            ];
+        }
+        // The array is rank 2 or more.
+        var subshape = shape.slice(1);
+        var substrides = strides.slice(1);
+        var stride = strides[0] * storagePerElement;
+        var lines = [];
+        if (size > FORMAT_LIMIT_NUM_VALS) {
+            for (var i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
+                var start = i * stride;
+                var end = start + stride;
+                lines.push.apply(lines, subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));
+            }
+            lines.push('...');
+            for (var i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
+                var start = i * stride;
+                var end = start + stride;
+                lines.push.apply(lines, subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));
+            }
+        }
+        else {
+            for (var i = 0; i < size; i++) {
+                var start = i * stride;
+                var end = start + stride;
+                lines.push.apply(lines, subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));
+            }
+        }
+        var sep = rank === 2 ? ',' : '';
+        lines[0] = '[' + lines[0] + sep;
+        for (var i = 1; i < lines.length - 1; i++) {
+            lines[i] = ' ' + lines[i] + sep;
+        }
+        var newLineSep = ',\n';
+        for (var i = 2; i < rank; i++) {
+            newLineSep += '\n';
+        }
+        lines[lines.length - 1] =
+            ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);
+        return lines;
+    }
+    function createComplexTuples(vals) {
+        var complexTuples = [];
+        for (var i = 0; i < vals.length; i += 2) {
+            complexTuples.push([vals[i], vals[i + 1]]);
+        }
+        return complexTuples;
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * A mutable object, similar to `tf.Tensor`, that allows users to set values
+     * at locations before converting to an immutable `tf.Tensor`.
+     *
+     * See `tf.buffer` for creating a tensor buffer.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    var TensorBuffer = /** @class */ (function () {
+        function TensorBuffer(shape, dtype, values) {
+            var _this = this;
+            this.dtype = dtype;
+            this.shape = shape.slice();
+            this.size = sizeFromShape(shape);
+            if (values != null) {
+                var n_1 = values.length;
+                assert(n_1 === this.size, function () { return "Length of values '" + n_1 + "' does not match the size " +
+                    ("inferred by the shape '" + _this.size + "'."); });
+            }
+            if (dtype === 'complex64') {
+                throw new Error("complex64 dtype TensorBuffers are not supported. Please create " +
+                    "a TensorBuffer for the real and imaginary parts separately and " +
+                    "call tf.complex(real, imag).");
+            }
+            this.values = values || getArrayFromDType(dtype, this.size);
+            this.strides = computeStrides(shape);
+        }
+        /**
+         * Sets a value in the buffer at a given location.
+         *
+         * @param value The value to set.
+         * @param locs  The location indices.
+         *
+         * @doc {heading: 'Tensors', subheading: 'Creation'}
+         */
+        TensorBuffer.prototype.set = function (value) {
+            var _this = this;
+            var locs = [];
+            for (var _i = 1; _i < arguments.length; _i++) {
+                locs[_i - 1] = arguments[_i];
+            }
+            if (locs.length === 0) {
+                locs = [0];
+            }
+            assert(locs.length === this.rank, function () { return "The number of provided coordinates (" + locs.length + ") must " +
+                ("match the rank (" + _this.rank + ")"); });
+            var index = this.locToIndex(locs);
+            this.values[index] = value;
+        };
+        /**
+         * Returns the value in the buffer at the provided location.
+         *
+         * @param locs The location indices.
+         *
+         * @doc {heading: 'Tensors', subheading: 'Creation'}
+         */
+        TensorBuffer.prototype.get = function () {
+            var locs = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                locs[_i] = arguments[_i];
+            }
+            if (locs.length === 0) {
+                locs = [0];
+            }
+            var i = 0;
+            for (var _a = 0, locs_1 = locs; _a < locs_1.length; _a++) {
+                var loc = locs_1[_a];
+                if (loc < 0 || loc >= this.shape[i]) {
+                    var msg = "Requested out of range element at " + locs + ". " +
+                        ("  Buffer shape=" + this.shape);
+                    throw new Error(msg);
+                }
+                i++;
+            }
+            var index = locs[locs.length - 1];
+            for (var i_1 = 0; i_1 < locs.length - 1; ++i_1) {
+                index += this.strides[i_1] * locs[i_1];
+            }
+            return this.values[index];
+        };
+        TensorBuffer.prototype.locToIndex = function (locs) {
+            if (this.rank === 0) {
+                return 0;
+            }
+            else if (this.rank === 1) {
+                return locs[0];
+            }
+            var index = locs[locs.length - 1];
+            for (var i = 0; i < locs.length - 1; ++i) {
+                index += this.strides[i] * locs[i];
+            }
+            return index;
+        };
+        TensorBuffer.prototype.indexToLoc = function (index) {
+            if (this.rank === 0) {
+                return [];
+            }
+            else if (this.rank === 1) {
+                return [index];
+            }
+            var locs = new Array(this.shape.length);
+            for (var i = 0; i < locs.length - 1; ++i) {
+                locs[i] = Math.floor(index / this.strides[i]);
+                index -= locs[i] * this.strides[i];
+            }
+            locs[locs.length - 1] = index;
+            return locs;
+        };
+        Object.defineProperty(TensorBuffer.prototype, "rank", {
+            get: function () {
+                return this.shape.length;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        /**
+         * Creates an immutable `tf.Tensor` object from the buffer.
+         *
+         * @doc {heading: 'Tensors', subheading: 'Creation'}
+         */
+        TensorBuffer.prototype.toTensor = function () {
+            return trackerFn().makeTensor(this.values, this.shape, this.dtype);
+        };
+        return TensorBuffer;
+    }());
+    // For tracking tensor creation and disposal.
+    var trackerFn = null;
+    // Used by chaining methods to call into ops.
+    var opHandler = null;
+    /**
+     * An external consumer can register itself as the tensor tracker. This way
+     * the Tensor class can notify the tracker for every tensor created and
+     * disposed.
+     */
+    function setTensorTracker(fn) {
+        trackerFn = fn;
+    }
+    /**
+     * An external consumer can register itself as the op handler. This way the
+     * Tensor class can have chaining methods that call into ops via the op
+     * handler.
+     */
+    function setOpHandler(handler) {
+        opHandler = handler;
+    }
+    /**
+     * A `tf.Tensor` object represents an immutable, multidimensional array of
+     * numbers that has a shape and a data type.
+     *
+     * For performance reasons, functions that create tensors do not necessarily
+     * perform a copy of the data passed to them (e.g. if the data is passed as a
+     * `Float32Array`), and changes to the data will change the tensor. This is not
+     * a feature and is not supported. To avoid this behavior, use the tensor before
+     * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.
+     *
+     * See `tf.tensor` for details on how to create a `tf.Tensor`.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    var Tensor = /** @class */ (function () {
+        function Tensor(shape, dtype, dataId, id) {
+            /** Whether this tensor has been globally kept. */
+            this.kept = false;
+            this.isDisposedInternal = false;
+            this.shape = shape.slice();
+            this.dtype = dtype || 'float32';
+            this.size = sizeFromShape(shape);
+            this.strides = computeStrides(shape);
+            this.dataId = dataId;
+            this.id = id;
+            this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');
+        }
+        Object.defineProperty(Tensor.prototype, "rank", {
+            get: function () {
+                return this.shape.length;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        /**
+         * Returns a promise of `tf.TensorBuffer` that holds the underlying data.
+         *
+         * @doc {heading: 'Tensors', subheading: 'Classes'}
+         */
+        Tensor.prototype.buffer = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var vals;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.data()];
+                        case 1:
+                            vals = _a.sent();
+                            return [2 /*return*/, opHandler.buffer(this.shape, this.dtype, vals)];
+                    }
+                });
+            });
+        };
+        /**
+         * Returns a `tf.TensorBuffer` that holds the underlying data.
+         * @doc {heading: 'Tensors', subheading: 'Classes'}
+         */
+        Tensor.prototype.bufferSync = function () {
+            return opHandler.buffer(this.shape, this.dtype, this.dataSync());
+        };
+        /**
+         * Returns the tensor data as a nested array. The transfer of data is done
+         * asynchronously.
+         *
+         * @doc {heading: 'Tensors', subheading: 'Classes'}
+         */
+        Tensor.prototype.array = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var vals;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.data()];
+                        case 1:
+                            vals = _a.sent();
+                            return [2 /*return*/, toNestedArray(this.shape, vals, this.dtype === 'complex64')];
+                    }
+                });
+            });
+        };
+        /**
+         * Returns the tensor data as a nested array. The transfer of data is done
+         * synchronously.
+         *
+         * @doc {heading: 'Tensors', subheading: 'Classes'}
+         */
+        Tensor.prototype.arraySync = function () {
+            return toNestedArray(this.shape, this.dataSync(), this.dtype === 'complex64');
+        };
+        /**
+         * Asynchronously downloads the values from the `tf.Tensor`. Returns a
+         * promise of `TypedArray` that resolves when the computation has finished.
+         *
+         * @doc {heading: 'Tensors', subheading: 'Classes'}
+         */
+        Tensor.prototype.data = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var data, bytes;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            this.throwIfDisposed();
+                            data = trackerFn().read(this.dataId);
+                            if (!(this.dtype === 'string')) return [3 /*break*/, 2];
+                            return [4 /*yield*/, data];
+                        case 1:
+                            bytes = _a.sent();
+                            try {
+                                return [2 /*return*/, bytes.map(function (b) { return decodeString(b); })];
+                            }
+                            catch (_b) {
+                                throw new Error('Failed to decode the string bytes into utf-8. ' +
+                                    'To get the original bytes, call tensor.bytes().');
+                            }
+                            _a.label = 2;
+                        case 2: return [2 /*return*/, data];
+                    }
+                });
+            });
+        };
+        /**
+         * Synchronously downloads the values from the `tf.Tensor`. This blocks the
+         * UI thread until the values are ready, which can cause performance issues.
+         *
+         * @doc {heading: 'Tensors', subheading: 'Classes'}
+         */
+        Tensor.prototype.dataSync = function () {
+            this.throwIfDisposed();
+            var data = trackerFn().readSync(this.dataId);
+            if (this.dtype === 'string') {
+                try {
+                    return data.map(function (b) { return decodeString(b); });
+                }
+                catch (_a) {
+                    throw new Error('Failed to decode the string bytes into utf-8. ' +
+                        'To get the original bytes, call tensor.bytes().');
+                }
+            }
+            return data;
+        };
+        /** Returns the underlying bytes of the tensor's data. */
+        Tensor.prototype.bytes = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var data;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            this.throwIfDisposed();
+                            return [4 /*yield*/, trackerFn().read(this.dataId)];
+                        case 1:
+                            data = _a.sent();
+                            if (this.dtype === 'string') {
+                                return [2 /*return*/, data];
+                            }
+                            else {
+                                return [2 /*return*/, new Uint8Array(data.buffer)];
+                            }
+                    }
+                });
+            });
+        };
+        /**
+         * Disposes `tf.Tensor` from memory.
+         *
+         * @doc {heading: 'Tensors', subheading: 'Classes'}
+         */
+        Tensor.prototype.dispose = function () {
+            if (this.isDisposed) {
+                return;
+            }
+            trackerFn().disposeTensor(this);
+            this.isDisposedInternal = true;
+        };
+        Object.defineProperty(Tensor.prototype, "isDisposed", {
+            get: function () {
+                return this.isDisposedInternal;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Tensor.prototype.throwIfDisposed = function () {
+            if (this.isDisposed) {
+                throw new Error("Tensor is disposed.");
+            }
+        };
+        /**
+         * Prints the `tf.Tensor`. See `tf.print` for details.
+         *
+         * @param verbose Whether to print verbose information about the tensor,
+         *    including dtype and size.
+         *
+         * @doc {heading: 'Tensors', subheading: 'Classes'}
+         */
+        Tensor.prototype.print = function (verbose) {
+            if (verbose === void 0) { verbose = false; }
+            return opHandler.print(this, verbose);
+        };
+        /**
+         * Returns a copy of the tensor. See `tf.clone` for details.
+         * @doc {heading: 'Tensors', subheading: 'Classes'}
+         */
+        Tensor.prototype.clone = function () {
+            this.throwIfDisposed();
+            return opHandler.clone(this);
+        };
+        /**
+         * Returns a human-readable description of the tensor. Useful for logging.
+         *
+         * @doc {heading: 'Tensors', subheading: 'Classes'}
+         */
+        Tensor.prototype.toString = function (verbose) {
+            if (verbose === void 0) { verbose = false; }
+            var vals = this.dataSync();
+            return tensorToString(vals, this.shape, this.dtype, verbose);
+        };
+        Tensor.prototype.cast = function (dtype) {
+            this.throwIfDisposed();
+            return opHandler.cast(this, dtype);
+        };
+        Tensor.prototype.variable = function (trainable, name, dtype) {
+            if (trainable === void 0) { trainable = true; }
+            this.throwIfDisposed();
+            return trackerFn().makeVariable(this, trainable, name, dtype);
+        };
+        return Tensor;
+    }());
+    Object.defineProperty(Tensor, Symbol.hasInstance, {
+        value: function (instance) {
+            // Implementation note: we should use properties of the object that will be
+            // defined before the constructor body has finished executing (methods).
+            // This is because when this code is transpiled by babel, babel will call
+            // classCallCheck before the constructor body is run.
+            // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.
+            return !!instance && instance.data != null && instance.dataSync != null &&
+                instance.throwIfDisposed != null;
+        }
+    });
+    function getGlobalTensorClass() {
+        // Use getGlobal so that we can augment the Tensor class across package
+        // boundaries becase the node resolution alg may result in different modules
+        // being returned for this file depending on the path they are loaded from.
+        return getGlobal('Tensor', function () {
+            return Tensor;
+        });
+    }
+    // Global side effect. Cache global reference to Tensor class
+    getGlobalTensorClass();
+    /**
+     * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    var Variable = /** @class */ (function (_super) {
+        __extends(Variable, _super);
+        function Variable(initialValue, trainable, name, tensorId) {
+            var _this = _super.call(this, initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId) || this;
+            _this.trainable = trainable;
+            _this.name = name;
+            return _this;
+        }
+        /**
+         * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have
+         * the same shape and dtype as the old `tf.Tensor`.
+         *
+         * @param newValue New tensor to be assigned to this variable.
+         *
+         * @doc {heading: 'Tensors', subheading: 'Classes'}
+         */
+        Variable.prototype.assign = function (newValue) {
+            if (newValue.dtype !== this.dtype) {
+                throw new Error("dtype of the new value (" + newValue.dtype + ") and " +
+                    ("previous value (" + this.dtype + ") must match"));
+            }
+            if (!arraysEqual(newValue.shape, this.shape)) {
+                throw new Error("shape of the new value (" + newValue.shape + ") and " +
+                    ("previous value (" + this.shape + ") must match"));
+            }
+            trackerFn().disposeTensor(this);
+            this.dataId = newValue.dataId;
+            trackerFn().incRef(this, null /* backend */);
+        };
+        Variable.prototype.dispose = function () {
+            trackerFn().disposeVariable(this);
+            this.isDisposedInternal = true;
+        };
+        return Variable;
+    }(Tensor));
+    Object.defineProperty(Variable, Symbol.hasInstance, {
+        value: function (instance) {
+            return instance instanceof Tensor && instance.assign != null &&
+                instance.assign instanceof Function;
+        }
+    });
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    (function (Rank) {
+        Rank["R0"] = "R0";
+        Rank["R1"] = "R1";
+        Rank["R2"] = "R2";
+        Rank["R3"] = "R3";
+        Rank["R4"] = "R4";
+        Rank["R5"] = "R5";
+        Rank["R6"] = "R6";
+    })(exports.Rank || (exports.Rank = {}));
+    // Looks for upcasting types. Used, for example, in operations with mixed dtype
+    // inputs.
+    var UpcastInt32AndMap;
+    (function (UpcastInt32AndMap) {
+        UpcastInt32AndMap["float32"] = "float32";
+        UpcastInt32AndMap["int32"] = "int32";
+        UpcastInt32AndMap["bool"] = "int32";
+        UpcastInt32AndMap["complex64"] = "complex64";
+    })(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
+    var UpcastBoolAndMap;
+    (function (UpcastBoolAndMap) {
+        UpcastBoolAndMap["float32"] = "float32";
+        UpcastBoolAndMap["int32"] = "int32";
+        UpcastBoolAndMap["bool"] = "bool";
+        UpcastBoolAndMap["complex64"] = "complex64";
+    })(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
+    var UpcastFloat32AndMap;
+    (function (UpcastFloat32AndMap) {
+        UpcastFloat32AndMap["float32"] = "float32";
+        UpcastFloat32AndMap["int32"] = "float32";
+        UpcastFloat32AndMap["bool"] = "float32";
+        UpcastFloat32AndMap["complex64"] = "complex64";
+    })(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
+    var UpcastComplex64AndMap;
+    (function (UpcastComplex64AndMap) {
+        UpcastComplex64AndMap["float32"] = "complex64";
+        UpcastComplex64AndMap["int32"] = "complex64";
+        UpcastComplex64AndMap["bool"] = "complex64";
+        UpcastComplex64AndMap["complex64"] = "complex64";
+    })(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));
+    var upcastTypeMap = {
+        'float32': UpcastFloat32AndMap,
+        'int32': UpcastInt32AndMap,
+        'bool': UpcastBoolAndMap,
+        'complex64': UpcastComplex64AndMap
+    };
+    function upcastType(typeA, typeB) {
+        if (typeA === 'string' || typeB === 'string') {
+            if (typeA === 'string' && typeB === 'string') {
+                return 'string';
+            }
+            throw new Error("Can not upcast " + typeA + " with " + typeB);
+        }
+        return upcastTypeMap[typeA][typeB];
+    }
+    /** Returns the output type after summation. */
+    function sumOutType(type) {
+        return upcastType(type, 'int32');
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function makeTypesMatch(a, b) {
+        if (a.dtype === b.dtype) {
+            return [a, b];
+        }
+        var dtype = upcastType(a.dtype, b.dtype);
+        return [a.cast(dtype), b.cast(dtype)];
+    }
+    function assertTypesMatch(a, b) {
+        assert(a.dtype === b.dtype, function () { return "The dtypes of the first(" + a.dtype + ") and" +
+            (" second(" + b.dtype + ") input must match"); });
+    }
+    function isTensorInList(tensor, tensorList) {
+        return tensorList.some(function (x) { return x.id === tensor.id; });
+    }
+    /**
+     * Extracts any `Tensor`s found within the provided object.
+     *
+     * @param container an object that may be a `Tensor` or may directly contain
+     *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it
+     *   is safe to pass any object here, except that `Promise`s are not
+     *   supported.
+     * @returns An array of `Tensors` found within the passed object. If the
+     *   argument is simply a `Tensor', a list containing that `Tensor` is
+     *   returned. If the object is not a `Tensor` or does not
+     *   contain `Tensors`, an empty list is returned.
+     */
+    function getTensorsInContainer(result) {
+        var list = [];
+        var seen = new Set();
+        walkTensorContainer(result, list, seen);
+        return list;
+    }
+    function walkTensorContainer(container, list, seen) {
+        if (container == null) {
+            return;
+        }
+        if (container instanceof Tensor) {
+            list.push(container);
+            return;
+        }
+        if (!isIterable(container)) {
+            return;
+        }
+        // Iteration over keys works also for arrays.
+        var iterable = container;
+        for (var k in iterable) {
+            var val = iterable[k];
+            if (!seen.has(val)) {
+                seen.add(val);
+                walkTensorContainer(val, list, seen);
+            }
+        }
+    }
+    // tslint:disable-next-line:no-any
+    function isIterable(obj) {
+        return Array.isArray(obj) || typeof obj === 'object';
+    }
+
+    var tensor_util = {
+        __proto__: null,
+        makeTypesMatch: makeTypesMatch,
+        assertTypesMatch: assertTypesMatch,
+        isTensorInList: isTensorInList,
+        getTensorsInContainer: getTensorsInContainer
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function isRegisteredKernelInvocation(kernelInvocation) {
+        return kernelInvocation.kernelName != null;
+    }
+    var EngineState = /** @class */ (function () {
+        function EngineState() {
+            // Public since optimizers will use it.
+            this.registeredVariables = {};
+            this.nextTapeNodeId = 0;
+            this.numBytes = 0;
+            this.numTensors = 0;
+            this.numStringTensors = 0;
+            this.numDataBuffers = 0;
+            // Number of nested tf.grad() statements when computing higher-order
+            // gradients. E.g. `1` for first-order gradients and `2` for second-order
+            // gradients. Used to track if the tape should be removed after a backprop.
+            this.gradientDepth = 0;
+            // Number of nested kernel calls. When kernel depth is greater than 1, we turn
+            // off the tape.
+            this.kernelDepth = 0;
+            this.scopeStack = [];
+            /**
+             * Keeps track of the number of data moves during a kernel execution. We
+             * maintain a stack since kernels can call other kernels, recursively.
+             */
+            this.numDataMovesStack = [];
+            this.nextScopeId = 0;
+            this.tensorInfo = new WeakMap();
+            this.profiling = false;
+            this.activeProfile = {
+                newBytes: 0,
+                newTensors: 0,
+                peakBytes: 0,
+                kernels: [],
+                result: null,
+                get kernelNames() {
+                    return Array.from(new Set(this.kernels.map(function (k) { return k.name; })));
+                }
+            };
+        }
+        EngineState.prototype.dispose = function () {
+            for (var variableName in this.registeredVariables) {
+                this.registeredVariables[variableName].dispose();
+            }
+        };
+        return EngineState;
+    }());
+    var Engine = /** @class */ (function () {
+        function Engine(ENV) {
+            this.ENV = ENV;
+            this.registry = {};
+            this.registryFactory = {};
+            this.pendingBackendInitId = 0;
+            this.state = new EngineState();
+        }
+        Engine.prototype.ready = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var sortedBackends, i, backendName, success;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            if (this.pendingBackendInit != null) {
+                                return [2 /*return*/, this.pendingBackendInit.then(function () { })];
+                            }
+                            if (this.backendInstance != null) {
+                                return [2 /*return*/];
+                            }
+                            sortedBackends = this.getSortedBackends();
+                            i = 0;
+                            _a.label = 1;
+                        case 1:
+                            if (!(i < sortedBackends.length)) return [3 /*break*/, 5];
+                            backendName = sortedBackends[i];
+                            return [4 /*yield*/, this.initializeBackend(backendName).success];
+                        case 2:
+                            success = _a.sent();
+                            if (!success) return [3 /*break*/, 4];
+                            return [4 /*yield*/, this.setBackend(backendName)];
+                        case 3:
+                            _a.sent();
+                            return [2 /*return*/];
+                        case 4:
+                            i++;
+                            return [3 /*break*/, 1];
+                        case 5: throw new Error("Could not initialize any backends, all backend initializations " +
+                            "failed.");
+                    }
+                });
+            });
+        };
+        Object.defineProperty(Engine.prototype, "backend", {
+            get: function () {
+                if (this.pendingBackendInit != null) {
+                    throw new Error("Backend '" + this.backendName + "' has not yet been initialized. Make " +
+                        "sure to await tf.ready() or await tf.setBackend() before calling " +
+                        "other methods");
+                }
+                if (this.backendInstance == null) {
+                    var _a = this.initializeBackendsAndReturnBest(), name_1 = _a.name, asyncInit = _a.asyncInit;
+                    if (asyncInit) {
+                        throw new Error("The highest priority backend '" + name_1 + "' has not yet been " +
+                            "initialized. Make sure to await tf.ready() or " +
+                            "await tf.setBackend() before calling other methods");
+                    }
+                    this.setBackend(name_1);
+                }
+                return this.backendInstance;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Engine.prototype.backendNames = function () {
+            return Object.keys(this.registryFactory);
+        };
+        Engine.prototype.findBackend = function (backendName) {
+            if (!(backendName in this.registry)) {
+                // If the backend hasn't been initialized but we have a registry entry for
+                // it, initialize it and return it.
+                if (backendName in this.registryFactory) {
+                    var asyncInit = this.initializeBackend(backendName).asyncInit;
+                    if (asyncInit) {
+                        // Backend is not ready yet.
+                        return null;
+                    }
+                }
+                else {
+                    return null;
+                }
+            }
+            return this.registry[backendName];
+        };
+        Engine.prototype.findBackendFactory = function (backendName) {
+            if (!(backendName in this.registryFactory)) {
+                return null;
+            }
+            return this.registryFactory[backendName].factory;
+        };
+        Engine.prototype.registerBackend = function (backendName, factory, priority) {
+            if (priority === void 0) { priority = 1; }
+            if (backendName in this.registryFactory) {
+                console.warn(backendName + " backend was already registered. " +
+                    "Reusing existing backend factory.");
+                return false;
+            }
+            this.registryFactory[backendName] = { factory: factory, priority: priority };
+            return true;
+        };
+        Engine.prototype.setBackend = function (backendName) {
+            return __awaiter(this, void 0, void 0, function () {
+                var _a, success, asyncInit, result, _b;
+                return __generator(this, function (_c) {
+                    switch (_c.label) {
+                        case 0:
+                            if (this.registryFactory[backendName] == null) {
+                                throw new Error("Backend name '" + backendName + "' not found in registry");
+                            }
+                            this.backendName = backendName;
+                            if (!(this.registry[backendName] == null)) return [3 /*break*/, 4];
+                            this.backendInstance = null;
+                            _a = this.initializeBackend(backendName), success = _a.success, asyncInit = _a.asyncInit;
+                            if (!asyncInit) return [3 /*break*/, 2];
+                            return [4 /*yield*/, success];
+                        case 1:
+                            _b = _c.sent();
+                            return [3 /*break*/, 3];
+                        case 2:
+                            _b = success;
+                            _c.label = 3;
+                        case 3:
+                            result = _b;
+                            if (!result) {
+                                return [2 /*return*/, false];
+                            }
+                            _c.label = 4;
+                        case 4:
+                            this.backendInstance = this.registry[backendName];
+                            this.setupRegisteredKernels();
+                            // Reset the profiler.
+                            this.profiler = new Profiler(this.backendInstance);
+                            return [2 /*return*/, true];
+                    }
+                });
+            });
+        };
+        Engine.prototype.setupRegisteredKernels = function () {
+            var _this = this;
+            var kernels = getKernelsForBackend(this.backendName);
+            kernels.forEach(function (kernel) {
+                if (kernel.setupFunc != null) {
+                    kernel.setupFunc(_this.backendInstance);
+                }
+            });
+        };
+        Engine.prototype.disposeRegisteredKernels = function (backendName) {
+            var _this = this;
+            var kernels = getKernelsForBackend(backendName);
+            kernels.forEach(function (kernel) {
+                if (kernel.disposeFunc != null) {
+                    kernel.disposeFunc(_this.registry[backendName]);
+                }
+            });
+        };
+        /**
+         * Initializes a backend by looking up the backend name in the factory
+         * registry and calling the factory method. Returns a boolean representing
+         * whether the initialization of the backend suceeded. Throws an error if
+         * there is no backend in the factory registry.
+         */
+        Engine.prototype.initializeBackend = function (backendName) {
+            var _this = this;
+            var registryFactoryEntry = this.registryFactory[backendName];
+            if (registryFactoryEntry == null) {
+                throw new Error("Cannot initialize backend " + backendName + ", no registration found.");
+            }
+            try {
+                var backend = registryFactoryEntry.factory();
+                /* Test if the factory returns a promise.
+                Done in a more liberal way than
+                previous 'Promise.resolve(backend)===backend'
+                as we needed to account for custom Promise
+                implementations (e.g. Angular) */
+                if (backend && !(backend instanceof KernelBackend) &&
+                    typeof backend.then === 'function') {
+                    var promiseId_1 = ++this.pendingBackendInitId;
+                    var success = backend
+                        .then(function (backendInstance) {
+                        // Outdated promise. Another backend was set in the meantime.
+                        if (promiseId_1 < _this.pendingBackendInitId) {
+                            return false;
+                        }
+                        _this.registry[backendName] = backendInstance;
+                        _this.pendingBackendInit = null;
+                        return true;
+                    })
+                        .catch(function (err) {
+                        // Outdated promise. Another backend was set in the meantime.
+                        if (promiseId_1 < _this.pendingBackendInitId) {
+                            return false;
+                        }
+                        _this.pendingBackendInit = null;
+                        console.warn("Initialization of backend " + backendName + " failed");
+                        console.warn(err.stack || err.message);
+                        return false;
+                    });
+                    this.pendingBackendInit = success;
+                    return { success: success, asyncInit: true };
+                }
+                else {
+                    this.registry[backendName] = backend;
+                    return { success: true, asyncInit: false };
+                }
+            }
+            catch (err) {
+                console.warn("Initialization of backend " + backendName + " failed");
+                console.warn(err.stack || err.message);
+                return { success: false, asyncInit: false };
+            }
+        };
+        Engine.prototype.removeBackend = function (backendName) {
+            if (!(backendName in this.registryFactory)) {
+                throw new Error(backendName + " backend not found in registry");
+            }
+            if (this.backendName === backendName && this.pendingBackendInit != null) {
+                // There is a pending promise of the backend we want to remove. Make it
+                // obsolete.
+                this.pendingBackendInitId++;
+            }
+            if (backendName in this.registry) {
+                this.disposeRegisteredKernels(backendName);
+                this.registry[backendName].dispose();
+                delete this.registry[backendName];
+            }
+            delete this.registryFactory[backendName];
+            // Unset the backend if it is active.
+            if (this.backendName === backendName) {
+                this.pendingBackendInit = null;
+                this.backendName = null;
+                this.backendInstance = null;
+            }
+        };
+        Engine.prototype.getSortedBackends = function () {
+            var _this = this;
+            if (Object.keys(this.registryFactory).length === 0) {
+                throw new Error('No backend found in registry.');
+            }
+            return Object.keys(this.registryFactory).sort(function (a, b) {
+                // Highest priority comes first.
+                return _this.registryFactory[b].priority -
+                    _this.registryFactory[a].priority;
+            });
+        };
+        Engine.prototype.initializeBackendsAndReturnBest = function () {
+            var sortedBackends = this.getSortedBackends();
+            for (var i = 0; i < sortedBackends.length; i++) {
+                var backendName = sortedBackends[i];
+                var _a = this.initializeBackend(backendName), success = _a.success, asyncInit = _a.asyncInit;
+                if (asyncInit || success) {
+                    return { name: backendName, asyncInit: asyncInit };
+                }
+            }
+            throw new Error("Could not initialize any backends, all backend initializations " +
+                "failed.");
+        };
+        Engine.prototype.moveData = function (backend, dataId) {
+            var info = this.state.tensorInfo.get(dataId);
+            var srcBackend = info.backend;
+            var values = this.readSync(dataId);
+            var refCount = srcBackend.refCount(dataId);
+            // Delete the tensor from the old backend and move it to the new
+            // backend.
+            srcBackend.disposeData(dataId, true);
+            info.backend = backend;
+            backend.move(dataId, values, info.shape, info.dtype, refCount);
+            if (this.shouldCheckForMemLeaks()) {
+                // Track the number of moves during a kernel execution to correctly
+                // detect memory leaks.
+                this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
+            }
+        };
+        Engine.prototype.tidy = function (nameOrFn, fn) {
+            var _this = this;
+            var name = null;
+            if (fn == null) {
+                // Called with only 1 argument.
+                if (typeof nameOrFn !== 'function') {
+                    throw new Error('Please provide a function to tidy()');
+                }
+                fn = nameOrFn;
+            }
+            else {
+                // Called with 2 arguments.
+                if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {
+                    throw new Error('When calling with two arguments, the first argument ' +
+                        'to tidy() must be a string');
+                }
+                if (typeof fn !== 'function') {
+                    throw new Error('When calling with two arguments, the 2nd argument ' +
+                        'to tidy() must be a function');
+                }
+                name = nameOrFn;
+                // TODO(nsthorat,smilkov): Do operation logging and performance
+                // profiling.
+            }
+            var result;
+            return this.scopedRun(function () { return _this.startScope(name); }, function () { return _this.endScope(result); }, function () {
+                result = fn();
+                if (result instanceof Promise) {
+                    console.error('Cannot return a Promise inside of tidy.');
+                }
+                return result;
+            });
+        };
+        Engine.prototype.scopedRun = function (start, end, f) {
+            start();
+            try {
+                var res = f();
+                end();
+                return res;
+            }
+            catch (ex) {
+                end();
+                throw ex;
+            }
+        };
+        Engine.prototype.nextTensorId = function () {
+            return Engine.nextTensorId++;
+        };
+        Engine.prototype.nextVariableId = function () {
+            return Engine.nextVariableId++;
+        };
+        /**
+         * This method is called instead of the public-facing tensor.clone() when
+         * saving a tensor for backwards pass. It makes sure to add the clone
+         * operation to the tape regardless of being called inside a kernel
+         * execution.
+         */
+        Engine.prototype.clone = function (x) {
+            var y = ENGINE.runKernel(Identity, { x: x });
+            var inputs = { x: x };
+            var grad = function (dy) { return ({
+                x: function () {
+                    var dtype = 'float32';
+                    var gradInputs = { x: dy };
+                    var attrs = { dtype: dtype };
+                    return ENGINE.runKernel(Cast, gradInputs, 
+                    // tslint:disable-next-line: no-unnecessary-type-assertion
+                    attrs);
+                }
+            }); };
+            var saved = [];
+            this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});
+            return y;
+        };
+        /**
+         * Execute a kernel with the given name and return the output tensor.
+         *
+         * @param kernelName The name of the kernel to execute.
+         * @param inputs A map of input names to tensors.
+         * @param attrs A map of attribute names to their values. An attribute is a
+         *     primitive (non-tensor) input to the kernel.
+         * @param inputsToSave A list of tensors, inputs to save for the backprop
+         *     computation.
+         * @param outputsToSave A list of booleans, specifying which output to save
+         *     for the backprop computation. These are booleans since the output
+         * tensors are not visible to the user.
+         */
+        Engine.prototype.runKernel = function (kernelName, inputs, attrs) {
+            var hasKernel = getKernel(kernelName, this.backendName) != null;
+            if (!hasKernel) {
+                throw new Error("Kernel '" + kernelName + "' not registered for backend '" + this.backendName + "'");
+            }
+            return this.runKernelFunc({ kernelName: kernelName, inputs: inputs, attrs: attrs });
+        };
+        Engine.prototype.shouldCheckForMemLeaks = function () {
+            return this.ENV.getBool('IS_TEST');
+        };
+        Engine.prototype.checkKernelForMemLeak = function (kernelName, numDataIdsBefore, outInfos) {
+            var numDataIdsAfter = this.backend.numDataIds();
+            // Count the number of data ids associated with the result of the kernel.
+            var numOutputDataIds = 0;
+            outInfos.forEach(function (info) {
+                // Complex numbers allocate 3 data ids, one for 'real', one for
+                // 'imaginary', and one for the container that holds the former two.
+                numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);
+            });
+            // Account for the number of moves during kernel execution. A "data move"
+            // can happen in the middle of a kernel execution, placing a new (key,value)
+            // pair in the data storage. Since data moves have net zero effect (we
+            // always remove the data from the old backend), we have to cancel them out
+            // when detecting memory leaks.
+            var numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];
+            var dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;
+            if (dataIdsLeaked > 0) {
+                throw new Error("Backend '" + this.backendName + "' has an internal memory leak " +
+                    ("(" + dataIdsLeaked + " data ids) after running '" + kernelName + "'"));
+            }
+        };
+        /**
+         * Internal helper method to execute a kernel Func
+         *
+         * Use `runKernel` to execute kernels from outside of engine.
+         */
+        Engine.prototype.runKernelFunc = function (kernelParams) {
+            var _this = this;
+            var outputs;
+            var saved = [];
+            var isTapeOn = this.isTapeOn();
+            var startingBytecount = this.state.numBytes;
+            var startingNumTensors = this.state.numTensors;
+            if (this.shouldCheckForMemLeaks()) {
+                this.state.numDataMovesStack.push(0);
+            }
+            var kernelFunc;
+            if (this.backendName == null) {
+                // backend has not been initialized yet (backend initialization is lazy
+                // can be deferred until an op/ kernel is run).
+                // The below getter has side effects that will try to initialize the
+                // backend and set properties like this.backendName
+                // tslint:disable-next-line: no-unused-expression
+                this.backend;
+            }
+            var out;
+            var kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ?
+                kernelParams.kernelName :
+                this.state.activeScope != null ? this.state.activeScope.name : '';
+            // Create the kernelFunc from either a registered kernel OR passed in
+            // forward/backward functions (used by custom grad). In this context a
+            // kernelFunc wraps a kernel implementation with some bookkeeping.
+            if (isRegisteredKernelInvocation(kernelParams)) {
+                var kernelName_1 = kernelParams.kernelName, inputs_1 = kernelParams.inputs, attrs_1 = kernelParams.attrs;
+                if (this.backendName == null) {
+                    // backend has not been initialized yet (backend initialization is lazy
+                    // can be deferred until an op/ kernel is run).
+                    // The below getter has side effects that will try to initialize the
+                    // backend and set properties like this.backendName
+                    // tslint:disable-next-line: no-unused-expression
+                    this.backend;
+                }
+                var kernel_1 = getKernel(kernelName_1, this.backendName);
+                assert(kernel_1 != null, function () { return "Cannot find registered kernel '" + kernelName_1 + "' for backend '" + _this.backendName + "'"; });
+                kernelFunc = function () {
+                    var numDataIdsBefore = _this.backend.numDataIds();
+                    out = kernel_1.kernelFunc({ inputs: inputs_1, attrs: attrs_1, backend: _this.backend });
+                    var outInfos = Array.isArray(out) ? out : [out];
+                    if (_this.shouldCheckForMemLeaks()) {
+                        _this.checkKernelForMemLeak(kernelName_1, numDataIdsBefore, outInfos);
+                    }
+                    var outTensors = outInfos.map(function (outInfo) {
+                        // todo (yassogba) remove this option (Tensor) when node backend
+                        // methods have been modularized and they all return tensorInfo.
+                        // TensorInfos do not have a rank attribute.
+                        if (outInfo.rank != null) {
+                            return outInfo;
+                        }
+                        var _a = outInfo, dataId = _a.dataId, shape = _a.shape, dtype = _a.dtype;
+                        return _this.makeTensorFromDataId(dataId, shape, dtype);
+                    });
+                    // Save any required inputs and outputs.
+                    // Do not save unless we are recording to the tape. Otherwise it would
+                    // cause a mem leak since there would be no backprop for these tensors
+                    // (which would otherwise dispose them).
+                    if (isTapeOn) {
+                        var tensorsToSave = _this.getTensorsForGradient(kernelName_1, inputs_1, outTensors);
+                        saved = _this.saveTensorsForBackwardMode(tensorsToSave);
+                    }
+                    return outTensors;
+                };
+            }
+            else {
+                var forwardFunc_1 = kernelParams.forwardFunc;
+                // Running a customGrad op.
+                var saveFunc_1 = function (tensors) {
+                    // Do not save unless we are recording to the tape. Otherwise it would
+                    // cause a mem leak since we would never run backprop, which disposes
+                    // the kept tensors.
+                    if (!isTapeOn) {
+                        return;
+                    }
+                    saved = tensors.map(function (tensor) { return _this.keep(_this.clone(tensor)); });
+                };
+                kernelFunc = function () {
+                    var numDataIdsBefore = _this.backend.numDataIds();
+                    out = _this.tidy(function () { return forwardFunc_1(_this.backend, saveFunc_1); });
+                    var outs = (Array.isArray(out) ? out : [out]);
+                    if (_this.shouldCheckForMemLeaks()) {
+                        // Scope name is used to print a more helpful error message if needed.
+                        _this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);
+                    }
+                    return outs;
+                };
+            }
+            //
+            // Run the kernelFunc. Optionally profiling it.
+            //
+            var inputs = kernelParams.inputs, attrs = kernelParams.attrs;
+            var backwardsFunc = isRegisteredKernelInvocation(kernelParams) ?
+                null :
+                kernelParams.backwardsFunc;
+            var kernelProfile;
+            this.scopedRun(
+            // Stop recording to a tape when running a kernel.
+            function () { return _this.state.kernelDepth++; }, function () { return _this.state.kernelDepth--; }, function () {
+                if (!_this.ENV.getBool('DEBUG') && !_this.state.profiling) {
+                    outputs = kernelFunc();
+                }
+                else {
+                    kernelProfile = _this.profiler.profileKernel(kernelOrScopeName, inputs, function () { return kernelFunc(); });
+                    if (_this.ENV.getBool('DEBUG')) {
+                        _this.profiler.logKernelProfile(kernelProfile);
+                    }
+                    outputs = kernelProfile.outputs;
+                }
+            });
+            if (isTapeOn) {
+                this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);
+            }
+            if (this.state.profiling) {
+                this.state.activeProfile.kernels.push({
+                    name: kernelOrScopeName,
+                    bytesAdded: this.state.numBytes - startingBytecount,
+                    totalBytesSnapshot: this.state.numBytes,
+                    tensorsAdded: this.state.numTensors - startingNumTensors,
+                    totalTensorsSnapshot: this.state.numTensors,
+                    inputShapes: Object.keys(inputs).map(function (key) { return inputs[key] != null ? inputs[key].shape : null; }),
+                    outputShapes: outputs.map(function (item) { return item.shape; }),
+                    kernelTimeMs: kernelProfile.timeMs,
+                    extraInfo: kernelProfile.extraInfo
+                });
+            }
+            return (Array.isArray(out) ? outputs : outputs[0]);
+        };
+        /**
+         * Saves tensors used in forward mode for use in backward mode.
+         *
+         * @param tensors the list of tensors to save.
+         */
+        Engine.prototype.saveTensorsForBackwardMode = function (tensors) {
+            var _this = this;
+            var saved = tensors.map(function (tensor) { return _this.keep(_this.clone(tensor)); });
+            return saved;
+        };
+        /**
+         * Returns a list of tensors to save for a given gradient calculation.
+         *
+         * @param kernelName name of kernel to look up gradient for.
+         * @param inputs a map of input tensors.
+         * @param outputs an array of output tensors from forward mode of kernel.
+         */
+        Engine.prototype.getTensorsForGradient = function (kernelName, inputs, outputs) {
+            var gradConfig = getGradient(kernelName);
+            if (gradConfig != null) {
+                var inputsToSave = gradConfig.inputsToSave || [];
+                var outputsToSave_1 = gradConfig.outputsToSave || [];
+                // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs
+                // specified in inputsToSave will be saved.
+                var inputTensorsToSave = void 0;
+                if (gradConfig.saveAllInputs) {
+                    assert(Array.isArray(inputs), function () { return 'saveAllInputs is true, expected inputs to be an array.'; });
+                    inputTensorsToSave = Object.keys(inputs).map(function (key) { return inputs[key]; });
+                }
+                else {
+                    inputTensorsToSave = inputsToSave.map(function (inputName) { return inputs[inputName]; });
+                }
+                var outputTensorsToSave = outputs.filter(function (_, i) { return outputsToSave_1[i]; });
+                return inputTensorsToSave.concat(outputTensorsToSave);
+            }
+            // We return an empty list rather than throw an error because the kernel we
+            // are looking up may not actually be relevant to backproping through the
+            // overall function
+            //
+            // See 'does not error if irrelevant (pruned) ops are missing grads' test
+            // in gradients_test.ts for an example.
+            return [];
+        };
+        /**
+         * Internal method used by public APIs for tensor creation. Makes a new
+         * tensor with the provided shape, dtype and values. It always
+         * creates a new data id and writes the values to the underlying backend.
+         */
+        Engine.prototype.makeTensor = function (values, shape, dtype, backend) {
+            if (values == null) {
+                throw new Error('Values passed to engine.makeTensor() are null');
+            }
+            dtype = dtype || 'float32';
+            backend = backend || this.backend;
+            var backendVals = values;
+            if (dtype === 'string' && isString(values[0])) {
+                backendVals = values.map(function (d) { return encodeString(d); });
+            }
+            var dataId = backend.write(backendVals, shape, dtype);
+            var t = new Tensor(shape, dtype, dataId, this.nextTensorId());
+            this.trackTensor(t, backend);
+            // Count bytes for string tensors.
+            if (dtype === 'string') {
+                var info = this.state.tensorInfo.get(dataId);
+                var newBytes = bytesFromStringArray(backendVals);
+                this.state.numBytes += newBytes - info.bytes;
+                info.bytes = newBytes;
+            }
+            return t;
+        };
+        /**
+         * Internal method used by backends. Makes a new tensor
+         * that is a wrapper around an existing data id. It doesn't create
+         * a new data id, only increments the ref count used in memory tracking.
+         */
+        Engine.prototype.makeTensorFromDataId = function (dataId, shape, dtype, backend) {
+            dtype = dtype || 'float32';
+            var t = new Tensor(shape, dtype, dataId, this.nextTensorId());
+            this.trackTensor(t, backend);
+            return t;
+        };
+        Engine.prototype.makeVariable = function (initialValue, trainable, name, dtype) {
+            if (trainable === void 0) { trainable = true; }
+            name = name || this.nextVariableId().toString();
+            if (dtype != null && dtype !== initialValue.dtype) {
+                initialValue = initialValue.cast(dtype);
+            }
+            var v = new Variable(initialValue, trainable, name, this.nextTensorId());
+            if (this.state.registeredVariables[v.name] != null) {
+                throw new Error("Variable with name " + v.name + " was already registered");
+            }
+            this.state.registeredVariables[v.name] = v;
+            this.incRef(v, this.backend);
+            return v;
+        };
+        Engine.prototype.trackTensor = function (a, backend) {
+            this.state.numTensors++;
+            if (a.dtype === 'string') {
+                this.state.numStringTensors++;
+            }
+            // Bytes for complex numbers are counted by their components. Bytes for
+            // string tensors are counted when writing values.
+            var bytes = 0;
+            if (a.dtype !== 'complex64' && a.dtype !== 'string') {
+                bytes = a.size * bytesPerElement(a.dtype);
+            }
+            this.state.numBytes += bytes;
+            if (!this.state.tensorInfo.has(a.dataId)) {
+                this.state.numDataBuffers++;
+                this.state.tensorInfo.set(a.dataId, {
+                    backend: backend || this.backend,
+                    dtype: a.dtype,
+                    shape: a.shape,
+                    bytes: bytes
+                });
+            }
+            if (!(a instanceof Variable)) {
+                this.track(a);
+            }
+        };
+        // Track the tensor by dataId and increase the refCount for the dataId in the
+        // backend.
+        // TODO(pyu10055): This is currently used by makeVariable method, to increase
+        // refCount on the backend for the dataId. It can potentially be replaced with
+        // Identity op indead of calling backend directly.
+        Engine.prototype.incRef = function (a, backend) {
+            this.trackTensor(a, backend);
+            this.backend.incRef(a.dataId);
+        };
+        Engine.prototype.removeDataId = function (dataId, backend) {
+            if (this.state.tensorInfo.has(dataId) &&
+                this.state.tensorInfo.get(dataId).backend === backend) {
+                this.state.tensorInfo.delete(dataId);
+                this.state.numDataBuffers--;
+            }
+        };
+        Engine.prototype.disposeTensor = function (a) {
+            if (!this.state.tensorInfo.has(a.dataId)) {
+                return;
+            }
+            var info = this.state.tensorInfo.get(a.dataId);
+            this.state.numTensors--;
+            if (a.dtype === 'string') {
+                this.state.numStringTensors--;
+                this.state.numBytes -= info.bytes;
+            }
+            // Don't count bytes for complex numbers as they are counted by their
+            // components.
+            if (a.dtype !== 'complex64' && a.dtype !== 'string') {
+                var bytes = a.size * bytesPerElement(a.dtype);
+                this.state.numBytes -= bytes;
+            }
+            // Remove the reference to dataId if backend dispose the data successfully
+            if (info.backend.disposeData(a.dataId)) {
+                this.removeDataId(a.dataId, info.backend);
+            }
+            // TODO(nsthorat): Construct an error and save the stack trace for
+            // debugging when in debug mode. Creating a stack trace is too expensive
+            // to do unconditionally.
+        };
+        Engine.prototype.disposeVariables = function () {
+            for (var varName in this.state.registeredVariables) {
+                var v = this.state.registeredVariables[varName];
+                this.disposeVariable(v);
+            }
+        };
+        Engine.prototype.disposeVariable = function (v) {
+            this.disposeTensor(v);
+            if (this.state.registeredVariables[v.name] != null) {
+                delete this.state.registeredVariables[v.name];
+            }
+        };
+        Engine.prototype.memory = function () {
+            var info = this.backend.memory();
+            info.numTensors = this.state.numTensors;
+            info.numDataBuffers = this.state.numDataBuffers;
+            info.numBytes = this.state.numBytes;
+            if (this.state.numStringTensors > 0) {
+                info.unreliable = true;
+                if (info.reasons == null) {
+                    info.reasons = [];
+                }
+                info.reasons.push('Memory usage by string tensors is approximate ' +
+                    '(2 bytes per character)');
+            }
+            return info;
+        };
+        Engine.prototype.profile = function (query) {
+            return __awaiter(this, void 0, void 0, function () {
+                var startBytes, startNumTensors, _a, _i, _b, kernel, _c, _d;
+                return __generator(this, function (_e) {
+                    switch (_e.label) {
+                        case 0:
+                            this.state.profiling = true;
+                            startBytes = this.state.numBytes;
+                            startNumTensors = this.state.numTensors;
+                            this.state.activeProfile.kernels = [];
+                            _a = this.state.activeProfile;
+                            return [4 /*yield*/, query()];
+                        case 1:
+                            _a.result = _e.sent();
+                            this.state.profiling = false;
+                            this.state.activeProfile.peakBytes = Math.max.apply(Math, this.state.activeProfile.kernels.map(function (d) { return d.totalBytesSnapshot; }));
+                            this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
+                            this.state.activeProfile.newTensors =
+                                this.state.numTensors - startNumTensors;
+                            _i = 0, _b = this.state.activeProfile.kernels;
+                            _e.label = 2;
+                        case 2:
+                            if (!(_i < _b.length)) return [3 /*break*/, 6];
+                            kernel = _b[_i];
+                            _c = kernel;
+                            return [4 /*yield*/, kernel.kernelTimeMs];
+                        case 3:
+                            _c.kernelTimeMs = _e.sent();
+                            _d = kernel;
+                            return [4 /*yield*/, kernel.extraInfo];
+                        case 4:
+                            _d.extraInfo = _e.sent();
+                            _e.label = 5;
+                        case 5:
+                            _i++;
+                            return [3 /*break*/, 2];
+                        case 6: return [2 /*return*/, this.state.activeProfile];
+                    }
+                });
+            });
+        };
+        Engine.prototype.isTapeOn = function () {
+            return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
+        };
+        Engine.prototype.addTapeNode = function (kernelName, inputs, outputs, gradientsFunc, saved, attrs) {
+            var _this = this;
+            var tapeNode = { id: this.state.nextTapeNodeId++, kernelName: kernelName, inputs: inputs, outputs: outputs, saved: saved };
+            var gradConfig = getGradient(kernelName);
+            if (gradConfig != null) {
+                gradientsFunc = gradConfig.gradFunc;
+            }
+            if (gradientsFunc != null) {
+                tapeNode.gradient = function (dys) {
+                    // TODO(smilkov): To optimize back-prop, pass dys that are not used in
+                    // the backprop graph to the user as null instead of zeros
+                    dys = dys.map(function (dy, i) {
+                        if (dy == null) {
+                            var output = outputs[i];
+                            var vals = makeZerosTypedArray(output.size, output.dtype);
+                            return _this.makeTensor(vals, output.shape, output.dtype);
+                        }
+                        return dy;
+                    });
+                    // Grad functions of ops with single outputs expect a dy, while ops
+                    // with multiple outputs expect dys (array of dy).
+                    return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);
+                };
+            }
+            this.state.activeTape.push(tapeNode);
+        };
+        Engine.prototype.keep = function (result) {
+            result.kept = true;
+            return result;
+        };
+        Engine.prototype.startTape = function () {
+            if (this.state.gradientDepth === 0) {
+                this.state.activeTape = [];
+            }
+            this.state.gradientDepth++;
+        };
+        Engine.prototype.endTape = function () {
+            this.state.gradientDepth--;
+        };
+        /**
+         * Start a scope. Use this with endScope() to achieve the same functionality
+         * as scope() without the need for a function closure.
+         */
+        Engine.prototype.startScope = function (name) {
+            var scopeInfo = {
+                track: [],
+                name: 'unnamed scope',
+                id: this.state.nextScopeId++
+            };
+            if (name) {
+                scopeInfo.name = name;
+            }
+            this.state.scopeStack.push(scopeInfo);
+            this.state.activeScope = scopeInfo;
+        };
+        /**
+         * End a scope. Use this with startScope() to achieve the same functionality
+         * as scope() without the need for a function closure.
+         */
+        Engine.prototype.endScope = function (result) {
+            var _this = this;
+            var tensorsToTrackInParent = getTensorsInContainer(result);
+            var tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(function (t) { return t.id; }));
+            // Dispose the arrays tracked in this scope.
+            for (var i = 0; i < this.state.activeScope.track.length; i++) {
+                var tensor = this.state.activeScope.track[i];
+                if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {
+                    tensor.dispose();
+                }
+            }
+            var oldScope = this.state.scopeStack.pop();
+            this.state.activeScope = this.state.scopeStack.length === 0 ?
+                null :
+                this.state.scopeStack[this.state.scopeStack.length - 1];
+            // Track the current result in the parent scope.
+            tensorsToTrackInParent.forEach(function (tensor) {
+                // Only track the tensor if was allocated in the inner scope and is not
+                // globally kept.
+                if (!tensor.kept && tensor.scopeId === oldScope.id) {
+                    _this.track(tensor);
+                }
+            });
+        };
+        /**
+         * Returns gradients of `f` with respect to each of the `xs`. The gradients
+         * returned are of the same length as `xs`, but some might be null if `f`
+         * was not a function of that `x`. It also takes optional dy to multiply the
+         * gradient, which defaults to `1`.
+         */
+        Engine.prototype.gradients = function (f, xs, dy, allowNoGradients) {
+            var _this = this;
+            if (allowNoGradients === void 0) { allowNoGradients = false; }
+            assert(xs.length > 0, function () { return 'gradients() received an empty list of xs.'; });
+            if (dy != null && dy.dtype !== 'float32') {
+                throw new Error("dy must have 'float32' dtype, but has '" + dy.dtype + "'");
+            }
+            var y = this.scopedRun(function () { return _this.startTape(); }, function () { return _this.endTape(); }, function () { return _this.tidy('forward', f); });
+            assert(y instanceof Tensor, function () { return 'The result y returned by f() must be a tensor.'; });
+            // Filter out the nodes that don't connect x => y.
+            var filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);
+            if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
+                throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +
+                    'that the f you passed encloses all operations that lead from x ' +
+                    'to y.');
+            }
+            return this.tidy('backward', function () {
+                var accumulatedGradientMap = {};
+                accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;
+                // Backprop gradients through the filtered nodes.
+                backpropagateGradients(accumulatedGradientMap, filteredTape, 
+                // Pass the tidy function to avoid circular dep with `tape.ts`.
+                function (f) { return _this.tidy(f); }, 
+                // Pass an add function to avoide a circular dep with `tape.ts`.
+                add);
+                var grads = xs.map(function (x) { return accumulatedGradientMap[x.id]; });
+                if (_this.state.gradientDepth === 0) {
+                    // This means that we are not computing higher-order gradients
+                    // and can clean up the tape.
+                    _this.state.activeTape.forEach(function (node) {
+                        for (var _i = 0, _a = node.saved; _i < _a.length; _i++) {
+                            var tensor = _a[_i];
+                            tensor.dispose();
+                        }
+                    });
+                    _this.state.activeTape = null;
+                }
+                return { value: y, grads: grads };
+            });
+        };
+        Engine.prototype.customGrad = function (f) {
+            var _this = this;
+            assert(isFunction(f), function () { return 'The f passed in customGrad(f) must be a function.'; });
+            return function () {
+                var inputs = [];
+                for (var _i = 0; _i < arguments.length; _i++) {
+                    inputs[_i] = arguments[_i];
+                }
+                assert(inputs.every(function (t) { return t instanceof Tensor; }), function () { return 'The args passed in customGrad(f)(x1, x2,...) must all be ' +
+                    'tensors'; });
+                var res;
+                var inputMap = {};
+                inputs.forEach(function (input, i) {
+                    inputMap[i] = input;
+                });
+                var forwardFunc = function (_, save) {
+                    res = f.apply(void 0, inputs.concat([save]));
+                    assert(res.value instanceof Tensor, function () { return 'The function f passed in customGrad(f) must return an ' +
+                        'object where `obj.value` is a tensor'; });
+                    assert(isFunction(res.gradFunc), function () { return 'The function f passed in customGrad(f) must return an ' +
+                        'object where `obj.gradFunc` is a function.'; });
+                    return res.value;
+                };
+                var backwardsFunc = function (dy, saved) {
+                    var gradRes = res.gradFunc(dy, saved);
+                    var grads = Array.isArray(gradRes) ? gradRes : [gradRes];
+                    assert(grads.length === inputs.length, function () { return 'The function f passed in customGrad(f) must return an ' +
+                        'object where `obj.gradFunc` is a function that returns ' +
+                        'the same number of tensors as inputs passed to f(...).'; });
+                    assert(grads.every(function (t) { return t instanceof Tensor; }), function () { return 'The function f passed in customGrad(f) must return an ' +
+                        'object where `obj.gradFunc` is a function that returns ' +
+                        'a list of only tensors.'; });
+                    var gradMap = {};
+                    grads.forEach(function (grad, i) {
+                        gradMap[i] = function () { return grad; };
+                    });
+                    return gradMap;
+                };
+                return _this.runKernelFunc({
+                    forwardFunc: forwardFunc,
+                    backwardsFunc: backwardsFunc,
+                    inputs: inputMap,
+                });
+            };
+        };
+        Engine.prototype.readSync = function (dataId) {
+            // Route the read to the correct backend.
+            var info = this.state.tensorInfo.get(dataId);
+            return info.backend.readSync(dataId);
+        };
+        Engine.prototype.read = function (dataId) {
+            // Route the read to the correct backend.
+            var info = this.state.tensorInfo.get(dataId);
+            return info.backend.read(dataId);
+        };
+        Engine.prototype.time = function (query) {
+            return __awaiter(this, void 0, void 0, function () {
+                var start, timingInfo;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            start = now();
+                            return [4 /*yield*/, this.backend.time(query)];
+                        case 1:
+                            timingInfo = _a.sent();
+                            timingInfo.wallMs = now() - start;
+                            return [2 /*return*/, timingInfo];
+                    }
+                });
+            });
+        };
+        /**
+         * Tracks a Tensor in the current scope to be automatically cleaned up
+         * when the current scope ends, and returns the value.
+         *
+         * @param result The Tensor to track in the current scope.
+         */
+        Engine.prototype.track = function (result) {
+            if (this.state.activeScope != null) {
+                result.scopeId = this.state.activeScope.id;
+                this.state.activeScope.track.push(result);
+            }
+            return result;
+        };
+        Object.defineProperty(Engine.prototype, "registeredVariables", {
+            get: function () {
+                return this.state.registeredVariables;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        /**
+         * Resets the engine state. Removes all backends but does not remove
+         * registered backend factories.
+         */
+        Engine.prototype.reset = function () {
+            // Make any pending promise obsolete.
+            this.pendingBackendInitId++;
+            this.state.dispose();
+            this.ENV.reset();
+            this.state = new EngineState();
+            for (var backendName in this.registry) {
+                this.disposeRegisteredKernels(backendName);
+                this.registry[backendName].dispose();
+                delete this.registry[backendName];
+            }
+            this.backendName = null;
+            this.backendInstance = null;
+            this.pendingBackendInit = null;
+        };
+        Engine.nextTensorId = 0;
+        Engine.nextVariableId = 0;
+        return Engine;
+    }());
+    function ones(shape) {
+        var values = makeOnesTypedArray(sizeFromShape(shape), 'float32');
+        return ENGINE.makeTensor(values, shape, 'float32');
+    }
+    function getOrMakeEngine() {
+        var ns = getGlobalNamespace();
+        if (ns._tfengine == null) {
+            var environment = new Environment(ns);
+            ns._tfengine = new Engine(environment);
+        }
+        setEnvironmentGlobal(ns._tfengine.ENV);
+        // Tell the current tensor interface that the global engine is responsible
+        // for tracking.
+        setTensorTracker(function () { return ns._tfengine; });
+        return ns._tfengine;
+    }
+    var ENGINE = getOrMakeEngine();
+    /**
+     * A implementation of the add op for use within engine and tape.
+     *
+     * This allows us to avoid a circular dependency between add.ts and engine.
+     * It is exported to be available in tape tests.
+     */
+    function add(a, b) {
+        // We duplicate Add here to avoid a circular dependency with add.ts.
+        var inputs = { a: a, b: b };
+        return ENGINE.runKernel(Add, inputs);
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // tslint:disable-next-line:no-any
+    function _isNavigatorDefined() {
+        return typeof navigator !== 'undefined' && navigator != null;
+    }
+    function isMobile(nav) {
+        if (nav || _isNavigatorDefined()) {
+            if (!nav) {
+                nav = navigator;
+            }
+            if (nav.product === 'ReactNative') {
+                return true;
+            }
+            // tslint:disable-next-line:no-any
+            var a = nav.userAgent || nav.vendor || window.opera;
+            // tslint:disable-next-line:max-line-length
+            return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i
+                .test(a) ||
+                // tslint:disable-next-line:max-line-length
+                /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i
+                    .test(a.substr(0, 4));
+        }
+        return false;
+    }
+    function isBrowser() {
+        return (typeof window !== 'undefined' && window.document != null) ||
+            //@ts-ignore
+            (typeof WorkerGlobalScope !== 'undefined');
+    }
+
+    var device_util = {
+        __proto__: null,
+        isMobile: isMobile,
+        isBrowser: isBrowser
+    };
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ENV = env();
+    /**
+     * This file contains environment-related flag registrations.
+     */
+    /** Whether to enable debug mode. */
+    ENV.registerFlag('DEBUG', function () { return false; }, function (debugValue) {
+        if (debugValue) {
+            console.warn('Debugging mode is ON. The output of every math call will ' +
+                'be downloaded to CPU and checked for NaNs. ' +
+                'This significantly impacts performance.');
+        }
+    });
+    /** Whether we are in a browser (as versus, say, node.js) environment. */
+    ENV.registerFlag('IS_BROWSER', function () { return isBrowser(); });
+    /** Whether we are in a browser (as versus, say, node.js) environment. */
+    ENV.registerFlag('IS_NODE', function () { return (typeof process !== 'undefined') &&
+        (typeof process.versions !== 'undefined') &&
+        (typeof process.versions.node !== 'undefined'); });
+    /** Whether this browser is Chrome. */
+    ENV.registerFlag('IS_CHROME', function () { return typeof navigator !== 'undefined' && navigator != null &&
+        navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&
+        /Google Inc/.test(navigator.vendor); });
+    /**
+     * True when the environment is "production" where we disable safety checks
+     * to gain performance.
+     */
+    ENV.registerFlag('PROD', function () { return false; });
+    /**
+     * Whether to do sanity checks when inferring a shape from user-provided
+     * values, used when creating a new tensor.
+     */
+    ENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', function () { return ENV.getBool('DEBUG'); });
+    /** Whether deprecation warnings are enabled. */
+    ENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', function () { return true; });
+    /** True if running unit tests. */
+    ENV.registerFlag('IS_TEST', function () { return false; });
+    /** Whether to check computation result for errors. */
+    ENV.registerFlag('CHECK_COMPUTATION_FOR_ERRORS', function () { return true; });
+    /** Whether the backend needs to wrap input to imageBitmap. */
+    ENV.registerFlag('WRAP_TO_IMAGEBITMAP', function () { return false; });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function inferShape(val, dtype) {
+        var firstElem = val;
+        if (isTypedArray(val)) {
+            return dtype === 'string' ? [] : [val.length];
+        }
+        if (!Array.isArray(val)) {
+            return []; // Scalar.
+        }
+        var shape = [];
+        while (Array.isArray(firstElem) ||
+            isTypedArray(firstElem) && dtype !== 'string') {
+            shape.push(firstElem.length);
+            firstElem = firstElem[0];
+        }
+        if (Array.isArray(val) &&
+            env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {
+            deepAssertShapeConsistency(val, shape, []);
+        }
+        return shape;
+    }
+    function deepAssertShapeConsistency(val, shape, indices) {
+        indices = indices || [];
+        if (!(Array.isArray(val)) && !isTypedArray(val)) {
+            assert(shape.length === 0, function () { return "Element arr[" + indices.join('][') + "] is a primitive, " +
+                ("but should be an array/TypedArray of " + shape[0] + " elements"); });
+            return;
+        }
+        assert(shape.length > 0, function () { return "Element arr[" + indices.join('][') + "] should be a primitive, " +
+            ("but is an array of " + val.length + " elements"); });
+        assert(val.length === shape[0], function () { return "Element arr[" + indices.join('][') + "] should have " + shape[0] + " " +
+            ("elements, but has " + val.length + " elements"); });
+        var subShape = shape.slice(1);
+        for (var i = 0; i < val.length; ++i) {
+            deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
+        }
+    }
+    function assertDtype(expectedDtype, actualDType, argName, functionName) {
+        if (expectedDtype === 'string_or_numeric') {
+            return;
+        }
+        if (expectedDtype == null) {
+            throw new Error("Expected dtype cannot be null.");
+        }
+        if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||
+            expectedDtype === 'numeric' && actualDType === 'string') {
+            throw new Error("Argument '" + argName + "' passed to '" + functionName + "' must " +
+                ("be " + expectedDtype + " tensor, but got " + actualDType + " tensor"));
+        }
+    }
+    function convertToTensor(x, argName, functionName, parseAsDtype) {
+        if (parseAsDtype === void 0) { parseAsDtype = 'numeric'; }
+        if (x instanceof Tensor) {
+            assertDtype(parseAsDtype, x.dtype, argName, functionName);
+            return x;
+        }
+        var inferredDtype = inferDtype(x);
+        // If the user expects a bool/int/float, use that info to update the
+        // inferredDtype when it is not a string.
+        if (inferredDtype !== 'string' &&
+            ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {
+            inferredDtype = parseAsDtype;
+        }
+        assertDtype(parseAsDtype, inferredDtype, argName, functionName);
+        if ((x == null) ||
+            (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&
+                typeof x !== 'boolean' && typeof x !== 'string')) {
+            var type = x == null ? 'null' : x.constructor.name;
+            throw new Error("Argument '" + argName + "' passed to '" + functionName + "' must be a " +
+                ("Tensor or TensorLike, but got '" + type + "'"));
+        }
+        var inferredShape = inferShape(x, inferredDtype);
+        if (!isTypedArray(x) && !Array.isArray(x)) {
+            x = [x];
+        }
+        var skipTypedArray = true;
+        var values = inferredDtype !== 'string' ?
+            toTypedArray(x, inferredDtype) :
+            flatten(x, [], skipTypedArray);
+        return ENGINE.makeTensor(values, inferredShape, inferredDtype);
+    }
+    function convertToTensorArray(arg, argName, functionName, parseAsDtype) {
+        if (parseAsDtype === void 0) { parseAsDtype = 'numeric'; }
+        if (!Array.isArray(arg)) {
+            throw new Error("Argument " + argName + " passed to " + functionName + " must be a " +
+                '`Tensor[]` or `TensorLike[]`');
+        }
+        var tensors = arg;
+        return tensors.map(function (t, i) {
+            return convertToTensor(t, argName + "[" + i + "]", functionName, parseAsDtype);
+        });
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var OP_SCOPE_SUFFIX = '__op';
+    /**
+     * Used for wrapping functions that perform math operations on
+     * Tensors. The function will be wrapped in a named scope that cleans all
+     * memory usage after the function is done.
+     */
+    function op(f) {
+        var keys = Object.keys(f);
+        if (keys.length !== 1) {
+            throw new Error("Please provide an object with a single key " +
+                "(operation name) mapping to a function. Got an object with " +
+                (keys.length + " keys."));
+        }
+        var opName = keys[0];
+        var fn = f[opName];
+        // Strip the underscore from the end of the function name.
+        if (opName.endsWith('_')) {
+            opName = opName.substring(0, opName.length - 1);
+        }
+        // add an __op suffix to distinguish ops from kernels in tf.profile
+        opName = opName + OP_SCOPE_SUFFIX;
+        // tslint:disable-next-line:no-any
+        var f2 = function () {
+            var args = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                args[_i] = arguments[_i];
+            }
+            ENGINE.startScope(opName);
+            try {
+                var result = fn.apply(void 0, args);
+                if (isPromise(result)) {
+                    console.error('Cannot return a Promise inside of tidy.');
+                }
+                ENGINE.endScope(result);
+                return result;
+            }
+            catch (ex) {
+                ENGINE.endScope(null);
+                throw ex;
+            }
+        };
+        Object.defineProperty(f2, 'name', { value: opName, configurable: true });
+        // tslint:disable-next-line:no-any
+        return f2;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Converts two real numbers to a complex number.
+     *
+     * Given a tensor `real` representing the real part of a complex number, and a
+     * tensor `imag` representing the imaginary part of a complex number, this
+     * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],
+     * where r represents the real part and i represents the imag part.
+     *
+     * The input tensors real and imag must have the same shape.
+     *
+     * ```js
+     * const real = tf.tensor1d([2.25, 3.25]);
+     * const imag = tf.tensor1d([4.75, 5.75]);
+     * const complex = tf.complex(real, imag);
+     *
+     * complex.print();
+     * ```
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function complex_(real, imag) {
+        var $real = convertToTensor(real, 'real', 'complex');
+        var $imag = convertToTensor(imag, 'imag', 'complex');
+        assertShapesMatch($real.shape, $imag.shape, "real and imag shapes, " + $real.shape + " and " + $imag.shape + ", " +
+            "must match in call to tf.complex().");
+        var inputs = { real: $real, imag: $imag };
+        return ENGINE.runKernel(Complex, inputs);
+    }
+    var complex = op({ complex_: complex_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /** This is shared code across all tensor creation methods. */
+    function makeTensor(values, shape, inferredShape, dtype) {
+        if (dtype == null) {
+            dtype = inferDtype(values);
+        }
+        if (dtype === 'complex64') {
+            throw new Error("Cannot construct a complex64 tensor directly. " +
+                "Please use tf.complex(real, imag).");
+        }
+        if (!isTypedArray(values) && !Array.isArray(values) &&
+            typeof values !== 'number' && typeof values !== 'boolean' &&
+            typeof values !== 'string') {
+            throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +
+                'an array of numbers/booleans/strings, or a TypedArray');
+        }
+        if (shape != null) {
+            assertNonNegativeIntegerDimensions(shape);
+            var providedSize_1 = sizeFromShape(shape);
+            var inferredSize_1 = sizeFromShape(inferredShape);
+            assert(providedSize_1 === inferredSize_1, function () {
+                return "Based on the provided shape, [" + shape + "], the tensor should have " +
+                    (providedSize_1 + " values but has " + inferredSize_1);
+            });
+            for (var i = 0; i < inferredShape.length; ++i) {
+                var inferred = inferredShape[i];
+                var flatDimsDontMatch = i === inferredShape.length - 1 ?
+                    inferred !== sizeFromShape(shape.slice(i)) :
+                    true;
+                assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, function () { return "Error creating a new Tensor. Inferred shape " +
+                    ("(" + inferredShape + ") does not match the provided ") +
+                    ("shape (" + shape + "). "); });
+            }
+        }
+        if (!isTypedArray(values) && !Array.isArray(values)) {
+            values = [values];
+        }
+        shape = shape || inferredShape;
+        values = dtype !== 'string' ?
+            toTypedArray(values, dtype) :
+            flatten(values, [], true);
+        return ENGINE.makeTensor(values, shape, dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a `tf.Tensor` with the provided values, shape and dtype.
+     *
+     * ```js
+     * // Pass an array of values to create a vector.
+     * tf.tensor([1, 2, 3, 4]).print();
+     * ```
+     *
+     * ```js
+     * // Pass a nested array of values to make a matrix or a higher
+     * // dimensional tensor.
+     * tf.tensor([[1, 2], [3, 4]]).print();
+     * ```
+     *
+     * ```js
+     * // Pass a flat array and specify a shape yourself.
+     * tf.tensor([1, 2, 3, 4], [2, 2]).print();
+     * ```
+     *
+     * @param values The values of the tensor. Can be nested array of numbers,
+     *     or a flat array, or a `TypedArray`. If the values are strings,
+     *     they will be encoded as utf-8 and kept as `Uint8Array[]`.
+     * @param shape The shape of the tensor. Optional. If not provided,
+     *   it is inferred from `values`.
+     * @param dtype The data type.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function tensor(values, shape, dtype) {
+        var inferredShape = inferShape(values, dtype);
+        return makeTensor(values, shape, inferredShape, dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /* Type definitions for exporting and importing of models. */
+    /**
+     * A map from Tensor dtype to number of bytes per element of the Tensor.
+     */
+    var DTYPE_VALUE_SIZE_MAP = {
+        'float32': 4,
+        'float16': 2,
+        'int32': 4,
+        'uint16': 2,
+        'uint8': 1,
+        'bool': 1,
+        'complex64': 8
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /** Number of bytes reserved for the length of the string. (32bit integer). */
+    var NUM_BYTES_STRING_LENGTH = 4;
+    /**
+     * Encode a map from names to weight values as an ArrayBuffer, along with an
+     * `Array` of `WeightsManifestEntry` as specification of the encoded weights.
+     *
+     * This function does not perform sharding.
+     *
+     * This function is the reverse of `decodeWeights`.
+     *
+     * @param tensors A map ("dict") from names to tensors.
+     * @param group Group to which the weights belong (optional).
+     * @returns A `Promise` of
+     *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s
+     *     concatenated.
+     *   - An `Array` of `WeightManifestEntry`s, carrying information including
+     *     tensor names, `dtype`s and shapes.
+     * @throws Error: on unsupported tensor `dtype`.
+     */
+    function encodeWeights(tensors, group) {
+        return __awaiter(this, void 0, void 0, function () {
+            var specs, dataPromises, names, _loop_1, i, tensorValues;
+            var _this = this;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        specs = [];
+                        dataPromises = [];
+                        names = Array.isArray(tensors) ?
+                            tensors.map(function (tensor) { return tensor.name; }) :
+                            Object.keys(tensors);
+                        _loop_1 = function (i) {
+                            var name_1 = names[i];
+                            var t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name_1];
+                            if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&
+                                t.dtype !== 'string' && t.dtype !== 'complex64') {
+                                throw new Error("Unsupported dtype in weight '" + name_1 + "': " + t.dtype);
+                            }
+                            var spec = { name: name_1, shape: t.shape, dtype: t.dtype };
+                            if (t.dtype === 'string') {
+                                var utf8bytes = new Promise(function (resolve) { return __awaiter(_this, void 0, void 0, function () {
+                                    var vals, totalNumBytes, bytes, offset, i_1, val, bytesOfLength;
+                                    return __generator(this, function (_a) {
+                                        switch (_a.label) {
+                                            case 0: return [4 /*yield*/, t.bytes()];
+                                            case 1:
+                                                vals = _a.sent();
+                                                totalNumBytes = vals.reduce(function (p, c) { return p + c.length; }, 0) +
+                                                    NUM_BYTES_STRING_LENGTH * vals.length;
+                                                bytes = new Uint8Array(totalNumBytes);
+                                                offset = 0;
+                                                for (i_1 = 0; i_1 < vals.length; i_1++) {
+                                                    val = vals[i_1];
+                                                    bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);
+                                                    bytes.set(bytesOfLength, offset);
+                                                    offset += NUM_BYTES_STRING_LENGTH;
+                                                    bytes.set(val, offset);
+                                                    offset += val.length;
+                                                }
+                                                resolve(bytes);
+                                                return [2 /*return*/];
+                                        }
+                                    });
+                                }); });
+                                dataPromises.push(utf8bytes);
+                            }
+                            else {
+                                dataPromises.push(t.data());
+                            }
+                            if (group != null) {
+                                spec.group = group;
+                            }
+                            specs.push(spec);
+                        };
+                        for (i = 0; i < names.length; ++i) {
+                            _loop_1(i);
+                        }
+                        return [4 /*yield*/, Promise.all(dataPromises)];
+                    case 1:
+                        tensorValues = _a.sent();
+                        return [2 /*return*/, { data: concatenateTypedArrays(tensorValues), specs: specs }];
+                }
+            });
+        });
+    }
+    /**
+     * Decode flat ArrayBuffer as weights.
+     *
+     * This function does not handle sharding.
+     *
+     * This function is the reverse of `encodeWeights`.
+     *
+     * @param buffer A flat ArrayBuffer carrying the binary values of the tensors
+     *   concatenated in the order specified in `specs`.
+     * @param specs Specifications of the names, dtypes and shapes of the tensors
+     *   whose value are encoded by `buffer`.
+     * @return A map from tensor name to tensor value, with the names corresponding
+     *   to names in `specs`.
+     * @throws Error, if any of the tensors has unsupported dtype.
+     */
+    function decodeWeights(buffer, specs) {
+        // TODO(adarob, cais): Support quantization.
+        var out = {};
+        var float16Decode;
+        var offset = 0;
+        for (var _i = 0, specs_1 = specs; _i < specs_1.length; _i++) {
+            var spec = specs_1[_i];
+            var name_2 = spec.name;
+            var dtype = spec.dtype;
+            var shape = spec.shape;
+            var size = sizeFromShape(shape);
+            var values = void 0;
+            if ('quantization' in spec) {
+                var quantization = spec.quantization;
+                if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {
+                    if (!('min' in quantization && 'scale' in quantization)) {
+                        throw new Error("Weight " + spec.name + " with quantization " + quantization.dtype + " " +
+                            "doesn't have corresponding metadata min and scale.");
+                    }
+                }
+                else if (quantization.dtype === 'float16') {
+                    if (dtype !== 'float32') {
+                        throw new Error("Weight " + spec.name + " is quantized with " + quantization.dtype + " " +
+                            ("which only supports weights of type float32 not " + dtype + "."));
+                    }
+                }
+                else {
+                    throw new Error("Weight " + spec.name + " has unknown " +
+                        ("quantization dtype " + quantization.dtype + ". ") +
+                        "Supported quantization dtypes are: " +
+                        "'uint8', 'uint16', and 'float16'.");
+                }
+                var quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];
+                var byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);
+                var quantizedArray = (quantization.dtype === 'uint8') ?
+                    new Uint8Array(byteBuffer) :
+                    new Uint16Array(byteBuffer);
+                if (dtype === 'float32') {
+                    if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {
+                        values = new Float32Array(quantizedArray.length);
+                        for (var i = 0; i < quantizedArray.length; i++) {
+                            var v = quantizedArray[i];
+                            values[i] = v * quantization.scale + quantization.min;
+                        }
+                    }
+                    else if (quantization.dtype === 'float16') {
+                        if (float16Decode === undefined) {
+                            float16Decode = getFloat16Decoder();
+                        }
+                        values = float16Decode(quantizedArray);
+                    }
+                    else {
+                        throw new Error("Unsupported quantization type " + quantization.dtype + " " +
+                            "for weight type float32.");
+                    }
+                }
+                else if (dtype === 'int32') {
+                    if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {
+                        throw new Error("Unsupported quantization type " + quantization.dtype + " " +
+                            "for weight type int32.");
+                    }
+                    values = new Int32Array(quantizedArray.length);
+                    for (var i = 0; i < quantizedArray.length; i++) {
+                        var v = quantizedArray[i];
+                        values[i] = Math.round(v * quantization.scale + quantization.min);
+                    }
+                }
+                else {
+                    throw new Error("Unsupported dtype in weight '" + name_2 + "': " + dtype);
+                }
+                offset += size * quantizationSizeFactor;
+            }
+            else if (dtype === 'string') {
+                var size_1 = sizeFromShape(spec.shape);
+                values = [];
+                for (var i = 0; i < size_1; i++) {
+                    var byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
+                    offset += NUM_BYTES_STRING_LENGTH;
+                    var bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));
+                    values.push(bytes);
+                    offset += byteLength;
+                }
+            }
+            else {
+                var dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
+                var byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);
+                if (dtype === 'float32') {
+                    values = new Float32Array(byteBuffer);
+                }
+                else if (dtype === 'int32') {
+                    values = new Int32Array(byteBuffer);
+                }
+                else if (dtype === 'bool') {
+                    values = new Uint8Array(byteBuffer);
+                }
+                else if (dtype === 'complex64') {
+                    values = new Float32Array(byteBuffer);
+                    var real = new Float32Array(values.length / 2);
+                    var image = new Float32Array(values.length / 2);
+                    for (var i = 0; i < real.length; i++) {
+                        real[i] = values[i * 2];
+                        image[i] = values[i * 2 + 1];
+                    }
+                    var realTensor = tensor(real, shape, 'float32');
+                    var imageTensor = tensor(image, shape, 'float32');
+                    out[name_2] = complex(realTensor, imageTensor);
+                    realTensor.dispose();
+                    imageTensor.dispose();
+                }
+                else {
+                    throw new Error("Unsupported dtype in weight '" + name_2 + "': " + dtype);
+                }
+                offset += size * dtypeFactor;
+            }
+            if (dtype !== 'complex64') {
+                out[name_2] = tensor(values, shape, dtype);
+            }
+        }
+        return out;
+    }
+    /**
+     * Concatenate TypedArrays into an ArrayBuffer.
+     */
+    function concatenateTypedArrays(xs) {
+        // TODO(adarob, cais): Support quantization.
+        if (xs === null) {
+            throw new Error("Invalid input value: " + JSON.stringify(xs));
+        }
+        var totalByteLength = 0;
+        // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'
+        // can have a different byte length from that of the `TypedArray` itself,
+        // for example, when the `TypedArray` is created from an offset in an
+        // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match
+        // the `TypedArray` in byte length. If an element of `xs` does not show
+        // this property, a new `TypedArray` that satisfy this property will be
+        // constructed and pushed into `normalizedXs`.
+        var normalizedXs = [];
+        xs.forEach(function (x) {
+            totalByteLength += x.byteLength;
+            // tslint:disable:no-any
+            normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :
+                new x.constructor(x));
+            if (!(x instanceof Float32Array || x instanceof Int32Array ||
+                x instanceof Uint8Array)) {
+                throw new Error("Unsupported TypedArray subtype: " + x.constructor.name);
+            }
+            // tslint:enable:no-any
+        });
+        var y = new Uint8Array(totalByteLength);
+        var offset = 0;
+        normalizedXs.forEach(function (x) {
+            y.set(new Uint8Array(x.buffer), offset);
+            offset += x.byteLength;
+        });
+        return y.buffer;
+    }
+    // Use Buffer on Node.js instead of Blob/atob/btoa
+    var useNodeBuffer = typeof Buffer !== 'undefined' &&
+        (typeof Blob === 'undefined' || typeof atob === 'undefined' ||
+            typeof btoa === 'undefined');
+    /**
+     * Calculate the byte length of a JavaScript string.
+     *
+     * Note that a JavaScript string can contain wide characters, therefore the
+     * length of the string is not necessarily equal to the byte length.
+     *
+     * @param str Input string.
+     * @returns Byte length.
+     */
+    function stringByteLength(str) {
+        if (useNodeBuffer) {
+            return Buffer.byteLength(str);
+        }
+        return new Blob([str]).size;
+    }
+    /**
+     * Encode an ArrayBuffer as a base64 encoded string.
+     *
+     * @param buffer `ArrayBuffer` to be converted.
+     * @returns A string that base64-encodes `buffer`.
+     */
+    function arrayBufferToBase64String(buffer) {
+        if (useNodeBuffer) {
+            return Buffer.from(buffer).toString('base64');
+        }
+        var buf = new Uint8Array(buffer);
+        var s = '';
+        for (var i = 0, l = buf.length; i < l; i++) {
+            s += String.fromCharCode(buf[i]);
+        }
+        return btoa(s);
+    }
+    /**
+     * Decode a base64 string as an ArrayBuffer.
+     *
+     * @param str Base64 string.
+     * @returns Decoded `ArrayBuffer`.
+     */
+    function base64StringToArrayBuffer(str) {
+        if (useNodeBuffer) {
+            var buf = Buffer.from(str, 'base64');
+            return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
+        }
+        var s = atob(str);
+        var buffer = new Uint8Array(s.length);
+        for (var i = 0; i < s.length; ++i) {
+            buffer.set([s.charCodeAt(i)], i);
+        }
+        return buffer.buffer;
+    }
+    /**
+     * Concatenate a number of ArrayBuffers into one.
+     *
+     * @param buffers A number of array buffers to concatenate.
+     * @returns Result of concatenating `buffers` in order.
+     */
+    function concatenateArrayBuffers(buffers) {
+        if (buffers.length === 1) {
+            return buffers[0];
+        }
+        var totalByteLength = 0;
+        buffers.forEach(function (buffer) {
+            totalByteLength += buffer.byteLength;
+        });
+        var temp = new Uint8Array(totalByteLength);
+        var offset = 0;
+        buffers.forEach(function (buffer) {
+            temp.set(new Uint8Array(buffer), offset);
+            offset += buffer.byteLength;
+        });
+        return temp.buffer;
+    }
+    /**
+     * Get the basename of a path.
+     *
+     * Behaves in a way analogous to Linux's basename command.
+     *
+     * @param path
+     */
+    function basename(path) {
+        var SEPARATOR = '/';
+        path = path.trim();
+        while (path.endsWith(SEPARATOR)) {
+            path = path.slice(0, path.length - 1);
+        }
+        var items = path.split(SEPARATOR);
+        return items[items.length - 1];
+    }
+    /**
+     * Populate ModelArtifactsInfo fields for a model with JSON topology.
+     * @param modelArtifacts
+     * @returns A ModelArtifactsInfo object.
+     */
+    function getModelArtifactsInfoForJSON(modelArtifacts) {
+        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+            throw new Error('Expected JSON model topology, received ArrayBuffer.');
+        }
+        return {
+            dateSaved: new Date(),
+            modelTopologyType: 'JSON',
+            modelTopologyBytes: modelArtifacts.modelTopology == null ?
+                0 :
+                stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
+            weightSpecsBytes: modelArtifacts.weightSpecs == null ?
+                0 :
+                stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
+            weightDataBytes: modelArtifacts.weightData == null ?
+                0 :
+                modelArtifacts.weightData.byteLength,
+        };
+    }
+    /**
+     * Computes mantisa table for casting Float16 to Float32
+     * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf
+     *
+     * @returns Uint32Array, 2048 mantissa lookup values.
+     */
+    function computeFloat16MantisaTable() {
+        var convertMantissa = function (i) {
+            var m = i << 13;
+            var e = 0;
+            while ((m & 0x00800000) === 0) {
+                e -= 0x00800000;
+                m <<= 1;
+            }
+            m &= ~0x00800000;
+            e += 0x38800000;
+            return m | e;
+        };
+        var mantisaTable = new Uint32Array(2048);
+        mantisaTable[0] = 0;
+        for (var i = 1; i < 1024; i++) {
+            mantisaTable[i] = convertMantissa(i);
+        }
+        for (var i = 1024; i < 2048; i++) {
+            mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);
+        }
+        return mantisaTable;
+    }
+    /**
+     * Computes exponent table for casting Float16 to Float32
+     * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf
+     *
+     * @returns Uint32Array, 64 exponent lookup values.
+     */
+    function computeFloat16ExponentTable() {
+        var exponentTable = new Uint32Array(64);
+        exponentTable[0] = 0;
+        exponentTable[31] = 0x47800000;
+        exponentTable[32] = 0x80000000;
+        exponentTable[63] = 0xc7800000;
+        for (var i = 1; i < 31; i++) {
+            exponentTable[i] = i << 23;
+        }
+        for (var i = 33; i < 63; i++) {
+            exponentTable[i] = 0x80000000 + ((i - 32) << 23);
+        }
+        return exponentTable;
+    }
+    /**
+     * Computes offset table for casting Float16 to Float32
+     * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf
+     *
+     * @returns Uint32Array, 6d offset values.
+     */
+    function computeFloat16OffsetTable() {
+        var offsetTable = new Uint32Array(64);
+        for (var i = 0; i < 64; i++) {
+            offsetTable[i] = 1024;
+        }
+        offsetTable[0] = offsetTable[32] = 0;
+        return offsetTable;
+    }
+    /**
+     * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values
+     * to a Float32Array.
+     *
+     * @returns Function (buffer: Uint16Array) => Float32Array which decodes
+     *          the Uint16Array of Float16 bytes to a Float32Array.
+     */
+    function getFloat16Decoder() {
+        // Algorithm is based off of
+        // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf
+        // Cache lookup tables
+        var mantisaTable = computeFloat16MantisaTable();
+        var exponentTable = computeFloat16ExponentTable();
+        var offsetTable = computeFloat16OffsetTable();
+        return function (quantizedArray) {
+            var buffer = new ArrayBuffer(4 * quantizedArray.length);
+            var bufferUint32View = new Uint32Array(buffer);
+            for (var index = 0; index < quantizedArray.length; index++) {
+                var float16Bits = quantizedArray[index];
+                var float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +
+                    exponentTable[float16Bits >> 10];
+                bufferUint32View[index] = float32Bits;
+            }
+            return new Float32Array(buffer);
+        };
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var IORouterRegistry = /** @class */ (function () {
+        function IORouterRegistry() {
+            this.saveRouters = [];
+            this.loadRouters = [];
+        }
+        IORouterRegistry.getInstance = function () {
+            if (IORouterRegistry.instance == null) {
+                IORouterRegistry.instance = new IORouterRegistry();
+            }
+            return IORouterRegistry.instance;
+        };
+        /**
+         * Register a save-handler router.
+         *
+         * @param saveRouter A function that maps a URL-like string onto an instance
+         * of `IOHandler` with the `save` method defined or `null`.
+         */
+        IORouterRegistry.registerSaveRouter = function (saveRouter) {
+            IORouterRegistry.getInstance().saveRouters.push(saveRouter);
+        };
+        /**
+         * Register a load-handler router.
+         *
+         * @param loadRouter A function that maps a URL-like string onto an instance
+         * of `IOHandler` with the `load` method defined or `null`.
+         */
+        IORouterRegistry.registerLoadRouter = function (loadRouter) {
+            IORouterRegistry.getInstance().loadRouters.push(loadRouter);
+        };
+        /**
+         * Look up IOHandler for saving, given a URL-like string.
+         *
+         * @param url
+         * @returns If only one match is found, an instance of IOHandler with the
+         * `save` method defined. If no match is found, `null`.
+         * @throws Error, if more than one match is found.
+         */
+        IORouterRegistry.getSaveHandlers = function (url) {
+            return IORouterRegistry.getHandlers(url, 'save');
+        };
+        /**
+         * Look up IOHandler for loading, given a URL-like string.
+         *
+         * @param url
+         * @param loadOptions Optional, custom load options.
+         * @returns All valid handlers for `url`, given the currently registered
+         *   handler routers.
+         */
+        IORouterRegistry.getLoadHandlers = function (url, loadOptions) {
+            return IORouterRegistry.getHandlers(url, 'load', loadOptions);
+        };
+        IORouterRegistry.getHandlers = function (url, handlerType, loadOptions) {
+            var validHandlers = [];
+            var routers = handlerType === 'load' ?
+                IORouterRegistry.getInstance().loadRouters :
+                IORouterRegistry.getInstance().saveRouters;
+            routers.forEach(function (router) {
+                var handler = router(url, loadOptions);
+                if (handler !== null) {
+                    validHandlers.push(handler);
+                }
+            });
+            return validHandlers;
+        };
+        return IORouterRegistry;
+    }());
+    var registerSaveRouter = function (loudRouter) {
+        return IORouterRegistry.registerSaveRouter(loudRouter);
+    };
+    var registerLoadRouter = function (loudRouter) {
+        return IORouterRegistry.registerLoadRouter(loudRouter);
+    };
+    var getSaveHandlers = function (url) {
+        return IORouterRegistry.getSaveHandlers(url);
+    };
+    var getLoadHandlers = function (url, loadOptions) {
+        return IORouterRegistry.getLoadHandlers(url, loadOptions);
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var DATABASE_NAME = 'tensorflowjs';
+    var DATABASE_VERSION = 1;
+    // Model data and ModelArtifactsInfo (metadata) are stored in two separate
+    // stores for efficient access of the list of stored models and their metadata.
+    // 1. The object store for model data: topology, weights and weight manifests.
+    var MODEL_STORE_NAME = 'models_store';
+    // 2. The object store for ModelArtifactsInfo, including meta-information such
+    //    as the type of topology (JSON vs binary), byte size of the topology, byte
+    //    size of the weights, etc.
+    var INFO_STORE_NAME = 'model_info_store';
+    function getIndexedDBFactory() {
+        if (!env().getBool('IS_BROWSER')) {
+            // TODO(cais): Add more info about what IOHandler subtypes are available.
+            //   Maybe point to a doc page on the web and/or automatically determine
+            //   the available IOHandlers and print them in the error message.
+            throw new Error('Failed to obtain IndexedDB factory because the current environment' +
+                'is not a web browser.');
+        }
+        // tslint:disable-next-line:no-any
+        var theWindow = typeof window === 'undefined' ? self : window;
+        var factory = theWindow.indexedDB || theWindow.mozIndexedDB ||
+            theWindow.webkitIndexedDB || theWindow.msIndexedDB ||
+            theWindow.shimIndexedDB;
+        if (factory == null) {
+            throw new Error('The current browser does not appear to support IndexedDB.');
+        }
+        return factory;
+    }
+    function setUpDatabase(openRequest) {
+        var db = openRequest.result;
+        db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });
+        db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });
+    }
+    /**
+     * IOHandler subclass: Browser IndexedDB.
+     *
+     * See the doc string of `browserIndexedDB` for more details.
+     */
+    var BrowserIndexedDB = /** @class */ (function () {
+        function BrowserIndexedDB(modelPath) {
+            this.indexedDB = getIndexedDBFactory();
+            if (modelPath == null || !modelPath) {
+                throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');
+            }
+            this.modelPath = modelPath;
+        }
+        BrowserIndexedDB.prototype.save = function (modelArtifacts) {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    // TODO(cais): Support saving GraphDef models.
+                    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+                        throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +
+                            'in binary formats yet.');
+                    }
+                    return [2 /*return*/, this.databaseAction(this.modelPath, modelArtifacts)];
+                });
+            });
+        };
+        BrowserIndexedDB.prototype.load = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    return [2 /*return*/, this.databaseAction(this.modelPath)];
+                });
+            });
+        };
+        /**
+         * Perform database action to put model artifacts into or read model artifacts
+         * from IndexedDB object store.
+         *
+         * Whether the action is put or get depends on whether `modelArtifacts` is
+         * specified. If it is specified, the action will be put; otherwise the action
+         * will be get.
+         *
+         * @param modelPath A unique string path for the model.
+         * @param modelArtifacts If specified, it will be the model artifacts to be
+         *   stored in IndexedDB.
+         * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`
+         *   of `ModelArtifacts`, if the action is get.
+         */
+        BrowserIndexedDB.prototype.databaseAction = function (modelPath, modelArtifacts) {
+            var _this = this;
+            return new Promise(function (resolve, reject) {
+                var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+                openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); };
+                openRequest.onsuccess = function () {
+                    var db = openRequest.result;
+                    if (modelArtifacts == null) {
+                        // Read model out from object store.
+                        var modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');
+                        var modelStore = modelTx.objectStore(MODEL_STORE_NAME);
+                        var getRequest_1 = modelStore.get(_this.modelPath);
+                        getRequest_1.onsuccess = function () {
+                            if (getRequest_1.result == null) {
+                                db.close();
+                                return reject(new Error("Cannot find model with path '" + _this.modelPath + "' " +
+                                    "in IndexedDB."));
+                            }
+                            else {
+                                resolve(getRequest_1.result.modelArtifacts);
+                            }
+                        };
+                        getRequest_1.onerror = function (error) {
+                            db.close();
+                            return reject(getRequest_1.error);
+                        };
+                        modelTx.oncomplete = function () { return db.close(); };
+                    }
+                    else {
+                        // Put model into object store.
+                        var modelArtifactsInfo_1 = getModelArtifactsInfoForJSON(modelArtifacts);
+                        // First, put ModelArtifactsInfo into info store.
+                        var infoTx_1 = db.transaction(INFO_STORE_NAME, 'readwrite');
+                        var infoStore_1 = infoTx_1.objectStore(INFO_STORE_NAME);
+                        var putInfoRequest_1 = infoStore_1.put({ modelPath: _this.modelPath, modelArtifactsInfo: modelArtifactsInfo_1 });
+                        var modelTx_1;
+                        putInfoRequest_1.onsuccess = function () {
+                            // Second, put model data into model store.
+                            modelTx_1 = db.transaction(MODEL_STORE_NAME, 'readwrite');
+                            var modelStore = modelTx_1.objectStore(MODEL_STORE_NAME);
+                            var putModelRequest = modelStore.put({
+                                modelPath: _this.modelPath,
+                                modelArtifacts: modelArtifacts,
+                                modelArtifactsInfo: modelArtifactsInfo_1
+                            });
+                            putModelRequest.onsuccess = function () { return resolve({ modelArtifactsInfo: modelArtifactsInfo_1 }); };
+                            putModelRequest.onerror = function (error) {
+                                // If the put-model request fails, roll back the info entry as
+                                // well.
+                                infoStore_1 = infoTx_1.objectStore(INFO_STORE_NAME);
+                                var deleteInfoRequest = infoStore_1.delete(_this.modelPath);
+                                deleteInfoRequest.onsuccess = function () {
+                                    db.close();
+                                    return reject(putModelRequest.error);
+                                };
+                                deleteInfoRequest.onerror = function (error) {
+                                    db.close();
+                                    return reject(putModelRequest.error);
+                                };
+                            };
+                        };
+                        putInfoRequest_1.onerror = function (error) {
+                            db.close();
+                            return reject(putInfoRequest_1.error);
+                        };
+                        infoTx_1.oncomplete = function () {
+                            if (modelTx_1 == null) {
+                                db.close();
+                            }
+                            else {
+                                modelTx_1.oncomplete = function () { return db.close(); };
+                            }
+                        };
+                    }
+                };
+                openRequest.onerror = function (error) { return reject(openRequest.error); };
+            });
+        };
+        BrowserIndexedDB.URL_SCHEME = 'indexeddb://';
+        return BrowserIndexedDB;
+    }());
+    var indexedDBRouter = function (url) {
+        if (!env().getBool('IS_BROWSER')) {
+            return null;
+        }
+        else {
+            if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {
+                return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));
+            }
+            else {
+                return null;
+            }
+        }
+    };
+    IORouterRegistry.registerSaveRouter(indexedDBRouter);
+    IORouterRegistry.registerLoadRouter(indexedDBRouter);
+    /**
+     * Creates a browser IndexedDB IOHandler for saving and loading models.
+     *
+     * ```js
+     * const model = tf.sequential();
+     * model.add(
+     *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
+     *
+     * const saveResult = await model.save('indexeddb://MyModel'));
+     * console.log(saveResult);
+     * ```
+     *
+     * @param modelPath A unique identifier for the model to be saved. Must be a
+     *   non-empty string.
+     * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),
+     *   which can be used with, e.g., `tf.Model.save`.
+     */
+    function browserIndexedDB(modelPath) {
+        return new BrowserIndexedDB(modelPath);
+    }
+    function maybeStripScheme(key) {
+        return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?
+            key.slice(BrowserIndexedDB.URL_SCHEME.length) :
+            key;
+    }
+    var BrowserIndexedDBManager = /** @class */ (function () {
+        function BrowserIndexedDBManager() {
+            this.indexedDB = getIndexedDBFactory();
+        }
+        BrowserIndexedDBManager.prototype.listModels = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var _this = this;
+                return __generator(this, function (_a) {
+                    return [2 /*return*/, new Promise(function (resolve, reject) {
+                            var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+                            openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); };
+                            openRequest.onsuccess = function () {
+                                var db = openRequest.result;
+                                var tx = db.transaction(INFO_STORE_NAME, 'readonly');
+                                var store = tx.objectStore(INFO_STORE_NAME);
+                                // tslint:disable:max-line-length
+                                // Need to cast `store` as `any` here because TypeScript's DOM
+                                // library does not have the `getAll()` method even though the
+                                // method is supported in the latest version of most mainstream
+                                // browsers:
+                                // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll
+                                // tslint:enable:max-line-length
+                                // tslint:disable-next-line:no-any
+                                var getAllInfoRequest = store.getAll();
+                                getAllInfoRequest.onsuccess = function () {
+                                    var out = {};
+                                    for (var _i = 0, _a = getAllInfoRequest.result; _i < _a.length; _i++) {
+                                        var item = _a[_i];
+                                        out[item.modelPath] = item.modelArtifactsInfo;
+                                    }
+                                    resolve(out);
+                                };
+                                getAllInfoRequest.onerror = function (error) {
+                                    db.close();
+                                    return reject(getAllInfoRequest.error);
+                                };
+                                tx.oncomplete = function () { return db.close(); };
+                            };
+                            openRequest.onerror = function (error) { return reject(openRequest.error); };
+                        })];
+                });
+            });
+        };
+        BrowserIndexedDBManager.prototype.removeModel = function (path) {
+            return __awaiter(this, void 0, void 0, function () {
+                var _this = this;
+                return __generator(this, function (_a) {
+                    path = maybeStripScheme(path);
+                    return [2 /*return*/, new Promise(function (resolve, reject) {
+                            var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+                            openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); };
+                            openRequest.onsuccess = function () {
+                                var db = openRequest.result;
+                                var infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');
+                                var infoStore = infoTx.objectStore(INFO_STORE_NAME);
+                                var getInfoRequest = infoStore.get(path);
+                                var modelTx;
+                                getInfoRequest.onsuccess = function () {
+                                    if (getInfoRequest.result == null) {
+                                        db.close();
+                                        return reject(new Error("Cannot find model with path '" + path + "' " +
+                                            "in IndexedDB."));
+                                    }
+                                    else {
+                                        // First, delete the entry in the info store.
+                                        var deleteInfoRequest = infoStore.delete(path);
+                                        var deleteModelData_1 = function () {
+                                            // Second, delete the entry in the model store.
+                                            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');
+                                            var modelStore = modelTx.objectStore(MODEL_STORE_NAME);
+                                            var deleteModelRequest = modelStore.delete(path);
+                                            deleteModelRequest.onsuccess = function () {
+                                                return resolve(getInfoRequest.result.modelArtifactsInfo);
+                                            };
+                                            deleteModelRequest.onerror = function (error) {
+                                                return reject(getInfoRequest.error);
+                                            };
+                                        };
+                                        // Proceed with deleting model data regardless of whether deletion
+                                        // of info data succeeds or not.
+                                        deleteInfoRequest.onsuccess = deleteModelData_1;
+                                        deleteInfoRequest.onerror = function (error) {
+                                            deleteModelData_1();
+                                            db.close();
+                                            return reject(getInfoRequest.error);
+                                        };
+                                    }
+                                };
+                                getInfoRequest.onerror = function (error) {
+                                    db.close();
+                                    return reject(getInfoRequest.error);
+                                };
+                                infoTx.oncomplete = function () {
+                                    if (modelTx == null) {
+                                        db.close();
+                                    }
+                                    else {
+                                        modelTx.oncomplete = function () { return db.close(); };
+                                    }
+                                };
+                            };
+                            openRequest.onerror = function (error) { return reject(openRequest.error); };
+                        })];
+                });
+            });
+        };
+        return BrowserIndexedDBManager;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var PATH_SEPARATOR = '/';
+    var PATH_PREFIX = 'tensorflowjs_models';
+    var INFO_SUFFIX = 'info';
+    var MODEL_TOPOLOGY_SUFFIX = 'model_topology';
+    var WEIGHT_SPECS_SUFFIX = 'weight_specs';
+    var WEIGHT_DATA_SUFFIX = 'weight_data';
+    var MODEL_METADATA_SUFFIX = 'model_metadata';
+    function getModelKeys(path) {
+        return {
+            info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),
+            topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),
+            weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),
+            weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),
+            modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)
+        };
+    }
+    /**
+     * Get model path from a local-storage key.
+     *
+     * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'
+     *
+     * @param key
+     */
+    function getModelPathFromKey(key) {
+        var items = key.split(PATH_SEPARATOR);
+        if (items.length < 3) {
+            throw new Error("Invalid key format: " + key);
+        }
+        return items.slice(1, items.length - 1).join(PATH_SEPARATOR);
+    }
+    function maybeStripScheme$1(key) {
+        return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?
+            key.slice(BrowserLocalStorage.URL_SCHEME.length) :
+            key;
+    }
+    /**
+     * IOHandler subclass: Browser Local Storage.
+     *
+     * See the doc string to `browserLocalStorage` for more details.
+     */
+    var BrowserLocalStorage = /** @class */ (function () {
+        function BrowserLocalStorage(modelPath) {
+            if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||
+                typeof window.localStorage === 'undefined') {
+                // TODO(cais): Add more info about what IOHandler subtypes are
+                // available.
+                //   Maybe point to a doc page on the web and/or automatically determine
+                //   the available IOHandlers and print them in the error message.
+                throw new Error('The current environment does not support local storage.');
+            }
+            this.LS = window.localStorage;
+            if (modelPath == null || !modelPath) {
+                throw new Error('For local storage, modelPath must not be null, undefined or empty.');
+            }
+            this.modelPath = modelPath;
+            this.keys = getModelKeys(this.modelPath);
+        }
+        /**
+         * Save model artifacts to browser local storage.
+         *
+         * See the documentation to `browserLocalStorage` for details on the saved
+         * artifacts.
+         *
+         * @param modelArtifacts The model artifacts to be stored.
+         * @returns An instance of SaveResult.
+         */
+        BrowserLocalStorage.prototype.save = function (modelArtifacts) {
+            return __awaiter(this, void 0, void 0, function () {
+                var topology, weightSpecs, modelArtifactsInfo, result;
+                return __generator(this, function (_a) {
+                    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+                        throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +
+                            'in binary formats yet.');
+                    }
+                    else {
+                        topology = JSON.stringify(modelArtifacts.modelTopology);
+                        weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);
+                        modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);
+                        try {
+                            this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));
+                            this.LS.setItem(this.keys.topology, topology);
+                            this.LS.setItem(this.keys.weightSpecs, weightSpecs);
+                            this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));
+                            result = {
+                                format: modelArtifacts.format,
+                                generatedBy: modelArtifacts.generatedBy,
+                                convertedBy: modelArtifacts.convertedBy
+                            };
+                            if (modelArtifacts.signature != null) {
+                                result.signature = modelArtifacts.signature;
+                            }
+                            if (modelArtifacts.userDefinedMetadata != null) {
+                                result.userDefinedMetadata = modelArtifacts.userDefinedMetadata;
+                            }
+                            if (modelArtifacts.modelInitializer != null) {
+                                result.modelInitializer = modelArtifacts.modelInitializer;
+                            }
+                            this.LS.setItem(this.keys.modelMetadata, JSON.stringify(result));
+                            return [2 /*return*/, { modelArtifactsInfo: modelArtifactsInfo }];
+                        }
+                        catch (err) {
+                            // If saving failed, clean up all items saved so far.
+                            this.LS.removeItem(this.keys.info);
+                            this.LS.removeItem(this.keys.topology);
+                            this.LS.removeItem(this.keys.weightSpecs);
+                            this.LS.removeItem(this.keys.weightData);
+                            this.LS.removeItem(this.keys.modelMetadata);
+                            throw new Error("Failed to save model '" + this.modelPath + "' to local storage: " +
+                                "size quota being exceeded is a possible cause of this failure: " +
+                                ("modelTopologyBytes=" + modelArtifactsInfo.modelTopologyBytes + ", ") +
+                                ("weightSpecsBytes=" + modelArtifactsInfo.weightSpecsBytes + ", ") +
+                                ("weightDataBytes=" + modelArtifactsInfo.weightDataBytes + "."));
+                        }
+                    }
+                    return [2 /*return*/];
+                });
+            });
+        };
+        /**
+         * Load a model from local storage.
+         *
+         * See the documentation to `browserLocalStorage` for details on the saved
+         * artifacts.
+         *
+         * @returns The loaded model (if loading succeeds).
+         */
+        BrowserLocalStorage.prototype.load = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var info, out, topology, weightSpecs, metadataString, metadata, weightDataBase64;
+                return __generator(this, function (_a) {
+                    info = JSON.parse(this.LS.getItem(this.keys.info));
+                    if (info == null) {
+                        throw new Error("In local storage, there is no model with name '" + this.modelPath + "'");
+                    }
+                    if (info.modelTopologyType !== 'JSON') {
+                        throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +
+                            'topology yet.');
+                    }
+                    out = {};
+                    topology = JSON.parse(this.LS.getItem(this.keys.topology));
+                    if (topology == null) {
+                        throw new Error("In local storage, the topology of model '" + this.modelPath + "' " +
+                            "is missing.");
+                    }
+                    out.modelTopology = topology;
+                    weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
+                    if (weightSpecs == null) {
+                        throw new Error("In local storage, the weight specs of model '" + this.modelPath + "' " +
+                            "are missing.");
+                    }
+                    out.weightSpecs = weightSpecs;
+                    metadataString = this.LS.getItem(this.keys.modelMetadata);
+                    if (metadataString != null) {
+                        metadata = JSON.parse(metadataString);
+                        out.format = metadata['format'];
+                        out.generatedBy = metadata['generatedBy'];
+                        out.convertedBy = metadata['convertedBy'];
+                        if (metadata['signature'] != null) {
+                            out.signature = metadata['signature'];
+                        }
+                        if (metadata['userDefinedMetadata'] != null) {
+                            out.userDefinedMetadata = metadata['userDefinedMetadata'];
+                        }
+                        if (metadata['modelInitializer'] != null) {
+                            out.modelInitializer = metadata['modelInitializer'];
+                        }
+                    }
+                    weightDataBase64 = this.LS.getItem(this.keys.weightData);
+                    if (weightDataBase64 == null) {
+                        throw new Error("In local storage, the binary weight values of model " +
+                            ("'" + this.modelPath + "' are missing."));
+                    }
+                    out.weightData = base64StringToArrayBuffer(weightDataBase64);
+                    return [2 /*return*/, out];
+                });
+            });
+        };
+        BrowserLocalStorage.URL_SCHEME = 'localstorage://';
+        return BrowserLocalStorage;
+    }());
+    var localStorageRouter = function (url) {
+        if (!env().getBool('IS_BROWSER')) {
+            return null;
+        }
+        else {
+            if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {
+                return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));
+            }
+            else {
+                return null;
+            }
+        }
+    };
+    IORouterRegistry.registerSaveRouter(localStorageRouter);
+    IORouterRegistry.registerLoadRouter(localStorageRouter);
+    /**
+     * Factory function for local storage IOHandler.
+     *
+     * This `IOHandler` supports both `save` and `load`.
+     *
+     * For each model's saved artifacts, four items are saved to local storage.
+     *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the
+     *     model, such as date saved, type of the topology, size in bytes, etc.
+     *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-
+     *     style models, this is a stringized JSON.
+     *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the
+     *     model, can be used to decode the saved binary weight values (see
+     *     item below).
+     *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary
+     *     weight values, stored as a base64-encoded string.
+     *
+     * Saving may throw an `Error` if the total size of the artifacts exceed the
+     * browser-specific quota.
+     *
+     * @param modelPath A unique identifier for the model to be saved. Must be a
+     *   non-empty string.
+     * @returns An instance of `IOHandler`, which can be used with, e.g.,
+     *   `tf.Model.save`.
+     */
+    function browserLocalStorage(modelPath) {
+        return new BrowserLocalStorage(modelPath);
+    }
+    var BrowserLocalStorageManager = /** @class */ (function () {
+        function BrowserLocalStorageManager() {
+            assert(env().getBool('IS_BROWSER'), function () { return 'Current environment is not a web browser'; });
+            assert(typeof window === 'undefined' ||
+                typeof window.localStorage !== 'undefined', function () { return 'Current browser does not appear to support localStorage'; });
+            this.LS = window.localStorage;
+        }
+        BrowserLocalStorageManager.prototype.listModels = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var out, prefix, suffix, i, key, modelPath;
+                return __generator(this, function (_a) {
+                    out = {};
+                    prefix = PATH_PREFIX + PATH_SEPARATOR;
+                    suffix = PATH_SEPARATOR + INFO_SUFFIX;
+                    for (i = 0; i < this.LS.length; ++i) {
+                        key = this.LS.key(i);
+                        if (key.startsWith(prefix) && key.endsWith(suffix)) {
+                            modelPath = getModelPathFromKey(key);
+                            out[modelPath] = JSON.parse(this.LS.getItem(key));
+                        }
+                    }
+                    return [2 /*return*/, out];
+                });
+            });
+        };
+        BrowserLocalStorageManager.prototype.removeModel = function (path) {
+            return __awaiter(this, void 0, void 0, function () {
+                var keys, info;
+                return __generator(this, function (_a) {
+                    path = maybeStripScheme$1(path);
+                    keys = getModelKeys(path);
+                    if (this.LS.getItem(keys.info) == null) {
+                        throw new Error("Cannot find model at path '" + path + "'");
+                    }
+                    info = JSON.parse(this.LS.getItem(keys.info));
+                    this.LS.removeItem(keys.info);
+                    this.LS.removeItem(keys.topology);
+                    this.LS.removeItem(keys.weightSpecs);
+                    this.LS.removeItem(keys.weightData);
+                    return [2 /*return*/, info];
+                });
+            });
+        };
+        return BrowserLocalStorageManager;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var URL_SCHEME_SUFFIX = '://';
+    var ModelStoreManagerRegistry = /** @class */ (function () {
+        function ModelStoreManagerRegistry() {
+            this.managers = {};
+        }
+        ModelStoreManagerRegistry.getInstance = function () {
+            if (ModelStoreManagerRegistry.instance == null) {
+                ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
+            }
+            return ModelStoreManagerRegistry.instance;
+        };
+        /**
+         * Register a save-handler router.
+         *
+         * @param saveRouter A function that maps a URL-like string onto an instance
+         * of `IOHandler` with the `save` method defined or `null`.
+         */
+        ModelStoreManagerRegistry.registerManager = function (scheme, manager) {
+            assert(scheme != null, function () { return 'scheme must not be undefined or null.'; });
+            if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
+                scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
+            }
+            assert(scheme.length > 0, function () { return 'scheme must not be an empty string.'; });
+            var registry = ModelStoreManagerRegistry.getInstance();
+            assert(registry.managers[scheme] == null, function () { return "A model store manager is already registered for scheme '" + scheme + "'."; });
+            registry.managers[scheme] = manager;
+        };
+        ModelStoreManagerRegistry.getManager = function (scheme) {
+            var manager = this.getInstance().managers[scheme];
+            if (manager == null) {
+                throw new Error("Cannot find model manager for scheme '" + scheme + "'");
+            }
+            return manager;
+        };
+        ModelStoreManagerRegistry.getSchemes = function () {
+            return Object.keys(this.getInstance().managers);
+        };
+        return ModelStoreManagerRegistry;
+    }());
+    /**
+     * Helper method for parsing a URL string into a scheme and a path.
+     *
+     * @param url E.g., 'localstorage://my-model'
+     * @returns A dictionary with two fields: scheme and path.
+     *   Scheme: e.g., 'localstorage' in the example above.
+     *   Path: e.g., 'my-model' in the example above.
+     */
+    function parseURL(url) {
+        if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
+            throw new Error("The url string provided does not contain a scheme. " +
+                "Supported schemes are: " +
+                ("" + ModelStoreManagerRegistry.getSchemes().join(',')));
+        }
+        return {
+            scheme: url.split(URL_SCHEME_SUFFIX)[0],
+            path: url.split(URL_SCHEME_SUFFIX)[1],
+        };
+    }
+    function cloneModelInternal(sourceURL, destURL, deleteSource) {
+        if (deleteSource === void 0) { deleteSource = false; }
+        return __awaiter(this, void 0, void 0, function () {
+            var loadHandlers, loadHandler, saveHandlers, saveHandler, sourceScheme, sourcePath, sameMedium, modelArtifacts, saveResult;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        assert(sourceURL !== destURL, function () { return "Old path and new path are the same: '" + sourceURL + "'"; });
+                        loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);
+                        assert(loadHandlers.length > 0, function () { return "Copying failed because no load handler is found for source URL " + sourceURL + "."; });
+                        assert(loadHandlers.length < 2, function () { return "Copying failed because more than one (" + loadHandlers.length + ") " +
+                            ("load handlers for source URL " + sourceURL + "."); });
+                        loadHandler = loadHandlers[0];
+                        saveHandlers = IORouterRegistry.getSaveHandlers(destURL);
+                        assert(saveHandlers.length > 0, function () { return "Copying failed because no save handler is found for destination " +
+                            ("URL " + destURL + "."); });
+                        assert(saveHandlers.length < 2, function () { return "Copying failed because more than one (" + loadHandlers.length + ") " +
+                            ("save handlers for destination URL " + destURL + "."); });
+                        saveHandler = saveHandlers[0];
+                        sourceScheme = parseURL(sourceURL).scheme;
+                        sourcePath = parseURL(sourceURL).path;
+                        sameMedium = sourceScheme === parseURL(sourceURL).scheme;
+                        return [4 /*yield*/, loadHandler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        if (!(deleteSource && sameMedium)) return [3 /*break*/, 3];
+                        return [4 /*yield*/, ModelStoreManagerRegistry.getManager(sourceScheme)
+                                .removeModel(sourcePath)];
+                    case 2:
+                        _a.sent();
+                        _a.label = 3;
+                    case 3: return [4 /*yield*/, saveHandler.save(modelArtifacts)];
+                    case 4:
+                        saveResult = _a.sent();
+                        if (!(deleteSource && !sameMedium)) return [3 /*break*/, 6];
+                        return [4 /*yield*/, ModelStoreManagerRegistry.getManager(sourceScheme)
+                                .removeModel(sourcePath)];
+                    case 5:
+                        _a.sent();
+                        _a.label = 6;
+                    case 6: return [2 /*return*/, saveResult.modelArtifactsInfo];
+                }
+            });
+        });
+    }
+    /**
+     * List all models stored in registered storage mediums.
+     *
+     * For a web browser environment, the registered mediums are Local Storage and
+     * IndexedDB.
+     *
+     * ```js
+     * // First create and save a model.
+     * const model = tf.sequential();
+     * model.add(tf.layers.dense(
+     *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+     * await model.save('localstorage://demo/management/model1');
+     *
+     * // Then list existing models.
+     * console.log(JSON.stringify(await tf.io.listModels()));
+     *
+     * // Delete the model.
+     * await tf.io.removeModel('localstorage://demo/management/model1');
+     *
+     * // List models again.
+     * console.log(JSON.stringify(await tf.io.listModels()));
+     * ```
+     *
+     * @returns A `Promise` of a dictionary mapping URLs of existing models to
+     * their model artifacts info. URLs include medium-specific schemes, e.g.,
+     *   'indexeddb://my/model/1'. Model artifacts info include type of the
+     * model's topology, byte sizes of the topology, weights, etc.
+     *
+     * @doc {
+     *   heading: 'Models',
+     *   subheading: 'Management',
+     *   namespace: 'io',
+     *   ignoreCI: true
+     * }
+     */
+    function listModels() {
+        return __awaiter(this, void 0, void 0, function () {
+            var schemes, out, _i, schemes_1, scheme, schemeOut, path, url;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        schemes = ModelStoreManagerRegistry.getSchemes();
+                        out = {};
+                        _i = 0, schemes_1 = schemes;
+                        _a.label = 1;
+                    case 1:
+                        if (!(_i < schemes_1.length)) return [3 /*break*/, 4];
+                        scheme = schemes_1[_i];
+                        return [4 /*yield*/, ModelStoreManagerRegistry.getManager(scheme).listModels()];
+                    case 2:
+                        schemeOut = _a.sent();
+                        for (path in schemeOut) {
+                            url = scheme + URL_SCHEME_SUFFIX + path;
+                            out[url] = schemeOut[path];
+                        }
+                        _a.label = 3;
+                    case 3:
+                        _i++;
+                        return [3 /*break*/, 1];
+                    case 4: return [2 /*return*/, out];
+                }
+            });
+        });
+    }
+    /**
+     * Remove a model specified by URL from a reigstered storage medium.
+     *
+     * ```js
+     * // First create and save a model.
+     * const model = tf.sequential();
+     * model.add(tf.layers.dense(
+     *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+     * await model.save('localstorage://demo/management/model1');
+     *
+     * // Then list existing models.
+     * console.log(JSON.stringify(await tf.io.listModels()));
+     *
+     * // Delete the model.
+     * await tf.io.removeModel('localstorage://demo/management/model1');
+     *
+     * // List models again.
+     * console.log(JSON.stringify(await tf.io.listModels()));
+     * ```
+     *
+     * @param url A URL to a stored model, with a scheme prefix, e.g.,
+     *   'localstorage://my-model-1', 'indexeddb://my/model/2'.
+     * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+     *   is successful).
+     * @throws Error if deletion fails, e.g., if no model exists at `path`.
+     *
+     * @doc {
+     *   heading: 'Models',
+     *   subheading: 'Management',
+     *   namespace: 'io',
+     *   ignoreCI: true
+     * }
+     */
+    function removeModel(url) {
+        return __awaiter(this, void 0, void 0, function () {
+            var schemeAndPath, manager;
+            return __generator(this, function (_a) {
+                schemeAndPath = parseURL(url);
+                manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
+                return [2 /*return*/, manager.removeModel(schemeAndPath.path)];
+            });
+        });
+    }
+    /**
+     * Copy a model from one URL to another.
+     *
+     * This function supports:
+     *
+     * 1. Copying within a storage medium, e.g.,
+     *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`
+     * 2. Copying between two storage mediums, e.g.,
+     *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`
+     *
+     * ```js
+     * // First create and save a model.
+     * const model = tf.sequential();
+     * model.add(tf.layers.dense(
+     *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+     * await model.save('localstorage://demo/management/model1');
+     *
+     * // Then list existing models.
+     * console.log(JSON.stringify(await tf.io.listModels()));
+     *
+     * // Copy the model, from Local Storage to IndexedDB.
+     * await tf.io.copyModel(
+     *     'localstorage://demo/management/model1',
+     *     'indexeddb://demo/management/model1');
+     *
+     * // List models again.
+     * console.log(JSON.stringify(await tf.io.listModels()));
+     *
+     * // Remove both models.
+     * await tf.io.removeModel('localstorage://demo/management/model1');
+     * await tf.io.removeModel('indexeddb://demo/management/model1');
+     * ```
+     *
+     * @param sourceURL Source URL of copying.
+     * @param destURL Destination URL of copying.
+     * @returns ModelArtifactsInfo of the copied model (if and only if copying
+     *   is successful).
+     * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or
+     *   if `oldPath` and `newPath` are identical.
+     *
+     * @doc {
+     *   heading: 'Models',
+     *   subheading: 'Management',
+     *   namespace: 'io',
+     *   ignoreCI: true
+     * }
+     */
+    function copyModel(sourceURL, destURL) {
+        return __awaiter(this, void 0, void 0, function () {
+            var deleteSource;
+            return __generator(this, function (_a) {
+                deleteSource = false;
+                return [2 /*return*/, cloneModelInternal(sourceURL, destURL, deleteSource)];
+            });
+        });
+    }
+    /**
+     * Move a model from one URL to another.
+     *
+     * This function supports:
+     *
+     * 1. Moving within a storage medium, e.g.,
+     *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`
+     * 2. Moving between two storage mediums, e.g.,
+     *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`
+     *
+     * ```js
+     * // First create and save a model.
+     * const model = tf.sequential();
+     * model.add(tf.layers.dense(
+     *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+     * await model.save('localstorage://demo/management/model1');
+     *
+     * // Then list existing models.
+     * console.log(JSON.stringify(await tf.io.listModels()));
+     *
+     * // Move the model, from Local Storage to IndexedDB.
+     * await tf.io.moveModel(
+     *     'localstorage://demo/management/model1',
+     *     'indexeddb://demo/management/model1');
+     *
+     * // List models again.
+     * console.log(JSON.stringify(await tf.io.listModels()));
+     *
+     * // Remove the moved model.
+     * await tf.io.removeModel('indexeddb://demo/management/model1');
+     * ```
+     *
+     * @param sourceURL Source URL of moving.
+     * @param destURL Destination URL of moving.
+     * @returns ModelArtifactsInfo of the copied model (if and only if copying
+     *   is successful).
+     * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or
+     *   if `oldPath` and `newPath` are identical.
+     *
+     * @doc {
+     *   heading: 'Models',
+     *   subheading: 'Management',
+     *   namespace: 'io',
+     *   ignoreCI: true
+     * }
+     */
+    function moveModel(sourceURL, destURL) {
+        return __awaiter(this, void 0, void 0, function () {
+            var deleteSource;
+            return __generator(this, function (_a) {
+                deleteSource = true;
+                return [2 /*return*/, cloneModelInternal(sourceURL, destURL, deleteSource)];
+            });
+        });
+    }
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var PlatformBrowser = /** @class */ (function () {
+        function PlatformBrowser() {
+        }
+        PlatformBrowser.prototype.fetch = function (path, init) {
+            return fetch(path, init);
+        };
+        PlatformBrowser.prototype.now = function () {
+            return performance.now();
+        };
+        PlatformBrowser.prototype.encode = function (text, encoding) {
+            if (encoding !== 'utf-8' && encoding !== 'utf8') {
+                throw new Error("Browser's encoder only supports utf-8, but got " + encoding);
+            }
+            if (this.textEncoder == null) {
+                this.textEncoder = new TextEncoder();
+            }
+            return this.textEncoder.encode(text);
+        };
+        PlatformBrowser.prototype.decode = function (bytes, encoding) {
+            return new TextDecoder(encoding).decode(bytes);
+        };
+        return PlatformBrowser;
+    }());
+    if (env().get('IS_BROWSER')) {
+        env().setPlatform('browser', new PlatformBrowser());
+        // Register LocalStorage IOHandler
+        try {
+            ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());
+        }
+        catch (err) {
+        }
+        // Register IndexedDB IOHandler
+        try {
+            ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());
+        }
+        catch (err) {
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // We are wrapping this within an object so it can be stubbed by Jasmine.
+    var getNodeFetch = {
+        // tslint:disable-next-line:no-require-imports
+        importFetch: function () { return require('node-fetch'); }
+    };
+    var systemFetch;
+    var PlatformNode = /** @class */ (function () {
+        function PlatformNode() {
+            // tslint:disable-next-line:no-require-imports
+            this.util = require('util');
+            // According to the spec, the built-in encoder can do only UTF-8 encoding.
+            // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder
+            this.textEncoder = new this.util.TextEncoder();
+        }
+        PlatformNode.prototype.fetch = function (path, requestInits) {
+            if (env().global.fetch != null) {
+                return env().global.fetch(path, requestInits);
+            }
+            if (systemFetch == null) {
+                systemFetch = getNodeFetch.importFetch();
+            }
+            return systemFetch(path, requestInits);
+        };
+        PlatformNode.prototype.now = function () {
+            var time = process.hrtime();
+            return time[0] * 1000 + time[1] / 1000000;
+        };
+        PlatformNode.prototype.encode = function (text, encoding) {
+            if (encoding !== 'utf-8' && encoding !== 'utf8') {
+                throw new Error("Node built-in encoder only supports utf-8, but got " + encoding);
+            }
+            return this.textEncoder.encode(text);
+        };
+        PlatformNode.prototype.decode = function (bytes, encoding) {
+            if (bytes.length === 0) {
+                return '';
+            }
+            return new this.util.TextDecoder(encoding).decode(bytes);
+        };
+        return PlatformNode;
+    }());
+    if (env().get('IS_NODE')) {
+        env().setPlatform('node', new PlatformNode());
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google Inc. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.
+     *
+     * The values are stored in CPU as `TypedArray`. Fill the buffer using
+     * `buffer.set()`, or by modifying directly `buffer.values`.
+     *
+     * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with
+     * those values.
+     *
+     * ```js
+     * // Create a buffer and set values at particular indices.
+     * const buffer = tf.buffer([2, 2]);
+     * buffer.set(3, 0, 0);
+     * buffer.set(5, 1, 0);
+     *
+     * // Convert the buffer back to a tensor.
+     * buffer.toTensor().print();
+     * ```
+     *
+     * @param shape An array of integers defining the output tensor shape.
+     * @param dtype The dtype of the buffer. Defaults to 'float32'.
+     * @param values The values of the buffer as `TypedArray`. Defaults to
+     * zeros.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function buffer(shape, dtype, values) {
+        if (dtype === void 0) { dtype = 'float32'; }
+        dtype = dtype || 'float32';
+        assertNonNegativeIntegerDimensions(shape);
+        return new TensorBuffer(shape, dtype, values);
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google Inc. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Casts a `tf.Tensor` to a new dtype.
+     *
+     * ```js
+     * const x = tf.tensor1d([1.5, 2.5, 3]);
+     * tf.cast(x, 'int32').print();
+     * ```
+     * @param x The input tensor to be casted.
+     * @param dtype The dtype to cast the input tensor to.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Transformations'}
+     */
+    function cast_(x, dtype) {
+        var $x = convertToTensor(x, 'x', 'cast');
+        // Sanity checks.
+        if (!isValidDtype(dtype)) {
+            throw new Error("Failed to cast to unknown dtype " + dtype);
+        }
+        if (dtype === 'string' && $x.dtype !== 'string' ||
+            dtype !== 'string' && $x.dtype === 'string') {
+            throw new Error('Only strings can be casted to strings');
+        }
+        var inputs = { x: $x };
+        var attrs = { dtype: dtype };
+        return ENGINE.runKernel(Cast, inputs, attrs);
+    }
+    var cast = op({ cast_: cast_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a new tensor with the same values and shape as the specified
+     * tensor.
+     *
+     * ```js
+     * const x = tf.tensor([1, 2]);
+     *
+     * x.clone().print();
+     * ```
+     *
+     * @param x The tensor to clone.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function clone_(x) {
+        var $x = convertToTensor(x, 'x', 'clone', 'string_or_numeric');
+        var inputs = { x: $x };
+        // Note this op is called tf.identity in python. Hence the kernel name used
+        // here.
+        return ENGINE.runKernel(Identity, inputs);
+    }
+    var clone = op({ clone_: clone_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google Inc. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Prints information about the `tf.Tensor` including its data.
+     *
+     * ```js
+     * const verbose = true;
+     * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);
+     * ```
+     * @param x The tensor to be printed.
+     * @param verbose Whether to print verbose information about the ` Tensor`,
+     * including dtype and size.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function print(x, verbose) {
+        if (verbose === void 0) { verbose = false; }
+        console.log(x.toString(verbose));
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google Inc. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    getOrMakeEngine();
+    var opHandler$1 = {
+        buffer: buffer,
+        cast: cast,
+        clone: clone,
+        print: print
+    };
+    setOpHandler(opHandler$1);
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var DEFAULT_FILE_NAME_PREFIX = 'model';
+    var DEFAULT_JSON_EXTENSION_NAME = '.json';
+    var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';
+    function defer(f) {
+        return new Promise(function (resolve) { return setTimeout(resolve); }).then(f);
+    }
+    var BrowserDownloads = /** @class */ (function () {
+        function BrowserDownloads(fileNamePrefix) {
+            if (!env().getBool('IS_BROWSER')) {
+                // TODO(cais): Provide info on what IOHandlers are available under the
+                //   current environment.
+                throw new Error('browserDownloads() cannot proceed because the current environment ' +
+                    'is not a browser.');
+            }
+            if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
+                fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
+            }
+            if (fileNamePrefix == null || fileNamePrefix.length === 0) {
+                fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
+            }
+            this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
+            this.weightDataFileName =
+                fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
+        }
+        BrowserDownloads.prototype.save = function (modelArtifacts) {
+            return __awaiter(this, void 0, void 0, function () {
+                var weightsURL, weightsManifest, modelTopologyAndWeightManifest, modelTopologyAndWeightManifestURL, jsonAnchor_1, weightDataAnchor_1;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            if (typeof (document) === 'undefined') {
+                                throw new Error('Browser downloads are not supported in ' +
+                                    'this environment since `document` is not present');
+                            }
+                            weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));
+                            if (!(modelArtifacts.modelTopology instanceof ArrayBuffer)) return [3 /*break*/, 1];
+                            throw new Error('BrowserDownloads.save() does not support saving model topology ' +
+                                'in binary formats yet.');
+                        case 1:
+                            weightsManifest = [{
+                                    paths: ['./' + this.weightDataFileName],
+                                    weights: modelArtifacts.weightSpecs
+                                }];
+                            modelTopologyAndWeightManifest = {
+                                modelTopology: modelArtifacts.modelTopology,
+                                format: modelArtifacts.format,
+                                generatedBy: modelArtifacts.generatedBy,
+                                convertedBy: modelArtifacts.convertedBy,
+                                weightsManifest: weightsManifest
+                            };
+                            if (modelArtifacts.signature != null) {
+                                modelTopologyAndWeightManifest.signature = modelArtifacts.signature;
+                            }
+                            if (modelArtifacts.userDefinedMetadata != null) {
+                                modelTopologyAndWeightManifest.userDefinedMetadata =
+                                    modelArtifacts.userDefinedMetadata;
+                            }
+                            if (modelArtifacts.modelInitializer != null) {
+                                modelTopologyAndWeightManifest.modelInitializer =
+                                    modelArtifacts.modelInitializer;
+                            }
+                            modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));
+                            jsonAnchor_1 = this.jsonAnchor == null ? document.createElement('a') :
+                                this.jsonAnchor;
+                            jsonAnchor_1.download = this.modelTopologyFileName;
+                            jsonAnchor_1.href = modelTopologyAndWeightManifestURL;
+                            // Trigger downloads by evoking a click event on the download anchors.
+                            // When multiple downloads are started synchronously, Firefox will only
+                            // save the last one.
+                            return [4 /*yield*/, defer(function () { return jsonAnchor_1.dispatchEvent(new MouseEvent('click')); })];
+                        case 2:
+                            // Trigger downloads by evoking a click event on the download anchors.
+                            // When multiple downloads are started synchronously, Firefox will only
+                            // save the last one.
+                            _a.sent();
+                            if (!(modelArtifacts.weightData != null)) return [3 /*break*/, 4];
+                            weightDataAnchor_1 = this.weightDataAnchor == null ?
+                                document.createElement('a') :
+                                this.weightDataAnchor;
+                            weightDataAnchor_1.download = this.weightDataFileName;
+                            weightDataAnchor_1.href = weightsURL;
+                            return [4 /*yield*/, defer(function () { return weightDataAnchor_1.dispatchEvent(new MouseEvent('click')); })];
+                        case 3:
+                            _a.sent();
+                            _a.label = 4;
+                        case 4: return [2 /*return*/, { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) }];
+                    }
+                });
+            });
+        };
+        BrowserDownloads.URL_SCHEME = 'downloads://';
+        return BrowserDownloads;
+    }());
+    var BrowserFiles = /** @class */ (function () {
+        function BrowserFiles(files) {
+            if (files == null || files.length < 1) {
+                throw new Error("When calling browserFiles, at least 1 file is required, " +
+                    ("but received " + files));
+            }
+            this.files = files;
+        }
+        BrowserFiles.prototype.load = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var jsonFile, weightFiles;
+                var _this = this;
+                return __generator(this, function (_a) {
+                    jsonFile = this.files[0];
+                    weightFiles = this.files.slice(1);
+                    return [2 /*return*/, new Promise(function (resolve, reject) {
+                            var jsonReader = new FileReader();
+                            jsonReader.onload = function (event) {
+                                // tslint:disable-next-line:no-any
+                                var modelJSON = JSON.parse(event.target.result);
+                                var modelTopology = modelJSON.modelTopology;
+                                if (modelTopology == null) {
+                                    reject(new Error("modelTopology field is missing from file " + jsonFile.name));
+                                    return;
+                                }
+                                if (weightFiles.length === 0) {
+                                    resolve({ modelTopology: modelTopology });
+                                }
+                                var weightsManifest = modelJSON.weightsManifest;
+                                if (weightsManifest == null) {
+                                    reject(new Error("weightManifest field is missing from file " + jsonFile.name));
+                                    return;
+                                }
+                                var pathToFile;
+                                try {
+                                    pathToFile =
+                                        _this.checkManifestAndWeightFiles(weightsManifest, weightFiles);
+                                }
+                                catch (err) {
+                                    reject(err);
+                                    return;
+                                }
+                                var weightSpecs = [];
+                                var paths = [];
+                                var perFileBuffers = [];
+                                weightsManifest.forEach(function (weightsGroup) {
+                                    weightsGroup.paths.forEach(function (path) {
+                                        paths.push(path);
+                                        perFileBuffers.push(null);
+                                    });
+                                    weightSpecs.push.apply(weightSpecs, weightsGroup.weights);
+                                });
+                                weightsManifest.forEach(function (weightsGroup) {
+                                    weightsGroup.paths.forEach(function (path) {
+                                        var weightFileReader = new FileReader();
+                                        weightFileReader.onload = function (event) {
+                                            // tslint:disable-next-line:no-any
+                                            var weightData = event.target.result;
+                                            var index = paths.indexOf(path);
+                                            perFileBuffers[index] = weightData;
+                                            if (perFileBuffers.indexOf(null) === -1) {
+                                                var result = {
+                                                    modelTopology: modelTopology,
+                                                    weightSpecs: weightSpecs,
+                                                    weightData: concatenateArrayBuffers(perFileBuffers),
+                                                    format: modelJSON.format,
+                                                    generatedBy: modelJSON.generatedBy,
+                                                    convertedBy: modelJSON.convertedBy
+                                                };
+                                                if (modelJSON.signature != null) {
+                                                    result.signature = modelJSON.signature;
+                                                }
+                                                if (modelJSON.userDefinedMetadata != null) {
+                                                    result.userDefinedMetadata = modelJSON.userDefinedMetadata;
+                                                }
+                                                if (modelJSON.modelInitializer != null) {
+                                                    result.modelInitializer = modelJSON.modelInitializer;
+                                                }
+                                                resolve(result);
+                                            }
+                                        };
+                                        weightFileReader.onerror = function (error) {
+                                            return reject("Failed to weights data from file of path '" + path + "'.");
+                                        };
+                                        weightFileReader.readAsArrayBuffer(pathToFile[path]);
+                                    });
+                                });
+                            };
+                            jsonReader.onerror = function (error) { return reject("Failed to read model topology and weights manifest JSON " +
+                                ("from file '" + jsonFile.name + "'. BrowserFiles supports loading ") +
+                                "Keras-style tf.Model artifacts only."); };
+                            jsonReader.readAsText(jsonFile);
+                        })];
+                });
+            });
+        };
+        /**
+         * Check the compatibility between weights manifest and weight files.
+         */
+        BrowserFiles.prototype.checkManifestAndWeightFiles = function (manifest, files) {
+            var basenames = [];
+            var fileNames = files.map(function (file) { return basename(file.name); });
+            var pathToFile = {};
+            for (var _i = 0, manifest_1 = manifest; _i < manifest_1.length; _i++) {
+                var group = manifest_1[_i];
+                group.paths.forEach(function (path) {
+                    var pathBasename = basename(path);
+                    if (basenames.indexOf(pathBasename) !== -1) {
+                        throw new Error("Duplicate file basename found in weights manifest: " +
+                            ("'" + pathBasename + "'"));
+                    }
+                    basenames.push(pathBasename);
+                    if (fileNames.indexOf(pathBasename) === -1) {
+                        throw new Error("Weight file with basename '" + pathBasename + "' is not provided.");
+                    }
+                    else {
+                        pathToFile[path] = files[fileNames.indexOf(pathBasename)];
+                    }
+                });
+            }
+            if (basenames.length !== files.length) {
+                throw new Error("Mismatch in the number of files in weights manifest " +
+                    ("(" + basenames.length + ") and the number of weight files provided ") +
+                    ("(" + files.length + ")."));
+            }
+            return pathToFile;
+        };
+        return BrowserFiles;
+    }());
+    var browserDownloadsRouter = function (url) {
+        if (!env().getBool('IS_BROWSER')) {
+            return null;
+        }
+        else {
+            if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
+                return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
+            }
+            else {
+                return null;
+            }
+        }
+    };
+    IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
+    /**
+     * Creates an IOHandler that triggers file downloads from the browser.
+     *
+     * The returned `IOHandler` instance can be used as model exporting methods such
+     * as `tf.Model.save` and supports only saving.
+     *
+     * ```js
+     * const model = tf.sequential();
+     * model.add(tf.layers.dense(
+     *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+     * const saveResult = await model.save('downloads://mymodel');
+     * // This will trigger downloading of two files:
+     * //   'mymodel.json' and 'mymodel.weights.bin'.
+     * console.log(saveResult);
+     * ```
+     *
+     * @param fileNamePrefix Prefix name of the files to be downloaded. For use with
+     *   `tf.Model`, `fileNamePrefix` should follow either of the following two
+     *   formats:
+     *   1. `null` or `undefined`, in which case the default file
+     *      names will be used:
+     *      - 'model.json' for the JSON file containing the model topology and
+     *        weights manifest.
+     *      - 'model.weights.bin' for the binary file containing the binary weight
+     *        values.
+     *   2. A single string or an Array of a single string, as the file name prefix.
+     *      For example, if `'foo'` is provided, the downloaded JSON
+     *      file and binary weights file will be named 'foo.json' and
+     *      'foo.weights.bin', respectively.
+     * @param config Additional configuration for triggering downloads.
+     * @returns An instance of `BrowserDownloads` `IOHandler`.
+     *
+     * @doc {
+     *   heading: 'Models',
+     *   subheading: 'Loading',
+     *   namespace: 'io',
+     *   ignoreCI: true
+     * }
+     */
+    function browserDownloads(fileNamePrefix) {
+        if (fileNamePrefix === void 0) { fileNamePrefix = 'model'; }
+        return new BrowserDownloads(fileNamePrefix);
+    }
+    /**
+     * Creates an IOHandler that loads model artifacts from user-selected files.
+     *
+     * This method can be used for loading from files such as user-selected files
+     * in the browser.
+     * When used in conjunction with `tf.loadLayersModel`, an instance of
+     * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+     *
+     * ```js
+     * // Note: This code snippet won't run properly without the actual file input
+     * //   elements in the HTML DOM.
+     *
+     * // Suppose there are two HTML file input (`<input type="file" ...>`)
+     * // elements.
+     * const uploadJSONInput = document.getElementById('upload-json');
+     * const uploadWeightsInput = document.getElementById('upload-weights');
+     * const model = await tf.loadLayersModel(tf.io.browserFiles(
+     *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
+     * ```
+     *
+     * @param files `File`s to load from. Currently, this function supports only
+     *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for
+     *   which an `Array` of `File`s is expected (in that order):
+     *   - A JSON file containing the model topology and weight manifest.
+     *   - Optionally, One or more binary files containing the binary weights.
+     *     These files must have names that match the paths in the `weightsManifest`
+     *     contained by the aforementioned JSON file, or errors will be thrown
+     *     during loading. These weights files have the same format as the ones
+     *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`
+     *     Python PIP package. If no weights files are provided, only the model
+     *     topology will be loaded from the JSON file above.
+     * @returns An instance of `Files` `IOHandler`.
+     *
+     * @doc {
+     *   heading: 'Models',
+     *   subheading: 'Loading',
+     *   namespace: 'io',
+     *   ignoreCI: true
+     * }
+     */
+    function browserFiles(files) {
+        return new BrowserFiles(files);
+    }
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Monitor Promise.all progress, fire onProgress callback function.
+     *
+     * @param promises Promise list going to be monitored
+     * @param onProgress Callback function. Fired when a promise resolved.
+     * @param startFraction Optional fraction start. Default to 0.
+     * @param endFraction Optional fraction end. Default to 1.
+     */
+    function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {
+        checkPromises(promises);
+        startFraction = startFraction == null ? 0 : startFraction;
+        endFraction = endFraction == null ? 1 : endFraction;
+        checkFraction(startFraction, endFraction);
+        var resolvedPromise = 0;
+        var registerMonitor = function (promise) {
+            promise.then(function (value) {
+                var fraction = startFraction +
+                    ++resolvedPromise / promises.length * (endFraction - startFraction);
+                // pass fraction as parameter to callback function.
+                onProgress(fraction);
+                return value;
+            });
+            return promise;
+        };
+        function checkPromises(promises) {
+            assert(promises != null && Array.isArray(promises) && promises.length > 0, function () { return 'promises must be a none empty array'; });
+        }
+        function checkFraction(startFraction, endFraction) {
+            assert(startFraction >= 0 && startFraction <= 1, function () { return "Progress fraction must be in range [0, 1], but " +
+                ("got startFraction " + startFraction); });
+            assert(endFraction >= 0 && endFraction <= 1, function () { return "Progress fraction must be in range [0, 1], but " +
+                ("got endFraction " + endFraction); });
+            assert(endFraction >= startFraction, function () { return "startFraction must be no more than endFraction, but " +
+                ("got startFraction " + startFraction + " and endFraction ") +
+                ("" + endFraction); });
+        }
+        return Promise.all(promises.map(registerMonitor));
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Reads binary weights data from a number of URLs.
+     *
+     * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.
+     * @param requestOptions RequestInit (options) for the HTTP requests.
+     * @param fetchFunc Optional overriding value for the `window.fetch` function.
+     * @param onProgress Optional, progress callback function, fired periodically
+     *   before the load is completed.
+     * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same
+     *   length as `fetchURLs`.
+     */
+    function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {
+        return __awaiter(this, void 0, void 0, function () {
+            var fetchFunc, requests, fetchStartFraction, fetchEndFraction, responses, _a, bufferPromises, bufferStartFraction, bufferEndFraction, buffers, _b;
+            return __generator(this, function (_c) {
+                switch (_c.label) {
+                    case 0:
+                        if (loadOptions == null) {
+                            loadOptions = {};
+                        }
+                        fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :
+                            loadOptions.fetchFunc;
+                        requests = fetchURLs.map(function (fetchURL) {
+                            return fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true });
+                        });
+                        fetchStartFraction = 0;
+                        fetchEndFraction = 0.5;
+                        if (!(loadOptions.onProgress == null)) return [3 /*break*/, 2];
+                        return [4 /*yield*/, Promise.all(requests)];
+                    case 1:
+                        _a = _c.sent();
+                        return [3 /*break*/, 4];
+                    case 2: return [4 /*yield*/, monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction)];
+                    case 3:
+                        _a = _c.sent();
+                        _c.label = 4;
+                    case 4:
+                        responses = _a;
+                        bufferPromises = responses.map(function (response) { return response.arrayBuffer(); });
+                        bufferStartFraction = 0.5;
+                        bufferEndFraction = 1;
+                        if (!(loadOptions.onProgress == null)) return [3 /*break*/, 6];
+                        return [4 /*yield*/, Promise.all(bufferPromises)];
+                    case 5:
+                        _b = _c.sent();
+                        return [3 /*break*/, 8];
+                    case 6: return [4 /*yield*/, monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction)];
+                    case 7:
+                        _b = _c.sent();
+                        _c.label = 8;
+                    case 8:
+                        buffers = _b;
+                        return [2 /*return*/, buffers];
+                }
+            });
+        });
+    }
+    /**
+     * Reads a weights manifest JSON configuration, fetches the weights and
+     * returns them as `Tensor`s.
+     *
+     * @param manifest The weights manifest JSON.
+     * @param filePathPrefix The path prefix for filenames given in the manifest.
+     *     Defaults to the empty string.
+     * @param weightNames The names of the weights to be fetched.
+     */
+    function loadWeights(manifest, filePathPrefix, weightNames, requestInit) {
+        if (filePathPrefix === void 0) { filePathPrefix = ''; }
+        return __awaiter(this, void 0, void 0, function () {
+            var fetchWeights, loadWeights;
+            return __generator(this, function (_a) {
+                fetchWeights = function (fetchUrls) {
+                    return loadWeightsAsArrayBuffer(fetchUrls, { requestInit: requestInit });
+                };
+                loadWeights = weightsLoaderFactory(fetchWeights);
+                return [2 /*return*/, loadWeights(manifest, filePathPrefix, weightNames)];
+            });
+        });
+    }
+    /**
+     * Creates a function, which reads a weights manifest JSON configuration,
+     * fetches the weight files using the specified function and returns them as
+     * `Tensor`s.
+     *
+     * ```js
+     * // example for creating a nodejs weight loader, which reads the weight files
+     * // from disk using fs.readFileSync
+     *
+     * import * as fs from 'fs'
+     *
+     * const fetchWeightsFromDisk = (filePaths: string[]) =>
+     *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)
+     *
+     * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
+     *
+     * const manifest = JSON.parse(
+     *   fs.readFileSync('./my_model-weights_manifest').toString()
+     * )
+     * const weightMap = await loadWeights(manifest, './')
+     * ```
+     * @param fetchWeightsFunction The function used for fetching the weight files.
+     * @returns Weight loading function.
+     */
+    function weightsLoaderFactory(fetchWeightsFunction) {
+        var _this = this;
+        return function (manifest, filePathPrefix, weightNames) {
+            if (filePathPrefix === void 0) { filePathPrefix = ''; }
+            return __awaiter(_this, void 0, void 0, function () {
+                var groupIndicesToFetchMap, groupWeightsToFetch, weightsFound, allManifestWeightNames, weightsNotFound, groupIndicesToFetch, fetchUrls, buffers, weightsTensorMap, bufferIndexOffset;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            groupIndicesToFetchMap = manifest.map(function () { return false; });
+                            groupWeightsToFetch = {};
+                            weightsFound = weightNames != null ? weightNames.map(function () { return false; }) : [];
+                            allManifestWeightNames = [];
+                            manifest.forEach(function (manifestGroupConfig, groupIndex) {
+                                var groupOffset = 0;
+                                manifestGroupConfig.weights.forEach(function (weightsEntry) {
+                                    var rawDtype = ('quantization' in weightsEntry) ?
+                                        weightsEntry.quantization.dtype :
+                                        weightsEntry.dtype;
+                                    var weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *
+                                        sizeFromShape(weightsEntry.shape);
+                                    var enqueueWeightsForFetchingFn = function () {
+                                        groupIndicesToFetchMap[groupIndex] = true;
+                                        if (groupWeightsToFetch[groupIndex] == null) {
+                                            groupWeightsToFetch[groupIndex] = [];
+                                        }
+                                        groupWeightsToFetch[groupIndex].push({
+                                            manifestEntry: weightsEntry,
+                                            groupOffset: groupOffset,
+                                            sizeBytes: weightsBytes
+                                        });
+                                    };
+                                    if (weightNames != null) {
+                                        weightNames.forEach(function (weightName, weightIndex) {
+                                            if (weightName === weightsEntry.name) {
+                                                enqueueWeightsForFetchingFn();
+                                                weightsFound[weightIndex] = true;
+                                            }
+                                        });
+                                    }
+                                    else {
+                                        enqueueWeightsForFetchingFn();
+                                    }
+                                    allManifestWeightNames.push(weightsEntry.name);
+                                    groupOffset += weightsBytes;
+                                });
+                            });
+                            if (!weightsFound.every(function (found) { return found; })) {
+                                weightsNotFound = weightNames.filter(function (_, i) { return !weightsFound[i]; });
+                                throw new Error("Could not find weights in manifest with names: " +
+                                    (weightsNotFound.join(', ') + ". \n") +
+                                    "Manifest JSON has weights with names: " +
+                                    (allManifestWeightNames.join(', ') + "."));
+                            }
+                            groupIndicesToFetch = groupIndicesToFetchMap.reduce(function (accumulator, shouldFetch, i) {
+                                if (shouldFetch) {
+                                    accumulator.push(i);
+                                }
+                                return accumulator;
+                            }, []);
+                            fetchUrls = [];
+                            groupIndicesToFetch.forEach(function (i) {
+                                manifest[i].paths.forEach(function (filepath) {
+                                    var fetchUrl = filePathPrefix +
+                                        (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;
+                                    fetchUrls.push(fetchUrl);
+                                });
+                            });
+                            return [4 /*yield*/, fetchWeightsFunction(fetchUrls)];
+                        case 1:
+                            buffers = _a.sent();
+                            weightsTensorMap = {};
+                            bufferIndexOffset = 0;
+                            groupIndicesToFetch.forEach(function (i) {
+                                var numBuffers = manifest[i].paths.length;
+                                var groupBytes = 0;
+                                for (var i_1 = 0; i_1 < numBuffers; i_1++) {
+                                    groupBytes += buffers[bufferIndexOffset + i_1].byteLength;
+                                }
+                                // Create a buffer for the whole group.
+                                var groupBuffer = new ArrayBuffer(groupBytes);
+                                var groupByteBuffer = new Uint8Array(groupBuffer);
+                                var groupBufferOffset = 0;
+                                for (var i_2 = 0; i_2 < numBuffers; i_2++) {
+                                    var buffer = new Uint8Array(buffers[bufferIndexOffset + i_2]);
+                                    groupByteBuffer.set(buffer, groupBufferOffset);
+                                    groupBufferOffset += buffer.byteLength;
+                                }
+                                var weightsEntries = groupWeightsToFetch[i];
+                                weightsEntries.forEach(function (weightsEntry) {
+                                    var byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
+                                    var nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
+                                    for (var name_1 in nameToTensorMap) {
+                                        weightsTensorMap[name_1] = nameToTensorMap[name_1];
+                                    }
+                                });
+                                bufferIndexOffset += numBuffers;
+                            });
+                            return [2 /*return*/, weightsTensorMap];
+                    }
+                });
+            });
+        };
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var OCTET_STREAM_MIME_TYPE = 'application/octet-stream';
+    var JSON_TYPE = 'application/json';
+    var HTTPRequest = /** @class */ (function () {
+        function HTTPRequest(path, loadOptions) {
+            this.DEFAULT_METHOD = 'POST';
+            if (loadOptions == null) {
+                loadOptions = {};
+            }
+            this.weightPathPrefix = loadOptions.weightPathPrefix;
+            this.onProgress = loadOptions.onProgress;
+            this.weightUrlConverter = loadOptions.weightUrlConverter;
+            if (loadOptions.fetchFunc != null) {
+                assert(typeof loadOptions.fetchFunc === 'function', function () { return 'Must pass a function that matches the signature of ' +
+                    '`fetch` (see ' +
+                    'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)'; });
+                this.fetch = loadOptions.fetchFunc;
+            }
+            else {
+                this.fetch = env().platform.fetch;
+            }
+            assert(path != null && path.length > 0, function () { return 'URL path for http must not be null, undefined or ' +
+                'empty.'; });
+            if (Array.isArray(path)) {
+                assert(path.length === 2, function () { return 'URL paths for http must have a length of 2, ' +
+                    ("(actual length is " + path.length + ")."); });
+            }
+            this.path = path;
+            if (loadOptions.requestInit != null &&
+                loadOptions.requestInit.body != null) {
+                throw new Error('requestInit is expected to have no pre-existing body, but has one.');
+            }
+            this.requestInit = loadOptions.requestInit || {};
+        }
+        HTTPRequest.prototype.save = function (modelArtifacts) {
+            return __awaiter(this, void 0, void 0, function () {
+                var init, weightsManifest, modelTopologyAndWeightManifest, response;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+                                throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +
+                                    'in binary formats yet.');
+                            }
+                            init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
+                            init.body = new FormData();
+                            weightsManifest = [{
+                                    paths: ['./model.weights.bin'],
+                                    weights: modelArtifacts.weightSpecs,
+                                }];
+                            modelTopologyAndWeightManifest = {
+                                modelTopology: modelArtifacts.modelTopology,
+                                format: modelArtifacts.format,
+                                generatedBy: modelArtifacts.generatedBy,
+                                convertedBy: modelArtifacts.convertedBy,
+                                weightsManifest: weightsManifest
+                            };
+                            if (modelArtifacts.signature != null) {
+                                modelTopologyAndWeightManifest.signature = modelArtifacts.signature;
+                            }
+                            if (modelArtifacts.userDefinedMetadata != null) {
+                                modelTopologyAndWeightManifest.userDefinedMetadata =
+                                    modelArtifacts.userDefinedMetadata;
+                            }
+                            if (modelArtifacts.modelInitializer != null) {
+                                modelTopologyAndWeightManifest.modelInitializer =
+                                    modelArtifacts.modelInitializer;
+                            }
+                            init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), 'model.json');
+                            if (modelArtifacts.weightData != null) {
+                                init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), 'model.weights.bin');
+                            }
+                            return [4 /*yield*/, this.fetch(this.path, init)];
+                        case 1:
+                            response = _a.sent();
+                            if (response.ok) {
+                                return [2 /*return*/, {
+                                        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),
+                                        responses: [response],
+                                    }];
+                            }
+                            else {
+                                throw new Error("BrowserHTTPRequest.save() failed due to HTTP response status " +
+                                    (response.status + "."));
+                            }
+                    }
+                });
+            });
+        };
+        /**
+         * Load model artifacts via HTTP request(s).
+         *
+         * See the documentation to `tf.io.http` for details on the saved
+         * artifacts.
+         *
+         * @returns The loaded model artifacts (if loading succeeds).
+         */
+        HTTPRequest.prototype.load = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var modelConfigRequest, modelConfig, e_1, message, modelTopology, weightsManifest, generatedBy, convertedBy, format, signature, userDefinedMetadata, weightSpecs, weightData, results, artifacts, initializer;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.fetch(this.path, this.requestInit)];
+                        case 1:
+                            modelConfigRequest = _a.sent();
+                            if (!modelConfigRequest.ok) {
+                                throw new Error("Request to " + this.path + " failed with status code " +
+                                    (modelConfigRequest.status + ". Please verify this URL points to ") +
+                                    "the model JSON of the model to load.");
+                            }
+                            _a.label = 2;
+                        case 2:
+                            _a.trys.push([2, 4, , 5]);
+                            return [4 /*yield*/, modelConfigRequest.json()];
+                        case 3:
+                            modelConfig = _a.sent();
+                            return [3 /*break*/, 5];
+                        case 4:
+                            e_1 = _a.sent();
+                            message = "Failed to parse model JSON of response from " + this.path + ".";
+                            // TODO(nsthorat): Remove this after some time when we're comfortable that
+                            // .pb files are mostly gone.
+                            if (this.path.endsWith('.pb')) {
+                                message += ' Your path contains a .pb file extension. ' +
+                                    'Support for .pb models have been removed in TensorFlow.js 1.0 ' +
+                                    'in favor of .json models. You can re-convert your Python ' +
+                                    'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +
+                                    'or you can convert your.pb models with the \'pb2json\'' +
+                                    'NPM script in the tensorflow/tfjs-converter repository.';
+                            }
+                            else {
+                                message += ' Please make sure the server is serving valid ' +
+                                    'JSON for this request.';
+                            }
+                            throw new Error(message);
+                        case 5:
+                            modelTopology = modelConfig.modelTopology;
+                            weightsManifest = modelConfig.weightsManifest;
+                            generatedBy = modelConfig.generatedBy;
+                            convertedBy = modelConfig.convertedBy;
+                            format = modelConfig.format;
+                            signature = modelConfig.signature;
+                            userDefinedMetadata = modelConfig.userDefinedMetadata;
+                            // We do not allow both modelTopology and weightsManifest to be missing.
+                            if (modelTopology == null && weightsManifest == null) {
+                                throw new Error("The JSON from HTTP path " + this.path + " contains neither model " +
+                                    "topology or manifest for weights.");
+                            }
+                            if (!(weightsManifest != null)) return [3 /*break*/, 7];
+                            return [4 /*yield*/, this.loadWeights(weightsManifest)];
+                        case 6:
+                            results = _a.sent();
+                            weightSpecs = results[0], weightData = results[1];
+                            _a.label = 7;
+                        case 7:
+                            artifacts = {
+                                modelTopology: modelTopology,
+                                weightSpecs: weightSpecs,
+                                weightData: weightData,
+                                generatedBy: generatedBy,
+                                convertedBy: convertedBy,
+                                format: format
+                            };
+                            if (signature != null) {
+                                artifacts.signature = signature;
+                            }
+                            if (userDefinedMetadata != null) {
+                                artifacts.userDefinedMetadata = userDefinedMetadata;
+                            }
+                            initializer = modelConfig.modelInitializer;
+                            if (initializer) {
+                                artifacts.modelInitializer = initializer;
+                            }
+                            return [2 /*return*/, artifacts];
+                    }
+                });
+            });
+        };
+        HTTPRequest.prototype.loadWeights = function (weightsManifest) {
+            return __awaiter(this, void 0, void 0, function () {
+                var weightPath, _a, prefix, suffix, pathPrefix, weightSpecs, _i, weightsManifest_1, entry, fetchURLs, urlPromises, _b, weightsManifest_2, weightsGroup, _c, _d, path, _e, _f, _g, buffers;
+                return __generator(this, function (_h) {
+                    switch (_h.label) {
+                        case 0:
+                            weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
+                            _a = parseUrl(weightPath), prefix = _a[0], suffix = _a[1];
+                            pathPrefix = this.weightPathPrefix || prefix;
+                            weightSpecs = [];
+                            for (_i = 0, weightsManifest_1 = weightsManifest; _i < weightsManifest_1.length; _i++) {
+                                entry = weightsManifest_1[_i];
+                                weightSpecs.push.apply(weightSpecs, entry.weights);
+                            }
+                            fetchURLs = [];
+                            urlPromises = [];
+                            for (_b = 0, weightsManifest_2 = weightsManifest; _b < weightsManifest_2.length; _b++) {
+                                weightsGroup = weightsManifest_2[_b];
+                                for (_c = 0, _d = weightsGroup.paths; _c < _d.length; _c++) {
+                                    path = _d[_c];
+                                    if (this.weightUrlConverter != null) {
+                                        urlPromises.push(this.weightUrlConverter(path));
+                                    }
+                                    else {
+                                        fetchURLs.push(pathPrefix + path + suffix);
+                                    }
+                                }
+                            }
+                            if (!this.weightUrlConverter) return [3 /*break*/, 2];
+                            _f = (_e = fetchURLs.push).apply;
+                            _g = [fetchURLs];
+                            return [4 /*yield*/, Promise.all(urlPromises)];
+                        case 1:
+                            _f.apply(_e, _g.concat([_h.sent()]));
+                            _h.label = 2;
+                        case 2: return [4 /*yield*/, loadWeightsAsArrayBuffer(fetchURLs, {
+                                requestInit: this.requestInit,
+                                fetchFunc: this.fetch,
+                                onProgress: this.onProgress
+                            })];
+                        case 3:
+                            buffers = _h.sent();
+                            return [2 /*return*/, [weightSpecs, concatenateArrayBuffers(buffers)]];
+                    }
+                });
+            });
+        };
+        HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
+        return HTTPRequest;
+    }());
+    /**
+     * Extract the prefix and suffix of the url, where the prefix is the path before
+     * the last file, and suffix is the search params after the last file.
+     * ```
+     * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'
+     * [prefix, suffix] = parseUrl(url)
+     * // prefix = 'http://tfhub.dev/model/1/'
+     * // suffix = '?tfjs-format=file'
+     * ```
+     * @param url the model url to be parsed.
+     */
+    function parseUrl(url) {
+        var lastSlash = url.lastIndexOf('/');
+        var lastSearchParam = url.lastIndexOf('?');
+        var prefix = url.substring(0, lastSlash);
+        var suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';
+        return [prefix + '/', suffix];
+    }
+    function isHTTPScheme(url) {
+        return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
+    }
+    var httpRouter = function (url, loadOptions) {
+        if (typeof fetch === 'undefined' &&
+            (loadOptions == null || loadOptions.fetchFunc == null)) {
+            // `http` uses `fetch` or `node-fetch`, if one wants to use it in
+            // an environment that is not the browser or node they have to setup a
+            // global fetch polyfill.
+            return null;
+        }
+        else {
+            var isHTTP = true;
+            if (Array.isArray(url)) {
+                isHTTP = url.every(function (urlItem) { return isHTTPScheme(urlItem); });
+            }
+            else {
+                isHTTP = isHTTPScheme(url);
+            }
+            if (isHTTP) {
+                return http(url, loadOptions);
+            }
+        }
+        return null;
+    };
+    IORouterRegistry.registerSaveRouter(httpRouter);
+    IORouterRegistry.registerLoadRouter(httpRouter);
+    /**
+     * Creates an IOHandler subtype that sends model artifacts to HTTP server.
+     *
+     * An HTTP request of the `multipart/form-data` mime type will be sent to the
+     * `path` URL. The form data includes artifacts that represent the topology
+     * and/or weights of the model. In the case of Keras-style `tf.Model`, two
+     * blobs (files) exist in form-data:
+     *   - A JSON file consisting of `modelTopology` and `weightsManifest`.
+     *   - A binary weights file consisting of the concatenated weight values.
+     * These files are in the same format as the one generated by
+     * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).
+     *
+     * The following code snippet exemplifies the client-side code that uses this
+     * function:
+     *
+     * ```js
+     * const model = tf.sequential();
+     * model.add(
+     *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
+     *
+     * const saveResult = await model.save(tf.io.http(
+     *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));
+     * console.log(saveResult);
+     * ```
+     *
+     * If the default `POST` method is to be used, without any custom parameters
+     * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:
+     *
+     * ```js
+     * const saveResult = await model.save('http://model-server:5000/upload');
+     * ```
+     *
+     * The following GitHub Gist
+     * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864
+     * implements a server based on [flask](https://github.com/pallets/flask) that
+     * can receive the request. Upon receiving the model artifacts via the requst,
+     * this particular server reconsistutes instances of [Keras
+     * Models](https://keras.io/models/model/) in memory.
+     *
+     *
+     * @param path A URL path to the model.
+     *   Can be an absolute HTTP path (e.g.,
+     *   'http://localhost:8000/model-upload)') or a relative path (e.g.,
+     *   './model-upload').
+     * @param requestInit Request configurations to be used when sending
+     *    HTTP request to server using `fetch`. It can contain fields such as
+     *    `method`, `credentials`, `headers`, `mode`, etc. See
+     *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request
+     *    for more information. `requestInit` must not have a body, because the
+     * body will be set by TensorFlow.js. File blobs representing the model
+     * topology (filename: 'model.json') and the weights of the model (filename:
+     * 'model.weights.bin') will be appended to the body. If `requestInit` has a
+     * `body`, an Error will be thrown.
+     * @param loadOptions Optional configuration for the loading. It includes the
+     *   following fields:
+     *   - weightPathPrefix Optional, this specifies the path prefix for weight
+     *     files, by default this is calculated from the path param.
+     *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,
+     *     the `fetch` from node-fetch can be used here.
+     *   - onProgress Optional, progress callback function, fired periodically
+     *     before the load is completed.
+     * @returns An instance of `IOHandler`.
+     *
+     * @doc {
+     *   heading: 'Models',
+     *   subheading: 'Loading',
+     *   namespace: 'io',
+     *   ignoreCI: true
+     * }
+     */
+    function http(path, loadOptions) {
+        return new HTTPRequest(path, loadOptions);
+    }
+    /**
+     * Deprecated. Use `tf.io.http`.
+     * @param path
+     * @param loadOptions
+     */
+    function browserHTTPRequest(path, loadOptions) {
+        return http(path, loadOptions);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var PassthroughLoader = /** @class */ (function () {
+        function PassthroughLoader(modelArtifacts) {
+            this.modelArtifacts = modelArtifacts;
+        }
+        PassthroughLoader.prototype.load = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    return [2 /*return*/, this.modelArtifacts];
+                });
+            });
+        };
+        return PassthroughLoader;
+    }());
+    var PassthroughSaver = /** @class */ (function () {
+        function PassthroughSaver(saveHandler) {
+            this.saveHandler = saveHandler;
+        }
+        PassthroughSaver.prototype.save = function (modelArtifacts) {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    return [2 /*return*/, this.saveHandler(modelArtifacts)];
+                });
+            });
+        };
+        return PassthroughSaver;
+    }());
+    /**
+     * Creates an IOHandler that loads model artifacts from memory.
+     *
+     * When used in conjunction with `tf.loadLayersModel`, an instance of
+     * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+     *
+     * ```js
+     * const model = await tf.loadLayersModel(tf.io.fromMemory(
+     *     modelTopology, weightSpecs, weightData));
+     * ```
+     *
+     * @param modelArtifacts a object containing model topology (i.e., parsed from
+     *   the JSON format).
+     * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+     *   names, shapes, types, and quantization of the weight data.
+     * @param weightData A single `ArrayBuffer` containing the weight data,
+     *   concatenated in the order described by the weightSpecs.
+     * @param trainingConfig Model training configuration. Optional.
+     *
+     * @returns A passthrough `IOHandler` that simply loads the provided data.
+     */
+    function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
+        if (arguments.length === 1) {
+            var isModelArtifacts = modelArtifacts.modelTopology != null ||
+                modelArtifacts.weightSpecs != null;
+            if (isModelArtifacts) {
+                return new PassthroughLoader(modelArtifacts);
+            }
+            else {
+                // Legacy support: with only modelTopology.
+                // TODO(cais): Remove this deprecated API.
+                console.warn('Please call tf.io.fromMemory() with only one argument. ' +
+                    'The argument should be of type ModelArtifacts. ' +
+                    'The multi-argument signature of tf.io.fromMemory() has been ' +
+                    'deprecated and will be removed in a future release.');
+                return new PassthroughLoader({ modelTopology: modelArtifacts });
+            }
+        }
+        else {
+            // Legacy support.
+            // TODO(cais): Remove this deprecated API.
+            console.warn('Please call tf.io.fromMemory() with only one argument. ' +
+                'The argument should be of type ModelArtifacts. ' +
+                'The multi-argument signature of tf.io.fromMemory() has been ' +
+                'deprecated and will be removed in a future release.');
+            return new PassthroughLoader({
+                modelTopology: modelArtifacts,
+                weightSpecs: weightSpecs,
+                weightData: weightData,
+                trainingConfig: trainingConfig
+            });
+        }
+    }
+    /**
+     * Creates an IOHandler that passes saved model artifacts to a callback.
+     *
+     * ```js
+     * function handleSave(artifacts) {
+     *   // ... do something with the artifacts ...
+     *   return {modelArtifactsInfo: {...}, ...};
+     * }
+     *
+     * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+     * ```
+     *
+     * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+     *     `SaveResult`.
+     */
+    function withSaveHandler(saveHandler) {
+        return new PassthroughSaver(saveHandler);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+
+    var io = {
+        __proto__: null,
+        browserFiles: browserFiles,
+        browserHTTPRequest: browserHTTPRequest,
+        concatenateArrayBuffers: concatenateArrayBuffers,
+        decodeWeights: decodeWeights,
+        encodeWeights: encodeWeights,
+        fromMemory: fromMemory,
+        getLoadHandlers: getLoadHandlers,
+        getModelArtifactsInfoForJSON: getModelArtifactsInfoForJSON,
+        getSaveHandlers: getSaveHandlers,
+        http: http,
+        isHTTPScheme: isHTTPScheme,
+        loadWeights: loadWeights,
+        registerLoadRouter: registerLoadRouter,
+        registerSaveRouter: registerSaveRouter,
+        weightsLoaderFactory: weightsLoaderFactory,
+        withSaveHandler: withSaveHandler,
+        copyModel: copyModel,
+        listModels: listModels,
+        moveModel: moveModel,
+        removeModel: removeModel
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the dot product of two matrices, A * B. These must be matrices.
+     *
+     * ```js
+     * const a = tf.tensor2d([1, 2], [1, 2]);
+     * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     *
+     * a.matMul(b).print();  // or tf.matMul(a, b)
+     * ```
+     * @param a First matrix in dot product operation.
+     * @param b Second matrix in dot product operation.
+     * @param transposeA If true, `a` is transposed before multiplication.
+     * @param transposeB If true, `b` is transposed before multiplication.
+     *
+     * @doc {heading: 'Operations', subheading: 'Matrices'}
+     */
+    function matMul_(a, b, transposeA, transposeB) {
+        var _a;
+        if (transposeA === void 0) { transposeA = false; }
+        if (transposeB === void 0) { transposeB = false; }
+        var $a = convertToTensor(a, 'a', 'matMul');
+        var $b = convertToTensor(b, 'b', 'matMul');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var inputs = { a: $a, b: $b };
+        var attrs = { transposeA: transposeA, transposeB: transposeB };
+        return ENGINE.runKernel(BatchMatMul, inputs, attrs);
+    }
+    var matMul = op({ matMul_: matMul_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take
+     * value `onValue` (defaults to 1), while all other locations take value
+     * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank
+     * `R+1` with the last axis of size `depth`.
+     *
+     * ```js
+     * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();
+     * ```
+     *
+     * @param indices `tf.Tensor` of indices with dtype `int32`.
+     * @param depth The depth of the one hot dimension.
+     * @param onValue A number used to fill in the output when the index matches
+     * the location.
+     * @param offValue A number used to fill in the output when the index does
+     *     not match the location.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function oneHot_(indices, depth, onValue, offValue) {
+        if (onValue === void 0) { onValue = 1; }
+        if (offValue === void 0) { offValue = 0; }
+        if (depth < 2) {
+            throw new Error("Error in oneHot: depth must be >=2, but it is " + depth);
+        }
+        var $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');
+        var inputs = { indices: $indices };
+        var attrs = { depth: depth, onValue: onValue, offValue: offValue };
+        return ENGINE.runKernel(OneHot, inputs, attrs);
+    }
+    var oneHot = op({ oneHot_: oneHot_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.
+     *
+     * The returned `tf.Tensor`'s dimension `i` will correspond to the input
+     * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,
+     * where `n` is the rank of the input `tf.Tensor`. Hence by default, this
+     * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.
+     *
+     * ```js
+     * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+     *
+     * a.transpose().print();  // or tf.transpose(a)
+     * ```
+     *
+     * @param x The tensor to transpose.
+     * @param perm The permutation of the dimensions of a.
+     *
+     * @doc {heading: 'Operations', subheading: 'Matrices'}
+     */
+    function transpose_(x, perm) {
+        var $x = convertToTensor(x, 'x', 'transpose');
+        if (perm == null) {
+            perm = $x.shape.map(function (s, i) { return i; }).reverse();
+        }
+        assert($x.rank === perm.length, function () { return "Error in transpose: rank of input " + $x.rank + " " +
+            ("must match length of perm " + perm + "."); });
+        perm.forEach(function (axis) {
+            assert(axis >= 0 && axis < $x.rank, function () { return "All entries in 'perm' must be between 0 and " + ($x.rank - 1) +
+                (" but got " + perm); });
+        });
+        if ($x.rank <= 1) {
+            return $x.clone();
+        }
+        var inputs = { x: $x };
+        var attrs = { perm: perm };
+        return ENGINE.runKernel(Transpose, inputs, attrs);
+    }
+    var transpose = op({ transpose_: transpose_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the confusion matrix from true labels and predicted labels.
+     *
+     * ```js
+     * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');
+     * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');
+     * const numClasses = 3;
+     * const out = tf.math.confusionMatrix(labels, predictions, numClasses);
+     * out.print();
+     * // Expected output matrix:
+     * // [[2, 0, 0],
+     * //  [0, 1, 1],
+     * //  [0, 0, 1]]
+     * ```
+     *
+     * @param labels The target labels, assumed to be 0-based integers
+     *   for the classes. The shape is `[numExamples]`, where
+     *   `numExamples` is the number of examples included.
+     * @param predictions The predicted classes, assumed to be
+     *   0-based integers for the classes. Must have the same shape as `labels`.
+     * @param numClasses Number of all classes, as an integer.
+     *   Its value must be larger than the largest element in `labels` and
+     *   `predictions`.
+     * @returns The confusion matrix as a int32-type 2D tensor. The value at
+     *   row `r` and column `c` is the number of times examples of actual class
+     *   `r` were predicted as class `c`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Evaluation'}
+     */
+    function confusionMatrix_(labels, predictions, numClasses) {
+        var $labels = convertToTensor(labels, 'labels', 'confusionMatrix');
+        var $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix');
+        assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), function () { return "If provided, numClasses must be a positive integer, " +
+            ("but got " + numClasses); });
+        assert($labels.rank === 1, function () { return "Expected the rank of labels to be 1, but got " + $labels.rank; });
+        assert($predictions.rank === 1, function () { return "Expected the rank of predictions to be 1, " +
+            ("but got " + $predictions.rank); });
+        assert($labels.shape[0] === $predictions.shape[0], function () { return "Mismatch in the number of examples: " +
+            ($labels.shape[0] + " vs. " + $predictions.shape[0] + ". ") +
+            "Labels and predictions should have the same number of elements."; });
+        assert(numClasses > 0 && Number.isInteger(numClasses), function () { return "numClasses is required to be a positive integer, but got " +
+            ("" + numClasses); });
+        // TODO(cais): In the future, if oneHot supports tensors inputs for
+        //   `numClasses`, `confusionMatrix` can make `numClasses` optional.
+        var oneHotLabels = oneHot(cast($labels, 'int32'), numClasses);
+        var oneHotPredictions = oneHot(cast($predictions, 'int32'), numClasses);
+        var oneHotLabelsT = transpose(oneHotLabels);
+        var product = matMul(oneHotLabelsT, oneHotPredictions);
+        return cast(product, 'int32');
+    }
+    var confusionMatrix = op({ confusionMatrix_: confusionMatrix_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+
+    var math = {
+        __proto__: null,
+        confusionMatrix: confusionMatrix
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.
+     *
+     * The same functionality can be achieved with `tf.tensor`, but in general
+     * we recommend using `tf.tensor3d` as it makes the code more readable.
+     *
+     *  ```js
+     * // Pass a nested array.
+     * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();
+     * ```
+     * ```js
+     * // Pass a flat array and specify a shape.
+     * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();
+     * ```
+     *
+     * @param values The values of the tensor. Can be nested array of numbers,
+     *     or a flat array, or a `TypedArray`.
+     * @param shape The shape of the tensor. If not provided,  it is inferred from
+     *     `values`.
+     * @param dtype The data type.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function tensor3d(values, shape, dtype) {
+        assertNonNull(values);
+        if (shape != null && shape.length !== 3) {
+            throw new Error('tensor3d() requires shape to have three numbers');
+        }
+        var inferredShape = inferShape(values, dtype);
+        if (inferredShape.length !== 3 && inferredShape.length !== 1) {
+            throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');
+        }
+        if (inferredShape.length === 1 && shape == null) {
+            throw new Error('tensor3d() requires shape to be provided when `values` ' +
+                'are a flat array');
+        }
+        return makeTensor(values, shape, inferredShape, dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var fromPixels2DContext;
+    /**
+     * Creates a `tf.Tensor` from an image.
+     *
+     * ```js
+     * const image = new ImageData(1, 1);
+     * image.data[0] = 100;
+     * image.data[1] = 150;
+     * image.data[2] = 200;
+     * image.data[3] = 255;
+     *
+     * tf.browser.fromPixels(image).print();
+     * ```
+     *
+     * @param pixels The input image to construct the tensor from. The
+     * supported image types are all 4-channel. You can also pass in an image
+     * object with following attributes:
+     * `{data: Uint8Array; width: number; height: number}`
+     * @param numChannels The number of channels of the output tensor. A
+     * numChannels value less than 4 allows you to ignore channels. Defaults to
+     * 3 (ignores alpha channel of input image).
+     *
+     * @returns A Tensor3D with the shape `[height, width, numChannels]`.
+     *
+     * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}
+     */
+    function fromPixels_(pixels, numChannels) {
+        if (numChannels === void 0) { numChannels = 3; }
+        // Sanity checks.
+        if (numChannels > 4) {
+            throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');
+        }
+        if (pixels == null) {
+            throw new Error('pixels passed to tf.browser.fromPixels() can not be null');
+        }
+        var isPixelData = false;
+        var isImageData = false;
+        var isVideo = false;
+        var isImage = false;
+        var isCanvasLike = false;
+        var isImageBitmap = false;
+        if (pixels.data instanceof Uint8Array) {
+            isPixelData = true;
+        }
+        else if (typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {
+            isImageData = true;
+        }
+        else if (typeof (HTMLVideoElement) !== 'undefined' &&
+            pixels instanceof HTMLVideoElement) {
+            isVideo = true;
+        }
+        else if (typeof (HTMLImageElement) !== 'undefined' &&
+            pixels instanceof HTMLImageElement) {
+            isImage = true;
+            // tslint:disable-next-line: no-any
+        }
+        else if (pixels.getContext != null) {
+            isCanvasLike = true;
+        }
+        else if (typeof (ImageBitmap) !== 'undefined' && pixels instanceof ImageBitmap) {
+            isImageBitmap = true;
+        }
+        else {
+            throw new Error('pixels passed to tf.browser.fromPixels() must be either an ' +
+                "HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData " +
+                "in browser, or OffscreenCanvas, ImageData in webworker" +
+                " or {data: Uint32Array, width: number, height: number}, " +
+                ("but was " + pixels.constructor.name));
+        }
+        if (isVideo) {
+            var HAVE_CURRENT_DATA_READY_STATE = 2;
+            if (isVideo &&
+                pixels.readyState <
+                    HAVE_CURRENT_DATA_READY_STATE) {
+                throw new Error('The video element has not loaded data yet. Please wait for ' +
+                    '`loadeddata` event on the <video> element.');
+            }
+        }
+        // If the current backend has 'FromPixels' registered, it has a more
+        // efficient way of handling pixel uploads, so we call that.
+        var kernel = getKernel(FromPixels, ENGINE.backendName);
+        if (kernel != null) {
+            var inputs = { pixels: pixels };
+            var attrs = { numChannels: numChannels };
+            return ENGINE.runKernel(FromPixels, inputs, attrs);
+        }
+        var _a = isVideo ?
+            [
+                pixels.videoWidth,
+                pixels.videoHeight
+            ] :
+            [pixels.width, pixels.height], width = _a[0], height = _a[1];
+        var vals;
+        if (isCanvasLike) {
+            vals =
+                // tslint:disable-next-line:no-any
+                pixels.getContext('2d').getImageData(0, 0, width, height).data;
+        }
+        else if (isImageData || isPixelData) {
+            vals = pixels.data;
+        }
+        else if (isImage || isVideo || isImageBitmap) {
+            if (fromPixels2DContext == null) {
+                fromPixels2DContext = document.createElement('canvas').getContext('2d');
+            }
+            fromPixels2DContext.canvas.width = width;
+            fromPixels2DContext.canvas.height = height;
+            fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
+            vals = fromPixels2DContext.getImageData(0, 0, width, height).data;
+        }
+        var values;
+        if (numChannels === 4) {
+            values = new Int32Array(vals);
+        }
+        else {
+            var numPixels = width * height;
+            values = new Int32Array(numPixels * numChannels);
+            for (var i = 0; i < numPixels; i++) {
+                for (var channel = 0; channel < numChannels; ++channel) {
+                    values[i * numChannels + channel] = vals[i * 4 + channel];
+                }
+            }
+        }
+        var outShape = [height, width, numChannels];
+        return tensor3d(values, outShape, 'int32');
+    }
+    // Helper functions for |fromPixelsAsync| to check whether the input can
+    // be wrapped into imageBitmap.
+    function isPixelData(pixels) {
+        return (pixels != null) && (pixels.data instanceof Uint8Array);
+    }
+    function isImageBitmapFullySupported() {
+        return typeof window !== 'undefined' &&
+            typeof (ImageBitmap) !== 'undefined' &&
+            window.hasOwnProperty('createImageBitmap');
+    }
+    function isNonEmptyPixels(pixels) {
+        return pixels != null && pixels.width !== 0 && pixels.height !== 0;
+    }
+    function canWrapPixelsToImageBitmap(pixels) {
+        return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) &&
+            isNonEmptyPixels(pixels) && !isPixelData(pixels);
+    }
+    /**
+     * Creates a `tf.Tensor` from an image in async way.
+     *
+     * ```js
+     * const image = new ImageData(1, 1);
+     * image.data[0] = 100;
+     * image.data[1] = 150;
+     * image.data[2] = 200;
+     * image.data[3] = 255;
+     *
+     * (await tf.browser.fromPixelsAsync(image)).print();
+     * ```
+     * This API is the async version of fromPixels. The API will first
+     * check |WRAP_TO_IMAGEBITMAP| flag, and try to wrap the input to
+     * imageBitmap if the flag is set to true.
+     *
+     * @param pixels The input image to construct the tensor from. The
+     * supported image types are all 4-channel. You can also pass in an image
+     * object with following attributes:
+     * `{data: Uint8Array; width: number; height: number}`
+     * @param numChannels The number of channels of the output tensor. A
+     * numChannels value less than 4 allows you to ignore channels. Defaults to
+     * 3 (ignores alpha channel of input image).
+     *
+     * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}
+     */
+    function fromPixelsAsync(pixels, numChannels) {
+        if (numChannels === void 0) { numChannels = 3; }
+        return __awaiter(this, void 0, void 0, function () {
+            var inputs, imageBitmap, e_1;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        inputs = null;
+                        if (!(env().getBool('WRAP_TO_IMAGEBITMAP') &&
+                            canWrapPixelsToImageBitmap(pixels))) return [3 /*break*/, 5];
+                        imageBitmap = void 0;
+                        _a.label = 1;
+                    case 1:
+                        _a.trys.push([1, 3, , 4]);
+                        return [4 /*yield*/, createImageBitmap(pixels, { premultiplyAlpha: 'none' })];
+                    case 2:
+                        // wrap in try-catch block, because createImageBitmap may not work
+                        // properly in some browsers, e.g.
+                        // https://bugzilla.mozilla.org/show_bug.cgi?id=1335594
+                        // tslint:disable-next-line: no-any
+                        imageBitmap = _a.sent();
+                        return [3 /*break*/, 4];
+                    case 3:
+                        e_1 = _a.sent();
+                        imageBitmap = null;
+                        return [3 /*break*/, 4];
+                    case 4:
+                        // createImageBitmap will clip the source size.
+                        // In some cases, the input will have larger size than its content.
+                        // E.g. new Image(10, 10) but with 1 x 1 content. Using
+                        // createImageBitmap will clip the size from 10 x 10 to 1 x 1, which
+                        // is not correct. We should avoid wrapping such resouce to
+                        // imageBitmap.
+                        if (imageBitmap != null && imageBitmap.width === pixels.width &&
+                            imageBitmap.height === pixels.height) {
+                            inputs = imageBitmap;
+                        }
+                        else {
+                            inputs = pixels;
+                        }
+                        return [3 /*break*/, 6];
+                    case 5:
+                        inputs = pixels;
+                        _a.label = 6;
+                    case 6: return [2 /*return*/, fromPixels_(inputs, numChannels)];
+                }
+            });
+        });
+    }
+    /**
+     * Draws a `tf.Tensor` of pixel values to a byte array or optionally a
+     * canvas.
+     *
+     * When the dtype of the input is 'float32', we assume values in the range
+     * [0-1]. Otherwise, when input is 'int32', we assume values in the range
+     * [0-255].
+     *
+     * Returns a promise that resolves when the canvas has been drawn to.
+     *
+     * @param img A rank-2 tensor with shape `[height, width]`, or a rank-3 tensor
+     * of shape `[height, width, numChannels]`. If rank-2, draws grayscale. If
+     * rank-3, must have depth of 1, 3 or 4. When depth of 1, draws
+     * grayscale. When depth of 3, we draw with the first three components of
+     * the depth dimension corresponding to r, g, b and alpha = 1. When depth of
+     * 4, all four components of the depth dimension correspond to r, g, b, a.
+     * @param canvas The canvas to draw to.
+     *
+     * @doc {heading: 'Browser', namespace: 'browser'}
+     */
+    function toPixels(img, canvas) {
+        return __awaiter(this, void 0, void 0, function () {
+            var $img, originalImgTensor, _a, height, width, depth, data, multiplier, bytes, i, rgba, d, value, j, ctx, imageData;
+            return __generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        $img = convertToTensor(img, 'img', 'toPixels');
+                        if (!(img instanceof Tensor)) {
+                            originalImgTensor = $img;
+                            $img = cast(originalImgTensor, 'int32');
+                            originalImgTensor.dispose();
+                        }
+                        if ($img.rank !== 2 && $img.rank !== 3) {
+                            throw new Error("toPixels only supports rank 2 or 3 tensors, got rank " + $img.rank + ".");
+                        }
+                        _a = $img.shape.slice(0, 2), height = _a[0], width = _a[1];
+                        depth = $img.rank === 2 ? 1 : $img.shape[2];
+                        if (depth > 4 || depth === 2) {
+                            throw new Error("toPixels only supports depth of size " +
+                                ("1, 3 or 4 but got " + depth));
+                        }
+                        if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {
+                            throw new Error("Unsupported type for toPixels: " + $img.dtype + "." +
+                                " Please use float32 or int32 tensors.");
+                        }
+                        return [4 /*yield*/, $img.data()];
+                    case 1:
+                        data = _b.sent();
+                        multiplier = $img.dtype === 'float32' ? 255 : 1;
+                        bytes = new Uint8ClampedArray(width * height * 4);
+                        for (i = 0; i < height * width; ++i) {
+                            rgba = [0, 0, 0, 255];
+                            for (d = 0; d < depth; d++) {
+                                value = data[i * depth + d];
+                                if ($img.dtype === 'float32') {
+                                    if (value < 0 || value > 1) {
+                                        throw new Error("Tensor values for a float32 Tensor must be in the " +
+                                            ("range [0 - 1] but encountered " + value + "."));
+                                    }
+                                }
+                                else if ($img.dtype === 'int32') {
+                                    if (value < 0 || value > 255) {
+                                        throw new Error("Tensor values for a int32 Tensor must be in the " +
+                                            ("range [0 - 255] but encountered " + value + "."));
+                                    }
+                                }
+                                if (depth === 1) {
+                                    rgba[0] = value * multiplier;
+                                    rgba[1] = value * multiplier;
+                                    rgba[2] = value * multiplier;
+                                }
+                                else {
+                                    rgba[d] = value * multiplier;
+                                }
+                            }
+                            j = i * 4;
+                            bytes[j + 0] = Math.round(rgba[0]);
+                            bytes[j + 1] = Math.round(rgba[1]);
+                            bytes[j + 2] = Math.round(rgba[2]);
+                            bytes[j + 3] = Math.round(rgba[3]);
+                        }
+                        if (canvas != null) {
+                            canvas.width = width;
+                            canvas.height = height;
+                            ctx = canvas.getContext('2d');
+                            imageData = new ImageData(bytes, width, height);
+                            ctx.putImageData(imageData, 0, 0);
+                        }
+                        if ($img !== img) {
+                            $img.dispose();
+                        }
+                        return [2 /*return*/, bytes];
+                }
+            });
+        });
+    }
+    var fromPixels = op({ fromPixels_: fromPixels_ });
+
+    var browser = {
+        __proto__: null,
+        fromPixelsAsync: fromPixelsAsync,
+        toPixels: toPixels,
+        fromPixels: fromPixels
+    };
+
+    /**
+     * Validate gather nd inputs.
+     *
+     * @param tensor The tensor contains the source values.
+     * @param indices The tensor contains the indices to slice the source.
+     *
+     * @returns [resultShape, numUpdates, sliceSize, strides]
+     */
+    function prepareAndValidate(tensor, indices) {
+        var tensorRank = tensor.shape.length;
+        var indicesRank = indices.shape.length;
+        if (tensorRank < 1) {
+            throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +
+                (" but the rank was " + tensorRank + "."));
+        }
+        if (indicesRank < 1) {
+            throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +
+                (" but the rank was " + indicesRank + "."));
+        }
+        if (indices.dtype !== 'int32') {
+            throw new Error('tf.gatherND() expects the indices to be int32 type,' +
+                (" but the dtype was " + indices.dtype + "."));
+        }
+        if (indices.shape[indicesRank - 1] > tensorRank) {
+            throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +
+                (indices.shape[indicesRank - 1] + " vs. " + tensorRank));
+        }
+        if (sizeFromShape(tensor.shape) === 0) {
+            throw new Error('Requested more than 0 entries, but input is empty.' +
+                (" Input shape: " + tensor.shape + "."));
+        }
+        var indicesShape = indices.shape;
+        var sliceRank = indicesShape[indicesShape.length - 1];
+        // The result shape is
+        //   indices.shape[:-1] + params.shape[indices.shape[-1]:]
+        var nResult = 1;
+        for (var i = 0; i < indicesShape.length - 1; ++i) {
+            nResult *= indicesShape[i];
+        }
+        var inputShape = tensor.shape;
+        var resultShape = indicesShape.slice();
+        resultShape.pop();
+        var sliceSize = 1;
+        for (var i = sliceRank; i < tensorRank; ++i) {
+            sliceSize *= inputShape[i];
+            resultShape.push(inputShape[i]);
+        }
+        var strides = computeStrides(tensor.shape).map(function (stride) { return stride / sliceSize; }).concat([1]).slice(0, sliceRank);
+        return [resultShape, nResult, sliceSize, strides];
+    }
+
+    var gather_nd_util = {
+        __proto__: null,
+        prepareAndValidate: prepareAndValidate
+    };
+
+    /**
+     * Check whether updates.shape = indices.shape[:batchDim] +
+     * shape[sliceDim:]
+     *
+     * @param x The input tensor.
+     */
+    function validateUpdateShape(shape, indices, updates) {
+        var sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;
+        var batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;
+        var shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +
+            ("shape[sliceDim:], got updates.shape: " + updates.shape) +
+            (", indices.shape: " + indices.shape + ", shape: " + shape) +
+            (", sliceDim: " + sliceDim + ", and batchDim: " + batchDim + ".");
+        if (updates.rank < batchDim) {
+            throw new Error(shapeError + (" update.rank < " + batchDim + ". "));
+        }
+        if (shape.length < sliceDim + (updates.rank - batchDim)) {
+            throw new Error(shapeError +
+                (" Output shape length < " + (sliceDim + (updates.rank - batchDim))));
+        }
+        if (updates.rank !== batchDim + shape.length - sliceDim) {
+            throw new Error(shapeError + (" update.rank != " + (batchDim + shape.length - sliceDim)));
+        }
+        for (var d = 0; d < batchDim; ++d) {
+            if (updates.shape[d] !== indices.shape[d]) {
+                throw new Error(shapeError +
+                    (" updates.shape[" + d + "] (" + updates.shape[d] + ") != indices.shape[" + d + "] (" + indices.shape[d] + ")."));
+            }
+        }
+        for (var d = 0; d < updates.rank - batchDim; ++d) {
+            if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
+                throw new Error(shapeError +
+                    (" updates.shape[" + (d + batchDim) + "] (" + updates.shape[d + batchDim] + ") != shape[" + (d + batchDim) + "] (" + shape[d + batchDim] + ")"));
+            }
+        }
+    }
+    /**
+     * Validate scatter nd inputs.
+     *
+     * @param update The tensor contains the update values.
+     * @param indices The tensor contains the indices for the update values.
+     * @param shape The shape of the output tensor.
+     */
+    function validateInput(updates, indices, shape) {
+        if (indices.rank < 1) {
+            throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +
+                (" but the rank was " + indices.rank + "."));
+        }
+        if (updates.rank < 1) {
+            throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +
+                (" but the rank was " + updates.rank + "."));
+        }
+        if (indices.dtype !== 'int32') {
+            throw new Error("The dtype of 'indices' should be int32, but got dtype: " + indices.dtype);
+        }
+        if (shape.length < 1) {
+            throw new Error("Output rank must be greater or equal to 1, but got shape: " + shape);
+        }
+        if (shape.length === 0) {
+            if (indices.size === 0) {
+                throw new Error("Indices specified for empty output. indices shape: " + indices.shape);
+            }
+            if (updates.size === 0) {
+                throw new Error("Updates specified for empty output. updates shape: " + updates.shape);
+            }
+        }
+        validateUpdateShape(shape, indices, updates);
+    }
+    /**
+     * Calculate the shape information for the output.
+     *
+     * @param update The tensor contains the update values.
+     * @param indices The tensor contains the indices for the update values.
+     * @param shape The shape of the output tensor.
+     *
+     * @returns ScatterShapeInfo
+     */
+    function calculateShapes(updates, indices, shape) {
+        // Calculate the number of dimensions in indices
+        var indicesRank = indices.shape.length;
+        var sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;
+        // Calculate the number of elements that make up each slice of our updated
+        // tensor. This allows us to work with flattened tensors and copy over whole
+        // slices at a time.
+        var totalNd = shape.length;
+        var sliceSize = 1;
+        for (var i = sliceRank; i < totalNd; ++i) {
+            sliceSize *= shape[i];
+        }
+        var safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;
+        var numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
+        var strides = computeStrides(shape.slice(0, sliceRank)).concat([1]);
+        var outputSize = sizeFromShape(shape);
+        return { sliceRank: sliceRank, numUpdates: numUpdates, sliceSize: sliceSize, strides: strides, outputSize: outputSize };
+    }
+
+    var scatter_nd_util = {
+        __proto__: null,
+        validateUpdateShape: validateUpdateShape,
+        validateInput: validateInput,
+        calculateShapes: calculateShapes
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function assertParamsValid(input, begin, size) {
+        var inputRank = input.shape.length;
+        assert(inputRank === begin.length, function () { return "Error in slice" + inputRank + "D: Length of begin " + begin + " must " +
+            ("match the rank of the array (" + inputRank + ")."); });
+        assert(inputRank === size.length, function () { return "Error in slice" + inputRank + "D: Length of size " + size + " must " +
+            ("match the rank of the array (" + inputRank + ")."); });
+        var _loop_1 = function (i) {
+            assert(begin[i] + size[i] <= input.shape[i], function () { return "Error in slice" + inputRank + "D: begin[" + i + "] + size[" + i + "] " +
+                ("(" + (begin[i] + size[i]) + ") would overflow input.shape[" + i + "] (" + input.shape[i] + ")"); });
+        };
+        for (var i = 0; i < inputRank; ++i) {
+            _loop_1(i);
+        }
+    }
+    /** Converts a binary mask to an array of axes. Used in stridedSlice(). */
+    function maskToAxes(mask) {
+        var axes = [];
+        var axis = 0;
+        while (mask > 0) {
+            if (mask & 1) {
+                axes.push(axis);
+            }
+            mask /= 2;
+            axis++;
+        }
+        return axes;
+    }
+    /** Computes the output shape given the strided slice params. */
+    function computeOutShape(begin, end, strides) {
+        var size = [];
+        for (var axis = 0; axis < begin.length; axis++) {
+            size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);
+        }
+        return size;
+    }
+    // Creates full selection at the elided dimensions. If the dimension matches
+    // the ellipsis mask, override the current stride value. Otherwise, insert.
+    function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {
+        var newStrides = strides.slice();
+        for (var i = newStrides.length; i < inputShape.length; i++) {
+            newStrides.push(1);
+        }
+        for (var i = 0; i < numElidedAxes; i++) {
+            if (i === 0) {
+                newStrides[ellipsisInsertionIndex] = 1;
+            }
+            else {
+                newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */, 1 /* element to add */);
+                newStrides.pop();
+            }
+        }
+        return newStrides;
+    }
+    function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {
+        if (normalizedAxis <= ellipsisInsertionIndex) {
+            return normalizedAxis;
+        }
+        return normalizedAxis - (numElidedAxes - 1);
+    }
+    function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {
+        var elidedAxes = [];
+        for (var i = 0; i < numElidedAxes; i++) {
+            elidedAxes.push(ellipsisInsertionIndex + i);
+        }
+        return elidedAxes;
+    }
+    // Normalize the start, end and strides.
+    function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {
+        var inputRank = inputShape.length;
+        var normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);
+        if (ellipsisAxes.length && numInterpolatedAxes > 0) {
+            var fullIndex = ellipsisAxes[0];
+            // The ellipsis applies to the masked index as well as any dimensions
+            // that are interpolated.
+            var numElidedAxes = numInterpolatedAxes + 1;
+            normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);
+            normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);
+            normalizedStrides =
+                stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);
+        }
+        else {
+            for (var axis = 0; axis < inputRank; axis++) {
+                normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);
+                normalizedEnd[axis] =
+                    stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);
+                normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);
+            }
+        }
+        return {
+            begin: normalizedBegin,
+            end: normalizedEnd,
+            strides: normalizedStrides
+        };
+    }
+    // Creates full selection at the elided dimensions. If the dimension matches
+    // the ellipsis mask, override the current start value. Otherwise, insert.
+    function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {
+        var newIndices = inputShape.slice();
+        var elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
+        for (var axis = 0; axis < newIndices.length; axis++) {
+            if (elidedAxes.indexOf(axis) > -1) {
+                newIndices[axis] = 0;
+            }
+            else {
+                var originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
+                var originalValue = originalBegin[originalAxis];
+                if (beginMask & 1 << originalAxis) {
+                    originalValue = 0;
+                }
+                newIndices[axis] = originalValue;
+            }
+        }
+        return newIndices;
+    }
+    // Creates full selection at the elided dimensions. If the dimension matches
+    // the ellipsis mask, override the current stop value. Otherwise, insert.
+    function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {
+        var newIndices = inputShape.slice();
+        var elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
+        for (var axis = 0; axis < newIndices.length; axis++) {
+            if (elidedAxes.indexOf(axis) > -1) {
+                newIndices[axis] = Number.MAX_SAFE_INTEGER;
+            }
+            else {
+                var originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
+                var originalValue = originalEnd[originalAxis];
+                if (endMask & 1 << originalAxis) {
+                    originalValue = Number.MAX_SAFE_INTEGER;
+                }
+                newIndices[axis] = originalValue;
+            }
+        }
+        for (var i = 0; i < newIndices.length; i++) {
+            // Handle negative indices
+            var axisSize = inputShape[i];
+            if (newIndices[i] < 0) {
+                newIndices[i] += axisSize;
+            }
+            newIndices[i] = clamp(0, newIndices[i], inputShape[i]);
+        }
+        return newIndices;
+    }
+    function stridesForAxis(strides, axis, ellipsisMask) {
+        var stride = strides[axis];
+        if (ellipsisMask & (1 << axis) || stride == null) {
+            stride = 1;
+        }
+        return stride;
+    }
+    function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {
+        // Begin with the specified index
+        var start = startIndices[axis];
+        var stride = strides[axis] || 1;
+        // Check the axis bit from right of masked axes, or the begin index is not set
+        // for the axis.
+        if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {
+            if (stride > 0) {
+                // Forward iteration - use the first element. These values will get
+                // clamped below (Note: We could have set them to 0 and axis_size-1, but
+                // use lowest() and max() to maintain symmetry with StopForAxis())
+                start = Number.MIN_SAFE_INTEGER;
+            }
+            else {
+                // Backward iteration - use the last element.
+                start = Number.MAX_SAFE_INTEGER;
+            }
+        }
+        // Handle negative indices
+        var axisSize = inputShape[axis];
+        if (start < 0) {
+            start += axisSize;
+        }
+        // Clamping
+        start = clamp(0, start, axisSize - 1);
+        return start;
+    }
+    function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {
+        // Begin with the specified index
+        var stop = stopIndices[axis];
+        var stride = strides[axis] || 1;
+        // Check the axis bit from right of masked axes, or if the stop index is not
+        // set for this axis.
+        if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {
+            if (stride > 0) {
+                // Forward iteration - use the last element. These values will get
+                // clamped below
+                stop = Number.MAX_SAFE_INTEGER;
+            }
+            else {
+                // Backward iteration - use the first element.
+                stop = Number.MIN_SAFE_INTEGER;
+            }
+        }
+        // Handle negative indices
+        var axisSize = inputShape[axis];
+        if (stop < 0) {
+            stop += axisSize;
+        }
+        // Clamping
+        // Because the end index points one past the last element, we need slightly
+        // different clamping ranges depending on the direction.
+        if (stride > 0) {
+            // Forward iteration
+            stop = clamp(0, stop, axisSize);
+        }
+        else {
+            // Backward iteration
+            stop = clamp(-1, stop, axisSize - 1);
+        }
+        return stop;
+    }
+    /**
+     * Returns true if the slice occupies a continous set of elements in the
+     * 'flat' space.
+     */
+    function isSliceContinous(shape, begin, size) {
+        // Index of the first axis that has size > 1.
+        var firstNonOneAxis = size.length;
+        for (var i = 0; i < size.length; i++) {
+            if (size[i] > 1) {
+                firstNonOneAxis = i;
+                break;
+            }
+        }
+        for (var i = firstNonOneAxis + 1; i < size.length; i++) {
+            if (begin[i] > 0 || size[i] !== shape[i]) {
+                return false;
+            }
+        }
+        return true;
+    }
+    function computeFlatOffset(begin, strides) {
+        var flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
+        for (var i = 0; i < begin.length - 1; i++) {
+            flatOffset += begin[i] * strides[i];
+        }
+        return flatOffset;
+    }
+    function parseSliceParams(x, begin, size) {
+        // The following logic allows for more ergonomic calls.
+        var begin_;
+        var xRank = x.shape.length;
+        if (typeof begin === 'number') {
+            begin_ = [begin].concat(new Array(xRank - 1).fill(0));
+        }
+        else if (begin.length < xRank) {
+            begin_ = begin.concat(new Array(xRank - begin.length).fill(0));
+        }
+        else {
+            begin_ = begin.slice();
+        }
+        begin_.forEach(function (d) {
+            assert(d !== -1, function () { return 'slice() does not support negative begin indexing.'; });
+        });
+        var size_;
+        if (size == null) {
+            size_ = new Array(xRank).fill(-1);
+        }
+        else if (typeof size === 'number') {
+            size_ = [size].concat(new Array(xRank - 1).fill(-1));
+        }
+        else if (size.length < xRank) {
+            size_ = size.concat(new Array(xRank - size.length).fill(-1));
+        }
+        else {
+            size_ = size;
+        }
+        size_ = size_.map(function (d, i) {
+            if (d >= 0) {
+                return d;
+            }
+            else {
+                assert(d === -1, function () { return "Negative size values should be exactly -1 but got " +
+                    (d + " for the slice() size at index " + i + "."); });
+                return x.shape[i] - begin_[i];
+            }
+        });
+        return [begin_, size_];
+    }
+    function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+        // make a copy because it may be modified further down.
+        var $begin = begin.slice();
+        var $end = end.slice();
+        var $strides = strides;
+        if (strides == null) {
+            $strides = new Array($begin.length);
+        }
+        var ellipsisAxes = maskToAxes(ellipsisMask);
+        if (ellipsisAxes.length > 1) {
+            throw new Error('Multiple ellipses in slice is not allowed.');
+        }
+        if (ellipsisMask !== 0 && newAxisMask !== 0) {
+            throw new Error('Using both ellipsisMask and newAxisMask is not yet supported.');
+        }
+        if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {
+            throw new Error('Using both ellipsisMask and shrinkAxisMask is not yet supported.');
+        }
+        var numInterpolatedAxes = xShape.length - $begin.length;
+        // Expand the dims of x based on the newAxisMask.
+        var expandAxes = maskToAxes(newAxisMask);
+        var newShape = xShape.slice();
+        expandAxes.forEach(function (axis) {
+            $begin[axis] = 0;
+            $end[axis] = 1;
+            newShape.splice(axis, 0, 1);
+        });
+        var _a = getNormalizedAxes(newShape, ellipsisAxes, numInterpolatedAxes, $begin, $end, $strides, beginMask, endMask, ellipsisMask), normalizedBegin = _a.begin, normalizedEnd = _a.end, normalizedStrides = _a.strides;
+        $begin = normalizedBegin;
+        $end = normalizedEnd;
+        $strides = normalizedStrides;
+        var shrinkAxes = maskToAxes(shrinkAxisMask);
+        // Adjust the ends based on the shrink mask.
+        shrinkAxes.forEach(function (axis) {
+            $end[axis] = $begin[axis] + 1;
+            $strides[axis] = 1;
+        });
+        // Figure out the output shape.
+        var size = computeOutShape($begin, $end, $strides);
+        // Remove the axes based on shrinkMask.
+        var outShape = size.filter(function (_, axis) { return shrinkAxes.indexOf(axis) === -1; });
+        var nonStrided = $strides.every(function (v) { return v === 1; });
+        return { nonStrided: nonStrided, $begin: $begin, $end: $end, $strides: $strides, size: size, newShape: newShape, outShape: outShape };
+    }
+
+    var slice_util = {
+        __proto__: null,
+        assertParamsValid: assertParamsValid,
+        maskToAxes: maskToAxes,
+        computeOutShape: computeOutShape,
+        stridesWithElidedDims: stridesWithElidedDims,
+        getNormalizedAxes: getNormalizedAxes,
+        startIndicesWithElidedDims: startIndicesWithElidedDims,
+        stopIndicesWithElidedDims: stopIndicesWithElidedDims,
+        stridesForAxis: stridesForAxis,
+        startForAxis: startForAxis,
+        stopForAxis: stopForAxis,
+        isSliceContinous: isSliceContinous,
+        computeFlatOffset: computeFlatOffset,
+        parseSliceParams: parseSliceParams,
+        sliceInfo: sliceInfo
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Serializable defines the serialization contract.
+     *
+     * TFJS requires serializable classes to return their className when asked
+     * to avoid issues with minification.
+     */
+    var Serializable = /** @class */ (function () {
+        function Serializable() {
+        }
+        /**
+         * Return the class name for this class to use in serialization contexts.
+         *
+         * Generally speaking this will be the same thing that constructor.name
+         * would have returned.  However, the class name needs to be robust
+         * against minification for serialization/deserialization to work properly.
+         *
+         * There's also places such as initializers.VarianceScaling, where
+         * implementation details between different languages led to different
+         * class hierarchies and a non-leaf node is used for serialization purposes.
+         */
+        Serializable.prototype.getClassName = function () {
+            return this.constructor
+                .className;
+        };
+        /**
+         * Creates an instance of T from a ConfigDict.
+         *
+         * This works for most descendants of serializable.  A few need to
+         * provide special handling.
+         * @param cls A Constructor for the class to instantiate.
+         * @param config The Configuration for the object.
+         */
+        /** @nocollapse */
+        Serializable.fromConfig = function (cls, config) {
+            return new cls(config);
+        };
+        return Serializable;
+    }());
+    /**
+     * Maps string keys to class constructors.
+     *
+     * Used during (de)serialization from the cross-language JSON format, which
+     * requires the class name in the serialization format matches the class
+     * names as used in Python, should it exist.
+     */
+    var SerializationMap = /** @class */ (function () {
+        function SerializationMap() {
+            this.classNameMap = {};
+        }
+        /**
+         * Returns the singleton instance of the map.
+         */
+        SerializationMap.getMap = function () {
+            if (SerializationMap.instance == null) {
+                SerializationMap.instance = new SerializationMap();
+            }
+            return SerializationMap.instance;
+        };
+        /**
+         * Registers the class as serializable.
+         */
+        SerializationMap.register = function (cls) {
+            SerializationMap.getMap().classNameMap[cls.className] =
+                [cls, cls.fromConfig];
+        };
+        return SerializationMap;
+    }());
+    /**
+     * Register a class with the serialization map of TensorFlow.js.
+     *
+     * This is often used for registering custom Layers, so they can be
+     * serialized and deserialized.
+     *
+     * Example:
+     *
+     * ```js
+     * class MyCustomLayer extends tf.layers.Layer {
+     *   static className = 'MyCustomLayer';
+     *
+     *   constructor(config) {
+     *     super(config);
+     *   }
+     * }
+     * tf.serialization.registerClass(MyCustomLayer);
+     * ```
+     *
+     * @param cls The class to be registered. It must have a public static member
+     *   called `className` defined and the value must be a non-empty string.
+     *
+     * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}
+     */
+    function registerClass(cls) {
+        assert(cls.className != null, function () { return "Class being registered does not have the static className " +
+            "property defined."; });
+        assert(typeof cls.className === 'string', function () { return "className is required to be a string, but got type " +
+            typeof cls.className; });
+        assert(cls.className.length > 0, function () { return "Class being registered has an empty-string as its className, " +
+            "which is disallowed."; });
+        SerializationMap.register(cls);
+    }
+
+    var serialization = {
+        __proto__: null,
+        Serializable: Serializable,
+        SerializationMap: SerializationMap,
+        registerClass: registerClass
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var TEST_EPSILON_FLOAT32 = 1e-3;
+    var TEST_EPSILON_FLOAT16 = 1e-1;
+    function expectArraysClose(actual, expected, epsilon) {
+        if (epsilon == null) {
+            epsilon = testEpsilon();
+        }
+        return expectArraysPredicate(actual, expected, function (a, b) { return areClose(a, b, epsilon); });
+    }
+    function testEpsilon() {
+        return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :
+            TEST_EPSILON_FLOAT16;
+    }
+    function expectArraysPredicate(actual, expected, predicate) {
+        var checkClassType = true;
+        if (isTypedArray(actual) || isTypedArray(expected)) {
+            checkClassType = false;
+        }
+        if (isTypedArray(actual) && isTypedArray(expected)) {
+            checkClassType = true;
+        }
+        if (checkClassType) {
+            var aType = actual.constructor.name;
+            var bType = expected.constructor.name;
+            if (aType !== bType) {
+                throw new Error("Arrays are of different type. Actual: " + aType + ". " +
+                    ("Expected: " + bType));
+            }
+        }
+        if (Array.isArray(actual) && Array.isArray(expected)) {
+            var actualShape = inferShape(actual);
+            var expectedShape = inferShape(expected);
+            if (!arraysEqual(actualShape, expectedShape)) {
+                throw new Error("Arrays have different shapes. " +
+                    ("Actual: [" + actualShape + "]. Expected: [" + expectedShape + "]"));
+            }
+        }
+        var actualFlat = isTypedArray(actual) ? actual : flatten(actual);
+        var expectedFlat = isTypedArray(expected) ?
+            expected :
+            flatten(expected);
+        if (actualFlat.length !== expectedFlat.length) {
+            throw new Error("Arrays have different lengths actual: " + actualFlat.length + " vs " +
+                ("expected: " + expectedFlat.length + ".\n") +
+                ("Actual:   " + actualFlat + ".\n") +
+                ("Expected: " + expectedFlat + "."));
+        }
+        for (var i = 0; i < expectedFlat.length; ++i) {
+            var a = actualFlat[i];
+            var e = expectedFlat[i];
+            if (!predicate(a, e)) {
+                throw new Error("Arrays differ: actual[" + i + "] = " + a + ", expected[" + i + "] = " + e + ".\n" +
+                    ("Actual:   " + actualFlat + ".\n") +
+                    ("Expected: " + expectedFlat + "."));
+            }
+        }
+    }
+    function expectPromiseToFail(fn, done) {
+        fn().then(function () { return done.fail(); }, function () { return done(); });
+    }
+    function expectArraysEqual(actual, expected) {
+        var exp = typeof expected === 'string' || typeof expected === 'number' ||
+            typeof expected === 'boolean' ?
+            [expected] :
+            expected;
+        if (isString(actual) || isString(actual[0]) ||
+            isString(expected) || isString(expected[0])) {
+            // tslint:disable-next-line: triple-equals
+            return expectArraysPredicate(actual, exp, function (a, b) { return a == b; });
+        }
+        return expectArraysPredicate(actual, expected, function (a, b) { return areClose(a, b, 0); });
+    }
+    function expectNumbersClose(a, e, epsilon) {
+        if (epsilon == null) {
+            epsilon = testEpsilon();
+        }
+        if (!areClose(a, e, epsilon)) {
+            throw new Error("Numbers differ: actual === " + a + ", expected === " + e);
+        }
+    }
+    function areClose(a, e, epsilon) {
+        if (!isFinite(a) && !isFinite(e)) {
+            return true;
+        }
+        if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {
+            return false;
+        }
+        return true;
+    }
+    function expectValuesInRange(actual, low, high) {
+        for (var i = 0; i < actual.length; i++) {
+            if (actual[i] < low || actual[i] > high) {
+                throw new Error("Value out of range:" + actual[i] + " low: " + low + ", high: " + high);
+            }
+        }
+    }
+    function expectArrayBuffersEqual(actual, expected) {
+        // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in
+        // a Float32Array solves this issue.
+        expect(new Float32Array(actual)).toEqual(new Float32Array(expected));
+    }
+    /** Encodes strings into utf-8 bytes. */
+    function encodeStrings(a) {
+        for (var i = 0; i < a.length; i++) {
+            var val = a[i];
+            if (Array.isArray(val)) {
+                encodeStrings(val);
+            }
+            else {
+                a[i] = encodeString(val);
+            }
+        }
+        return a;
+    }
+
+    var test_util = {
+        __proto__: null,
+        TEST_EPSILON_FLOAT16: TEST_EPSILON_FLOAT16,
+        expectArraysClose: expectArraysClose,
+        testEpsilon: testEpsilon,
+        expectPromiseToFail: expectPromiseToFail,
+        expectArraysEqual: expectArraysEqual,
+        expectNumbersClose: expectNumbersClose,
+        expectValuesInRange: expectValuesInRange,
+        expectArrayBuffersEqual: expectArrayBuffersEqual,
+        encodeStrings: encodeStrings
+    };
+
+    /** @license See the LICENSE file. */
+    // This code is auto-generated, do not modify this file!
+    var version = '3.7.0';
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Enables production mode which disables correctness checks in favor of
+     * performance.
+     *
+     * @doc {heading: 'Environment'}
+     */
+    function enableProdMode() {
+        env().set('PROD', true);
+    }
+    /**
+     * Enables debug mode which will log information about all executed kernels:
+     * the elapsed time of the kernel execution, as well as the rank, shape, and
+     * size of the output tensor.
+     *
+     * Debug mode will significantly slow down your application as it will
+     * download the result of every operation to the CPU. This should not be used in
+     * production. Debug mode does not affect the timing information of the kernel
+     * execution as we do not measure download time in the kernel execution time.
+     *
+     * See also: `tf.profile`, `tf.memory`.
+     *
+     * @doc {heading: 'Environment'}
+     */
+    function enableDebugMode() {
+        env().set('DEBUG', true);
+    }
+    /** Globally disables deprecation warnings */
+    function disableDeprecationWarnings() {
+        env().set('DEPRECATION_WARNINGS_ENABLED', false);
+        console.warn("TensorFlow.js deprecation warnings have been disabled.");
+    }
+    /** Warn users about deprecated functionality. */
+    function deprecationWarn(msg) {
+        if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {
+            console.warn(msg + ' You can disable deprecation warnings with ' +
+                'tf.disableDeprecationWarnings().');
+        }
+    }
+    /**
+     * Dispose all variables kept in backend engine.
+     *
+     * @doc {heading: 'Environment'}
+     */
+    function disposeVariables() {
+        ENGINE.disposeVariables();
+    }
+    /**
+     * It returns the global engine that keeps track of all tensors and backends.
+     *
+     * @doc {heading: 'Environment'}
+     */
+    function engine() {
+        return ENGINE;
+    }
+    /**
+     * Returns memory info at the current time in the program. The result is an
+     * object with the following properties:
+     *
+     * - `numBytes`: Number of bytes allocated (undisposed) at this time.
+     * - `numTensors`: Number of unique tensors allocated.
+     * - `numDataBuffers`: Number of unique data buffers allocated
+     *   (undisposed) at this time, which is ≤ the number of tensors
+     *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same
+     *   data buffer with `a`).
+     * - `unreliable`: True if the memory usage is unreliable. See `reasons` when
+     *    `unreliable` is true.
+     * - `reasons`: `string[]`, reasons why the memory is unreliable, present if
+     *    `unreliable` is true.
+     *
+     * WebGL Properties:
+     * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at
+     *     this time.
+     *
+     * @doc {heading: 'Performance', subheading: 'Memory'}
+     */
+    function memory() {
+        return ENGINE.memory();
+    }
+    /**
+     * Executes the provided function `f()` and returns a promise that resolves
+     * with information about the function's memory use:
+     * - `newBytes`: the number of new bytes allocated
+     * - `newTensors`: the number of new tensors created
+     * - `peakBytes`: the peak number of bytes allocated
+     * - `kernels`: an array of objects for each kernel involved that reports
+     * their input and output shapes, number of bytes used, and number of new
+     * tensors created.
+     * - `kernelNames`: an array of unique strings with just the names of the
+     * kernels in the `kernels` array.
+     *
+     * ```js
+     * const profile = await tf.profile(() => {
+     *   const x = tf.tensor1d([1, 2, 3]);
+     *   let x2 = x.square();
+     *   x2.dispose();
+     *   x2 = x.square();
+     *   x2.dispose();
+     *   return x;
+     * });
+     *
+     * console.log(`newBytes: ${profile.newBytes}`);
+     * console.log(`newTensors: ${profile.newTensors}`);
+     * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>
+     * k.totalBytesSnapshot)}`);
+     * ```
+     *
+     *
+     * @doc {heading: 'Performance', subheading: 'Profile'}
+     */
+    function profile(f) {
+        return ENGINE.profile(f);
+    }
+    /**
+     * Executes the provided function `fn` and after it is executed, cleans up all
+     * intermediate tensors allocated by `fn` except those returned by `fn`.
+     * `fn` must not return a Promise (async functions not allowed). The returned
+     * result can be a complex object.
+     *
+     * Using this method helps avoid memory leaks. In general, wrap calls to
+     * operations in `tf.tidy` for automatic memory cleanup.
+     *
+     * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to
+     * dispose variables, please use `tf.disposeVariables` or call dispose()
+     * directly on variables.
+     *
+     * ```js
+     * // y = 2 ^ 2 + 1
+     * const y = tf.tidy(() => {
+     *   // a, b, and one will be cleaned up when the tidy ends.
+     *   const one = tf.scalar(1);
+     *   const a = tf.scalar(2);
+     *   const b = a.square();
+     *
+     *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);
+     *
+     *   // The value returned inside the tidy function will return
+     *   // through the tidy, in this case to the variable y.
+     *   return b.add(one);
+     * });
+     *
+     * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
+     * y.print();
+     * ```
+     *
+     * @param nameOrFn The name of the closure, or the function to execute.
+     *     If a name is provided, the 2nd argument should be the function.
+     *     If debug mode is on, the timing and the memory usage of the function
+     *     will be tracked and displayed on the console using the provided name.
+     * @param fn The function to execute.
+     *
+     * @doc {heading: 'Performance', subheading: 'Memory'}
+     */
+    function tidy(nameOrFn, fn) {
+        return ENGINE.tidy(nameOrFn, fn);
+    }
+    /**
+     * Disposes any `tf.Tensor`s found within the provided object.
+     *
+     * @param container an object that may be a `tf.Tensor` or may directly
+     *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If
+     *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing
+     *     happens. In general it is safe to pass any object here, except that
+     *     `Promise`s are not supported.
+     *
+     * @doc {heading: 'Performance', subheading: 'Memory'}
+     */
+    function dispose(container) {
+        var tensors = getTensorsInContainer(container);
+        tensors.forEach(function (tensor) { return tensor.dispose(); });
+    }
+    /**
+     * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed
+     * automatically.
+     *
+     * ```js
+     * let b;
+     * const y = tf.tidy(() => {
+     *   const one = tf.scalar(1);
+     *   const a = tf.scalar(2);
+     *
+     *   // b will not be cleaned up by the tidy. a and one will be cleaned up
+     *   // when the tidy ends.
+     *   b = tf.keep(a.square());
+     *
+     *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);
+     *
+     *   // The value returned inside the tidy function will return
+     *   // through the tidy, in this case to the variable y.
+     *   return b.add(one);
+     * });
+     *
+     * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
+     * console.log('y:');
+     * y.print();
+     * console.log('b:');
+     * b.print();
+     * ```
+     *
+     * @param result The tensor to keep from being disposed.
+     *
+     * @doc {heading: 'Performance', subheading: 'Memory'}
+     */
+    function keep(result) {
+        return ENGINE.keep(result);
+    }
+    /**
+     * Executes `f()` and returns a promise that resolves with timing
+     * information.
+     *
+     * The result is an object with the following properties:
+     *
+     * - `wallMs`: Wall execution time.
+     * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the
+     * WebGL backend and the query timer extension is not available, this will
+     * return an error object.
+     * - On `WebGL` The following additional properties exist:
+     *   - `uploadWaitMs`: CPU blocking time on texture uploads.
+     *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).
+     *
+     * ```js
+     * const x = tf.randomNormal([20, 20]);
+     * const time = await tf.time(() => x.matMul(x));
+     *
+     * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);
+     * ```
+     *
+     * @param f The function to execute and time.
+     *
+     * @doc {heading: 'Performance', subheading: 'Timing'}
+     */
+    function time(f) {
+        return ENGINE.time(f);
+    }
+    /**
+     * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and
+     * executing operations on those tensors. Returns a promise that resolves
+     * to a boolean if the backend initialization was successful.
+     *
+     * Note this disposes the current backend, if any, as well as any tensors
+     * associated with it. A new backend is initialized, even if it is of the
+     * same type as the previous one.
+     *
+     * @param backendName The name of the backend. Currently supports
+     *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js
+     *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).
+     *
+     * @doc {heading: 'Backends'}
+     */
+    function setBackend(backendName) {
+        return ENGINE.setBackend(backendName);
+    }
+    /**
+     * Returns a promise that resolves when the currently selected backend (or the
+     * highest priority one) has initialized. Await this promise when you are using
+     * a backend that has async initialization.
+     *
+     * @doc {heading: 'Backends'}
+     */
+    function ready() {
+        return ENGINE.ready();
+    }
+    /**
+     * Returns the current backend name (cpu, webgl, etc). The backend is
+     * responsible for creating tensors and executing operations on those tensors.
+     *
+     * @doc {heading: 'Backends'}
+     */
+    function getBackend() {
+        return ENGINE.backendName;
+    }
+    /**
+     * Removes a backend and the registered factory.
+     *
+     * @doc {heading: 'Backends'}
+     */
+    function removeBackend(name) {
+        ENGINE.removeBackend(name);
+    }
+    /**
+     * Finds the backend registered under the provided name. Returns null if the
+     * name is not in the registry, or the registration hasn't finished yet.
+     */
+    function findBackend(name) {
+        return ENGINE.findBackend(name);
+    }
+    /**
+     * Finds the backend factory registered under the provided name. Returns a
+     * function that produces a new backend when called. Returns null if the name
+     * is not in the registry.
+     */
+    function findBackendFactory(name) {
+        return ENGINE.findBackendFactory(name);
+    }
+    /**
+     * Registers a global backend. The registration should happen when importing
+     * a module file (e.g. when importing `backend_webgl.ts`), and is used for
+     * modular builds (e.g. custom tfjs bundle with only webgl support).
+     *
+     * @param factory The backend factory function. When called, it should
+     * return a backend instance, or a promise of an instance.
+     * @param priority The priority of the backend (higher = more important).
+     *     In case multiple backends are registered, the priority is used to find
+     *     the best backend. Defaults to 1.
+     * @return False if there is already a registered backend under this name, true
+     *     if not.
+     *
+     * @doc {heading: 'Backends'}
+     */
+    function registerBackend(name, factory, priority) {
+        if (priority === void 0) { priority = 1; }
+        return ENGINE.registerBackend(name, factory, priority);
+    }
+    /**
+     * Gets the current backend. If no backends have been initialized, this will
+     * attempt to initialize the best backend. Will throw an error if the highest
+     * priority backend has async initialization, in which case, you should call
+     * 'await tf.ready()' before running other code.
+     *
+     * @doc {heading: 'Backends'}
+     */
+    function backend() {
+        return ENGINE.backend;
+    }
+    /**
+     * Sets the global platform.
+     *
+     * @param platformName The name of this platform.
+     * @param platform A platform implementation.
+     */
+    function setPlatform(platformName, platform) {
+        env().setPlatform(platformName, platform);
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.
+     *
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2, 3, 4]);
+     * const b = tf.tensor1d([10, 20, 30, 40]);
+     *
+     * a.add(b).print();  // or tf.add(a, b)
+     * ```
+     *
+     * ```js
+     * // Broadcast add a with b.
+     * const a = tf.scalar(5);
+     * const b = tf.tensor1d([10, 20, 30, 40]);
+     *
+     * a.add(b).print();  // or tf.add(a, b)
+     * ```
+     * @param a The first `tf.Tensor` to add.
+     * @param b The second `tf.Tensor` to add. Must have the same type as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Arithmetic'}
+     */
+    function add_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'add');
+        var $b = convertToTensor(b, 'b', 'add');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(Add, inputs);
+    }
+    var add$1 = op({ add_: add_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
+     * The result is rounded with floor function.
+     *
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 4, 9, 16]);
+     * const b = tf.tensor1d([1, 2, 3, 4]);
+     *
+     * a.floorDiv(b).print();  // or tf.div(a, b)
+     * ```
+     *
+     * ```js
+     * // Broadcast div a with b.
+     * const a = tf.tensor1d([2, 4, 6, 8]);
+     * const b = tf.scalar(2);
+     *
+     * a.floorDiv(b).print();  // or tf.floorDiv(a, b)
+     * ```
+     *
+     * @param a The first tensor as the numerator.
+     * @param b The second tensor as the denominator. Must have the same dtype as
+     * `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Arithmetic'}
+     */
+    function floorDiv_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'floorDiv');
+        var $b = convertToTensor(b, 'b', 'floorDiv');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(FloorDiv, inputs);
+    }
+    var floorDiv = op({ floorDiv_: floorDiv_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 4, 9, 16]);
+     * const b = tf.tensor1d([1, 2, 3, 4]);
+     *
+     * a.div(b).print();  // or tf.div(a, b)
+     * ```
+     *
+     * ```js
+     * // Broadcast div a with b.
+     * const a = tf.tensor1d([2, 4, 6, 8]);
+     * const b = tf.scalar(2);
+     *
+     * a.div(b).print();  // or tf.div(a, b)
+     * ```
+     *
+     * @param a The first tensor as the numerator.
+     * @param b The second tensor as the denominator. Must have the same dtype as
+     * `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Arithmetic'}
+     */
+    function div_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'div');
+        var $b = convertToTensor(b, 'b', 'div');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        if ($a.dtype === 'int32' && $b.dtype === 'int32') {
+            return floorDiv($a, $b);
+        }
+        var inputs = { a: $a, b: $b };
+        var attrs = {};
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        return ENGINE.runKernel(RealDiv, inputs, attrs);
+    }
+    var div = op({ div_: div_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.
+     *
+     * We also expose `tf.mulStrict` which has the same signature as this op and
+     * asserts that `a` and `b` are the same shape (does not broadcast).
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2, 3, 4]);
+     * const b = tf.tensor1d([2, 3, 4, 5]);
+     *
+     * a.mul(b).print();  // or tf.mul(a, b)
+     * ```
+     *
+     * ```js
+     * // Broadcast mul a with b.
+     * const a = tf.tensor1d([1, 2, 3, 4]);
+     * const b = tf.scalar(5);
+     *
+     * a.mul(b).print();  // or tf.mul(a, b)
+     * ```
+     * @param a The first tensor to multiply.
+     * @param b The second tensor to multiply. Must have the same dtype as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Arithmetic'}
+     */
+    function mul_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'mul');
+        var $b = convertToTensor(b, 'b', 'mul');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(Multiply, inputs);
+    }
+    var mul = op({ mul_: mul_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes absolute value element-wise: `abs(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([-1, 2, -3, 4]);
+     *
+     * x.abs().print();  // or tf.abs(x)
+     * ```
+     * @param x The input `tf.Tensor`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function abs_(x) {
+        var $x = convertToTensor(x, 'x', 'abs');
+        if ($x.dtype === 'complex64') {
+            var inputs = { x: $x };
+            return ENGINE.runKernel(ComplexAbs, inputs);
+        }
+        else {
+            var inputs = { x: $x };
+            return ENGINE.runKernel(Abs, inputs);
+        }
+    }
+    var abs = op({ abs_: abs_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, 1, -1, .7]);
+     *
+     * x.acos().print();  // or tf.acos(x)
+     * ```
+     * @param x The input tensor.
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function acos_(x) {
+        var $x = convertToTensor(x, 'x', 'acos');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Acos, inputs);
+    }
+    var acos = op({ acos_: acos_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:
+     * `acosh(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([10, 1, 3, 5.7]);
+     *
+     * x.acosh().print();  // or tf.acosh(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function acosh_(x) {
+        var $x = convertToTensor(x, 'x', 'acosh');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Acosh, inputs);
+    }
+    var acosh = op({ acosh_: acosh_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2]);
+     * const b = tf.tensor1d([3, 4]);
+     * const c = tf.tensor1d([5, 6]);
+     *
+     * tf.addN([a, b, c]).print();
+     * ```
+     * @param tensors A list of tensors with the same shape and dtype.
+     * @doc {heading: 'Operations', subheading: 'Arithmetic'}
+     */
+    function addN_(tensors) {
+        assert(Array.isArray(tensors), function () { return 'The argument passed to tf.addN() must be a list of tensors'; });
+        assert(tensors.length >= 1, function () { return "Must pass at least one tensor to tf.addN(), but got " +
+            ("" + tensors.length); });
+        var $tensors = tensors.map(function (t, i) { return convertToTensor(t, "tensors" + i, 'addN'); });
+        var firstTensor = $tensors[0];
+        $tensors.forEach(function (t) {
+            if (t.dtype !== firstTensor.dtype) {
+                throw new Error('All tensors passed to tf.addN() must have the same dtype');
+            }
+        });
+        $tensors.forEach(function (t) {
+            if (!arraysEqual(t.shape, firstTensor.shape)) {
+                throw new Error('All tensors passed to tf.addN() must have the same shape');
+            }
+        });
+        var inputs = $tensors;
+        return ENGINE.runKernel(AddN, inputs);
+    }
+    var addN = op({ addN_: addN_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the logical and of elements across dimensions of a `tf.Tensor`.
+     *
+     * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
+     * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
+     * `axes`. If `keepDims` is true, the reduced dimensions are retained with
+     * length 1. If `axes` has no entries, all dimensions are reduced, and an
+     * `tf.Tensor` with a single element is returned.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 1, 1], 'bool');
+     *
+     * x.all().print();  // or tf.all(x)
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
+     *
+     * const axis = 1;
+     * x.all(axis).print();  // or tf.all(x, axis)
+     * ```
+     *
+     * @param x The input tensor. Must be of dtype bool.
+     * @param axis The dimension(s) to reduce. By default it reduces
+     *     all dimensions.
+     * @param keepDims If true, retains reduced dimensions with size 1.
+     *
+     * @doc {heading: 'Operations', subheading: 'Reduction'}
+     */
+    function all_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'all', 'bool');
+        var inputs = { x: $x };
+        var attrs = { axis: axis, keepDims: keepDims };
+        return ENGINE.runKernel(All, inputs, attrs);
+    }
+    var all = op({ all_: all_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the logical or of elements across dimensions of a `tf.Tensor`.
+     *
+     * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
+     * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
+     * `axes`. If `keepDims` is true, the reduced dimensions are retained with
+     * length 1. If `axes` has no entries, all dimensions are reduced, and an
+     * `tf.Tensor` with a single element is returned.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 1, 1], 'bool');
+     *
+     * x.any().print();  // or tf.any(x)
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
+     *
+     * const axis = 1;
+     * x.any(axis).print();  // or tf.any(x, axis)
+     * ```
+     *
+     * @param x The input tensor. Must be of dtype bool.
+     * @param axis The dimension(s) to reduce. By default it reduces
+     *     all dimensions.
+     * @param keepDims If true, retains reduced dimensions with size 1.
+     *
+     * @doc {heading: 'Operations', subheading: 'Reduction'}
+     */
+    function any_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'any', 'bool');
+        var inputs = { x: $x };
+        var attrs = { axis: axis, keepDims: keepDims };
+        return ENGINE.runKernel(Any, inputs, attrs);
+    }
+    // tslint:disable-next-line:variable-name
+    var any = op({ any_: any_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google Inc. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the indices of the maximum values along an `axis`.
+     *
+     * The result has the same shape as `input` with the dimension along `axis`
+     * removed.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3]);
+     *
+     * x.argMax().print();  // or tf.argMax(x)
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
+     *
+     * const axis = 1;
+     * x.argMax(axis).print();  // or tf.argMax(x, axis)
+     * ```
+     *
+     * @param x The input tensor.
+     * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).
+     *
+     * @doc {heading: 'Operations', subheading: 'Reduction'}
+     */
+    function argMax_(x, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $x = convertToTensor(x, 'x', 'argMax');
+        var inputs = { x: $x };
+        var attrs = { axis: axis };
+        return ENGINE.runKernel(ArgMax, inputs, attrs);
+    }
+    var argMax = op({ argMax_: argMax_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google Inc. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the indices of the minimum values along an `axis`.
+     *
+     * The result has the same shape as `input` with the dimension along `axis`
+     * removed.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3]);
+     *
+     * x.argMin().print();  // or tf.argMin(x)
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
+     *
+     * const axis = 1;
+     * x.argMin(axis).print();  // or tf.argMin(x, axis)
+     * ```
+     *
+     * @param x The input tensor.
+     * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).
+     *
+     * @doc {heading: 'Operations', subheading: 'Reduction'}
+     */
+    function argMin_(x, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $x = convertToTensor(x, 'x', 'argMin');
+        var inputs = { x: $x };
+        var attrs = { axis: axis };
+        return ENGINE.runKernel(ArgMin, inputs, attrs);
+    }
+    var argMin = op({ argMin_: argMin_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, 1, -1, .7]);
+     *
+     * x.asin().print();  // or tf.asin(x)
+     * ```
+     * @param x The input tensor.
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function asin_(x) {
+        var $x = convertToTensor(x, 'x', 'asin');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Asin, inputs);
+    }
+    var asin = op({ asin_: asin_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:
+     * `asinh(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, 1, -1, .7]);
+     *
+     * x.asinh().print();  // or tf.asinh(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function asinh_(x) {
+        var $x = convertToTensor(x, 'x', 'asinh');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Asinh, inputs);
+    }
+    var asinh = op({ asinh_: asinh_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, 1, -1, .7]);
+     *
+     * x.atan().print();  // or tf.atan(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function atan_(x) {
+        var $x = convertToTensor(x, 'x', 'atan');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Atan, inputs);
+    }
+    var atan = op({ atan_: atan_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.
+     * Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);
+     * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);
+     *
+     * tf.atan2(a, b).print()
+     * ```
+     *
+     * @param a The first tensor.
+     * @param b The second tensor. Must have the same dtype as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function atan2_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'atan2');
+        var $b = convertToTensor(b, 'b', 'atan2');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(Atan2, inputs);
+    }
+    var atan2 = op({ atan2_: atan2_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:
+     * `atanh(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, .1, -.1, .7]);
+     *
+     * x.atanh().print();  // or tf.atanh(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function atanh_(x) {
+        var $x = convertToTensor(x, 'x', 'atanh');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Atanh, inputs);
+    }
+    var atanh = op({ atanh_: atanh_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     *
+     * @param inputShape Input tensor shape is of the following dimensions:
+     *     `[batch, height, width, inChannels]`.
+     * @param filterShape The filter shape is of the following dimensions:
+     *     `[filterHeight, filterWidth, depth]`.
+     * @param strides The strides of the sliding window for each dimension of the
+     *     input tensor: `[strideHeight, strideWidth]`.
+     *     If `strides` is a single number,
+     *     then `strideHeight == strideWidth`.
+     * @param pad The type of padding algorithm.
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1*1x1.
+     *    - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dataFormat The data format of the input and output data.
+     *     Defaults to 'NHWC'.
+     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.
+     *     Defaults to `[1, 1]`. If `dilations` is a single number, then
+     *     `dilationHeight == dilationWidth`.
+     */
+    function computeDilation2DInfo(inputShape, filterShape, strides, pad, dataFormat, dilations) {
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        // `computerConv2DInfo` require filterShape to be in the dimension of:
+        // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have
+        // outDepth, it should have the same depth as the input.
+        // Input shape: [batch, height, width, inChannels]
+        var inputChannels = inputShape[3];
+        var $filterShape = filterShape.concat([inputChannels]);
+        var $dataFormat = convertConv2DDataFormat(dataFormat);
+        return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad, null /* roundingMode */, null /* depthWise */, $dataFormat);
+    }
+    function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat) {
+        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+        var _a = parseTupleParam(filterSize), filterHeight = _a[0], filterWidth = _a[1];
+        var filterShape;
+        if (dataFormat === 'channelsLast') {
+            filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
+        }
+        else if (dataFormat === 'channelsFirst') {
+            filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
+        }
+        else {
+            throw new Error("Unknown dataFormat " + dataFormat);
+        }
+        return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);
+    }
+    /**
+     * Computes the information for a forward pass of a pooling3D operation.
+     */
+    function computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat) {
+        if (dataFormat === void 0) { dataFormat = 'NDHWC'; }
+        var _a = parse3TupleParam(filterSize), filterDepth = _a[0], filterHeight = _a[1], filterWidth = _a[2];
+        var filterShape;
+        var $dataFormat;
+        if (dataFormat === 'NDHWC') {
+            $dataFormat = 'channelsLast';
+            filterShape =
+                [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];
+        }
+        else if (dataFormat === 'NCDHW') {
+            $dataFormat = 'channelsFirst';
+            filterShape =
+                [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];
+        }
+        else {
+            throw new Error("Unknown dataFormat " + dataFormat);
+        }
+        return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);
+    }
+    /**
+     * Computes the information for a forward pass of a convolution/pooling
+     * operation.
+     */
+    function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise, dataFormat) {
+        if (depthwise === void 0) { depthwise = false; }
+        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+        var _a = [-1, -1, -1, -1], batchSize = _a[0], inHeight = _a[1], inWidth = _a[2], inChannels = _a[3];
+        if (dataFormat === 'channelsLast') {
+            batchSize = inShape[0], inHeight = inShape[1], inWidth = inShape[2], inChannels = inShape[3];
+        }
+        else if (dataFormat === 'channelsFirst') {
+            batchSize = inShape[0], inChannels = inShape[1], inHeight = inShape[2], inWidth = inShape[3];
+        }
+        else {
+            throw new Error("Unknown dataFormat " + dataFormat);
+        }
+        var filterHeight = filterShape[0], filterWidth = filterShape[1], filterChannels = filterShape[3];
+        var _b = parseTupleParam(strides), strideHeight = _b[0], strideWidth = _b[1];
+        var _c = parseTupleParam(dilations), dilationHeight = _c[0], dilationWidth = _c[1];
+        var effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+        var effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+        var _d = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat), padInfo = _d.padInfo, outHeight = _d.outHeight, outWidth = _d.outWidth;
+        var outChannels = depthwise ? filterChannels * inChannels : filterChannels;
+        var outShape;
+        if (dataFormat === 'channelsFirst') {
+            outShape = [batchSize, outChannels, outHeight, outWidth];
+        }
+        else if (dataFormat === 'channelsLast') {
+            outShape = [batchSize, outHeight, outWidth, outChannels];
+        }
+        return {
+            batchSize: batchSize,
+            dataFormat: dataFormat,
+            inHeight: inHeight,
+            inWidth: inWidth,
+            inChannels: inChannels,
+            outHeight: outHeight,
+            outWidth: outWidth,
+            outChannels: outChannels,
+            padInfo: padInfo,
+            strideHeight: strideHeight,
+            strideWidth: strideWidth,
+            filterHeight: filterHeight,
+            filterWidth: filterWidth,
+            effectiveFilterHeight: effectiveFilterHeight,
+            effectiveFilterWidth: effectiveFilterWidth,
+            dilationHeight: dilationHeight,
+            dilationWidth: dilationWidth,
+            inShape: inShape,
+            outShape: outShape,
+            filterShape: filterShape
+        };
+    }
+    /**
+     * Computes the information for a forward pass of a 3D convolution/pooling
+     * operation.
+     */
+    function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise, dataFormat, roundingMode) {
+        if (depthwise === void 0) { depthwise = false; }
+        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+        var _a = [-1, -1, -1, -1, -1], batchSize = _a[0], inDepth = _a[1], inHeight = _a[2], inWidth = _a[3], inChannels = _a[4];
+        if (dataFormat === 'channelsLast') {
+            batchSize = inShape[0], inDepth = inShape[1], inHeight = inShape[2], inWidth = inShape[3], inChannels = inShape[4];
+        }
+        else if (dataFormat === 'channelsFirst') {
+            batchSize = inShape[0], inChannels = inShape[1], inDepth = inShape[2], inHeight = inShape[3], inWidth = inShape[4];
+        }
+        else {
+            throw new Error("Unknown dataFormat " + dataFormat);
+        }
+        var filterDepth = filterShape[0], filterHeight = filterShape[1], filterWidth = filterShape[2], filterChannels = filterShape[4];
+        var _b = parse3TupleParam(strides), strideDepth = _b[0], strideHeight = _b[1], strideWidth = _b[2];
+        var _c = parse3TupleParam(dilations), dilationDepth = _c[0], dilationHeight = _c[1], dilationWidth = _c[2];
+        var effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
+        var effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+        var effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+        var _d = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode), padInfo = _d.padInfo, outDepth = _d.outDepth, outHeight = _d.outHeight, outWidth = _d.outWidth;
+        var outChannels = depthwise ? filterChannels * inChannels : filterChannels;
+        var outShape;
+        if (dataFormat === 'channelsFirst') {
+            outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
+        }
+        else if (dataFormat === 'channelsLast') {
+            outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
+        }
+        return {
+            batchSize: batchSize,
+            dataFormat: dataFormat,
+            inDepth: inDepth,
+            inHeight: inHeight,
+            inWidth: inWidth,
+            inChannels: inChannels,
+            outDepth: outDepth,
+            outHeight: outHeight,
+            outWidth: outWidth,
+            outChannels: outChannels,
+            padInfo: padInfo,
+            strideDepth: strideDepth,
+            strideHeight: strideHeight,
+            strideWidth: strideWidth,
+            filterDepth: filterDepth,
+            filterHeight: filterHeight,
+            filterWidth: filterWidth,
+            effectiveFilterDepth: effectiveFilterDepth,
+            effectiveFilterHeight: effectiveFilterHeight,
+            effectiveFilterWidth: effectiveFilterWidth,
+            dilationDepth: dilationDepth,
+            dilationHeight: dilationHeight,
+            dilationWidth: dilationWidth,
+            inShape: inShape,
+            outShape: outShape,
+            filterShape: filterShape
+        };
+    }
+    function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {
+        if (zeroPad == null) {
+            zeroPad = computeDefaultPad(inShape, fieldSize, stride);
+        }
+        var inputRows = inShape[0];
+        var inputCols = inShape[1];
+        var outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+        var outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+        return [outputRows, outputCols];
+    }
+    function computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {
+        if (zeroPad == null) {
+            zeroPad = computeDefaultPad(inShape, fieldSize, stride);
+        }
+        var inputDepth = inShape[0];
+        var inputRows = inShape[1];
+        var inputCols = inShape[2];
+        var outputDepths = round((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+        var outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+        var outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+        return [outputDepths, outputRows, outputCols, outChannels];
+    }
+    function computeDefaultPad(inputShape, fieldSize, stride, dilation) {
+        if (dilation === void 0) { dilation = 1; }
+        var effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
+        return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
+    }
+    function parseTupleParam(param) {
+        if (typeof param === 'number') {
+            return [param, param, param];
+        }
+        if (param.length === 2) {
+            return [param[0], param[1], 1];
+        }
+        return param;
+    }
+    function parse3TupleParam(param) {
+        return typeof param === 'number' ? [param, param, param] : param;
+    }
+    /* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d
+     * Atrous convolution is equivalent to standard convolution with upsampled
+     * filters with effective_filter_height =
+     * filter_height + (filter_height - 1) * (dilation - 1)
+     * and effective_filter_width =
+     * filter_width + (filter_width - 1) * (dilation - 1),
+     * produced by inserting dilation - 1 zeros along consecutive elements across
+     * the filters' spatial dimensions.
+     * When there is a dilation, this converts a filter dimension to the
+     * effective filter dimension, so it can be used in a standard convolution.
+     */
+    function getEffectiveFilterSize(filterSize, dilation) {
+        if (dilation <= 1) {
+            return filterSize;
+        }
+        return filterSize + (filterSize - 1) * (dilation - 1);
+    }
+    function getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {
+        var padInfo;
+        var outHeight;
+        var outWidth;
+        if (typeof pad === 'number') {
+            var padType = (pad === 0) ? 'VALID' : 'NUMBER';
+            padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };
+            var outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);
+            outHeight = outShape[0];
+            outWidth = outShape[1];
+        }
+        else if (pad === 'same') {
+            outHeight = Math.ceil(inHeight / strideHeight);
+            outWidth = Math.ceil(inWidth / strideWidth);
+            var padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);
+            var padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);
+            var top_1 = Math.floor(padAlongHeight / 2);
+            var bottom = padAlongHeight - top_1;
+            var left = Math.floor(padAlongWidth / 2);
+            var right = padAlongWidth - left;
+            padInfo = { top: top_1, bottom: bottom, left: left, right: right, type: 'SAME' };
+        }
+        else if (pad === 'valid') {
+            padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };
+            outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
+            outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
+        }
+        else if (typeof pad === 'object') {
+            var top_2 = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];
+            var bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];
+            var left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];
+            var right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];
+            var padType = (top_2 === 0 && bottom === 0 && left === 0 && right === 0) ?
+                'VALID' :
+                'EXPLICIT';
+            padInfo = { top: top_2, bottom: bottom, left: left, right: right, type: padType };
+            outHeight = round((inHeight - filterHeight + top_2 + bottom) / strideHeight + 1, roundingMode);
+            outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
+        }
+        else {
+            throw Error("Unknown padding parameter: " + pad);
+        }
+        return { padInfo: padInfo, outHeight: outHeight, outWidth: outWidth };
+    }
+    function get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {
+        var padInfo;
+        var outDepth;
+        var outHeight;
+        var outWidth;
+        if (typeof pad === 'number') {
+            var padType = (pad === 0) ? 'VALID' : 'NUMBER';
+            padInfo = {
+                top: pad,
+                bottom: pad,
+                left: pad,
+                right: pad,
+                front: pad,
+                back: pad,
+                type: padType
+            };
+            var outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad, roundingMode);
+            outDepth = outShape[0];
+            outHeight = outShape[1];
+            outWidth = outShape[2];
+        }
+        else if (pad === 'same') {
+            outDepth = Math.ceil(inDepth / strideDepth);
+            outHeight = Math.ceil(inHeight / strideHeight);
+            outWidth = Math.ceil(inWidth / strideWidth);
+            var padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
+            var padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
+            var padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
+            var front = Math.floor(padAlongDepth / 2);
+            var back = padAlongDepth - front;
+            var top_3 = Math.floor(padAlongHeight / 2);
+            var bottom = padAlongHeight - top_3;
+            var left = Math.floor(padAlongWidth / 2);
+            var right = padAlongWidth - left;
+            padInfo = { top: top_3, bottom: bottom, left: left, right: right, front: front, back: back, type: 'SAME' };
+        }
+        else if (pad === 'valid') {
+            padInfo = {
+                top: 0,
+                bottom: 0,
+                left: 0,
+                right: 0,
+                front: 0,
+                back: 0,
+                type: 'VALID'
+            };
+            outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);
+            outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
+            outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
+        }
+        else {
+            throw Error("Unknown padding parameter: " + pad);
+        }
+        return { padInfo: padInfo, outDepth: outDepth, outHeight: outHeight, outWidth: outWidth };
+    }
+    /**
+     * Rounds a value depending on the rounding mode
+     * @param value
+     * @param roundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     */
+    function round(value, roundingMode) {
+        if (!roundingMode) {
+            return Math.trunc(value);
+        }
+        switch (roundingMode) {
+            case 'round':
+                // used for Caffe Conv
+                return Math.round(value);
+            case 'ceil':
+                // used for Caffe Pool
+                return Math.ceil(value);
+            case 'floor':
+                return Math.floor(value);
+            default:
+                throw new Error("Unknown roundingMode " + roundingMode);
+        }
+    }
+    function tupleValuesAreOne(param) {
+        var _a = parseTupleParam(param), dimA = _a[0], dimB = _a[1], dimC = _a[2];
+        return dimA === 1 && dimB === 1 && dimC === 1;
+    }
+    function eitherStridesOrDilationsAreOne(strides, dilations) {
+        return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
+    }
+    /**
+     * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to
+     *    'channelsLast'|'channelsFirst'
+     * @param dataFormat in 'NHWC'|'NCHW' mode
+     * @return dataFormat in 'channelsLast'|'channelsFirst' mode
+     * @throws unknown dataFormat
+     */
+    function convertConv2DDataFormat(dataFormat) {
+        if (dataFormat === 'NHWC') {
+            return 'channelsLast';
+        }
+        else if (dataFormat === 'NCHW') {
+            return 'channelsFirst';
+        }
+        else {
+            throw new Error("Unknown dataFormat " + dataFormat);
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Reshapes a `tf.Tensor` to a given shape.
+     *
+     * Given an input tensor, returns a new tensor with the same values as the
+     * input tensor with shape `shape`.
+     *
+     * If one component of shape is the special value -1, the size of that
+     * dimension is computed so that the total size remains constant. In
+     * particular, a shape of [-1] flattens into 1-D. At most one component of
+     * shape can be -1.
+     *
+     * If shape is 1-D or higher, then the operation returns a tensor with shape
+     * shape filled with the values of tensor. In this case, the number of
+     * elements implied by shape must be the same as the number of elements in
+     * tensor.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3, 4]);
+     * x.reshape([2, 2]).print();
+     * ```
+     *
+     * @param x The input tensor to be reshaped.
+     * @param shape An array of integers defining the output tensor shape.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Transformations'}
+     */
+    function reshape_(x, shape) {
+        var $x = convertToTensor(x, 'x', 'reshape', 'string_or_numeric');
+        var inputs = { x: $x };
+        var attrs = { shape: shape };
+        return ENGINE.runKernel(Reshape, inputs, attrs);
+    }
+    var reshape = op({ reshape_: reshape_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the 2D average pooling of an image.
+     *
+     * @param x The input tensor, of rank 4 or rank 3 of shape
+     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
+     * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
+     *     `filterSize` is a single number, then `filterHeight == filterWidth`.
+     * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
+     *     `strides` is a single number, then `strideHeight == strideWidth`.
+     * @param pad The type of padding algorithm:
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1x1.
+     *    - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *         https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     */
+    function avgPool_(x, filterSize, strides, pad, dimRoundingMode) {
+        var $x = convertToTensor(x, 'x', 'avgPool', 'float32');
+        var dilations = 1;
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in avgPool: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+        }
+        assert(x4D.rank === 4, function () { return "Error in avgPool: x must be rank 4 but got rank " + x4D.rank + "."; });
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), function () { return "Error in avgPool: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+        }
+        var inputs = { x: x4D };
+        var attrs = { filterSize: filterSize, strides: strides, pad: pad, dimRoundingMode: dimRoundingMode };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(AvgPool, inputs, attrs);
+        res = cast(res, $x.dtype);
+        if (reshapedTo4D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+        }
+        return res;
+    }
+    var avgPool = op({ avgPool_: avgPool_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the 3D average pooling.
+     *
+     * ```js
+     * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
+     * const result = tf.avgPool3d(x, 2, 1, 'valid');
+     * result.print();
+     * ```
+     *
+     * @param x The input tensor, of rank 5 or rank 4 of shape
+     *     `[batch, depth, height, width, inChannels]`.
+     * @param filterSize The filter size:
+     *     `[filterDepth, filterHeight, filterWidth]`.
+     *     If `filterSize` is a single number,
+     *     then `filterDepth == filterHeight == filterWidth`.
+     * @param strides The strides of the pooling:
+     *     `[strideDepth, strideHeight, strideWidth]`.
+     *     If `strides` is a single number,
+     *     then `strideDepth == strideHeight == strideWidth`.
+     * @param pad The type of padding algorithm.
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1*1x1.
+     *    - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
+     *     "NDHWC". Specify the data format of the input and output data. With the
+     *     default format "NDHWC", the data is stored in the order of: [batch,
+     *     depth, height, width, channels]. Only "NDHWC" is currently supported.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat) {
+        if (dataFormat === void 0) { dataFormat = 'NDHWC'; }
+        var $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');
+        var x5D = $x;
+        var reshapedTo5D = false;
+        if ($x.rank === 4) {
+            reshapedTo5D = true;
+            x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
+        }
+        assert(x5D.rank === 5, function () { return "Error in avgPool3d: x must be rank 5 but got rank " + x5D.rank + "."; });
+        assert(dataFormat === 'NDHWC', function () { return "Error in avgPool3d: Only NDHWC is currently supported, " +
+            ("but got dataFormat of " + dataFormat); });
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), function () { return "Error in avgPool3d: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+        }
+        var inputs = { x: x5D };
+        var attrs = { filterSize: filterSize, strides: strides, pad: pad, dimRoundingMode: dimRoundingMode, dataFormat: dataFormat };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(AvgPool3D, inputs, attrs);
+        res = cast(res, x5D.dtype);
+        if (reshapedTo5D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+        }
+        return res;
+    }
+    var avgPool3d = op({ avgPool3d_: avgPool3d_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Concatenates a list of `tf.Tensor`s along a given axis.
+     *
+     * The tensors ranks and types must match, and their sizes must match in all
+     * dimensions except `axis`.
+     *
+     * Also available are stricter rank-specific methods that assert that
+     * `tensors` are of the given rank:
+     *   - `tf.concat1d`
+     *   - `tf.concat2d`
+     *   - `tf.concat3d`
+     *   - `tf.concat4d`
+     *
+     * Except `tf.concat1d` (which does not have axis param), all methods have
+     * same signature as this method.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2]);
+     * const b = tf.tensor1d([3, 4]);
+     * a.concat(b).print();  // or a.concat(b)
+     * ```
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2]);
+     * const b = tf.tensor1d([3, 4]);
+     * const c = tf.tensor1d([5, 6]);
+     * tf.concat([a, b, c]).print();
+     * ```
+     *
+     * ```js
+     * const a = tf.tensor2d([[1, 2], [10, 20]]);
+     * const b = tf.tensor2d([[3, 4], [30, 40]]);
+     * const axis = 1;
+     * tf.concat([a, b], axis).print();
+     * ```
+     * @param tensors A list of tensors to concatenate.
+     * @param axis The axis to concate along. Defaults to 0 (the first dim).
+     *
+     * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
+     */
+    function concat_(tensors, axis) {
+        if (axis === void 0) { axis = 0; }
+        assert(tensors.length >= 1, function () { return 'Pass at least one tensor to concat'; });
+        var $tensors = convertToTensorArray(tensors, 'tensors', 'concat', 'string_or_numeric');
+        if ($tensors[0].dtype === 'complex64') {
+            $tensors.forEach(function (tensor) {
+                if (tensor.dtype !== 'complex64') {
+                    throw new Error("Cannot concatenate complex64 tensors with a tensor\n          with dtype " + tensor.dtype + ". ");
+                }
+            });
+        }
+        if ($tensors.length === 1) {
+            return clone($tensors[0]);
+        }
+        var inputs = $tensors;
+        var attr = { axis: axis };
+        return ENGINE.runKernel(Concat, inputs, attr);
+    }
+    var concat = op({ concat_: concat_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes sigmoid element-wise, `1 / (1 + exp(-x))`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, -1, 2, -3]);
+     *
+     * x.sigmoid().print();  // or tf.sigmoid(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function sigmoid_(x) {
+        var $x = convertToTensor(x, 'x', 'sigmoid');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Sigmoid, inputs);
+    }
+    var sigmoid = op({ sigmoid_: sigmoid_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`
+     * and is of size `size`.
+     *
+     * Also available are stricter rank-specific methods with the same signature
+     * as this method that assert that `x` is of the given rank:
+     *   - `tf.slice1d`
+     *   - `tf.slice2d`
+     *   - `tf.slice3d`
+     *   - `tf.slice4d`
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3, 4]);
+     *
+     * x.slice([1], [2]).print();
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     *
+     * x.slice([1, 0], [1, 2]).print();
+     * ```
+     * @param x The input `tf.Tensor` to slice from.
+     * @param begin The coordinates to start the slice from. The length can be
+     *     less than the rank of x - the rest of the axes will have implicit 0 as
+     *     start. Can also be a single number, in which case it specifies the
+     *     first axis.
+     * @param size The size of the slice. The length can be less than the rank of
+     *     x - the rest of the axes will have implicit -1. A value of -1 requests
+     *     the rest of the dimensions in the axis. Can also be a single number,
+     *     in which case it specifies the size of the first axis.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
+     */
+    function slice_(x, begin, size) {
+        var $x = convertToTensor(x, 'x', 'slice', 'string_or_numeric');
+        if ($x.rank === 0) {
+            throw new Error('Slicing scalar is not possible');
+        }
+        var inputs = { x: $x };
+        var attrs = { begin: begin, size: size };
+        return ENGINE.runKernel(Slice, inputs, attrs);
+    }
+    var slice = op({ slice_: slice_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, 1, -1, 70]);
+     *
+     * x.tanh().print();  // or tf.tanh(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function tanh_(x) {
+        var $x = convertToTensor(x, 'x', 'tanh');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Tanh, inputs);
+    }
+    var tanh$1 = op({ tanh_: tanh_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the next state and output of a BasicLSTMCell.
+     *
+     * Returns `[newC, newH]`.
+     *
+     * Derived from tf.contrib.rnn.BasicLSTMCell.
+     *
+     * @param forgetBias Forget bias for the cell.
+     * @param lstmKernel The weights for the cell.
+     * @param lstmBias The bias for the cell.
+     * @param data The input to the cell.
+     * @param c Previous cell state.
+     * @param h Previous cell output.
+     *
+     * @doc {heading: 'Operations', subheading: 'RNN'}
+     */
+    function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
+        var $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');
+        var $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');
+        var $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');
+        var $data = convertToTensor(data, 'data', 'basicLSTMCell');
+        var $c = convertToTensor(c, 'c', 'basicLSTMCell');
+        var $h = convertToTensor(h, 'h', 'basicLSTMCell');
+        var combined = concat([$data, $h], 1);
+        var weighted = matMul(combined, $lstmKernel);
+        var res = add$1(weighted, $lstmBias);
+        // i = input_gate, j = new_input, f = forget_gate, o = output_gate
+        var batchSize = res.shape[0];
+        var sliceCols = res.shape[1] / 4;
+        var sliceSize = [batchSize, sliceCols];
+        var i = slice(res, [0, 0], sliceSize);
+        var j = slice(res, [0, sliceCols], sliceSize);
+        var f = slice(res, [0, sliceCols * 2], sliceSize);
+        var o = slice(res, [0, sliceCols * 3], sliceSize);
+        var newC = add$1(mul(sigmoid(i), tanh$1(j)), mul($c, sigmoid(add$1($forgetBias, f))));
+        var newH = mul(tanh$1(newC), sigmoid(o));
+        return [newC, newH];
+    }
+    var basicLSTMCell = op({ basicLSTMCell_: basicLSTMCell_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of
+     * shape `blockShape + [batch]`, interleaves these blocks back into the grid
+     * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with
+     * the same rank as the input. The spatial dimensions of this intermediate
+     * result are then optionally cropped according to `crops` to produce the
+     * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise
+     * description.
+     *
+     * ```js
+     * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
+     * const blockShape = [2, 2];
+     * const crops = [[0, 0], [0, 0]];
+     *
+     * x.batchToSpaceND(blockShape, crops).print();
+     * ```
+     *
+     * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +
+     * remainingShape`, where spatialShape has `M` dimensions.
+     * @param blockShape A 1-D array. Must have shape `[M]`, all values must
+     * be >= 1.
+     * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.
+     * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input
+     * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required
+     * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`
+     *
+     * This operation is equivalent to the following steps:
+     *
+     * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,
+     * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,
+     * x.shape[N-1]]`
+     *
+     * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /
+     * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],
+     * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`
+     *
+     * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /
+     * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *
+     * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`
+     *
+     * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`
+     * according to `crops` to produce the output of shape: `[batch /
+     * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],
+     * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -
+     * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`
+     *
+     * @doc {heading: 'Tensors', subheading: 'Transformations'}
+     */
+    function batchToSpaceND_(x, blockShape, crops) {
+        var $x = convertToTensor(x, 'x', 'batchToSpaceND');
+        var prod = blockShape.reduce(function (a, b) { return a * b; });
+        assert($x.rank >= 1 + blockShape.length, function () { return "input rank is " + $x.rank + " but should be > than blockShape.length " + blockShape.length; });
+        assert(crops.length === blockShape.length, function () { return "crops.length is " + crops.length + " but should be equal to blockShape.length  " + blockShape.length; });
+        assert($x.shape[0] % prod === 0, function () { return "input tensor batch is " + $x.shape[0] + " but is not divisible by the product of " +
+            ("the elements of blockShape " + blockShape.join(' * ') + " === " + prod); });
+        var inputs = { x: $x };
+        var attrs = { blockShape: blockShape, crops: crops };
+        return ENGINE.runKernel(BatchToSpaceND, inputs, attrs);
+    }
+    var batchToSpaceND = op({ batchToSpaceND_: batchToSpaceND_ });
+
+    function xAs4D(x) {
+        var x4D;
+        if (x.rank === 0 || x.rank === 1) {
+            x4D = reshape(x, [1, 1, 1, x.size]);
+        }
+        else if (x.rank === 2) {
+            x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);
+        }
+        else if (x.rank === 3) {
+            x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
+        }
+        else {
+            x4D = x;
+        }
+        return x4D;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Batch normalization.
+     *
+     * As described in
+     * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).
+     *
+     * Mean, variance, scale, and offset can be of two shapes:
+     *   - The same shape as the input.
+     *   - In the common case, the depth dimension is the last dimension of x, so
+     *     the values would be an `tf.Tensor1D` of shape [depth].
+     *
+     * Also available are stricter rank-specific methods with the same signature
+     * as this method that assert that parameters passed are of given rank
+     *   - `tf.batchNorm2d`
+     *   - `tf.batchNorm3d`
+     *   - `tf.batchNorm4d`
+     *
+     * @param x The input Tensor.
+     * @param mean A mean Tensor.
+     * @param variance A variance Tensor.
+     * @param offset An offset Tensor.
+     * @param scale A scale Tensor.
+     * @param varianceEpsilon A small float number to avoid dividing by 0.
+     *
+     * @doc {heading: 'Operations', subheading: 'Normalization'}
+     */
+    function batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {
+        if (varianceEpsilon == null) {
+            varianceEpsilon = 0.001;
+        }
+        var $x = convertToTensor(x, 'x', 'batchNorm');
+        var $mean = convertToTensor(mean, 'mean', 'batchNorm');
+        var $variance = convertToTensor(variance, 'variance', 'batchNorm');
+        var $scale;
+        if (scale != null) {
+            $scale = convertToTensor(scale, 'scale', 'batchNorm');
+        }
+        var $offset;
+        if (offset != null) {
+            $offset = convertToTensor(offset, 'offset', 'batchNorm');
+        }
+        assert($mean.rank === $variance.rank, function () { return 'Batch normalization gradient requires mean and variance to have ' +
+            'equal ranks.'; });
+        assert($offset == null || $mean.rank === $offset.rank, function () { return 'Batch normalization gradient requires mean and offset to have ' +
+            'equal ranks.'; });
+        assert($scale == null || $mean.rank === $scale.rank, function () { return 'Batch normalization gradient requires mean and scale to have ' +
+            'equal ranks.'; });
+        var x4D = xAs4D($x);
+        var inputs = {
+            x: x4D,
+            scale: $scale,
+            offset: $offset,
+            mean: $mean,
+            variance: $variance
+        };
+        var attrs = { varianceEpsilon: varianceEpsilon };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs);
+        return reshape(res, $x.shape);
+    }
+    var batchNorm = op({ batchNorm_: batchNorm_ });
+
+    /**
+     * Batch normalization, strictly for 2D. For the more relaxed version, see
+     * `tf.batchNorm`.
+     *
+     * @param x The input Tensor.
+     * @param mean A mean Tensor.
+     * @param variance A variance Tensor.
+     * @param offset An offset Tensor.
+     * @param scale A scale Tensor.
+     * @param varianceEpsilon A small float number to avoid dividing by 0.
+     */
+    function batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {
+        var $x = convertToTensor(x, 'x', 'batchNorm');
+        var $mean = convertToTensor(mean, 'mean', 'batchNorm');
+        var $variance = convertToTensor(variance, 'variance', 'batchNorm');
+        var $scale;
+        if (scale != null) {
+            $scale = convertToTensor(scale, 'scale', 'batchNorm');
+        }
+        var $offset;
+        if (offset != null) {
+            $offset = convertToTensor(offset, 'offset', 'batchNorm');
+        }
+        assert($x.rank === 2, function () { return "Error in batchNorm2D: x must be rank 2 but got rank " +
+            ($x.rank + "."); });
+        assert($mean.rank === 2 || $mean.rank === 1, function () { return "Error in batchNorm2D: mean must be rank 2 or rank 1 but " +
+            ("got rank " + $mean.rank + "."); });
+        assert($variance.rank === 2 || $variance.rank === 1, function () { return "Error in batchNorm2D: variance must be rank 2 or rank 1 " +
+            ("but got rank " + $variance.rank + "."); });
+        if ($scale != null) {
+            assert($scale.rank === 2 || $scale.rank === 1, function () { return "Error in batchNorm2D: scale must be rank 2 or rank 1 " +
+                ("but got rank " + $scale.rank + "."); });
+        }
+        if ($offset != null) {
+            assert($offset.rank === 2 || $offset.rank === 1, function () { return "Error in batchNorm2D: offset must be rank 2 or rank 1 " +
+                ("but got rank " + $offset.rank + "."); });
+        }
+        return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
+    }
+    var batchNorm2d = op({ batchNorm2d_: batchNorm2d_ });
+
+    /**
+     * Batch normalization, strictly for 3D. For the more relaxed version, see
+     * `tf.batchNorm`.
+     *
+     * @param x The input Tensor.
+     * @param mean A mean Tensor.
+     * @param variance A variance Tensor.
+     * @param offset An offset Tensor.
+     * @param scale A scale Tensor.
+     * @param varianceEpsilon A small float number to avoid dividing by 0.
+     */
+    function batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {
+        var $x = convertToTensor(x, 'x', 'batchNorm');
+        var $mean = convertToTensor(mean, 'mean', 'batchNorm');
+        var $variance = convertToTensor(variance, 'variance', 'batchNorm');
+        var $scale;
+        if (scale != null) {
+            $scale = convertToTensor(scale, 'scale', 'batchNorm');
+        }
+        var $offset;
+        if (offset != null) {
+            $offset = convertToTensor(offset, 'offset', 'batchNorm');
+        }
+        assert($x.rank === 3, function () { return "Error in batchNorm3D: x must be rank 3 but got rank " +
+            ($x.rank + "."); });
+        assert($mean.rank === 3 || $mean.rank === 1, function () { return "Error in batchNorm3D: mean must be rank 3 or rank 1 but " +
+            ("got rank " + $mean.rank + "."); });
+        assert($variance.rank === 3 || $variance.rank === 1, function () { return "Error in batchNorm3D: variance must be rank 3 or rank 1 " +
+            ("but got rank " + $variance.rank + "."); });
+        if ($scale != null) {
+            assert($scale.rank === 3 || $scale.rank === 1, function () { return "Error in batchNorm3D: scale must be rank 3 or rank 1 " +
+                ("but got rank " + $scale.rank + "."); });
+        }
+        if ($offset != null) {
+            assert($offset.rank === 3 || $offset.rank === 1, function () { return "Error in batchNorm3D: offset must be rank 3 or rank 1 " +
+                ("but got rank " + $offset.rank + "."); });
+        }
+        return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
+    }
+    var batchNorm3d = op({ batchNorm3d_: batchNorm3d_ });
+
+    /**
+     * Batch normalization, strictly for 4D. For the more relaxed version, see
+     * `tf.batchNorm`.
+     *
+     * @param x The input Tensor.
+     * @param mean A mean Tensor.
+     * @param variance A variance Tensor.
+     * @param offset An offset Tensor.
+     * @param scale A scale Tensor.
+     * @param varianceEpsilon A small float number to avoid dividing by 0.
+     */
+    function batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {
+        var $x = convertToTensor(x, 'x', 'batchNorm');
+        var $mean = convertToTensor(mean, 'mean', 'batchNorm');
+        var $variance = convertToTensor(variance, 'variance', 'batchNorm');
+        var $scale;
+        if (scale != null) {
+            $scale = convertToTensor(scale, 'scale', 'batchNorm');
+        }
+        var $offset;
+        if (offset != null) {
+            $offset = convertToTensor(offset, 'offset', 'batchNorm');
+        }
+        assert($x.rank === 4, function () { return "Error in batchNorm4D: x must be rank 4 but got rank " +
+            ($x.rank + "."); });
+        assert($mean.rank === 4 || $mean.rank === 1, function () { return "Error in batchNorm4D: mean must be rank 4 or rank 1 but " +
+            ("got rank " + $mean.rank + "."); });
+        assert($variance.rank === 4 || $variance.rank === 1, function () { return "Error in batchNorm4D: variance must be rank 4 or rank 1 " +
+            ("but got rank " + $variance.rank + "."); });
+        if ($scale != null) {
+            assert($scale.rank === 4 || $scale.rank === 1, function () { return "Error in batchNorm4D: scale must be rank 4 or rank 1 " +
+                ("but got rank " + $scale.rank + "."); });
+        }
+        if ($offset != null) {
+            assert($offset.rank === 4 || $offset.rank === 1, function () { return "Error in batchNorm4D: offset must be rank 4 or rank 1 " +
+                ("but got rank " + $offset.rank + "."); });
+        }
+        return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
+    }
+    var batchNorm4d = op({ batchNorm4d_: batchNorm4d_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Outputs a vector with length `size` and the same dtype as `weights`.
+     *
+     * If `weights` are empty, then index `i` stores the number of times the value
+     * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the
+     * sum of the value in `weights` at each index where the corresponding value in
+     * `x` is `i`.
+     *
+     * Values in `x` outside of the range [0, size) are ignored.
+     *
+     * @param x The input int tensor, rank 1.
+     * @param weights The weights tensor, must have the same shape as x, or a
+     *     length-0 Tensor, in which case it acts as all weights equal to 1.
+     * @param size Non-negative integer.
+     *
+     * @doc {heading: 'Operations', subheading: 'Reduction'}
+     */
+    function bincount_(x, weights, size) {
+        var $x = convertToTensor(x, 'x', 'bincount');
+        var $weights = convertToTensor(weights, 'weights', 'bincount');
+        assert($x.dtype === 'int32', function () { return "Error in bincount: input " +
+            ("dtype must be int32, but got " + $x.dtype); });
+        assert(size >= 0, function () { return "size must be non-negative, but got " + size + "."; });
+        assert($weights.size === $x.size || $weights.size === 0, function () { return "Error in bincount: weights must have the same size as input or" +
+            ("0-length, but got input shape: " + $x.shape + ", weights shape: ") +
+            ($weights.shape + "."); });
+        var inputs = { x: $x, weights: $weights };
+        var attrs = { size: size };
+        return ENGINE.runKernel(Bincount, inputs, attrs);
+    }
+    var bincount = op({ bincount_: bincount_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Broadcast an array to a compatible shape NumPy-style.
+     *
+     * The tensor's shape is compared to the broadcast shape from end to beginning.
+     * Ones are prepended to the tensor's shape until is has the same length as
+     * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is
+     * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then
+     * the input tensor is tiled N times along that axis (using tf.tile).
+     *
+     * @param input The tensor that is to be broadcasted.
+     * @param shape The input is to be broadcast to this shape.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Transformations'}
+     */
+    function broadcastTo_(x, shape) {
+        var input = convertToTensor(x, 'broadcastTo', 'x');
+        var xShape = input.shape;
+        if (shape.some(function (d) { return !(d > 0) || d % 1 !== 0; })) {
+            throw new Error("broadcastTo(): Invalid broadcast shape [" + shape + "].");
+        }
+        if (shape.length < input.rank) {
+            throw new Error("broadcastTo(): shape.length=" + shape.length + " < input.rank=" + input.rank + ".");
+        }
+        if (shape.length > input.rank) {
+            var newShape = input.shape.slice();
+            while (newShape.length < shape.length) {
+                newShape.unshift(1);
+            }
+            input = reshape(input, newShape);
+        }
+        var inputShape = input.shape;
+        var reps = Array.from(shape);
+        for (var i = shape.length - 1; i >= 0; i--) {
+            if (inputShape[i] === shape[i]) {
+                reps[i] = 1;
+            }
+            else if (input.shape[i] !== 1) {
+                throw new Error("broadcastTo(): [" + xShape + "] cannot be broadcast to [" + shape + "].");
+            }
+        }
+        var axes = reps.map(function (n, i) { return n > 1 ? i : -1; }).filter(function (i) { return i >= 0; });
+        if (axes.length === 0) {
+            return clone(input);
+        }
+        // TODO call broadcastTo kernel directly once backends implement broadcstTo
+        var inputs = { x: input };
+        var attrs = { reps: reps };
+        return ENGINE.runKernel(Tile, inputs, attrs);
+    }
+    var broadcastTo = op({ broadcastTo_: broadcastTo_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([.6, 1.1, -3.3]);
+     *
+     * x.ceil().print();  // or tf.ceil(x)
+     * ```
+     * @param x The input Tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function ceil_(x) {
+        var $x = convertToTensor(x, 'x', 'ceil');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Ceil, inputs);
+    }
+    var ceil = op({ ceil_: ceil_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`
+     *
+     * ```js
+     * const x = tf.tensor1d([-1, 2, -3, 4]);
+     *
+     * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)
+     * ```
+     * @param x The input tensor.
+     * @param clipValueMin Lower-bound of range to be clipped to.
+     * @param clipValueMax Upper-bound of range to be clipped to.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function clipByValue_(x, clipValueMin, clipValueMax) {
+        var $x = convertToTensor(x, 'x', 'clipByValue');
+        assert((clipValueMin <= clipValueMax), function () { return "Error in clip: min (" + clipValueMin + ") must be " +
+            ("less than or equal to max (" + clipValueMax + ")."); });
+        var inputs = { x: $x };
+        var attrs = { clipValueMin: clipValueMin, clipValueMax: clipValueMax };
+        return ENGINE.runKernel(ClipByValue, inputs, attrs);
+    }
+    var clipByValue = op({ clipByValue_: clipByValue_ });
+
+    /**
+     * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.
+     *
+     * For example, if:
+     * A: shape(3) = |r1, g1, b1|
+     * B: shape(2) = |r2, g2|
+     * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|
+     *
+     * @param tensors A list of`tf.Tensor`s to concatenate.
+     * @return The concatenated array.
+     */
+    function concat1d_(tensors) {
+        return concat(tensors, 0 /* axis */);
+    }
+    var concat1d = op({ concat1d_: concat1d_ });
+
+    /**
+     * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.
+     *
+     * For example, if:
+     * A: shape(2, 3) = | r1, g1, b1 |
+     *                  | r2, g2, b2 |
+     *
+     * B: shape(2, 3) = | r3, g3, b3 |
+     *                  | r4, g4, b4 |
+     *
+     * C = tf.concat2d([A, B], axis)
+     *
+     * if axis = 0:
+     * C: shape(4, 3) = | r1, g1, b1 |
+     *                  | r2, g2, b2 |
+     *                  | r3, g3, b3 |
+     *                  | r4, g4, b4 |
+     *
+     * if axis = 1:
+     * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |
+     *                   | r2, g2, b2, r4, g4, b4 |
+     *
+     *
+     * @param tensors A list of `tf.Tensor`s to concatenate.
+     * @param axis The axis to concatenate along.
+     * @return The concatenated array.
+     */
+    function concat2d_(tensors, axis) {
+        return concat(tensors, axis);
+    }
+    var concat2d = op({ concat2d_: concat2d_ });
+
+    /**
+     * Concatenates a list of `tf.Tensor3D`s along an axis.
+     * See `concat` for details.
+     *
+     * For example, if:
+     * A: shape(2, 1, 3) = | r1, g1, b1 |
+     *                     | r2, g2, b2 |
+     *
+     * B: shape(2, 1, 3) = | r3, g3, b3 |
+     *                     | r4, g4, b4 |
+     *
+     * C = tf.concat3d([A, B], axis)
+     *
+     * if axis = 0:
+     * C: shape(4, 1, 3) = | r1, g1, b1 |
+     *                     | r2, g2, b2 |
+     *                     | r3, g3, b3 |
+     *                     | r4, g4, b4 |
+     *
+     * if axis = 1:
+     * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |
+     *                     | r2, g2, b2, r4, g4, b4 |
+     *
+     * if axis = 2:
+     * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |
+     *                      | r2, g2, b2, r4, g4, b4 |
+     *
+     * @param tensors A list of`tf.Tensor`s to concatenate.
+     * @param axis The axis to concate along.
+     * @return The concatenated array.
+     */
+    function concat3d_(tensors, axis) {
+        return concat(tensors, axis);
+    }
+    var concat3d = op({ concat3d_: concat3d_ });
+
+    /**
+     * Concatenates a list of `tf.Tensor4D`s along an axis.
+     * See `concat` for details.
+     *
+     * @param tensors A list of `tf.Tensor`s to concatenate.
+     * @param axis The axis to concate along.
+     * @return The concatenated array.
+     */
+    function concat4d_(tensors, axis) {
+        return concat(tensors, axis);
+    }
+    var concat4d = op({ concat4d_: concat4d_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes a 2D convolution over the input x.
+     *
+     * @param x The input tensor, of rank 4 or rank 3, of shape
+     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
+     * assumed.
+     * @param filter The filter, rank 4, of shape
+     *     `[filterHeight, filterWidth, inDepth, outDepth]`.
+     * @param strides The strides of the convolution: `[strideHeight,
+     * strideWidth]`.
+     * @param pad The type of padding algorithm.
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1x1.
+     *   - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
+     *     "NHWC". Specify the data format of the input and output data. With the
+     *     default format "NHWC", the data is stored in the order of: [batch,
+     *     height, width, channels].
+     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
+     *     in which we sample input values across the height and width dimensions
+     *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single
+     *     number, then `dilationHeight == dilationWidth`. If it is greater than
+     *     1, then all values of `strides` must be 1.
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function conv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        if (dilations === void 0) { dilations = [1, 1]; }
+        var $x = convertToTensor(x, 'x', 'conv2d');
+        var $filter = convertToTensor(filter, 'filter', 'conv2d');
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+        }
+        assert(x4D.rank === 4, function () { return "Error in conv2d: input must be rank 4, but got rank " + x4D.rank + "."; });
+        assert($filter.rank === 4, function () { return "Error in conv2d: filter must be rank 4, but got rank " +
+            ($filter.rank + "."); });
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), function () { return "Error in conv2d: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+        }
+        var inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];
+        assert(inDepth === $filter.shape[2], function () { return "Error in conv2d: depth of input (" + inDepth + ") must match " +
+            ("input depth for filter " + $filter.shape[2] + "."); });
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in conv2D: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
+        var inputs = { x: x4D, filter: $filter };
+        var attrs = { strides: strides, pad: pad, dataFormat: dataFormat, dilations: dilations, dimRoundingMode: dimRoundingMode };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(Conv2D, inputs, attrs);
+        if (reshapedTo4D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+        }
+        return res;
+    }
+    var conv2d = op({ conv2d_: conv2d_ });
+
+    /**
+     * Computes a 1D convolution over the input x.
+     *
+     * @param x The input tensor, of rank 3 or rank 2, of shape
+     *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.
+     * @param filter The filter, rank 3, of shape
+     *     `[filterWidth, inDepth, outDepth]`.
+     * @param stride The number of entries by which the filter is moved right at
+     *     each step.
+     * @param pad The type of padding algorithm.
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1x1.
+     *   - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dataFormat An optional string from "NWC", "NCW". Defaults to "NWC",
+     *     the data is stored in the order of [batch, in_width, in_channels]. Only
+     *     "NWC" is currently supported.
+     * @param dilation The dilation rate in which we sample input values in
+     *     atrous convolution. Defaults to `1`. If it is greater than 1, then
+     *     stride must be `1`.
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function conv1d_(x, filter, stride, pad, dataFormat, dilation, dimRoundingMode) {
+        if (dataFormat === void 0) { dataFormat = 'NWC'; }
+        if (dilation === void 0) { dilation = 1; }
+        var $x = convertToTensor(x, 'x', 'conv1d');
+        var $filter = convertToTensor(filter, 'filter', 'conv1d');
+        var x3D = $x;
+        var reshapedTo3D = false;
+        if ($x.rank === 2) {
+            reshapedTo3D = true;
+            x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);
+        }
+        assert(x3D.rank === 3, function () { return "Error in conv1d: input must be rank 3, but got rank " + x3D.rank + "."; });
+        assert($filter.rank === 3, function () { return "Error in conv1d: filter must be rank 3, but got rank " +
+            ($filter.rank + "."); });
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), function () { return "Error in conv1d: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+        }
+        assert(x3D.shape[2] === $filter.shape[1], function () { return "Error in conv1d: depth of input (" + x3D.shape[2] + ") must match " +
+            ("input depth for filter " + $filter.shape[1] + "."); });
+        assert(eitherStridesOrDilationsAreOne(stride, dilation), function () { return 'Error in conv1D: Either stride or dilation must be 1. ' +
+            ("Got stride " + stride + " and dilation '" + dilation + "'"); });
+        assert(dataFormat === 'NWC', function () { return "Error in conv1d: got dataFormat of " + dataFormat + " but only NWC is currently supported."; });
+        var filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);
+        var input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);
+        var strides = [1, stride];
+        var dilations = [1, dilation];
+        var conv2dDataFormat = 'NHWC';
+        var res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);
+        if (reshapedTo3D) {
+            return reshape(res, [res.shape[2], res.shape[3]]);
+        }
+        return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);
+    }
+    var conv1d = op({ conv1d_: conv1d_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the derivative of the input of a 2D convolution.
+     *
+     * @param xShape The shape of the input: [batch, height, width, inDepth].
+     * If length of 3, batch of 1 is assumed.
+     * @param dy The derivative of the output, of rank 4 or rank 3 of shape
+     *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is
+     * assumed.
+     * @param filter The filter, rank 4, of shape
+     *     `[filterHeight, filterWidth, inDepth, outDepth]`.
+     * @param strides The strides of the convolution: `[strideHeight,
+     * strideWidth]`.
+     * @param pad The type of padding algorithm used:
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1x1.
+     * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
+     *     "NHWC". Specify the data format of the input and output data. With the
+     *     default format "NHWC", the data is stored in the order of: [batch,
+     *     height, width, channels].
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     */
+    function conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat, dimRoundingMode) {
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        assert(xShape.length === dy.rank, function () { return "Length of inShape " +
+            ("(" + xShape.length + ") and rank of dy (" + dy.rank + ") must match"); });
+        var xShape4D = xShape;
+        var dy4D = dy;
+        var reshapedTo4D = false;
+        if (dy.rank === 3) {
+            reshapedTo4D = true;
+            dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
+            xShape4D = [1, xShape[0], xShape[1], xShape[2]];
+        }
+        assert(xShape4D.length === 4, function () {
+            return "Error in conv2dDerInput: inShape must be length 4, but got length " +
+                (xShape4D.length + ".");
+        });
+        assert(dy4D.rank === 4, function () { return "Error in conv2dDerInput: dy must be rank 4, but got " +
+            ("rank " + dy4D.rank); });
+        assert(filter.rank === 4, function () { return "Error in conv2dDerInput: filter must be rank 4, but got " +
+            ("rank " + filter.rank); });
+        var inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];
+        var outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];
+        assert(inDepth === filter.shape[2], function () { return "Error in conv2dDerInput: depth of input (" + inDepth + ") must " +
+            ("match input depth for filter " + filter.shape[2] + "."); });
+        assert(outDepth === filter.shape[3], function () { return "Error in conv2dDerInput: depth of output (" + outDepth + ") must " +
+            ("match output depth for filter " + filter.shape[3] + "."); });
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), function () { return "Error in conv2dDerInput: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+        }
+        var inputs = { dy: dy4D, filter: filter };
+        var attrs = { strides: strides, pad: pad, dataFormat: dataFormat, dimRoundingMode: dimRoundingMode, inputShape: xShape4D };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs);
+        if (reshapedTo4D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+        }
+        return res;
+    }
+    var conv2DBackpropInput = op({ conv2DBackpropInput_: conv2DBackpropInput_ });
+
+    /**
+     * Computes the transposed 2D convolution of an image, also known as a
+     * deconvolution.
+     *
+     * @param x The input image, of rank 4 or rank 3, of shape
+     *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.
+     * @param filter The filter, rank 4, of shape
+     *     `[filterHeight, filterWidth, outDepth, inDepth]`.
+     *     `inDepth` must match `inDepth` in `x`.
+     * @param outputShape Output shape, of rank 4 or rank 3:
+     *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.
+     * @param strides The strides of the original convolution:
+     *     `[strideHeight, strideWidth]`.
+     * @param pad  The type of padding algorithm used in the non-transpose version
+     *    of the op.
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {
+        var $x = convertToTensor(x, 'x', 'conv2dTranspose');
+        var $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');
+        return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);
+    }
+    var conv2dTranspose = op({ conv2dTranspose_: conv2dTranspose_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes a 3D convolution over the input x.
+     *
+     * @param x The input tensor, of rank 5 or rank 4, of shape
+     *     `[batch, depth, height, width, channels]`. If rank 4,
+     * batch of 1 is assumed.
+     * @param filter The filter, rank 5, of shape
+     *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.
+     *      inChannels must match between input and filter.
+     * @param strides The strides of the convolution: `[strideDepth, strideHeight,
+     * strideWidth]`.
+     * @param pad The type of padding algorithm.
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1x1.
+     *   - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dataFormat: An optional string from: "NDHWC", "NCDHW". Defaults to
+     *     "NDHWC". Specify the data format of the input and output data. With the
+     *     default format "NDHWC", the data is stored in the order of: [batch,
+     *     depth, height, width, channels]. Only "NDHWC" is currently supported.
+     * @param dilations The dilation rates: `[dilationDepth, dilationHeight,
+     *     dilationWidth]` in which we sample input values across the height
+     *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.
+     *     If `dilations` is a single number, then
+     *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater
+     *     than 1, then all values of `strides` must be 1.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function conv3d_(x, filter, strides, pad, dataFormat, dilations) {
+        if (dataFormat === void 0) { dataFormat = 'NDHWC'; }
+        if (dilations === void 0) { dilations = [1, 1, 1]; }
+        var $x = convertToTensor(x, 'x', 'conv3d');
+        var $filter = convertToTensor(filter, 'filter', 'conv3d');
+        var x5D = $x;
+        var reshapedTo5D = false;
+        if ($x.rank === 4) {
+            reshapedTo5D = true;
+            x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
+        }
+        assert(x5D.rank === 5, function () { return "Error in conv3d: input must be rank 5, but got rank " + x5D.rank + "."; });
+        assert($filter.rank === 5, function () { return "Error in conv3d: filter must be rank 5, but got rank " +
+            ($filter.rank + "."); });
+        assert(x5D.shape[4] === $filter.shape[3], function () { return "Error in conv3d: depth of input (" + x5D.shape[4] + ") must match " +
+            ("input depth for filter " + $filter.shape[3] + "."); });
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in conv3D: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
+        assert(dataFormat === 'NDHWC', function () { return "Error in conv3d: got dataFormat of " + dataFormat + " but only NDHWC is currently supported."; });
+        var inputs = { x: x5D, filter: $filter };
+        var attrs = { strides: strides, pad: pad, dataFormat: dataFormat, dilations: dilations };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(Conv3D, inputs, attrs);
+        if (reshapedTo5D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+        }
+        return res;
+    }
+    var conv3d = op({ conv3d_: conv3d_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the derivative of the input of a 3D convolution.
+     *
+     * @param xShape The shape of the input: [batch, depth, height, width,
+     * in_channels]. If length of 4, batch of 1 is assumed.
+     * @param dy The derivative of the output, of rank 5 or rank 4 of shape
+     *   `[batch, outDepth, outHeight, outWidth, in_channels]`.
+     * If rank 4, batch of 1 is assumed.
+     * @param filter The filter, rank 5, of shape
+     *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.
+     * @param strides The strides of the convolution: `[strideDepth, strideHeight,
+     * strideWidth]`.
+     * @param pad The type of padding algorithm used:
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1x1.
+     */
+    function conv3DBackpropInput_(xShape, dy, filter, strides, pad) {
+        assert(xShape.length === dy.rank, function () { return "Length of inShape " +
+            ("(" + xShape.length + ") and rank of dy (" + dy.rank + ") must match"); });
+        var xShape5D = xShape;
+        var dy5D = dy;
+        var reshapedTo5D = false;
+        if (dy.rank === 4) {
+            reshapedTo5D = true;
+            dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
+            xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
+        }
+        var inDepth = xShape5D[4];
+        var outDepth = dy5D.shape[4];
+        assert(xShape5D.length === 5, function () {
+            return "Error in conv3dDerInput: inShape must be length 5, but got length " +
+                (xShape5D.length + ".");
+        });
+        assert(dy5D.rank === 5, function () { return "Error in conv3dDerInput: dy must be rank 5, but got " +
+            ("rank " + dy5D.rank); });
+        assert(filter.rank === 5, function () { return "Error in conv3dDerInput: filter must be rank 5, but got " +
+            ("rank " + filter.rank); });
+        assert(inDepth === filter.shape[3], function () { return "Error in conv3dDerInput: depth of input (" + inDepth + ") must " +
+            ("match input depth for filter " + filter.shape[3] + "."); });
+        assert(outDepth === filter.shape[4], function () { return "Error in conv3dDerInput: depth of output (" + outDepth + ") must " +
+            ("match output depth for filter " + filter.shape[4] + "."); });
+        var inputs = { dy: dy5D, filter: filter };
+        var attrs = { pad: pad, strides: strides, inputShape: xShape5D };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs);
+        if (reshapedTo5D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+        }
+        return res;
+    }
+    var conv3DBackpropInput = op({ conv3DBackpropInput_: conv3DBackpropInput_ });
+
+    /**
+     * Computes the transposed 3D convolution of a volume, also known as a
+     * deconvolution.
+     *
+     * @param x The input image, of rank 5 or rank 4, of shape
+     *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.
+     * @param filter The filter, rank 4, of shape
+     *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.
+     *     `inDepth` must match `inDepth` in `x`.
+     * @param outputShape Output shape, of rank 5 or rank 4:
+     *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is
+     *    assumed.
+     * @param strides The strides of the original convolution:
+     *     `[strideDepth, strideHeight, strideWidth]`.
+     * @param pad  The type of padding algorithm used in the non-transpose version
+     *    of the op.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function conv3dTranspose_(x, filter, outputShape, strides, pad) {
+        var $x = convertToTensor(x, 'x', 'conv3dTranspose');
+        var $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');
+        return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);
+    }
+    var conv3dTranspose = op({ conv3dTranspose_: conv3dTranspose_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
+     *
+     * x.cos().print();  // or tf.cos(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function cos_(x) {
+        var $x = convertToTensor(x, 'x', 'cos');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Cos, inputs);
+    }
+    var cos = op({ cos_: cos_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, 1, -1, .7]);
+     *
+     * x.cosh().print();  // or tf.cosh(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function cosh_(x) {
+        var $x = convertToTensor(x, 'x', 'cosh');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Cosh, inputs);
+    }
+    var cosh = op({ cosh_: cosh_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the cumulative sum of a `tf.Tensor` along `axis`.
+     *
+     * ```js
+     * const x = tf.tensor([1, 2, 3, 4]);
+     * x.cumsum().print();
+     * ```
+     * ```js
+     * const x = tf.tensor([[1, 2], [3, 4]]);
+     * x.cumsum().print();
+     * ```
+     *
+     * @param x The input tensor to be summed.
+     * @param axis The axis along which to sum. Optional. Defaults to 0.
+     * @param exclusive Whether to perform exclusive cumulative sum. Optional.
+     *     Defaults to false. If set to true then the sum of each tensor entry
+     *     does not include its own value, but only the values previous to it
+     *     along the specified axis.
+     * @param reverse Whether to sum in the opposite direction. Optional.
+     *     Defaults to false.
+     *
+     * @doc {heading: 'Operations', subheading: 'Scan'}
+     */
+    function cumsum_(x, axis, exclusive, reverse) {
+        if (axis === void 0) { axis = 0; }
+        if (exclusive === void 0) { exclusive = false; }
+        if (reverse === void 0) { reverse = false; }
+        var $x = convertToTensor(x, 'x', 'cumsum');
+        var inputs = { x: $x };
+        var attrs = { axis: axis, exclusive: exclusive, reverse: reverse };
+        return ENGINE.runKernel(Cumsum, inputs, attrs);
+    }
+    var cumsum = op({ cumsum_: cumsum_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Outputs a vector with length `size` and the same dtype as `weights`.
+     *
+     * If `weights` are empty, then index `i` stores the number of times the value
+     * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the
+     * sum of the value in `weights` at each index where the corresponding value in
+     * `x` is `i`.
+     *
+     * Values in `x` outside of the range [0, size) are ignored.
+     *
+     * @param x The input int tensor, rank 1 or rank 2.
+     * @param weights The weights tensor, must have the same shape as x, or a
+     *     length-0 Tensor, in which case it acts as all weights equal to 1.
+     * @param size Non-negative integer.
+     * @param binaryOutput Optional. Whether the kernel should count the appearance
+     *     or number of occurrences. Defaults to False.
+     *
+     * @doc {heading: 'Operations', subheading: 'Reduction'}
+     */
+    function denseBincount_(x, weights, size, binaryOutput) {
+        if (binaryOutput === void 0) { binaryOutput = false; }
+        var $x = convertToTensor(x, 'x', 'denseBincount');
+        var $weights = convertToTensor(weights, 'weights', 'denseBincount');
+        assert($x.dtype === 'int32', function () { return "Error in denseBincount: input " +
+            ("dtype must be int32, but got " + $x.dtype); });
+        assert($x.rank <= 2, function () { return "Error in denseBincount: input must be at most rank 2, but got " +
+            ("rank " + $x.rank + "."); });
+        assert(size >= 0, function () { return "size must be non-negative, but got " + size + "."; });
+        assert($weights.size === $x.size || $weights.size === 0, function () {
+            return "Error in denseBincount: weights must have the same shape as x or " +
+                ("0-length, but got x shape: " + $x.shape + ", weights shape: ") +
+                ($weights.shape + ".");
+        });
+        var inputs = { x: $x, weights: $weights };
+        var attrs = { size: size, binaryOutput: binaryOutput };
+        return ENGINE.runKernel(DenseBincount, inputs, attrs);
+    }
+    var denseBincount = op({ denseBincount_: denseBincount_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Rearranges data from depth into blocks of spatial data. More specifically,
+     * this op outputs a copy of the input tensor where values from the `depth`
+     * dimension are moved in spatial blocks to the `height` and `width` dimensions.
+     * The attr `blockSize` indicates the input block size and how the data is
+     * moved.
+     *
+     *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged
+     * into non-overlapping blocks of size `blockSize x blockSize`
+     *
+     *  - The width the output tensor is `inputWidth * blockSize`, whereas the
+     * height is `inputHeight * blockSize`
+     *
+     *  - The Y, X coordinates within each block of the output image are determined
+     * by the high order component of the input channel index
+     *
+     *  - The depth of the input tensor must be divisible by `blockSize *
+     * blockSize`
+     *
+     * The `dataFormat` attr specifies the layout of the input and output tensors
+     * with the following options: "NHWC": [ `batch, height, width, channels` ]
+     * "NCHW": [ `batch, channels, height, width` ]
+     *
+     * ```js
+     * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);
+     * const blockSize = 2;
+     * const dataFormat = "NHWC";
+     *
+     * tf.depthToSpace(x, blockSize, dataFormat).print();
+     * ```
+     *
+     * @param x The input tensor of rank 4
+     * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block
+     * @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to "NHWC"
+     *
+     * @doc {heading: 'Tensors', subheading: 'Transformations'}
+     */
+    function depthToSpace_(x, blockSize, dataFormat) {
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        var $x = convertToTensor(x, 'x', 'depthToSpace');
+        var inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];
+        var inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];
+        var inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];
+        assert(inputHeight * blockSize >= 0, function () { return "Negative dimension size caused by overflow when multiplying\n    " + inputHeight + " and " + blockSize + "  for depthToSpace with input shape\n    " + $x.shape; });
+        assert(inputWidth * blockSize >= 0, function () { return "Negative dimension size caused by overflow when multiplying\n    " + inputWidth + " and " + blockSize + " for depthToSpace with input shape\n        " + $x.shape; });
+        assert((inputDepth % (blockSize * blockSize) === 0), function () { return "Dimension size must be evenly divisible by " + blockSize * blockSize + " but is " + inputDepth + " for depthToSpace with input shape " + $x.shape; });
+        var inputs = { x: $x };
+        var attrs = { blockSize: blockSize, dataFormat: dataFormat };
+        return ENGINE.runKernel(DepthToSpace, inputs, attrs);
+    }
+    var depthToSpace = op({ depthToSpace_: depthToSpace_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Depthwise 2D convolution.
+     *
+     * Given a 4D `input` array and a `filter` array of shape
+     * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
+     * `inChannels` convolutional filters of depth 1, this op applies a
+     * different filter to each input channel (expanding from 1 channel to
+     * `channelMultiplier` channels for each), then concatenates the results
+     * together. The output has `inChannels * channelMultiplier` channels.
+     *
+     * See
+     * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
+     *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
+     * for more details.
+     *
+     * @param x The input tensor, of rank 4 or rank 3, of shape
+     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
+     * assumed.
+     * @param filter The filter tensor, rank 4, of shape
+     *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
+     * @param strides The strides of the convolution: `[strideHeight,
+     * strideWidth]`. If strides is a single number, then `strideHeight ==
+     * strideWidth`.
+     * @param pad The type of padding algorithm.
+     *   - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *   - `valid`: output will be smaller than input if filter is larger
+     *       than 1x1.
+     *   - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
+     *     in which we sample input values across the height and width dimensions
+     *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
+     *     number, then `dilationHeight == dilationWidth`. If it is greater than
+     *     1, then all values of `strides` must be 1.
+     * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
+     *     "NHWC". Specify the data format of the input and output data. With the
+     *     default format "NHWC", the data is stored in the order of: [batch,
+     *     height, width, channels]. Only "NHWC" is currently supported.
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function depthwiseConv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        if (dilations === void 0) { dilations = [1, 1]; }
+        var $x = convertToTensor(x, 'x', 'depthwiseConv2d');
+        var $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+        }
+        assert(x4D.rank === 4, function () { return "Error in depthwiseConv2d: input must be rank 4, but got " +
+            ("rank " + x4D.rank + "."); });
+        assert($filter.rank === 4, function () { return "Error in depthwiseConv2d: filter must be rank 4, but got rank " +
+            ($filter.rank + "."); });
+        assert(x4D.shape[3] === $filter.shape[2], function () { return "Error in depthwiseConv2d: number of input channels " +
+            ("(" + x4D.shape[3] + ") must match the inChannels dimension in ") +
+            ("filter " + $filter.shape[2] + "."); });
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), function () { return "Error in depthwiseConv2d: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+        }
+        var inputs = { x: x4D, filter: $filter };
+        var attrs = { strides: strides, pad: pad, dataFormat: dataFormat, dilations: dilations, dimRoundingMode: dimRoundingMode };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs);
+        if (reshapedTo4D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+        }
+        return res;
+    }
+    var depthwiseConv2d = op({ depthwiseConv2d_: depthwiseConv2d_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns a diagonal tensor with a given diagonal values.
+     *
+     * Given a diagonal, this operation returns a tensor with the diagonal and
+     * everything else padded with zeros.
+     *
+     * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor
+     * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3, 4]);
+     *
+     * tf.diag(x).print()
+     * ```
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])
+     *
+     * tf.diag(x).print()
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function diag_(x) {
+        var $x = convertToTensor(x, 'x', 'diag');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Diag, inputs);
+    }
+    var diag = op({ diag_: diag_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the grayscale dilation over the input `x`.
+     *
+     * @param x The input tensor, rank 3 or rank 4 of shape
+     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
+     * @param filter The filter tensor, rank 3, of shape
+     *     `[filterHeight, filterWidth, depth]`.
+     * @param strides The strides of the sliding window for each dimension of the
+     *     input tensor: `[strideHeight, strideWidth]`.
+     *     If `strides` is a single number,
+     *     then `strideHeight == strideWidth`.
+     * @param pad The type of padding algorithm.
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1*1x1.
+     *    - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dataFormat Specify the data format of the input and output data.
+     *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the
+     *      default format "NHWC", the data is stored in the order of: [batch,
+     *      height, width, channels].
+     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
+     *     in which we sample input values across the height and width dimensions
+     *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`
+     *     is a single number, then `dilationHeight == dilationWidth`. If it is
+     *     greater than 1, then all values of `strides` must be 1.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function dilation2d_(x, filter, strides, pad, dilations, dataFormat) {
+        if (dilations === void 0) { dilations = [1, 1]; }
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        var $x = convertToTensor(x, 'x', 'dilation2d');
+        var $filter = convertToTensor(filter, 'filter', 'dilation2d');
+        assert($x.rank === 3 || $x.rank === 4, function () { return "Error in dilation2d: input must be rank 3 or 4, but got rank " +
+            ($x.rank + "."); });
+        assert($filter.rank === 3, function () { return "Error in dilation2d: filter must be rank 3, but got rank " +
+            ($filter.rank + "."); });
+        assert(dataFormat === 'NHWC', function () { return "Error in dilation2d: Only NHWC is currently supported, " +
+            ("but got dataFormat of " + dataFormat); });
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+            reshapedTo4D = true;
+        }
+        var inputs = { x: x4D, filter: $filter };
+        var attrs = { strides: strides, pad: pad, dilations: dilations };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(Dilation2D, inputs, attrs);
+        if (reshapedTo4D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+        }
+        return res;
+    }
+    var dilation2d = op({ dilation2d_: dilation2d_ });
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the dimensions in the input shape that are broadcasted to
+     * produce the provided output shape.
+     *
+     * The returned dimensions are 0-indexed and sorted. An example:
+     * inShape = [4, 1, 3]
+     * outShape = [5, 4, 3, 3]
+     * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.
+     */
+    function getBroadcastDims(inShape, outShape) {
+        var inRank = inShape.length;
+        var dims = [];
+        for (var i = 0; i < inRank; i++) {
+            var dim = inRank - 1 - i;
+            var a = inShape[dim] || 1;
+            var b = outShape[outShape.length - 1 - i] || 1;
+            if (b > 1 && a === 1) {
+                dims.unshift(dim);
+            }
+        }
+        return dims;
+    }
+    /**
+     * Returns the axes in the output space that should be reduced to produce
+     * the input space.
+     */
+    function getReductionAxes(inShape, outShape) {
+        var result = [];
+        for (var i = 0; i < outShape.length; i++) {
+            var inDim = inShape[inShape.length - i - 1];
+            var outAxis = outShape.length - i - 1;
+            var outDim = outShape[outAxis];
+            if (inDim == null || (inDim === 1 && outDim > 1)) {
+                result.unshift(outAxis);
+            }
+        }
+        return result;
+    }
+    function assertAndGetBroadcastShape(shapeA, shapeB) {
+        var result = [];
+        var l = Math.max(shapeA.length, shapeB.length);
+        for (var i = 0; i < l; i++) {
+            var a = shapeA[shapeA.length - i - 1];
+            if (a == null) {
+                a = 1;
+            }
+            var b = shapeB[shapeB.length - i - 1];
+            if (b == null) {
+                b = 1;
+            }
+            if (a === 1) {
+                result.unshift(b);
+            }
+            else if (b === 1) {
+                result.unshift(a);
+            }
+            else if (a !== b) {
+                var errMsg = "Operands could not be broadcast together with shapes " +
+                    (shapeA + " and " + shapeB + ".");
+                throw Error(errMsg);
+            }
+            else {
+                result.unshift(a);
+            }
+        }
+        return result;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the truth value of (a == b) element-wise. Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2, 3]);
+     * const b = tf.tensor1d([2, 2, 2]);
+     *
+     * a.equal(b).print();
+     * ```
+     *
+     * @param a The first input tensor.
+     * @param b The second input tensor. Must have the same dtype as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Logical'}
+     */
+    function equal_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'equal', 'string_or_numeric');
+        var $b = convertToTensor(b, 'b', 'equal', 'string_or_numeric');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(Equal, inputs);
+    }
+    var equal = op({ equal_: equal_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the elements, either `a` or `b` depending on the `condition`.
+     *
+     * If the condition is true, select from `a`, otherwise select from `b`.
+     *
+     * ```js
+     * const cond = tf.tensor1d([false, false, true], 'bool');
+     * const a = tf.tensor1d([1 , 2, 3]);
+     * const b = tf.tensor1d([-1, -2, -3]);
+     *
+     * a.where(cond, b).print();
+     * ```
+     *
+     * @param condition The input condition. Must be of dtype bool.
+     * @param a If `condition` is rank 1, `a` may have a higher rank but
+     *     its first dimension must match the size of `condition`.
+     * @param b A tensor with the same dtype as `a` and with shape that is
+     *     compatible with `a`.
+     * @return A tensor with same dtype as `a` and `b`, and shape that is
+     *     broadcastable from `a` and `b`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Logical'}
+     */
+    function where_(condition, a, b) {
+        var $a = convertToTensor(a, 'a', 'where');
+        var $b = convertToTensor(b, 'b', 'where');
+        var $condition = convertToTensor(condition, 'condition', 'where', 'bool');
+        // TODO: move this logic to forward function when the broadcastTo op is
+        // implemented in WASM.
+        // Find the broadcastable shape for $condition, $a, and $b.
+        var broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape);
+        var $broadcastedCondition = broadcastTo($condition, broadcastShape);
+        var $broadcastedA = broadcastTo($a, broadcastShape);
+        var $broadcastedB = broadcastTo($b, broadcastShape);
+        var inputs = {
+            condition: $broadcastedCondition,
+            t: $broadcastedA,
+            e: $broadcastedB
+        };
+        return ENGINE.runKernel(Select, inputs);
+    }
+    var where = op({ where_: where_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the
+     * given tensor.
+     *
+     * ```js
+     * const x = tf.tensor([1, 2]);
+     * tf.zerosLike(x).print();
+     * ```
+     *
+     * @param x The tensor of required shape.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function zerosLike_(x) {
+        var $x = convertToTensor(x, 'x', 'zerosLike');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(ZerosLike, inputs);
+    }
+    var zerosLike = op({ zerosLike_: zerosLike_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0
+     * if denominator is 0.
+     *
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 4, 9, 16]);
+     * const b = tf.tensor1d([1, 2, 3, 4]);
+     * const c = tf.tensor1d([0, 0, 0, 0]);
+     *
+     * a.divNoNan(b).print();  // or tf.divNoNan(a, b)
+     * a.divNoNan(c).print();  // or tf.divNoNan(a, c)
+     * ```
+     *
+     * ```js
+     * // Broadcast div a with b.
+     * const a = tf.tensor1d([2, 4, 6, 8]);
+     * const b = tf.scalar(2);
+     * const c = tf.scalar(0);
+     *
+     * a.divNoNan(b).print();  // or tf.divNoNan(a, b)
+     * a.divNoNan(c).print();  // or tf.divNoNan(a, c)
+     * ```
+     *
+     * @param a The first tensor as the numerator.
+     * @param b The second tensor as the denominator. Must have the same dtype as
+     * `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Arithmetic'}
+     */
+    function divNoNan_(a, b) {
+        var _a;
+        // TODO: Make this into its own kernel.
+        var $a = convertToTensor(a, 'a', 'div');
+        var $b = convertToTensor(b, 'b', 'div');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var divResult = div($a, $b);
+        var zeros = zerosLike(divResult);
+        var bEqualsZero = equal($b, zeros);
+        return where(bEqualsZero, zeros, divResult);
+    }
+    var divNoNan = op({ divNoNan_: divNoNan_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2]);
+     * const b = tf.tensor2d([[1, 2], [3, 4]]);
+     * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
+     *
+     * a.dot(b).print();  // or tf.dot(a, b)
+     * b.dot(a).print();
+     * b.dot(c).print();
+     * ```
+     * @param t1 The first tensor in the dot operation.
+     * @param t2 The second tensor in the dot operation.
+     *
+     * @doc {heading: 'Operations', subheading: 'Matrices'}
+     */
+    function dot_(t1, t2) {
+        var $t1 = convertToTensor(t1, 't1', 'dot');
+        var $t2 = convertToTensor(t2, 't2', 'dot');
+        assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), function () { return "Error in dot: inputs must all be rank 1 or 2, but got ranks " +
+            ($t1.rank + " and " + $t2.rank + "."); });
+        var t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);
+        var t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);
+        assert(t1Inner === t2Inner, function () { return "Error in dot: inner dimensions of inputs must match, but got " +
+            (t1Inner + " and " + t2Inner + "."); });
+        if ($t1.rank === 1 && $t2.rank === 1) {
+            var t12D = reshape($t1, [1, -1]);
+            var t22D = reshape($t2, [-1, 1]);
+            var t1t2 = matMul(t12D, t22D);
+            return reshape(t1t2, []);
+        }
+        else if ($t1.rank === 1 && $t2.rank === 2) {
+            var t12D = reshape($t1, [1, -1]);
+            var t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
+            var t1t2 = matMul(t12D, t22D);
+            return reshape(t1t2, [t1t2.size]);
+        }
+        else if ($t1.rank === 2 && $t2.rank === 1) {
+            var t22D = reshape($t2, [-1, 1]);
+            var t1t2 = matMul($t1, t22D);
+            return reshape(t1t2, [t1t2.size]);
+        }
+        else {
+            var t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
+            var t1t2 = matMul($t1, t22D);
+            return t1t2;
+        }
+    }
+    var dot = op({ dot_: dot_ });
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Tensor contraction over specified indices and outer product.
+     *
+     * `einsum` allows defining Tensors by defining their element-wise computation.
+     * This computation is based on
+     * [Einstein summation](https://en.wikipedia.org/wiki/Einstein_notation).
+     *
+     * Some special cases include:
+     *
+     * Matrix multiplication:
+     * ```js
+     * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
+     * const y = tf.tensor2d([[0, 1], [2, 3], [4, 5]]);
+     * x.print();
+     * y.print();
+     * tf.einsum('ij,jk->ik', x, y).print();
+     * ```
+     *
+     * Dot product:
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3]);
+     * const y = tf.tensor1d([0, 1, 2]);
+     * x.print();
+     * y.print();
+     * tf.einsum('i,i->', x, y).print();
+     * ```
+     *
+     * Batch dot product:
+     * ```js
+     * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
+     * const y = tf.tensor2d([[0, 1, 2], [3, 4, 5]]);
+     * x.print();
+     * y.print();
+     * tf.einsum('bi,bi->b', x, y).print();
+     * ```
+     *
+     * Outer prouduct:
+     * ```js
+     * const x = tf.tensor1d([1, 3, 5]);
+     * const y = tf.tensor1d([2, 4, 6]);
+     * x.print();
+     * y.print();
+     * tf.einsum('i,j->ij', x, y).print();
+     * ```
+     *
+     * Matrix transpose:
+     * ```js
+     * const x = tf.tensor2d([[1, 2], [3, 4]]);
+     * x.print();
+     * tf.einsum('ij->ji', x).print();
+     * ```
+     *
+     * Batch matrix transpose:
+     * ```js
+     * const x = tf.tensor3d([[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]);
+     * x.print();
+     * tf.einsum('bij->bji', x).print();
+     * ```
+     *
+     * Limitations:
+     *
+     * This implementation of einsum has the following limitations:
+     *
+     * - Does not support >2 input tensors.
+     * - Does not support duplicate axes for any given input tensor. E.g., equation
+     *   'ii->' is not suppoted.
+     * - The `...` notation is not supported.
+     *
+     * @param equation a string describing the contraction, in the same format as
+     * [numpy.einsum](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html).
+     * @param tensors the input(s) to contract (each one a Tensor), whose shapes
+     *     should be consistent with equation.
+     * @returns The output tensor.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Matrices'}
+     */
+    function einsum_(equation) {
+        var tensors = [];
+        for (var _i = 1; _i < arguments.length; _i++) {
+            tensors[_i - 1] = arguments[_i];
+        }
+        var $tensors = tensors.map(function (t, i) { return convertToTensor(t, "tensors" + i, 'einsum'); });
+        var attrs = { equation: equation };
+        return ENGINE.runKernel(Einsum, $tensors, attrs);
+    }
+    var einsum = op({ einsum_: einsum_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.
+     *
+     * ```js
+     * const x = tf.tensor1d([-1, 1, -3, 2]);
+     *
+     * x.elu().print();  // or tf.elu(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function elu_(x) {
+        var $x = convertToTensor(x, 'x', 'elu');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Elu, inputs);
+    }
+    var elu = op({ elu_: elu_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes gause error function of the input `tf.Tensor` element-wise:
+     * `erf(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, .1, -.1, .7]);
+     *
+     * x.erf().print(); // or tf.erf(x);
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function erf_(x) {
+        var $x = convertToTensor(x, 'x', 'erf');
+        assert($x.dtype === 'int32' || $x.dtype === 'float32', function () { return 'Input dtype must be `int32` or `float32`.'; });
+        if ($x.dtype === 'int32') {
+            $x = cast($x, 'float32');
+        }
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Erf, inputs);
+    }
+    var erf = op({ erf_: erf_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, -3]);
+     *
+     * x.exp().print();  // or tf.exp(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function exp_(x) {
+        var $x = convertToTensor(x, 'x', 'exp');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Exp, inputs);
+    }
+    var exp = op({ exp_: exp_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension
+     * into the tensor's shape.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3, 4]);
+     * const axis = 1;
+     * x.expandDims(axis).print();
+     * ```
+     *
+     * @param x The input tensor whose dimensions to be expanded.
+     * @param axis The dimension index at which to insert shape of `1`. Defaults
+     *     to 0 (the first dimension).
+     *
+     * @doc {heading: 'Tensors', subheading: 'Transformations'}
+     */
+    function expandDims_(x, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $x = convertToTensor(x, 'x', 'expandDims', 'string_or_numeric');
+        assert(axis <= $x.rank, function () { return 'Axis must be <= rank of the tensor'; });
+        var inputs = { input: $x };
+        var attrs = { dim: axis };
+        return ENGINE.runKernel(ExpandDims, inputs, attrs);
+    }
+    var expandDims = op({ expandDims_: expandDims_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes exponential of the input `tf.Tensor` minus one element-wise.
+     * `e ^ x - 1`
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, -3]);
+     *
+     * x.expm1().print();  // or tf.expm1(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function expm1_(x) {
+        var $x = convertToTensor(x, 'x', 'expm1');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Expm1, inputs);
+    }
+    var expm1 = op({ expm1_: expm1_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Construct a tensor by repeating it the number of times given by reps.
+     *
+     * This operation creates a new tensor by replicating `input` `reps`
+     * times. The output tensor's i'th dimension has `input.shape[i] *
+     * reps[i]` elements, and the values of `input` are replicated
+     * `reps[i]` times along the i'th dimension. For example, tiling
+     * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2]);
+     *
+     * a.tile([2]).print();    // or a.tile([2])
+     * ```
+     *
+     * ```js
+     * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     *
+     * a.tile([1, 2]).print();  // or a.tile([1, 2])
+     * ```
+     * @param x The tensor to tile.
+     * @param reps Determines the number of replications per dimension.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
+     */
+    function tile_(x, reps) {
+        var $x = convertToTensor(x, 'x', 'tile', 'string_or_numeric');
+        assert($x.rank === reps.length, function () { return "Error in transpose: rank of input " + $x.rank + " " +
+            ("must match length of reps " + reps + "."); });
+        var inputs = { x: $x };
+        var attrs = { reps: reps };
+        return ENGINE.runKernel(Tile, inputs, attrs);
+    }
+    var tile = op({ tile_: tile_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Create an identity matrix.
+     *
+     * @param numRows Number of rows.
+     * @param numColumns Number of columns. Defaults to `numRows`.
+     * @param batchShape If provided, will add the batch shape to the beginning
+     *   of the shape of the returned `tf.Tensor` by repeating the identity
+     *   matrix.
+     * @param dtype Data type.
+     * @returns Identity matrix of the specified size and data type, possibly
+     *   with batch repetition if `batchShape` is specified.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function eye_(numRows, numColumns, batchShape, dtype) {
+        if (dtype === void 0) { dtype = 'float32'; }
+        if (numColumns == null) {
+            numColumns = numRows;
+        }
+        var buff = buffer([numRows, numColumns], dtype);
+        var n = numRows <= numColumns ? numRows : numColumns;
+        for (var i = 0; i < n; ++i) {
+            buff.set(1, i, i);
+        }
+        var out = reshape(buff.toTensor(), [numRows, numColumns]);
+        if (batchShape == null) {
+            return out;
+        }
+        else {
+            if (batchShape.length === 1) {
+                return tile(expandDims(out, 0), [batchShape[0], 1, 1]);
+            }
+            else if (batchShape.length === 2) {
+                // tslint:disable-next-line:no-unnecessary-type-assertion
+                return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
+            }
+            else if (batchShape.length === 3) {
+                // tslint:disable-next-line:no-unnecessary-type-assertion
+                return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [
+                    batchShape[0], batchShape[1], batchShape[2], 1, 1
+                ]);
+            }
+            else {
+                throw new Error("eye() currently supports only 1D and 2D " +
+                    (
+                    // tslint:disable-next-line:no-any
+                    "batchShapes, but received " + batchShape.length + "D."));
+            }
+        }
+    }
+    var eye = op({ eye_: eye_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a `tf.Tensor` filled with a scalar value.
+     *
+     * ```js
+     * tf.fill([2, 2], 4).print();
+     * ```
+     *
+     * @param shape An array of integers defining the output tensor shape.
+     * @param value The scalar value to fill the tensor with.
+     * @param dtype The type of an element in the resulting tensor. Defaults to
+     * 'float'.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function fill(shape, value, dtype) {
+        var attrs = { shape: shape, value: value, dtype: dtype };
+        return ENGINE.runKernel(Fill, {}, attrs);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.
+     *
+     * ```js
+     * const x = tf.tensor1d([.6, 1.1, -3.3]);
+     *
+     * x.floor().print();  // or tf.floor(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function floor_(x) {
+        var $x = convertToTensor(x, 'x', 'floor');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Floor, inputs);
+    }
+    var floor = op({ floor_: floor_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Gather slices from tensor `x`'s axis `axis` according to `indices`.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3, 4]);
+     * const indices = tf.tensor1d([1, 3, 3], 'int32');
+     *
+     * x.gather(indices).print();
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     * const indices = tf.tensor1d([1, 1, 0], 'int32');
+     *
+     * x.gather(indices).print();
+     * ```
+     * @param x The input tensor whose slices to be gathered.
+     * @param indices The indices of the values to extract.
+     * @param axis The axis over which to select values. Defaults to 0.
+     * @param batchDims Optional. The number of batch dimensions. It must be less
+     *     than or equal to rank(indices). Defaults to 0.
+     *     The output tensor will have shape of
+     *     `x.shape[:axis] + indices.shape[batchDims:] + x.shape[axis + 1:]`
+     *
+     * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
+     */
+    function gather_(x, indices, axis, batchDims) {
+        if (axis === void 0) { axis = 0; }
+        if (batchDims === void 0) { batchDims = 0; }
+        var $x = convertToTensor(x, 'x', 'gather');
+        var $indices = convertToTensor(indices, 'indices', 'gather', 'int32');
+        var inputs = { x: $x, indices: $indices };
+        var attrs = { axis: axis, batchDims: batchDims };
+        return ENGINE.runKernel(GatherV2, inputs, attrs);
+    }
+    var gather = op({ gather_: gather_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the truth value of (a > b) element-wise. Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2, 3]);
+     * const b = tf.tensor1d([2, 2, 2]);
+     *
+     * a.greater(b).print();
+     * ```
+     *
+     * @param a The first input tensor.
+     * @param b The second input tensor. Must have the same dtype as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Logical'}
+     */
+    function greater_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'greater', 'string_or_numeric');
+        var $b = convertToTensor(b, 'b', 'greater', 'string_or_numeric');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(Greater, inputs);
+    }
+    var greater = op({ greater_: greater_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the truth value of (a >= b) element-wise. Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2, 3]);
+     * const b = tf.tensor1d([2, 2, 2]);
+     *
+     * a.greaterEqual(b).print();
+     * ```
+     *
+     * @param a The first input tensor.
+     * @param b The second input tensor. Must have the same dtype as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Logical'}
+     */
+    function greaterEqual_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'greaterEqual', 'string_or_numeric');
+        var $b = convertToTensor(b, 'b', 'greaterEqual', 'string_or_numeric');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(GreaterEqual, inputs);
+    }
+    var greaterEqual = op({ greaterEqual_: greaterEqual_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the imaginary part of a complex (or real) tensor.
+     *
+     * Given a tensor input, this operation returns a tensor of type float that is
+     * the imaginary part of each element in input considered as a complex number.
+     * If input is real, a tensor of all zeros is returned.
+     *
+     * ```js
+     * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);
+     * tf.imag(x).print();
+     * ```
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function imag_(input) {
+        var $input = convertToTensor(input, 'input', 'imag');
+        var inputs = { input: $input };
+        return ENGINE.runKernel(Imag, inputs);
+    }
+    var imag = op({ imag_: imag_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns which elements of x are finite.
+     *
+     * ```js
+     * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
+     *
+     * x.isFinite().print();  // or tf.isNaN(x)
+     * ```
+     * @param x The input Tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function isFinite_(x) {
+        var $x = convertToTensor(x, 'x', 'isFinite');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(IsFinite, inputs);
+    }
+    var isFinite$1 = op({ isFinite_: isFinite_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns which elements of x are Infinity or -Infinity.
+     *
+     * ```js
+     * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
+     *
+     * x.isInf().print();  // or tf.isNaN(x)
+     * ```
+     * @param x The input Tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function isInf_(x) {
+        var $x = convertToTensor(x, 'x', 'isInf');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(IsInf, inputs);
+    }
+    var isInf = op({ isInf_: isInf_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * RReturns which elements of x are NaN.
+     *
+     * ```js
+     * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
+     *
+     * x.isNaN().print();  // or tf.isNaN(x)
+     * ```
+     * @param x The input Tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function isNaN_(x) {
+        var $x = convertToTensor(x, 'x', 'isNaN');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(IsNan, inputs);
+    }
+    var isNaN$1 = op({ isNaN_: isNaN_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes leaky rectified linear element-wise.
+     *
+     * See
+     * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](
+     *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)
+     *
+     * ```js
+     * const x = tf.tensor1d([-1, 2, -3, 4]);
+     *
+     * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)
+     * ```
+     * @param x The input tensor.
+     * @param alpha The scaling factor for negative values, defaults to 0.2.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function leakyRelu_(x, alpha) {
+        if (alpha === void 0) { alpha = 0.2; }
+        var $x = convertToTensor(x, 'x', 'leakyRelu');
+        var inputs = { x: $x };
+        var attrs = { alpha: alpha };
+        return ENGINE.runKernel(LeakyRelu, inputs, attrs);
+    }
+    var leakyRelu = op({ leakyRelu_: leakyRelu_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the truth value of (a < b) element-wise. Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2, 3]);
+     * const b = tf.tensor1d([2, 2, 2]);
+     *
+     * a.less(b).print();
+     * ```
+     * @param a The first input tensor.
+     * @param b The second input tensor. Must have the same dtype as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Logical'}
+     */
+    function less_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'less', 'string_or_numeric');
+        var $b = convertToTensor(b, 'b', 'less', 'string_or_numeric');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(Less, inputs);
+    }
+    var less = op({ less_: less_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the truth value of (a <= b) element-wise. Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2, 3]);
+     * const b = tf.tensor1d([2, 2, 2]);
+     *
+     * a.lessEqual(b).print();
+     * ```
+     *
+     * @param a The first input tensor.
+     * @param b The second input tensor. Must have the same dtype as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Logical'}
+     */
+    function lessEqual_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'lessEqual', 'string_or_numeric');
+        var $b = convertToTensor(b, 'b', 'lessEqual', 'string_or_numeric');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(LessEqual, inputs);
+    }
+    var lessEqual = op({ lessEqual_: lessEqual_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Return an evenly spaced sequence of numbers over the given interval.
+     *
+     * ```js
+     * tf.linspace(0, 9, 10).print();
+     * ```
+     * @param start The start value of the sequence.
+     * @param stop The end value of the sequence.
+     * @param num The number of values to generate.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function linspace(start, stop, num) {
+        if (num <= 0) {
+            throw new Error('The number of values should be positive.');
+        }
+        var attrs = { start: start, stop: stop, num: num };
+        return ENGINE.runKernel(LinSpace, {}, attrs);
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Normalizes the activation of a local neighborhood across or within
+     * channels.
+     *
+     * @param x The input tensor. The 4-D input tensor is treated as a 3-D array
+     *     of 1D vectors (along the last dimension), and each vector is
+     *     normalized independently.
+     * @param depthRadius The number of adjacent channels in the 1D normalization
+     *     window.
+     * @param bias A constant bias term for the basis.
+     * @param alpha A scale factor, usually positive.
+     * @param beta An exponent.
+     *
+     * @doc {heading: 'Operations', subheading: 'Normalization'}
+     */
+    function localResponseNormalization_(x, depthRadius, bias, alpha, beta) {
+        if (depthRadius === void 0) { depthRadius = 5; }
+        if (bias === void 0) { bias = 1; }
+        if (alpha === void 0) { alpha = 1; }
+        if (beta === void 0) { beta = 0.5; }
+        var $x = convertToTensor(x, 'x', 'localResponseNormalization');
+        assert($x.rank === 4 || $x.rank === 3, function () { return "Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank " + $x.rank + "."; });
+        assert(isInt(depthRadius), function () { return "Error in localResponseNormalization: depthRadius must be an " +
+            ("integer but got depthRadius " + depthRadius + "."); });
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+        }
+        var inputs = { x: x4D };
+        var attrs = { depthRadius: depthRadius, bias: bias, alpha: alpha, beta: beta };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(LRN, inputs, attrs);
+        if (reshapedTo4D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+        }
+        else {
+            return res;
+        }
+    }
+    var localResponseNormalization = op({ localResponseNormalization_: localResponseNormalization_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, Math.E]);
+     *
+     * x.log().print();  // or tf.log(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function log_(x) {
+        var $x = convertToTensor(x, 'x', 'log');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Log, inputs);
+    }
+    var log = op({ log_: log_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes natural logarithm of the input `tf.Tensor` plus one
+     * element-wise: `ln(1 + x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, Math.E - 1]);
+     *
+     * x.log1p().print();  // or tf.log1p(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function log1p_(x) {
+        var $x = convertToTensor(x, 'x', 'log1p');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Log1p, inputs);
+    }
+    var log1p = op({ log1p_: log1p_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the
+     * gradient of `f(x)` with respect to `x`.
+     *
+     * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to
+     * `x` is computed instead. `f(x)` must take a single tensor `x` and return a
+     * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.
+     *
+     * ```js
+     * // f(x) = x ^ 2
+     * const f = x => x.square();
+     * // f'(x) = 2x
+     * const g = tf.grad(f);
+     *
+     * const x = tf.tensor1d([2, 3]);
+     * g(x).print();
+     * ```
+     *
+     * ```js
+     * // f(x) = x ^ 3
+     * const f = x => x.pow(tf.scalar(3, 'int32'));
+     * // f'(x) = 3x ^ 2
+     * const g = tf.grad(f);
+     * // f''(x) = 6x
+     * const gg = tf.grad(g);
+     *
+     * const x = tf.tensor1d([2, 3]);
+     * gg(x).print();
+     * ```
+     *
+     * @param f The function f(x), to compute gradient for.
+     *
+     * @doc {heading: 'Training', subheading: 'Gradients'}
+     */
+    function grad(f) {
+        assert(isFunction(f), function () { return 'The f passed in grad(f) must be a function'; });
+        return function (x, dy) {
+            // x can be of any dtype, thus null as the last argument.
+            var $x = convertToTensor(x, 'x', 'tf.grad', 'string_or_numeric');
+            var $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;
+            return ENGINE.tidy(function () {
+                var _a = ENGINE.gradients(function () { return f($x); }, [$x], $dy), value = _a.value, grads = _a.grads;
+                if ($dy != null) {
+                    assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +
+                        'returned by f(x)');
+                }
+                checkGrads(grads);
+                return grads[0];
+            });
+        };
+    }
+    /**
+     * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,
+     * which gives an array of gradients of `f()` with respect to each input
+     * [`x1`,`x2`,...].
+     *
+     * If `dy` is passed when calling `g()`, the gradient of
+     * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.
+     * The provided `f` must take one or more tensors and return a single tensor
+     * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.
+     *
+     * ```js
+     * // f(a, b) = a * b
+     * const f = (a, b) => a.mul(b);
+     * // df / da = b, df / db = a
+     * const g = tf.grads(f);
+     *
+     * const a = tf.tensor1d([2, 3]);
+     * const b = tf.tensor1d([-2, -3]);
+     * const [da, db] = g([a, b]);
+     * console.log('da');
+     * da.print();
+     * console.log('db');
+     * db.print();
+     * ```
+     *
+     * @param f The function `f(x1, x2,...)` to compute gradients for.
+     *
+     * @doc {heading: 'Training', subheading: 'Gradients'}
+     */
+    function grads(f) {
+        assert(isFunction(f), function () { return 'The f passed in grads(f) must be a function'; });
+        return function (args, dy) {
+            assert(Array.isArray(args), function () { return 'The args passed in grads(f)(args) must be an array ' +
+                'of `Tensor`s or `TensorLike`s'; });
+            // args can be of any dtype, thus null as the last argument.
+            var $args = convertToTensorArray(args, 'args', 'tf.grads', 'string_or_numeric');
+            var $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;
+            return ENGINE.tidy(function () {
+                var _a = ENGINE.gradients(function () { return f.apply(void 0, $args); }, $args, $dy), value = _a.value, grads = _a.grads;
+                if ($dy != null) {
+                    assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +
+                        'match the shape returned by f([x1,...])');
+                }
+                checkGrads(grads);
+                return grads;
+            });
+        };
+    }
+    /**
+     * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`
+     * returns a metric you want to show.
+     *
+     * The result is a rich object with the following properties:
+     * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).
+     * - value: The value returned by `f(x)`.
+     *
+     * ```js
+     * // f(x) = x ^ 2
+     * const f = x => x.square();
+     * // f'(x) = 2x
+     * const g = tf.valueAndGrad(f);
+     *
+     * const x = tf.tensor1d([2, 3]);
+     * const {value, grad} = g(x);
+     *
+     * console.log('value');
+     * value.print();
+     * console.log('grad');
+     * grad.print();
+     * ```
+     *
+     * @doc {heading: 'Training', subheading: 'Gradients'}
+     */
+    function valueAndGrad(f) {
+        assert(isFunction(f), function () { return 'The f passed in valueAndGrad(f) must be a function'; });
+        return function (x, dy) {
+            assert(x instanceof Tensor, function () { return 'The x passed in valueAndGrad(f)(x) must be a tensor'; });
+            assert(dy == null || dy instanceof Tensor, function () { return 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor'; });
+            var _a = ENGINE.gradients(function () { return f(x); }, [x], dy), grads = _a.grads, value = _a.value;
+            checkGrads(grads);
+            return { grad: grads[0], value: value };
+        };
+    }
+    /**
+     * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`
+     * returns a metric you want to show.
+     *
+     * The result is a rich object with the following properties:
+     * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).
+     * - value: The value returned by `f(x)`.
+     *
+     * ```js
+     * // f(a, b) = a * b
+     * const f = (a, b) => a.mul(b);
+     * // df/da = b, df/db = a
+     * const g = tf.valueAndGrads(f);
+     *
+     * const a = tf.tensor1d([2, 3]);
+     * const b = tf.tensor1d([-2, -3]);
+     * const {value, grads} = g([a, b]);
+     *
+     * const [da, db] = grads;
+     *
+     * console.log('value');
+     * value.print();
+     *
+     * console.log('da');
+     * da.print();
+     * console.log('db');
+     * db.print();
+     * ```
+     *
+     * @doc {heading: 'Training', subheading: 'Gradients'}
+     */
+    function valueAndGrads(f) {
+        assert(isFunction(f), function () { return 'The f passed in valueAndGrads(f) must be a function'; });
+        return function (args, dy) {
+            assert(Array.isArray(args) && args.every(function (arg) { return arg instanceof Tensor; }), function () { return 'The args passed in valueAndGrads(f)(args) must be array of ' +
+                'tensors'; });
+            assert(dy == null || dy instanceof Tensor, function () { return 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor'; });
+            var res = ENGINE.gradients(function () { return f.apply(void 0, args); }, args, dy);
+            if (dy != null) {
+                assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +
+                    'match the shape returned by f([x1,...])');
+            }
+            checkGrads(res.grads);
+            return res;
+        };
+    }
+    /**
+     * Computes and returns the gradient of f(x) with respect to the list of
+     * trainable variables provided by `varList`. If no list is provided, it
+     * defaults to all trainable variables.
+     *
+     * ```js
+     * const a = tf.variable(tf.tensor1d([3, 4]));
+     * const b = tf.variable(tf.tensor1d([5, 6]));
+     * const x = tf.tensor1d([1, 2]);
+     *
+     * // f(a, b) = a * x ^ 2 + b * x
+     * const f = () => a.mul(x.square()).add(b.mul(x)).sum();
+     * // df/da = x ^ 2, df/db = x
+     * const {value, grads} = tf.variableGrads(f);
+     *
+     * Object.keys(grads).forEach(varName => grads[varName].print());
+     * ```
+     *
+     * @param f The function to execute. f() should return a scalar.
+     * @param varList The list of variables to compute the gradients with respect
+     *     to. Defaults to all trainable variables.
+     * @returns An object with the following keys and values:
+     *   - `value`: The value of the function `f`.
+     *   - `grads`: A map from the names of the variables to the gradients.
+     *     If the `varList` argument is provided explicitly and contains a subset of
+     *     non-trainable variables, this map in the return value will contain keys
+     *     that map the names of the non-trainable variables to `null`.
+     *
+     * @doc {heading: 'Training', subheading: 'Gradients'}
+     */
+    function variableGrads(f, varList) {
+        assert(isFunction(f), function () { return 'The f passed in variableGrads(f) must be a function'; });
+        assert(varList == null ||
+            Array.isArray(varList) && varList.every(function (v) { return v instanceof Variable; }), function () {
+            return 'The varList passed in variableGrads(f, varList) must be an array ' +
+                'of variables';
+        });
+        var specifiedVarList = varList != null;
+        if (!specifiedVarList) {
+            // Get all of the trainable variables.
+            varList = [];
+            for (var varName in ENGINE.registeredVariables) {
+                varList.push(ENGINE.registeredVariables[varName]);
+            }
+        }
+        var specifiedNonTrainable = specifiedVarList ? varList.filter(function (variable) { return !variable.trainable; }) : null;
+        // Prune non-trainable variables.
+        var originalVarCount = varList.length;
+        varList = varList.filter(function (variable) { return variable.trainable; });
+        assert(varList.length > 0, function () { return "variableGrads() expects at least one of the input variables to " +
+            ("be trainable, but none of the " + originalVarCount + " variables is ") +
+            "trainable."; });
+        var allowNoGradients = true;
+        var _a = ENGINE.gradients(f, varList, null, allowNoGradients), value = _a.value, grads = _a.grads;
+        assert(grads.some(function (g) { return g != null; }), function () { return 'Cannot find a connection between any variable and the result of ' +
+            'the loss function y=f(x). Please make sure the operations that ' +
+            'use variables are inside the function f passed to minimize().'; });
+        assert(value.rank === 0, function () { return "The f passed in variableGrads(f) must return a scalar, but it " +
+            ("returned a rank-" + value.rank + " tensor"); });
+        var namedGrads = {};
+        varList.forEach(function (v, i) {
+            if (grads[i] != null) {
+                namedGrads[v.name] = grads[i];
+            }
+        });
+        if (specifiedNonTrainable != null) {
+            // If varList is explicitly provided and contains non-trainable values,
+            // add them to the returned gradients with `null` values.
+            specifiedNonTrainable.forEach(function (v) { return namedGrads[v.name] = null; });
+        }
+        return { value: value, grads: namedGrads };
+    }
+    /**
+     * Overrides the gradient computation of a function `f`.
+     *
+     * Takes a function
+     * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`
+     * and returns another function `g(...inputs)` which takes the same inputs as
+     * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients
+     * with respect to each input of `f` are computed using `f().gradFunc`.
+     *
+     * The `save` function passsed to `f` should be used for saving tensors needed
+     * in the gradient. And the `saved` passed to the `gradFunc` is a
+     * `NamedTensorMap`, which contains those saved tensor.
+     *
+     * ```js
+     * const customOp = tf.customGrad((x, save) => {
+     *   // Save x to make sure it's available later for the gradient.
+     *   save([x]);
+     *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);
+     *   return {
+     *     value: x.square(),
+     *     // Note `saved.x` which points to the `x` we saved earlier.
+     *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]
+     *   };
+     * });
+     *
+     * const x = tf.tensor1d([-1, -2, 3]);
+     * const dx = tf.grad(x => customOp(x));
+     *
+     * console.log(`f(x):`);
+     * customOp(x).print();
+     * console.log(`f'(x):`);
+     * dx(x).print();
+     * ```
+     *
+     * @param f The function to evaluate in forward mode, which should return
+     *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`
+     *     returns the custom gradients of `f` with respect to its inputs.
+     *
+     * @doc {heading: 'Training', subheading: 'Gradients'}
+     */
+    function customGrad(f) {
+        return ENGINE.customGrad(f);
+    }
+    function checkGrads(grads) {
+        var numNullGradients = grads.filter(function (g) { return g == null; }).length;
+        if (numNullGradients > 0) {
+            throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.");
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes `-1 * x` element-wise.
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);
+     *
+     * x.neg().print();  // or tf.neg(x)
+     * ```
+     *
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function neg_(x) {
+        var $x = convertToTensor(x, 'x', 'neg');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Neg, inputs);
+    }
+    var neg = op({ neg_: neg_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, 1, -1, .7]);
+     *
+     * x.softplus().print();  // or tf.softplus(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function softplus_(x) {
+        var $x = convertToTensor(x, 'x', 'softplus');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Softplus, inputs);
+    }
+    var softplus = op({ softplus_: softplus_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes log sigmoid of the input `tf.Tensor` element-wise:
+     * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.
+     *
+     * ```js
+     * const x = tf.tensor1d([0, 1, -1, .7]);
+     *
+     * x.logSigmoid().print();  // or tf.logSigmoid(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function logSigmoid_(x) {
+        var $x = convertToTensor(x, 'x', 'logSigmoid');
+        // Use a custom gradient to maintain previous implementation.
+        // There is no LogSigmoid kernel in TF so we can't use engine.runKernel
+        // directly
+        var customOp = customGrad(function (x) {
+            // TODO(yassogba) we can remove the chained softplus call here only
+            // after backends have modualrized softplus at which point we can call
+            // engine runKernel(..., Sotfplus, ...) directly.
+            var value = neg(softplus(neg(x)));
+            var gradFunc = function (dy) {
+                var derX = mul(dy, sigmoid(neg(x)));
+                return derX;
+            };
+            return { value: value, gradFunc: gradFunc };
+        });
+        return customOp($x);
+    }
+    var logSigmoid = op({ logSigmoid_: logSigmoid_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the maximum of elements across dimensions of a `tf.Tensor`.
+     *
+     * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
+     * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
+     * `axes`. If `keepDims` is true, the reduced dimensions are retained with
+     * length 1. If `axes` has no entries, all dimensions are reduced, and an
+     * `tf.Tensor` with a single element is returned.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3]);
+     *
+     * x.max().print();  // or tf.max(x)
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     *
+     * const axis = 1;
+     * x.max(axis).print();  // or tf.max(x, axis)
+     * ```
+     *
+     * @param x The input tensor.
+     * @param axis The dimension(s) to reduce. By default it reduces
+     *     all dimensions.
+     * @param keepDims If true, retains reduced dimensions with size 1.
+     *
+     * @doc {heading: 'Operations', subheading: 'Reduction'}
+     */
+    function max_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'max');
+        var inputs = { x: $x };
+        var attrs = { reductionIndices: axis, keepDims: keepDims };
+        return ENGINE.runKernel(Max, inputs, attrs);
+    }
+    var max = op({ max_: max_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([10, 20, 30, 40]);
+     * const b = tf.tensor1d([1, 2, 3, 4]);
+     *
+     * a.sub(b).print();  // or tf.sub(a, b)
+     * ```
+     *
+     * ```js
+     * // Broadcast subtract a with b.
+     * const a = tf.tensor1d([10, 20, 30, 40]);
+     * const b = tf.scalar(5);
+     *
+     * a.sub(b).print();  // or tf.sub(a, b)
+     * ```
+     * @param a The first `tf.Tensor` to subtract from.
+     * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as
+     * `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Arithmetic'}
+     */
+    function sub_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'sub');
+        var $b = convertToTensor(b, 'b', 'sub');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(Sub, inputs);
+    }
+    var sub = op({ sub_: sub_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the sum of elements across dimensions of a `tf.Tensor`.
+     *
+     * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
+     * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
+     * `axes`. If `keepDims` is true, the reduced dimensions are retained with
+     * length 1. If axes has no entries, all dimensions are reduced, and a
+     * `tf.Tensor` with a single element is returned.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3]);
+     *
+     * x.sum().print();  // or tf.sum(x)
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     *
+     * const axis = 1;
+     * x.sum(axis).print();  // or tf.sum(x, axis)
+     * ```
+     *
+     * @param x The input tensor to compute the sum over. If the dtype is `bool`
+     *   it will be converted to `int32` and the output dtype will be `int32`.
+     * @param axis The dimension(s) to reduce. By default it reduces
+     *     all dimensions.
+     * @param keepDims If true, retains reduced dimensions with size 1.
+     *
+     * @doc {heading: 'Operations', subheading: 'Reduction'}
+     */
+    function sum_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'sum');
+        if ($x.dtype === 'bool') {
+            $x = cast($x, 'int32');
+        }
+        var inputs = { x: $x };
+        var attrs = { axis: axis, keepDims: keepDims };
+        return ENGINE.runKernel(Sum, inputs, attrs);
+    }
+    var sum$1 = op({ sum_: sum_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google Inc. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the log softmax.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2, 3]);
+     *
+     * a.logSoftmax().print();  // or tf.logSoftmax(a)
+     * ```
+     *
+     * ```js
+     * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);
+     *
+     * a.logSoftmax().print();  // or tf.logSoftmax(a)
+     * ```
+     *
+     * @param logits The logits array.
+     * @param axis The dimension softmax would be performed on. Defaults to `-1`
+     *     which indicates the last dimension.
+     *
+     * @doc {heading: 'Operations', subheading: 'Normalization'}
+     */
+    function logSoftmax_(logits, axis) {
+        if (axis === void 0) { axis = -1; }
+        var $logits = convertToTensor(logits, 'logits', 'logSoftmax');
+        if (axis === -1) {
+            axis = $logits.rank - 1;
+        }
+        if (axis !== $logits.rank - 1) {
+            throw Error('Log Softmax along a non-last dimension is not yet supported. ' +
+                ("Logits was rank " + $logits.rank + " and axis was " + axis));
+        }
+        // const forward: ForwardFunc<Tensor> = (backend, save) => {
+        //   const keepDims = true;
+        //   const xMax = max(logits, axis, true);
+        //   const shifted = sub(logits, xMax);
+        //   const value =
+        //       sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis,
+        //       keepDims)));
+        //   save([value]);
+        //   return value;
+        // };
+        // Use a custom gradient for numerical stability.
+        var customOp = customGrad(function (logits, save) {
+            var keepDims = true;
+            var xMax = max(logits, axis, true);
+            var shifted = sub(logits, xMax);
+            var value = sub(cast(shifted, 'float32'), log(sum$1(exp(shifted), axis, keepDims)));
+            save([value]);
+            var gradFunc = function (dy, saved) {
+                var value = saved[0];
+                var keepDims = true;
+                var softmax = exp(value);
+                return sub(dy, mul(sum$1(dy, axis, keepDims), softmax));
+            };
+            return { value: value, gradFunc: gradFunc };
+        });
+        return customOp($logits);
+        // TODO Use Engine.runKernel when CPU/WebGL/WASM backends implement this.
+        // const inputs: LogSoftmaxInputs = {logits: $logits};
+        // const attrs: LogSoftmaxAttrs = {axis};
+        // return ENGINE.runKernel(
+        //            LogSoftmax, inputs as {} as NamedTensorMap,
+        //            attrs as {} as NamedAttrMap);
+    }
+    var logSoftmax = op({ logSoftmax_: logSoftmax_ });
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns true if the axis specifies the inner most dimensions of the
+     * array.
+     */
+    function axesAreInnerMostDims(axes, rank) {
+        for (var i = 0; i < axes.length; ++i) {
+            if (axes[axes.length - i - 1] !== rank - 1 - i) {
+                return false;
+            }
+        }
+        return true;
+    }
+    function combineLocations(outputLoc, reduceLoc, axes) {
+        var rank = outputLoc.length + reduceLoc.length;
+        var loc = [];
+        var outIdx = 0;
+        var reduceIdx = 0;
+        for (var dim = 0; dim < rank; dim++) {
+            if (axes.indexOf(dim) === -1) {
+                loc.push(outputLoc[outIdx++]);
+            }
+            else {
+                loc.push(reduceLoc[reduceIdx++]);
+            }
+        }
+        return loc;
+    }
+    function computeOutAndReduceShapes(aShape, axes) {
+        var outShape = [];
+        var rank = aShape.length;
+        for (var dim = 0; dim < rank; dim++) {
+            if (axes.indexOf(dim) === -1) {
+                outShape.push(aShape[dim]);
+            }
+        }
+        var reduceShape = axes.map(function (dim) { return aShape[dim]; });
+        return [outShape, reduceShape];
+    }
+    function expandShapeToKeepDim(shape, axes) {
+        var reduceSubShape = axes.map(function (x) { return 1; });
+        return combineLocations(shape, reduceSubShape, axes);
+    }
+    function assertAxesAreInnerMostDims(msg, axes, rank) {
+        assert(axesAreInnerMostDims(axes, rank), function () { return msg + " supports only inner-most axes for now. " +
+            ("Got axes " + axes + " and rank-" + rank + " input."); });
+    }
+    /**
+     * Returns the axes permutation to be used with `tf.transpose`, if such
+     * permutation is necessary. Otherwise it returns null. This method is used by
+     * operations that operate only on inner-most axes.
+     */
+    function getAxesPermutation(axes, rank) {
+        if (axesAreInnerMostDims(axes, rank)) {
+            return null;
+        }
+        var result = [];
+        for (var i = 0; i < rank; ++i) {
+            if (axes.indexOf(i) === -1) {
+                result.push(i);
+            }
+        }
+        axes.forEach(function (axis) { return result.push(axis); });
+        return result;
+    }
+    /** Returns the axes permutation that undoes the original permutation. */
+    function getUndoAxesPermutation(axes) {
+        return axes.map(function (axis, i) { return [i, axis]; })
+            .sort(function (a, b) { return a[1] - b[1]; })
+            .map(function (x) { return x[0]; });
+    }
+    function getInnerMostAxes(numAxes, rank) {
+        var res = [];
+        for (var i = rank - numAxes; i < rank; ++i) {
+            res.push(i);
+        }
+        return res;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the log(sum(exp(elements across the reduction dimensions)).
+     *
+     * Reduces the input along the dimensions given in `axis`. Unless `keepDims`
+     * is true, the rank of the array is reduced by 1 for each entry in `axis`.
+     * If `keepDims` is true, the reduced dimensions are retained with length 1.
+     * If `axis` has no entries, all dimensions are reduced, and an array with a
+     * single element is returned.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3]);
+     *
+     * x.logSumExp().print();  // or tf.logSumExp(x)
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     *
+     * const axis = 1;
+     * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)
+     * ```
+     * @param x The input tensor.
+     * @param axis The dimension(s) to reduce. If null (the default),
+     *     reduces all dimensions.
+     * @param keepDims If true, retains reduced dimensions with length
+     *     of 1. Defaults to false.
+     *
+     * @doc {heading: 'Operations', subheading: 'Reduction'}
+     */
+    function logSumExp_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'logSumExp');
+        var axes = parseAxisParam(axis, $x.shape);
+        var xMax = max($x, axes, true /* keepDims */);
+        var a = sub($x, xMax);
+        var b = exp(a);
+        var c = sum$1(b, axes);
+        var d = log(c);
+        var res = add$1(reshape(xMax, d.shape), d);
+        if (keepDims) {
+            var newShape = expandShapeToKeepDim(res.shape, axes);
+            return reshape(res, newShape);
+        }
+        return res;
+    }
+    var logSumExp = op({ logSumExp_: logSumExp_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the truth value of `a AND b` element-wise. Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([false, false, true, true], 'bool');
+     * const b = tf.tensor1d([false, true, false, true], 'bool');
+     *
+     * a.logicalAnd(b).print();
+     * ```
+     *
+     * @param a The first input tensor. Must be of dtype bool.
+     * @param b The second input tensor. Must be of dtype bool.
+     *
+     * @doc {heading: 'Operations', subheading: 'Logical'}
+     */
+    function logicalAnd_(a, b) {
+        var $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');
+        var $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(LogicalAnd, inputs);
+    }
+    var logicalAnd = op({ logicalAnd_: logicalAnd_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the truth value of `NOT x` element-wise.
+     *
+     * ```js
+     * const a = tf.tensor1d([false, true], 'bool');
+     *
+     * a.logicalNot().print();
+     * ```
+     *
+     * @param x The input tensor. Must be of dtype 'bool'.
+     *
+     * @doc {heading: 'Operations', subheading: 'Logical'}
+     */
+    function logicalNot_(x) {
+        var $x = convertToTensor(x, 'x', 'logicalNot', 'bool');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(LogicalNot, inputs);
+    }
+    var logicalNot = op({ logicalNot_: logicalNot_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the truth value of `a OR b` element-wise. Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([false, false, true, true], 'bool');
+     * const b = tf.tensor1d([false, true, false, true], 'bool');
+     *
+     * a.logicalOr(b).print();
+     * ```
+     * @param a The first input tensor. Must be of dtype bool.
+     * @param b The second input tensor. Must be of dtype bool.
+     *
+     * @doc {heading: 'Operations', subheading: 'Logical'}
+     */
+    function logicalOr_(a, b) {
+        var $a = convertToTensor(a, 'a', 'logicalOr', 'bool');
+        var $b = convertToTensor(b, 'b', 'logicalOr', 'bool');
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(LogicalOr, inputs);
+    }
+    var logicalOr = op({ logicalOr_: logicalOr_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([false, false, true, true], 'bool');
+     * const b = tf.tensor1d([false, true, false, true], 'bool');
+     *
+     * a.logicalXor(b).print();
+     * ```
+     *
+     * @param a The first input tensor. Must be of dtype bool.
+     * @param b The second input tensor. Must be of dtype bool.
+     *
+     * @doc {heading: 'Operations', subheading: 'Logical'}
+     */
+    function logicalXor_(a, b) {
+        var $a = convertToTensor(a, 'a', 'logicalXor', 'bool');
+        var $b = convertToTensor(b, 'b', 'logicalXor', 'bool');
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        // x ^ y = (x | y) & ~(x & y)
+        return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));
+    }
+    var logicalXor = op({ logicalXor_: logicalXor_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the 2D max pooling of an image.
+     *
+     * @param x The input tensor, of rank 4 or rank 3 of shape
+     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
+     * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
+     *     `filterSize` is a single number, then `filterHeight == filterWidth`.
+     * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
+     *     `strides` is a single number, then `strideHeight == strideWidth`.
+     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
+     *     in which we sample input values across the height and width dimensions
+     *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single
+     *     number, then `dilationHeight == dilationWidth`. If it is greater than
+     *     1, then all values of `strides` must be 1.
+     * @param pad The type of padding algorithm.
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1x1.
+     *    - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     */
+    function maxPool_(x, filterSize, strides, pad, dimRoundingMode) {
+        var $x = convertToTensor(x, 'x', 'maxPool');
+        var dilations = 1;
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+        }
+        assert(x4D.rank === 4, function () { return "Error in maxPool: input must be rank 4 but got rank " + x4D.rank + "."; });
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in maxPool: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), function () { return "Error in maxPool: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+        }
+        var inputs = { x: x4D };
+        var attrs = { filterSize: filterSize, strides: strides, pad: pad, dimRoundingMode: dimRoundingMode };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(MaxPool, inputs, attrs);
+        if (reshapedTo4D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+        }
+        return res;
+    }
+    var maxPool = op({ maxPool_: maxPool_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the 3D max pooling.
+     *
+     * ```js
+     * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
+     * const result = tf.maxPool3d(x, 2, 1, 'valid');
+     * result.print();
+     * ```
+     *
+     * @param x The input tensor, of rank 5 or rank 4 of shape
+     *     `[batch, depth, height, width, inChannels]`.
+     * @param filterSize The filter size:
+     *     `[filterDepth, filterHeight, filterWidth]`.
+     *     If `filterSize` is a single number,
+     *     then `filterDepth == filterHeight == filterWidth`.
+     * @param strides The strides of the pooling:
+     *     `[strideDepth, strideHeight, strideWidth]`.
+     *     If `strides` is a single number,
+     *     then `strideDepth == strideHeight == strideWidth`.
+     * @param pad The type of padding algorithm.
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1*1x1.
+     *    - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
+     *     "NDHWC". Specify the data format of the input and output data. With the
+     *     default format "NDHWC", the data is stored in the order of: [batch,
+     *     depth, height, width, channels]. Only "NDHWC" is currently supported.
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function maxPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat) {
+        if (filterSize === void 0) { filterSize = [1, 1, 1]; }
+        if (dataFormat === void 0) { dataFormat = 'NDHWC'; }
+        var $x = convertToTensor(x, 'x', 'maxPool3d');
+        var x5D = $x;
+        var reshapedTo5D = false;
+        if ($x.rank === 4) {
+            reshapedTo5D = true;
+            x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
+        }
+        assert(x5D.rank === 5, function () { return "Error in maxPool3d: x must be rank 5 but got rank " + x5D.rank + "."; });
+        assert(dataFormat === 'NDHWC', function () { return "Error in maxPool3d: Only NDHWC is currently supported, " +
+            ("but got dataFormat of " + dataFormat); });
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), function () { return "Error in maxPool3d: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+        }
+        var inputs = { x: x5D };
+        var attrs = { filterSize: filterSize, strides: strides, pad: pad, dimRoundingMode: dimRoundingMode, dataFormat: dataFormat };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(MaxPool3D, inputs, attrs);
+        if (reshapedTo5D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+        }
+        return res;
+    }
+    var maxPool3d = op({ maxPool3d_: maxPool3d_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the 2D max pooling of an image with Argmax index.
+     * The indices in argmax are flattened, so that a maximum value at position `[b,
+     * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if
+     * include_batch_in_index is False; `((b * height + y) * width + x) * channels
+     * +c` if include_batch_in_index is True.
+     *
+     * The indices returned are always in `[0, height) x [0, width)` before
+     * flattening.
+     *
+     * @param x The input tensor, of rank 4 or rank 3 of shape
+     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
+     * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
+     *     `filterSize` is a single number, then `filterHeight == filterWidth`.
+     * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
+     *     `strides` is a single number, then `strideHeight == strideWidth`.
+     * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
+     *     "NDHWC". Specify the data format of the input and output data. With the
+     *     default format "NDHWC", the data is stored in the order of: [batch,
+     *     depth, height, width, channels]. Only "NDHWC" is currently supported.
+     * @param pad The type of padding algorithm.
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1x1.
+     *    - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param includeBatchIndex Defaults to False. Whether to include batch
+     *    dimension in flattened index of argmax.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex) {
+        if (includeBatchInIndex === void 0) { includeBatchInIndex = false; }
+        var $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');
+        var inputs = { x: $x };
+        var attrs = { filterSize: filterSize, strides: strides, pad: pad, includeBatchInIndex: includeBatchInIndex };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);
+        return { result: result[0], indexes: result[1] };
+    }
+    var maxPoolWithArgmax = op({ maxPoolWithArgmax_: maxPoolWithArgmax_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the max of a and b (`a > b ? a : b`) element-wise.
+     * Supports broadcasting.
+     *
+     * We also expose `tf.maximumStrict` which has the same signature as this op and
+     * asserts that `a` and `b` are the same shape (does not broadcast).
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 4, 3, 16]);
+     * const b = tf.tensor1d([1, 2, 9, 4]);
+     *
+     * a.maximum(b).print();  // or tf.maximum(a, b)
+     * ```
+     *
+     * ```js
+     * // Broadcast maximum a with b.
+     * const a = tf.tensor1d([2, 4, 6, 8]);
+     * const b = tf.scalar(5);
+     *
+     * a.maximum(b).print();  // or tf.maximum(a, b)
+     * ```
+     *
+     * @param a The first tensor.
+     * @param b The second tensor. Must have the same type as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Arithmetic'}
+     */
+    function maximum_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'maximum');
+        var $b = convertToTensor(b, 'b', 'maximum');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        if ($a.dtype === 'bool') {
+            $a = cast($a, 'int32');
+            $b = cast($b, 'int32');
+        }
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(Maximum, inputs);
+    }
+    var maximum = op({ maximum_: maximum_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google Inc. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the mean of elements across dimensions of a `tf.Tensor`.
+     *
+     * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is
+     * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.
+     * If `keepDims` is true, the reduced dimensions are retained with length 1.
+     * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with
+     * a single element is returned.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3]);
+     *
+     * x.mean().print();  // or tf.mean(a)
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     *
+     * const axis = 1;
+     * x.mean(axis).print();  // or tf.mean(x, axis)
+     * ```
+     *
+     * @param x The input tensor.
+     * @param axis The dimension(s) to reduce. By default it reduces
+     *     all dimensions.
+     * @param keepDims If true, retains reduced dimensions with size 1.
+     *
+     * @doc {heading: 'Operations', subheading: 'Reduction'}
+     */
+    function mean_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'mean');
+        var inputs = { x: $x };
+        var attrs = { axis: axis, keepDims: keepDims };
+        return ENGINE.runKernel(Mean, inputs, attrs);
+    }
+    var mean = op({ mean_: mean_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a `tf.Tensor` with all elements set to 0.
+     *
+     * ```js
+     * tf.zeros([2, 2]).print();
+     * ```
+     *
+     * @param shape An array of integers defining the output tensor shape.
+     * @param dtype The type of an element in the resulting tensor. Can
+     *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function zeros(shape, dtype) {
+        if (dtype === void 0) { dtype = 'float32'; }
+        if (dtype === 'complex64') {
+            var real = zeros(shape, 'float32');
+            var imag = zeros(shape, 'float32');
+            return complex(real, imag);
+        }
+        var values = makeZerosTypedArray(sizeFromShape(shape), dtype);
+        return ENGINE.makeTensor(values, shape, dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a `tf.Tensor` with all elements set to 1.
+     *
+     * ```js
+     * tf.ones([2, 2]).print();
+     * ```
+     *
+     * @param shape An array of integers defining the output tensor shape.
+     * @param dtype The type of an element in the resulting tensor. Defaults to
+     *     'float'.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function ones$1(shape, dtype) {
+        if (dtype === void 0) { dtype = 'float32'; }
+        if (dtype === 'complex64') {
+            var real = ones$1(shape, 'float32');
+            var imag = zeros(shape, 'float32');
+            return complex(real, imag);
+        }
+        var values = makeOnesTypedArray(sizeFromShape(shape), dtype);
+        return ENGINE.makeTensor(values, shape, dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Broadcasts parameters for evaluation on an N-D grid.
+     *
+     * Given N one-dimensional coordinate arrays `*args`, returns a list `outputs`
+     * of N-D coordinate arrays for evaluating expressions on an N-D grid.
+     *
+     * Notes:
+     * `meshgrid` supports cartesian ('xy') and matrix ('ij') indexing conventions.
+     * When the `indexing` argument is set to 'xy' (the default), the broadcasting
+     * instructions for the first two dimensions are swapped.
+     * Examples:
+     * Calling `const [X, Y] = meshgrid(x, y)` with the tensors
+     *
+     * ```javascript
+     * const x = [1, 2, 3];
+     * const y = [4, 5, 6];
+     * const [X, Y] = tf.meshgrid(x, y);
+     * // X = [[1, 2, 3],
+     * //      [1, 2, 3],
+     * //      [1, 2, 3]]
+     * // Y = [[4, 4, 4],
+     * //      [5, 5, 5],
+     * //      [6, 6, 6]]
+     * ```
+     *
+     * @param x Tensor with rank geq 1.
+     * @param y Tensor with rank geq 1.
+     * @param indexing
+     *
+     * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
+     */
+    function meshgrid(x, y, _a) {
+        var _b = (_a === void 0 ? {} : _a).indexing, indexing = _b === void 0 ? 'xy' : _b;
+        if (indexing !== 'xy' && indexing !== 'ij') {
+            throw new TypeError(indexing + " is not a valid third argument to meshgrid");
+        }
+        if (x === undefined) {
+            return [];
+        }
+        var $x = convertToTensor(x, 'x', 'meshgrid', x instanceof Tensor ? x.dtype : 'float32');
+        if (y === undefined) {
+            return [$x];
+        }
+        var $y = convertToTensor(y, 'y', 'meshgrid', y instanceof Tensor ? y.dtype : 'float32');
+        var w = sizeFromShape($x.shape);
+        var h = sizeFromShape($y.shape);
+        if (indexing === 'xy') {
+            $x = reshape($x, [1, -1]);
+            $y = reshape($y, [-1, 1]);
+            return [
+                matMul(ones$1([h, 1], $x.dtype), $x),
+                matMul($y, ones$1([1, w], $y.dtype)),
+            ];
+        }
+        $x = reshape($x, [-1, 1]);
+        $y = reshape($y, [1, -1]);
+        return [
+            matMul($x, ones$1([1, h], $x.dtype)),
+            matMul(ones$1([w, 1], $y.dtype), $y),
+        ];
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google Inc. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the minimum value from the input.
+     *
+     * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
+     * is true, the rank of the array is reduced by 1 for each entry in `axes`.
+     * If `keepDims` is true, the reduced dimensions are retained with length 1.
+     * If `axes` has no entries, all dimensions are reduced, and an array with a
+     * single element is returned.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3]);
+     *
+     * x.min().print();  // or tf.min(x)
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     *
+     * const axis = 1;
+     * x.min(axis).print();  // or tf.min(x, axis)
+     * ```
+     *
+     * @param x The input Tensor.
+     * @param axis The dimension(s) to reduce. By default it reduces
+     *     all dimensions.
+     * @param keepDims If true, retains reduced dimensions with size 1.
+     *
+     * @doc {heading: 'Operations', subheading: 'Reduction'}
+     */
+    function min_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'min');
+        var inputs = { x: $x };
+        var attrs = { axis: axis, keepDims: keepDims };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        return ENGINE.runKernel(Min, inputs, attrs);
+    }
+    var min = op({ min_: min_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the min of a and b (`a < b ? a : b`) element-wise.
+     * Supports broadcasting.
+     *
+     * We also expose `minimumStrict` which has the same signature as this op and
+     * asserts that `a` and `b` are the same shape (does not broadcast).
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 4, 3, 16]);
+     * const b = tf.tensor1d([1, 2, 9, 4]);
+     *
+     * a.minimum(b).print();  // or tf.minimum(a, b)
+     * ```
+     *
+     * ```js
+     * // Broadcast minimum a with b.
+     * const a = tf.tensor1d([2, 4, 6, 8]);
+     * const b = tf.scalar(5);
+     *
+     * a.minimum(b).print();  // or tf.minimum(a, b)
+     * ```
+     *
+     * @param a The first tensor.
+     * @param b The second tensor. Must have the same type as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Arithmetic'}
+     */
+    function minimum_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'minimum');
+        var $b = convertToTensor(b, 'b', 'minimum');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        if ($a.dtype === 'bool') {
+            $a = cast($a, 'int32');
+            $b = cast($b, 'int32');
+        }
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(Minimum, inputs);
+    }
+    var minimum = op({ minimum_: minimum_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Pads a `tf.Tensor` using mirror padding.
+     *
+     * This operation implements the `REFLECT` and `SYMMETRIC` modes of pad.
+     *
+     * ```js
+     * const x = tf.range(0, 9).reshape([1, 1, 3, 3]);
+     * x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print();
+     * ```
+     * @param x The tensor to pad.
+     * @param paddings An array of length `R` (the rank of the tensor), where
+     * each element is a length-2 tuple of ints `[padBefore, padAfter]`,
+     * specifying how much to pad along each dimension of the tensor.
+     * In "reflect" mode, the padded regions do not include the borders,
+     * while in "symmetric" mode the padded regions do include the borders.
+     * For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`,
+     * then the output is `[1, 2, 3, 2, 1]` in "reflect" mode, and
+     * `[1, 2, 3, 3, 2]` in "symmetric" mode.
+     * If `mode` is "reflect" then both `paddings[D, 0]` and `paddings[D, 1]`
+     * must be no greater than `x.shape[D] - 1`. If mode is "symmetric"
+     * then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than
+     * `x.shape[D]`
+     * @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'`
+     */
+    /** @doc {heading: 'Tensors', subheading: 'Transformations'} */
+    function mirrorPad_(x, paddings, mode) {
+        assert(mode === 'reflect' || mode === 'symmetric', function () { return "Invalid mode. Mode must be either reflect or symmetric. " +
+            ("Got " + mode + "."); });
+        var $x = convertToTensor(x, 'x', 'mirrorPad');
+        if ($x.rank === 0) {
+            throw new Error('mirrorPad(scalar) is not defined. ' +
+                'Pass non-scalar to mirrorPad');
+        }
+        assert(paddings.length === $x.rank, function () { return "Padding doesn't match input. Must be " + $x.rank + ". " +
+            ("Got " + paddings.length + "."); });
+        var shapeOffset = mode === 'reflect' ? 1 : 0;
+        var _loop_1 = function (i) {
+            assert(paddings[i].length === 2, function () { return "Invalid number of paddings. Must be length of 2 each."; });
+            assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset &&
+                paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, function () { return "Padding in dimension " + i + " cannot be greater than or equal " +
+                ("to " + ($x.shape[i] - shapeOffset) + " or less than 0 for input of ") +
+                ("shape " + $x.shape); });
+        };
+        for (var i = 0; i < $x.rank; i++) {
+            _loop_1(i);
+        }
+        var attrs = { paddings: paddings, mode: mode };
+        var inputs = { x: $x };
+        return ENGINE.runKernel(MirrorPad, inputs, attrs);
+    }
+    var mirrorPad = op({ mirrorPad_: mirrorPad_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the mod of a and b element-wise.
+     * `floor(x / y) * y + mod(x, y) = x`
+     * Supports broadcasting.
+     *
+     * We also expose `tf.modStrict` which has the same signature as this op and
+     * asserts that `a` and `b` are the same shape (does not broadcast).
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 4, 3, 16]);
+     * const b = tf.tensor1d([1, 2, 9, 4]);
+     *
+     * a.mod(b).print();  // or tf.mod(a, b)
+     * ```
+     *
+     * ```js
+     * // Broadcast a mod b.
+     * const a = tf.tensor1d([2, 4, 6, 8]);
+     * const b = tf.scalar(5);
+     *
+     * a.mod(b).print();  // or tf.mod(a, b)
+     * ```
+     *
+     * @param a The first tensor.
+     * @param b The second tensor. Must have the same type as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Arithmetic'}
+     */
+    function mod_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'mod');
+        var $b = convertToTensor(b, 'b', 'mod');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(Mod, inputs);
+    }
+    var mod = op({ mod_: mod_ });
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes square of `x` element-wise: `x ^ 2`
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);
+     *
+     * x.square().print();  // or tf.square(x)
+     * ```
+     * @param x The input Tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function square_(x) {
+        var $x = convertToTensor(x, 'x', 'square');
+        var attrs = {};
+        return ENGINE.runKernel('Square', { x: $x }, attrs);
+    }
+    var square = op({ square_: square_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Calculates the mean and variance of `x`. The mean and variance are
+     * calculated by aggregating the contents of `x` across `axes`. If `x` is
+     * 1-D and `axes = [0]` this is just the mean and variance of a vector.
+     *
+     * @param x The input tensor.
+     * @param axis The dimension(s) along with to compute mean and
+     *     variance. By default it reduces all dimensions.
+     * @param keepDims If true, the moments have the same dimensionality as the
+     *     input.
+     * @return An object with two keys: `mean` and `variance`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Normalization'}
+     */
+    function moments_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        x = convertToTensor(x, 'x', 'moments');
+        var axes = parseAxisParam(axis, x.shape);
+        var xMean = mean(x, axes, keepDims);
+        var keepDimsShape = xMean.shape;
+        if (!keepDims) {
+            keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);
+        }
+        var devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));
+        var variance = mean(devSquared, axes, keepDims);
+        return { mean: xMean, variance: variance };
+    }
+    var moments = op({ moments_: moments_ });
+
+    /**
+     * Computes the next states and outputs of a stack of LSTMCells.
+     *
+     * Each cell output is used as input to the next cell.
+     *
+     * Returns `[cellState, cellOutput]`.
+     *
+     * Derived from tf.contrib.rn.MultiRNNCell.
+     *
+     * @param lstmCells Array of LSTMCell functions.
+     * @param data The input to the cell.
+     * @param c Array of previous cell states.
+     * @param h Array of previous cell outputs.
+     *
+     * @doc {heading: 'Operations', subheading: 'RNN'}
+     */
+    function multiRNNCell_(lstmCells, data, c, h) {
+        var $data = convertToTensor(data, 'data', 'multiRNNCell');
+        var $c = convertToTensorArray(c, 'c', 'multiRNNCell');
+        var $h = convertToTensorArray(h, 'h', 'multiRNNCell');
+        var input = $data;
+        var newStates = [];
+        for (var i = 0; i < lstmCells.length; i++) {
+            var output = lstmCells[i](input, $c[i], $h[i]);
+            newStates.push(output[0]);
+            newStates.push(output[1]);
+            input = output[1];
+        }
+        var newC = [];
+        var newH = [];
+        for (var i = 0; i < newStates.length; i += 2) {
+            newC.push(newStates[i]);
+            newH.push(newStates[i + 1]);
+        }
+        return [newC, newH];
+    }
+    var multiRNNCell = op({ multiRNNCell_: multiRNNCell_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a `tf.Tensor` with values drawn from a multinomial distribution.
+     *
+     * ```js
+     * const probs = tf.tensor([.75, .25]);
+     * tf.multinomial(probs, 3).print();
+     * ```
+     *
+     * @param logits 1D array with unnormalized log-probabilities, or
+     *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`
+     *     parameter.
+     * @param numSamples Number of samples to draw for each row slice.
+     * @param seed The seed number.
+     * @param normalized Whether the provided `logits` are normalized true
+     *     probabilities (sum to 1). Defaults to false.
+     * @return 1D array of shape `[numSamples]`, or 2D array of shape
+     *     `[batchSize, numSamples]`, depending on the rank of the input.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Random'}
+     */
+    function multinomial_(logits, numSamples, seed, normalized) {
+        if (normalized === void 0) { normalized = false; }
+        var $logits = convertToTensor(logits, 'logits', 'multinomial');
+        var numOutcomes = $logits.size;
+        var origRank = $logits.rank;
+        if (numOutcomes < 2) {
+            throw new Error("Error in multinomial: you need at least 2 outcomes, but got " +
+                (numOutcomes + "."));
+        }
+        if (origRank > 2) {
+            throw new Error("Rank of probabilities must be 1 or 2, but is " + origRank);
+        }
+        // TODO(lina128): Investigate correct seed behavior. The code seems not allow
+        // setting see to 0.
+        seed = seed || Math.random();
+        // The kernel only accepts (and returns) rank 2 tensors.
+        var logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;
+        var inputs = { logits: logits2D };
+        var attrs = { numSamples: numSamples, seed: seed, normalized: normalized };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(Multinomial, inputs, attrs);
+        // tslint:disable-next-line:no-unnecessary-type-assertion
+        return origRank === 1 ? reshape(res, [res.size]) : res;
+    }
+    var multinomial = op({ multinomial_: multinomial_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the truth value of (a != b) element-wise. Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2, 3]);
+     * const b = tf.tensor1d([0, 2, 3]);
+     *
+     * a.notEqual(b).print();
+     * ```
+     * @param a The first input tensor.
+     * @param b The second input tensor. Must have the same dtype as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Logical'}
+     */
+    function notEqual_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'notEqual', 'string_or_numeric');
+        var $b = convertToTensor(b, 'b', 'notEqual', 'string_or_numeric');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(NotEqual, inputs);
+    }
+    var notEqual = op({ notEqual_: notEqual_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the
+     * given tensor.
+     *
+     * ```js
+     * const x = tf.tensor([1, 2]);
+     * tf.onesLike(x).print();
+     * ```
+     * @param x A tensor.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function onesLike_(x) {
+        var $x = convertToTensor(x, 'x', 'onesLike');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(OnesLike, inputs);
+    }
+    var onesLike = op({ onesLike_: onesLike_ });
+
+    /**
+     * Computes the outer product of two vectors, `v1` and `v2`.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2, 3]);
+     * const b = tf.tensor1d([3, 4, 5]);
+     *
+     * tf.outerProduct(a, b).print();
+     * ```
+     * @param v1 The first vector in the outer product operation.
+     * @param v2 The second vector in the outer product operation.
+     *
+     * @doc {heading: 'Operations', subheading: 'Matrices'}
+     */
+    function outerProduct_(v1, v2) {
+        var $v1 = convertToTensor(v1, 'v1', 'outerProduct');
+        var $v2 = convertToTensor(v2, 'v2', 'outerProduct');
+        assert($v1.rank === 1 && $v2.rank === 1, function () { return "Error in outerProduct: inputs must be rank 1, but got ranks " +
+            ($v1.rank + " and " + $v2.rank + "."); });
+        var v12D = reshape($v1, [-1, 1]);
+        var v22D = reshape($v2, [1, -1]);
+        return matMul(v12D, v22D);
+    }
+    var outerProduct = op({ outerProduct_: outerProduct_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Pads a `tf.Tensor` with a given value and paddings.
+     *
+     * This operation implements `CONSTANT` mode. For `REFLECT` and `SYMMETRIC`,
+     * refer to `tf.mirrorPad`
+     *
+     * Also available are stricter rank-specific methods with the same signature
+     * as this method that assert that `paddings` is of given length.
+     *   - `tf.pad1d`
+     *   - `tf.pad2d`
+     *   - `tf.pad3d`
+     *   - `tf.pad4d`
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3, 4]);
+     * x.pad([[1, 2]]).print();
+     * ```
+     * @param x The tensor to pad.
+     * @param paddings An array of length `R` (the rank of the tensor), where
+     * each element is a length-2 tuple of ints `[padBefore, padAfter]`,
+     * specifying how much to pad along each dimension of the tensor.
+     * @param constantValue The pad value to use. Defaults to 0.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Transformations'}
+     */
+    function pad_(x, paddings, constantValue) {
+        if (constantValue === void 0) { constantValue = 0; }
+        var $x = convertToTensor(x, 'x', 'pad');
+        if ($x.rank === 0) {
+            throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');
+        }
+        var attrs = { paddings: paddings, constantValue: constantValue };
+        var inputs = { x: $x };
+        return ENGINE.runKernel(PadV2, inputs, attrs);
+    }
+    var pad = op({ pad_: pad_ });
+
+    /**
+     * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.
+     */
+    function pad1d_(x, paddings, constantValue) {
+        if (constantValue === void 0) { constantValue = 0; }
+        assert(paddings.length === 2, function () { return 'Invalid number of paddings. Must be length of 2.'; });
+        return pad(x, [paddings], constantValue);
+    }
+    var pad1d = op({ pad1d_: pad1d_ });
+
+    /**
+     * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.
+     */
+    function pad2d_(x, paddings, constantValue) {
+        if (constantValue === void 0) { constantValue = 0; }
+        assert(paddings.length === 2 && paddings[0].length === 2 &&
+            paddings[1].length === 2, function () { return 'Invalid number of paddings. Must be length of 2 each.'; });
+        return pad(x, paddings, constantValue);
+    }
+    var pad2d = op({ pad2d_: pad2d_ });
+
+    /**
+     * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.
+     */
+    function pad3d_(x, paddings, constantValue) {
+        if (constantValue === void 0) { constantValue = 0; }
+        assert(paddings.length === 3 && paddings[0].length === 2 &&
+            paddings[1].length === 2 && paddings[2].length === 2, function () { return 'Invalid number of paddings. Must be length of 2 each.'; });
+        return pad(x, paddings, constantValue);
+    }
+    var pad3d = op({ pad3d_: pad3d_ });
+
+    /**
+     * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.
+     */
+    function pad4d_(x, paddings, constantValue) {
+        if (constantValue === void 0) { constantValue = 0; }
+        assert(paddings.length === 4 && paddings[0].length === 2 &&
+            paddings[1].length === 2 && paddings[2].length === 2 &&
+            paddings[3].length === 2, function () { return 'Invalid number of paddings. Must be length of 2 each.'; });
+        return pad(x, paddings, constantValue);
+    }
+    var pad4d = op({ pad4d_: pad4d_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * This operation divides "spatial" dimensions `[1, ..., M]` of the input into
+     * a grid of blocks of shape `blockShape`, and interleaves these blocks with
+     * the "batch" dimension (0) such that in the output, the spatial
+     * dimensions `[1, ..., M]` correspond to the position within the grid,
+     * and the batch dimension combines both the position within a spatial block
+     * and the original batch position. Prior to division into blocks,
+     * the spatial dimensions of the input are optionally zero padded
+     * according to `paddings`. See below for a precise description.
+     *
+     * ```js
+     * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+     * const blockShape = [2, 2];
+     * const paddings = [[0, 0], [0, 0]];
+     *
+     * x.spaceToBatchND(blockShape, paddings).print();
+     * ```
+     *
+     * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +
+     * remainingShape`, where spatialShape has `M` dimensions.
+     * @param blockShape A 1-D array. Must have shape `[M]`, all values must
+     * be >= 1.
+     * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=
+     *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad
+     * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It
+     * is required that
+     * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`
+     *
+     * This operation is equivalent to the following steps:
+     *
+     * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input
+     * according to `paddings` to produce `padded` of shape paddedShape.
+     *
+     * 2. Reshape `padded` to `reshapedPadded` of shape:
+     * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,
+     * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`
+     *
+     * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`
+     * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,
+     * paddedShape[M] / blockShape[M-1]] + remainingShape`
+     *
+     * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the
+     * batch dimension, producing an output tensor of shape:
+     * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,
+     * paddedShape[M] / blockShape[M-1]] + remainingShape`
+     *
+     * @doc {heading: 'Tensors', subheading: 'Transformations'}
+     */
+    function spaceToBatchND_(x, blockShape, paddings) {
+        var $x = convertToTensor(x, 'x', 'spaceToBatchND');
+        assert($x.rank >= 1 + blockShape.length, function () { return "input rank " + $x.rank + " should be > than [blockShape] " + blockShape.length; });
+        assert(paddings.length === blockShape.length, function () { return "paddings.shape[0] " + paddings.length + " must be equal to [blockShape] " + blockShape.length; });
+        assert($x.shape.reduce(function (a, b, i) {
+            if (i > 0 && i <= blockShape.length) {
+                return a &&
+                    ((b + paddings[i - 1][0] + paddings[i - 1][1]) %
+                        blockShape[i - 1] ===
+                        0);
+            }
+            return a;
+        }, true), function () { return "input spatial dimensions " + $x.shape.slice(1) + " with paddings " + paddings.toString() + " must be divisible by blockShapes " + blockShape.toString(); });
+        var inputs = { x: $x };
+        var attrs = { blockShape: blockShape, paddings: paddings };
+        return ENGINE.runKernel(SpaceToBatchND, inputs, attrs);
+    }
+    var spaceToBatchND = op({ spaceToBatchND_: spaceToBatchND_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Performs an N-D pooling operation
+     *
+     * @param input The input tensor, of rank 4 or rank 3 of shape
+     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
+     * @param windowShape The filter size: `[filterHeight, filterWidth]`. If
+     *     `filterSize` is a single number, then `filterHeight == filterWidth`.
+     * @param poolingType The type of pooling, either 'max' or 'avg'.
+     * @param pad The type of padding algorithm:
+     *    - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *    - `valid`: output will be smaller than input if filter is larger
+     *       than 1x1.
+     *    - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *         https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
+     *     in which we sample input values across the height and width dimensions
+     *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single
+     *     number, then `dilationHeight == dilationWidth`. If it is greater than
+     *     1, then all values of `strides` must be 1.
+     * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
+     *     `strides` is a single number, then `strideHeight == strideWidth`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function pool_(input, windowShape, poolingType, pad, dilations, strides) {
+        if (dilations == null) {
+            dilations = [1, 1];
+        }
+        if (strides == null) {
+            strides = 1;
+        }
+        if (pad === 0) {
+            pad = 'valid';
+        }
+        var $x = convertToTensor(input, 'x', 'maxPool');
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+        }
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in pool: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
+        var convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);
+        var dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
+        // The following implementation does batchToSpace(pool(spaceToBatch(x)))
+        // whenever dilation > 1 since the TF kernels do not support dilation > 1.
+        // tslint:disable-next-line:max-line-length
+        // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037
+        var basePadding;
+        if (pad === 'same') {
+            basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);
+        }
+        else {
+            basePadding = [[0, 0], [0, 0]];
+        }
+        var isDilationOne = dilation[0] === 1 && dilation[1] === 1;
+        var _a = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding), adjustedPadding = _a[0], adjustedCrops = _a[1];
+        var convertedPad = isDilationOne ? pad : 'valid';
+        var convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
+        var forwardOp = poolingType === 'avg' ?
+            function () { return avgPool(convertedX, windowShape, strides, convertedPad); } :
+            function () { return maxPool(convertedX, windowShape, strides, convertedPad); };
+        var y = forwardOp();
+        var res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
+        if (reshapedTo4D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+        }
+        return res;
+    }
+    // Helper function to compute crops and paddings for pool with dilation > 1.
+    // tslint:disable-next-line:max-line-length
+    // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184
+    function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
+        var padStart = basePadding.map(function (b) { return b[0]; });
+        var origPadEnd = basePadding.map(function (b) { return b[1]; });
+        var fullInputShape = inputShape.concat(padStart, origPadEnd);
+        var padEndExtra = blockShape.map(function (b, i) { return (b - fullInputShape[i] % b) % b; });
+        var padEnd = origPadEnd.map(function (s, i) { return s + padEndExtra[i]; });
+        var paddings = blockShape.map(function (_, i) { return [padStart[i], padEnd[i]]; });
+        var crops = blockShape.map(function (_, i) { return [0, padEndExtra[i]]; });
+        return [paddings, crops];
+    }
+    // Helper function to compute base paddings for pool with dilation > 1.
+    // tslint:disable-next-line:max-line-length
+    // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524
+    function withSpaceToBatchBasePaddings(filterShape, dilation) {
+        // Spatial dimensions of the filters and the upsampled filters in which we
+        // introduce (rate - 1) zeros between consecutive filter values.
+        var dilatedFilterShape = filterShape.map(function (s, i) {
+            return s + (s - 1) * (dilation[i] - 1);
+        });
+        var padExtraShape = dilatedFilterShape.map(function (s) { return s - 1; });
+        // When padding is odd, we pad more at end, following the same
+        // convention as conv2d.
+        var padExtraStart = padExtraShape.map(function (s) { return Math.floor(s / 2); });
+        var padExtraEnd = padExtraShape.map(function (s, i) { return s - padExtraStart[i]; });
+        return padExtraShape.map(function (_, i) {
+            return [padExtraStart[i], padExtraEnd[i]];
+        });
+    }
+    var pool = op({ pool_: pool_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the power of one `tf.Tensor` to another. Supports broadcasting.
+     *
+     * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for
+     * corresponding elements in x and y. The result's dtype will be the upcasted
+     * type of the `base` and `exp` dtypes.
+     *
+     * ```js
+     * const a = tf.tensor([[2, 3], [4, 5]])
+     * const b = tf.tensor([[1, 2], [3, 0]]).toInt();
+     *
+     * a.pow(b).print();  // or tf.pow(a, b)
+     * ```
+     *
+     * ```js
+     * const a = tf.tensor([[1, 2], [3, 4]])
+     * const b = tf.tensor(2).toInt();
+     *
+     * a.pow(b).print();  // or tf.pow(a, b)
+     * ```
+     * We also expose `powStrict` which has the same signature as this op and
+     * asserts that `base` and `exp` are the same shape (does not broadcast).
+     *
+     * @param base The base `tf.Tensor` to pow element-wise.
+     * @param exp The exponent `tf.Tensor` to pow element-wise.
+     *
+     * @doc {heading: 'Operations', subheading: 'Arithmetic'}
+     */
+    function pow_(base, exp) {
+        var _a;
+        var $base = convertToTensor(base, 'base', 'pow');
+        var $exp = convertToTensor(exp, 'exp', 'pow');
+        _a = makeTypesMatch($base, $exp), $base = _a[0], $exp = _a[1];
+        var inputs = { a: $base, b: $exp };
+        return ENGINE.runKernel(Pow, inputs);
+    }
+    var pow = op({ pow_: pow_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes leaky rectified linear element-wise with parametric alphas.
+     *
+     * `x < 0 ? alpha * x : f(x) = x`
+     *
+     * ```js
+     * const x = tf.tensor1d([-1, 2, -3, 4]);
+     * const alpha = tf.scalar(0.1);
+     *
+     * x.prelu(alpha).print();  // or tf.prelu(x, alpha)
+     * ```
+     * @param x The input tensor.
+     * @param alpha Scaling factor for negative values.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function prelu_(x, alpha) {
+        var $x = convertToTensor(x, 'x', 'prelu');
+        var $alpha = convertToTensor(alpha, 'alpha', 'prelu');
+        var inputs = { x: $x, alpha: $alpha };
+        return ENGINE.runKernel(Prelu, inputs);
+    }
+    var prelu = op({ prelu_: prelu_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the product of elements across dimensions of a `tf.Tensor`.
+     *
+     * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
+     * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
+     * `axes`. If `keepDims` is true, the reduced dimensions are retained with
+     * length 1. If `axes` has no entries, all dimensions are reduced, and a
+     * `tf.Tensor` with a single element is returned.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3]);
+     *
+     * x.prod().print();  // or tf.prod(x)
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     *
+     * const axis = 1;
+     * x.prod(axis).print();  // or tf.prod(x, axis)
+     * ```
+     *
+     * @param x The input tensor to compute the product over. If the dtype is `bool`
+     *   it will be converted to `int32` and the output dtype will be `int32`.
+     * @param axis The dimension(s) to reduce. By default it reduces
+     *     all dimensions.
+     * @param keepDims If true, retains reduced dimensions with size 1.
+     *
+     * @doc {heading: 'Operations', subheading: 'Reduction'}
+     */
+    function prod_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'prod');
+        if ($x.dtype === 'bool') {
+            // bool is not an allowed type for the underlying kernel.
+            $x = cast($x, 'int32');
+        }
+        var inputs = { x: $x };
+        var attrs = { axis: axis, keepDims: keepDims };
+        return ENGINE.runKernel(Prod, inputs, attrs);
+    }
+    var prod = op({ prod_: prod_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a `tf.Tensor` with values sampled from a random number generator
+     * function defined by the user.
+     *
+     * @param shape An array of integers defining the output tensor shape.
+     * @param randFunction A random number generator function which is called
+     * for each element in the output tensor.
+     * @param dtype The data type of the output tensor. Defaults to 'float32'.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Random'}
+     */
+    function rand_(shape, randFunction, dtype) {
+        var size = sizeFromShape(shape);
+        var values = null;
+        if (dtype == null || dtype === 'float32') {
+            values = new Float32Array(size);
+        }
+        else if (dtype === 'int32') {
+            values = new Int32Array(size);
+        }
+        else if (dtype === 'bool') {
+            values = new Uint8Array(size);
+        }
+        else {
+            throw new Error("Unknown data type " + dtype);
+        }
+        for (var i = 0; i < size; i++) {
+            values[i] = randFunction();
+        }
+        return ENGINE.makeTensor(values, shape, dtype);
+    }
+    var rand = op({ rand_: rand_ });
+
+    var commonjsGlobal = typeof globalThis !== 'undefined' ? globalThis : typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {};
+
+    function createCommonjsModule(fn, module) {
+    	return module = { exports: {} }, fn(module, module.exports), module.exports;
+    }
+
+    var alea = createCommonjsModule(function (module) {
+    // A port of an algorithm by Johannes Baagøe <baagoe@baagoe.com>, 2010
+    // http://baagoe.com/en/RandomMusings/javascript/
+    // https://github.com/nquinlan/better-random-numbers-for-javascript-mirror
+    // Original work is under MIT license -
+
+    // Copyright (C) 2010 by Johannes Baagøe <baagoe@baagoe.org>
+    //
+    // Permission is hereby granted, free of charge, to any person obtaining a copy
+    // of this software and associated documentation files (the "Software"), to deal
+    // in the Software without restriction, including without limitation the rights
+    // to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    // copies of the Software, and to permit persons to whom the Software is
+    // furnished to do so, subject to the following conditions:
+    // 
+    // The above copyright notice and this permission notice shall be included in
+    // all copies or substantial portions of the Software.
+    // 
+    // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    // IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    // FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    // AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    // LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    // THE SOFTWARE.
+
+
+
+    (function(global, module, define) {
+
+    function Alea(seed) {
+      var me = this, mash = Mash();
+
+      me.next = function() {
+        var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32
+        me.s0 = me.s1;
+        me.s1 = me.s2;
+        return me.s2 = t - (me.c = t | 0);
+      };
+
+      // Apply the seeding algorithm from Baagoe.
+      me.c = 1;
+      me.s0 = mash(' ');
+      me.s1 = mash(' ');
+      me.s2 = mash(' ');
+      me.s0 -= mash(seed);
+      if (me.s0 < 0) { me.s0 += 1; }
+      me.s1 -= mash(seed);
+      if (me.s1 < 0) { me.s1 += 1; }
+      me.s2 -= mash(seed);
+      if (me.s2 < 0) { me.s2 += 1; }
+      mash = null;
+    }
+
+    function copy(f, t) {
+      t.c = f.c;
+      t.s0 = f.s0;
+      t.s1 = f.s1;
+      t.s2 = f.s2;
+      return t;
+    }
+
+    function impl(seed, opts) {
+      var xg = new Alea(seed),
+          state = opts && opts.state,
+          prng = xg.next;
+      prng.int32 = function() { return (xg.next() * 0x100000000) | 0; };
+      prng.double = function() {
+        return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53
+      };
+      prng.quick = prng;
+      if (state) {
+        if (typeof(state) == 'object') copy(state, xg);
+        prng.state = function() { return copy(xg, {}); };
+      }
+      return prng;
+    }
+
+    function Mash() {
+      var n = 0xefc8249d;
+
+      var mash = function(data) {
+        data = data.toString();
+        for (var i = 0; i < data.length; i++) {
+          n += data.charCodeAt(i);
+          var h = 0.02519603282416938 * n;
+          n = h >>> 0;
+          h -= n;
+          h *= n;
+          n = h >>> 0;
+          h -= n;
+          n += h * 0x100000000; // 2^32
+        }
+        return (n >>> 0) * 2.3283064365386963e-10; // 2^-32
+      };
+
+      return mash;
+    }
+
+
+    if (module && module.exports) {
+      module.exports = impl;
+    } else if (define && define.amd) {
+      define(function() { return impl; });
+    } else {
+      this.alea = impl;
+    }
+
+    })(
+      commonjsGlobal,
+       module,    // present in node.js
+      (typeof undefined) == 'function'    // present with an AMD loader
+    );
+    });
+
+    var xor128 = createCommonjsModule(function (module) {
+    // A Javascript implementaion of the "xor128" prng algorithm by
+    // George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper
+
+    (function(global, module, define) {
+
+    function XorGen(seed) {
+      var me = this, strseed = '';
+
+      me.x = 0;
+      me.y = 0;
+      me.z = 0;
+      me.w = 0;
+
+      // Set up generator function.
+      me.next = function() {
+        var t = me.x ^ (me.x << 11);
+        me.x = me.y;
+        me.y = me.z;
+        me.z = me.w;
+        return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);
+      };
+
+      if (seed === (seed | 0)) {
+        // Integer seed.
+        me.x = seed;
+      } else {
+        // String seed.
+        strseed += seed;
+      }
+
+      // Mix in string seed, then discard an initial batch of 64 values.
+      for (var k = 0; k < strseed.length + 64; k++) {
+        me.x ^= strseed.charCodeAt(k) | 0;
+        me.next();
+      }
+    }
+
+    function copy(f, t) {
+      t.x = f.x;
+      t.y = f.y;
+      t.z = f.z;
+      t.w = f.w;
+      return t;
+    }
+
+    function impl(seed, opts) {
+      var xg = new XorGen(seed),
+          state = opts && opts.state,
+          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+      prng.double = function() {
+        do {
+          var top = xg.next() >>> 11,
+              bot = (xg.next() >>> 0) / 0x100000000,
+              result = (top + bot) / (1 << 21);
+        } while (result === 0);
+        return result;
+      };
+      prng.int32 = xg.next;
+      prng.quick = prng;
+      if (state) {
+        if (typeof(state) == 'object') copy(state, xg);
+        prng.state = function() { return copy(xg, {}); };
+      }
+      return prng;
+    }
+
+    if (module && module.exports) {
+      module.exports = impl;
+    } else if (define && define.amd) {
+      define(function() { return impl; });
+    } else {
+      this.xor128 = impl;
+    }
+
+    })(
+      commonjsGlobal,
+       module,    // present in node.js
+      (typeof undefined) == 'function'    // present with an AMD loader
+    );
+    });
+
+    var xorwow = createCommonjsModule(function (module) {
+    // A Javascript implementaion of the "xorwow" prng algorithm by
+    // George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper
+
+    (function(global, module, define) {
+
+    function XorGen(seed) {
+      var me = this, strseed = '';
+
+      // Set up generator function.
+      me.next = function() {
+        var t = (me.x ^ (me.x >>> 2));
+        me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;
+        return (me.d = (me.d + 362437 | 0)) +
+           (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;
+      };
+
+      me.x = 0;
+      me.y = 0;
+      me.z = 0;
+      me.w = 0;
+      me.v = 0;
+
+      if (seed === (seed | 0)) {
+        // Integer seed.
+        me.x = seed;
+      } else {
+        // String seed.
+        strseed += seed;
+      }
+
+      // Mix in string seed, then discard an initial batch of 64 values.
+      for (var k = 0; k < strseed.length + 64; k++) {
+        me.x ^= strseed.charCodeAt(k) | 0;
+        if (k == strseed.length) {
+          me.d = me.x << 10 ^ me.x >>> 4;
+        }
+        me.next();
+      }
+    }
+
+    function copy(f, t) {
+      t.x = f.x;
+      t.y = f.y;
+      t.z = f.z;
+      t.w = f.w;
+      t.v = f.v;
+      t.d = f.d;
+      return t;
+    }
+
+    function impl(seed, opts) {
+      var xg = new XorGen(seed),
+          state = opts && opts.state,
+          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+      prng.double = function() {
+        do {
+          var top = xg.next() >>> 11,
+              bot = (xg.next() >>> 0) / 0x100000000,
+              result = (top + bot) / (1 << 21);
+        } while (result === 0);
+        return result;
+      };
+      prng.int32 = xg.next;
+      prng.quick = prng;
+      if (state) {
+        if (typeof(state) == 'object') copy(state, xg);
+        prng.state = function() { return copy(xg, {}); };
+      }
+      return prng;
+    }
+
+    if (module && module.exports) {
+      module.exports = impl;
+    } else if (define && define.amd) {
+      define(function() { return impl; });
+    } else {
+      this.xorwow = impl;
+    }
+
+    })(
+      commonjsGlobal,
+       module,    // present in node.js
+      (typeof undefined) == 'function'    // present with an AMD loader
+    );
+    });
+
+    var xorshift7 = createCommonjsModule(function (module) {
+    // A Javascript implementaion of the "xorshift7" algorithm by
+    // François Panneton and Pierre L'ecuyer:
+    // "On the Xorgshift Random Number Generators"
+    // http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf
+
+    (function(global, module, define) {
+
+    function XorGen(seed) {
+      var me = this;
+
+      // Set up generator function.
+      me.next = function() {
+        // Update xor generator.
+        var X = me.x, i = me.i, t, v;
+        t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);
+        t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);
+        t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);
+        t = X[(i + 4) & 7]; v ^= t ^ (t << 7);
+        t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);
+        X[i] = v;
+        me.i = (i + 1) & 7;
+        return v;
+      };
+
+      function init(me, seed) {
+        var j, w, X = [];
+
+        if (seed === (seed | 0)) {
+          // Seed state array using a 32-bit integer.
+          w = X[0] = seed;
+        } else {
+          // Seed state using a string.
+          seed = '' + seed;
+          for (j = 0; j < seed.length; ++j) {
+            X[j & 7] = (X[j & 7] << 15) ^
+                (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);
+          }
+        }
+        // Enforce an array length of 8, not all zeroes.
+        while (X.length < 8) X.push(0);
+        for (j = 0; j < 8 && X[j] === 0; ++j);
+        if (j == 8) w = X[7] = -1; else w = X[j];
+
+        me.x = X;
+        me.i = 0;
+
+        // Discard an initial 256 values.
+        for (j = 256; j > 0; --j) {
+          me.next();
+        }
+      }
+
+      init(me, seed);
+    }
+
+    function copy(f, t) {
+      t.x = f.x.slice();
+      t.i = f.i;
+      return t;
+    }
+
+    function impl(seed, opts) {
+      if (seed == null) seed = +(new Date);
+      var xg = new XorGen(seed),
+          state = opts && opts.state,
+          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+      prng.double = function() {
+        do {
+          var top = xg.next() >>> 11,
+              bot = (xg.next() >>> 0) / 0x100000000,
+              result = (top + bot) / (1 << 21);
+        } while (result === 0);
+        return result;
+      };
+      prng.int32 = xg.next;
+      prng.quick = prng;
+      if (state) {
+        if (state.x) copy(state, xg);
+        prng.state = function() { return copy(xg, {}); };
+      }
+      return prng;
+    }
+
+    if (module && module.exports) {
+      module.exports = impl;
+    } else if (define && define.amd) {
+      define(function() { return impl; });
+    } else {
+      this.xorshift7 = impl;
+    }
+
+    })(
+      commonjsGlobal,
+       module,    // present in node.js
+      (typeof undefined) == 'function'    // present with an AMD loader
+    );
+    });
+
+    var xor4096 = createCommonjsModule(function (module) {
+    // A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.
+    //
+    // This fast non-cryptographic random number generator is designed for
+    // use in Monte-Carlo algorithms. It combines a long-period xorshift
+    // generator with a Weyl generator, and it passes all common batteries
+    // of stasticial tests for randomness while consuming only a few nanoseconds
+    // for each prng generated.  For background on the generator, see Brent's
+    // paper: "Some long-period random number generators using shifts and xors."
+    // http://arxiv.org/pdf/1004.3115v1.pdf
+    //
+    // Usage:
+    //
+    // var xor4096 = require('xor4096');
+    // random = xor4096(1);                        // Seed with int32 or string.
+    // assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.
+    // assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.
+    //
+    // For nonzero numeric keys, this impelementation provides a sequence
+    // identical to that by Brent's xorgens 3 implementaion in C.  This
+    // implementation also provides for initalizing the generator with
+    // string seeds, or for saving and restoring the state of the generator.
+    //
+    // On Chrome, this prng benchmarks about 2.1 times slower than
+    // Javascript's built-in Math.random().
+
+    (function(global, module, define) {
+
+    function XorGen(seed) {
+      var me = this;
+
+      // Set up generator function.
+      me.next = function() {
+        var w = me.w,
+            X = me.X, i = me.i, t, v;
+        // Update Weyl generator.
+        me.w = w = (w + 0x61c88647) | 0;
+        // Update xor generator.
+        v = X[(i + 34) & 127];
+        t = X[i = ((i + 1) & 127)];
+        v ^= v << 13;
+        t ^= t << 17;
+        v ^= v >>> 15;
+        t ^= t >>> 12;
+        // Update Xor generator array state.
+        v = X[i] = v ^ t;
+        me.i = i;
+        // Result is the combination.
+        return (v + (w ^ (w >>> 16))) | 0;
+      };
+
+      function init(me, seed) {
+        var t, v, i, j, w, X = [], limit = 128;
+        if (seed === (seed | 0)) {
+          // Numeric seeds initialize v, which is used to generates X.
+          v = seed;
+          seed = null;
+        } else {
+          // String seeds are mixed into v and X one character at a time.
+          seed = seed + '\0';
+          v = 0;
+          limit = Math.max(limit, seed.length);
+        }
+        // Initialize circular array and weyl value.
+        for (i = 0, j = -32; j < limit; ++j) {
+          // Put the unicode characters into the array, and shuffle them.
+          if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);
+          // After 32 shuffles, take v as the starting w value.
+          if (j === 0) w = v;
+          v ^= v << 10;
+          v ^= v >>> 15;
+          v ^= v << 4;
+          v ^= v >>> 13;
+          if (j >= 0) {
+            w = (w + 0x61c88647) | 0;     // Weyl.
+            t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.
+            i = (0 == t) ? i + 1 : 0;     // Count zeroes.
+          }
+        }
+        // We have detected all zeroes; make the key nonzero.
+        if (i >= 128) {
+          X[(seed && seed.length || 0) & 127] = -1;
+        }
+        // Run the generator 512 times to further mix the state before using it.
+        // Factoring this as a function slows the main generator, so it is just
+        // unrolled here.  The weyl generator is not advanced while warming up.
+        i = 127;
+        for (j = 4 * 128; j > 0; --j) {
+          v = X[(i + 34) & 127];
+          t = X[i = ((i + 1) & 127)];
+          v ^= v << 13;
+          t ^= t << 17;
+          v ^= v >>> 15;
+          t ^= t >>> 12;
+          X[i] = v ^ t;
+        }
+        // Storing state as object members is faster than using closure variables.
+        me.w = w;
+        me.X = X;
+        me.i = i;
+      }
+
+      init(me, seed);
+    }
+
+    function copy(f, t) {
+      t.i = f.i;
+      t.w = f.w;
+      t.X = f.X.slice();
+      return t;
+    }
+    function impl(seed, opts) {
+      if (seed == null) seed = +(new Date);
+      var xg = new XorGen(seed),
+          state = opts && opts.state,
+          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+      prng.double = function() {
+        do {
+          var top = xg.next() >>> 11,
+              bot = (xg.next() >>> 0) / 0x100000000,
+              result = (top + bot) / (1 << 21);
+        } while (result === 0);
+        return result;
+      };
+      prng.int32 = xg.next;
+      prng.quick = prng;
+      if (state) {
+        if (state.X) copy(state, xg);
+        prng.state = function() { return copy(xg, {}); };
+      }
+      return prng;
+    }
+
+    if (module && module.exports) {
+      module.exports = impl;
+    } else if (define && define.amd) {
+      define(function() { return impl; });
+    } else {
+      this.xor4096 = impl;
+    }
+
+    })(
+      commonjsGlobal,                                     // window object or global
+       module,    // present in node.js
+      (typeof undefined) == 'function'    // present with an AMD loader
+    );
+    });
+
+    var tychei = createCommonjsModule(function (module) {
+    // A Javascript implementaion of the "Tyche-i" prng algorithm by
+    // Samuel Neves and Filipe Araujo.
+    // See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf
+
+    (function(global, module, define) {
+
+    function XorGen(seed) {
+      var me = this, strseed = '';
+
+      // Set up generator function.
+      me.next = function() {
+        var b = me.b, c = me.c, d = me.d, a = me.a;
+        b = (b << 25) ^ (b >>> 7) ^ c;
+        c = (c - d) | 0;
+        d = (d << 24) ^ (d >>> 8) ^ a;
+        a = (a - b) | 0;
+        me.b = b = (b << 20) ^ (b >>> 12) ^ c;
+        me.c = c = (c - d) | 0;
+        me.d = (d << 16) ^ (c >>> 16) ^ a;
+        return me.a = (a - b) | 0;
+      };
+
+      /* The following is non-inverted tyche, which has better internal
+       * bit diffusion, but which is about 25% slower than tyche-i in JS.
+      me.next = function() {
+        var a = me.a, b = me.b, c = me.c, d = me.d;
+        a = (me.a + me.b | 0) >>> 0;
+        d = me.d ^ a; d = d << 16 ^ d >>> 16;
+        c = me.c + d | 0;
+        b = me.b ^ c; b = b << 12 ^ d >>> 20;
+        me.a = a = a + b | 0;
+        d = d ^ a; me.d = d = d << 8 ^ d >>> 24;
+        me.c = c = c + d | 0;
+        b = b ^ c;
+        return me.b = (b << 7 ^ b >>> 25);
+      }
+      */
+
+      me.a = 0;
+      me.b = 0;
+      me.c = 2654435769 | 0;
+      me.d = 1367130551;
+
+      if (seed === Math.floor(seed)) {
+        // Integer seed.
+        me.a = (seed / 0x100000000) | 0;
+        me.b = seed | 0;
+      } else {
+        // String seed.
+        strseed += seed;
+      }
+
+      // Mix in string seed, then discard an initial batch of 64 values.
+      for (var k = 0; k < strseed.length + 20; k++) {
+        me.b ^= strseed.charCodeAt(k) | 0;
+        me.next();
+      }
+    }
+
+    function copy(f, t) {
+      t.a = f.a;
+      t.b = f.b;
+      t.c = f.c;
+      t.d = f.d;
+      return t;
+    }
+    function impl(seed, opts) {
+      var xg = new XorGen(seed),
+          state = opts && opts.state,
+          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+      prng.double = function() {
+        do {
+          var top = xg.next() >>> 11,
+              bot = (xg.next() >>> 0) / 0x100000000,
+              result = (top + bot) / (1 << 21);
+        } while (result === 0);
+        return result;
+      };
+      prng.int32 = xg.next;
+      prng.quick = prng;
+      if (state) {
+        if (typeof(state) == 'object') copy(state, xg);
+        prng.state = function() { return copy(xg, {}); };
+      }
+      return prng;
+    }
+
+    if (module && module.exports) {
+      module.exports = impl;
+    } else if (define && define.amd) {
+      define(function() { return impl; });
+    } else {
+      this.tychei = impl;
+    }
+
+    })(
+      commonjsGlobal,
+       module,    // present in node.js
+      (typeof undefined) == 'function'    // present with an AMD loader
+    );
+    });
+
+    var seedrandom = createCommonjsModule(function (module) {
+    /*
+    Copyright 2014 David Bau.
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal in the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+
+    The above copyright notice and this permission notice shall be
+    included in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+    IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+    CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+    TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+    SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+    */
+
+    (function (pool, math) {
+    //
+    // The following constants are related to IEEE 754 limits.
+    //
+    var global = this,
+        width = 256,        // each RC4 output is 0 <= x < 256
+        chunks = 6,         // at least six RC4 outputs for each double
+        digits = 52,        // there are 52 significant digits in a double
+        rngname = 'random', // rngname: name for Math.random and Math.seedrandom
+        startdenom = math.pow(width, chunks),
+        significance = math.pow(2, digits),
+        overflow = significance * 2,
+        mask = width - 1,
+        nodecrypto;         // node.js crypto module, initialized at the bottom.
+
+    //
+    // seedrandom()
+    // This is the seedrandom function described above.
+    //
+    function seedrandom(seed, options, callback) {
+      var key = [];
+      options = (options == true) ? { entropy: true } : (options || {});
+
+      // Flatten the seed string or build one from local entropy if needed.
+      var shortseed = mixkey(flatten(
+        options.entropy ? [seed, tostring(pool)] :
+        (seed == null) ? autoseed() : seed, 3), key);
+
+      // Use the seed to initialize an ARC4 generator.
+      var arc4 = new ARC4(key);
+
+      // This function returns a random double in [0, 1) that contains
+      // randomness in every bit of the mantissa of the IEEE 754 value.
+      var prng = function() {
+        var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48
+            d = startdenom,                 //   and denominator d = 2 ^ 48.
+            x = 0;                          //   and no 'extra last byte'.
+        while (n < significance) {          // Fill up all significant digits by
+          n = (n + x) * width;              //   shifting numerator and
+          d *= width;                       //   denominator and generating a
+          x = arc4.g(1);                    //   new least-significant-byte.
+        }
+        while (n >= overflow) {             // To avoid rounding up, before adding
+          n /= 2;                           //   last byte, shift everything
+          d /= 2;                           //   right using integer math until
+          x >>>= 1;                         //   we have exactly the desired bits.
+        }
+        return (n + x) / d;                 // Form the number within [0, 1).
+      };
+
+      prng.int32 = function() { return arc4.g(4) | 0; };
+      prng.quick = function() { return arc4.g(4) / 0x100000000; };
+      prng.double = prng;
+
+      // Mix the randomness into accumulated entropy.
+      mixkey(tostring(arc4.S), pool);
+
+      // Calling convention: what to return as a function of prng, seed, is_math.
+      return (options.pass || callback ||
+          function(prng, seed, is_math_call, state) {
+            if (state) {
+              // Load the arc4 state from the given state if it has an S array.
+              if (state.S) { copy(state, arc4); }
+              // Only provide the .state method if requested via options.state.
+              prng.state = function() { return copy(arc4, {}); };
+            }
+
+            // If called as a method of Math (Math.seedrandom()), mutate
+            // Math.random because that is how seedrandom.js has worked since v1.0.
+            if (is_math_call) { math[rngname] = prng; return seed; }
+
+            // Otherwise, it is a newer calling convention, so return the
+            // prng directly.
+            else return prng;
+          })(
+      prng,
+      shortseed,
+      'global' in options ? options.global : (this == math),
+      options.state);
+    }
+    math['seed' + rngname] = seedrandom;
+
+    //
+    // ARC4
+    //
+    // An ARC4 implementation.  The constructor takes a key in the form of
+    // an array of at most (width) integers that should be 0 <= x < (width).
+    //
+    // The g(count) method returns a pseudorandom integer that concatenates
+    // the next (count) outputs from ARC4.  Its return value is a number x
+    // that is in the range 0 <= x < (width ^ count).
+    //
+    function ARC4(key) {
+      var t, keylen = key.length,
+          me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
+
+      // The empty key [] is treated as [0].
+      if (!keylen) { key = [keylen++]; }
+
+      // Set up S using the standard key scheduling algorithm.
+      while (i < width) {
+        s[i] = i++;
+      }
+      for (i = 0; i < width; i++) {
+        s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];
+        s[j] = t;
+      }
+
+      // The "g" method returns the next (count) outputs as one number.
+      (me.g = function(count) {
+        // Using instance members instead of closure state nearly doubles speed.
+        var t, r = 0,
+            i = me.i, j = me.j, s = me.S;
+        while (count--) {
+          t = s[i = mask & (i + 1)];
+          r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];
+        }
+        me.i = i; me.j = j;
+        return r;
+        // For robust unpredictability, the function call below automatically
+        // discards an initial batch of values.  This is called RC4-drop[256].
+        // See http://google.com/search?q=rsa+fluhrer+response&btnI
+      })(width);
+    }
+
+    //
+    // copy()
+    // Copies internal state of ARC4 to or from a plain object.
+    //
+    function copy(f, t) {
+      t.i = f.i;
+      t.j = f.j;
+      t.S = f.S.slice();
+      return t;
+    }
+    //
+    // flatten()
+    // Converts an object tree to nested arrays of strings.
+    //
+    function flatten(obj, depth) {
+      var result = [], typ = (typeof obj), prop;
+      if (depth && typ == 'object') {
+        for (prop in obj) {
+          try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}
+        }
+      }
+      return (result.length ? result : typ == 'string' ? obj : obj + '\0');
+    }
+
+    //
+    // mixkey()
+    // Mixes a string seed into a key that is an array of integers, and
+    // returns a shortened string seed that is equivalent to the result key.
+    //
+    function mixkey(seed, key) {
+      var stringseed = seed + '', smear, j = 0;
+      while (j < stringseed.length) {
+        key[mask & j] =
+          mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));
+      }
+      return tostring(key);
+    }
+
+    //
+    // autoseed()
+    // Returns an object for autoseeding, using window.crypto and Node crypto
+    // module if available.
+    //
+    function autoseed() {
+      try {
+        var out;
+        if (nodecrypto && (out = nodecrypto.randomBytes)) {
+          // The use of 'out' to remember randomBytes makes tight minified code.
+          out = out(width);
+        } else {
+          out = new Uint8Array(width);
+          (global.crypto || global.msCrypto).getRandomValues(out);
+        }
+        return tostring(out);
+      } catch (e) {
+        var browser = global.navigator,
+            plugins = browser && browser.plugins;
+        return [+new Date, global, plugins, global.screen, tostring(pool)];
+      }
+    }
+
+    //
+    // tostring()
+    // Converts an array of charcodes to a string
+    //
+    function tostring(a) {
+      return String.fromCharCode.apply(0, a);
+    }
+
+    //
+    // When seedrandom.js is loaded, we immediately mix a few bits
+    // from the built-in RNG into the entropy pool.  Because we do
+    // not want to interfere with deterministic PRNG state later,
+    // seedrandom will not call math.random on its own again after
+    // initialization.
+    //
+    mixkey(math.random(), pool);
+
+    //
+    // Nodejs and AMD support: export the implementation as a module using
+    // either convention.
+    //
+    if ( module.exports) {
+      module.exports = seedrandom;
+      // When in node.js, try using crypto package for autoseeding.
+      try {
+        nodecrypto = require('crypto');
+      } catch (ex) {}
+    }
+
+    // End anonymous scope, and pass initial values.
+    })(
+      [],     // pool: entropy pool starts empty
+      Math    // math: package containing random, pow, and seedrandom
+    );
+    });
+
+    // A library of seedable RNGs implemented in Javascript.
+    //
+    // Usage:
+    //
+    // var seedrandom = require('seedrandom');
+    // var random = seedrandom(1); // or any seed.
+    // var x = random();       // 0 <= x < 1.  Every bit is random.
+    // var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.
+
+    // alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.
+    // Period: ~2^116
+    // Reported to pass all BigCrush tests.
+
+
+    // xor128, a pure xor-shift generator by George Marsaglia.
+    // Period: 2^128-1.
+    // Reported to fail: MatrixRank and LinearComp.
+
+
+    // xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.
+    // Period: 2^192-2^32
+    // Reported to fail: CollisionOver, SimpPoker, and LinearComp.
+
+
+    // xorshift7, by François Panneton and Pierre L'ecuyer, takes
+    // a different approach: it adds robustness by allowing more shifts
+    // than Marsaglia's original three.  It is a 7-shift generator
+    // with 256 bits, that passes BigCrush with no systmatic failures.
+    // Period 2^256-1.
+    // No systematic BigCrush failures reported.
+
+
+    // xor4096, by Richard Brent, is a 4096-bit xor-shift with a
+    // very long period that also adds a Weyl generator. It also passes
+    // BigCrush with no systematic failures.  Its long period may
+    // be useful if you have many generators and need to avoid
+    // collisions.
+    // Period: 2^4128-2^32.
+    // No systematic BigCrush failures reported.
+
+
+    // Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random
+    // number generator derived from ChaCha, a modern stream cipher.
+    // https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf
+    // Period: ~2^127
+    // No systematic BigCrush failures reported.
+
+
+    // The original ARC4-based prng included in this library.
+    // Period: ~2^1600
+
+
+    seedrandom.alea = alea;
+    seedrandom.xor128 = xor128;
+    seedrandom.xorwow = xorwow;
+    seedrandom.xorshift7 = xorshift7;
+    seedrandom.xor4096 = xor4096;
+    seedrandom.tychei = tychei;
+
+    var seedrandom$1 = seedrandom;
+    var seedrandom_1 = seedrandom$1.alea;
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // https://en.wikipedia.org/wiki/Marsaglia_polar_method
+    var MPRandGauss = /** @class */ (function () {
+        function MPRandGauss(mean, stdDeviation, dtype, truncated, seed) {
+            this.mean = mean;
+            this.stdDev = stdDeviation;
+            this.dtype = dtype;
+            this.nextVal = NaN;
+            this.truncated = truncated;
+            if (this.truncated) {
+                this.upper = this.mean + this.stdDev * 2;
+                this.lower = this.mean - this.stdDev * 2;
+            }
+            var seedValue = seed ? seed : Math.random();
+            this.random = seedrandom_1(seedValue.toString());
+        }
+        /** Returns next sample from a Gaussian distribution. */
+        MPRandGauss.prototype.nextValue = function () {
+            if (!isNaN(this.nextVal)) {
+                var value = this.nextVal;
+                this.nextVal = NaN;
+                return value;
+            }
+            var resultX, resultY;
+            var isValid = false;
+            while (!isValid) {
+                var v1 = void 0, v2 = void 0, s = void 0;
+                do {
+                    v1 = 2 * this.random() - 1;
+                    v2 = 2 * this.random() - 1;
+                    s = v1 * v1 + v2 * v2;
+                } while (s >= 1 || s === 0);
+                var mul = Math.sqrt(-2.0 * Math.log(s) / s);
+                resultX = this.mean + this.stdDev * v1 * mul;
+                resultY = this.mean + this.stdDev * v2 * mul;
+                if (!this.truncated || this.isValidTruncated(resultX)) {
+                    isValid = true;
+                }
+            }
+            if (!this.truncated || this.isValidTruncated(resultY)) {
+                this.nextVal = this.convertValue(resultY);
+            }
+            return this.convertValue(resultX);
+        };
+        /** Handles proper rounding for non-floating-point numbers. */
+        MPRandGauss.prototype.convertValue = function (value) {
+            if (this.dtype == null || this.dtype === 'float32') {
+                return value;
+            }
+            return Math.round(value);
+        };
+        /** Returns true if less than 2-standard-deviations from the mean. */
+        MPRandGauss.prototype.isValidTruncated = function (value) {
+            return value <= this.upper && value >= this.lower;
+        };
+        return MPRandGauss;
+    }());
+    // Marsaglia, George, and Wai Wan Tsang. 2000. "A Simple Method for Generating
+    // Gamma Variables."
+    var RandGamma = /** @class */ (function () {
+        function RandGamma(alpha, beta, dtype, seed) {
+            this.alpha = alpha;
+            this.beta = 1 / beta; // convert rate to scale parameter
+            this.dtype = dtype;
+            var seedValue = seed ? seed : Math.random();
+            this.randu = seedrandom_1(seedValue.toString());
+            this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
+            if (alpha < 1) {
+                this.d = alpha + (2 / 3);
+            }
+            else {
+                this.d = alpha - (1 / 3);
+            }
+            this.c = 1 / Math.sqrt(9 * this.d);
+        }
+        /** Returns next sample from a gamma distribution. */
+        RandGamma.prototype.nextValue = function () {
+            var x2, v0, v1, x, u, v;
+            while (true) {
+                do {
+                    x = this.randn.nextValue();
+                    v = 1 + (this.c * x);
+                } while (v <= 0);
+                v *= v * v;
+                x2 = x * x;
+                v0 = 1 - (0.331 * x2 * x2);
+                v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));
+                u = this.randu();
+                if (u < v0 || Math.log(u) < v1) {
+                    break;
+                }
+            }
+            v = (1 / this.beta) * this.d * v;
+            if (this.alpha < 1) {
+                v *= Math.pow(this.randu(), 1 / this.alpha);
+            }
+            return this.convertValue(v);
+        };
+        /** Handles proper rounding for non-floating-point numbers. */
+        RandGamma.prototype.convertValue = function (value) {
+            if (this.dtype === 'float32') {
+                return value;
+            }
+            return Math.round(value);
+        };
+        return RandGamma;
+    }());
+    var UniformRandom = /** @class */ (function () {
+        function UniformRandom(min, max, dtype, seed) {
+            var _this = this;
+            if (min === void 0) { min = 0; }
+            if (max === void 0) { max = 1; }
+            /** Handles proper rounding for non floating point numbers. */
+            this.canReturnFloat = function () {
+                return (_this.dtype == null || _this.dtype === 'float32');
+            };
+            this.min = min;
+            this.range = max - min;
+            this.dtype = dtype;
+            if (seed == null) {
+                seed = Math.random();
+            }
+            if (typeof seed === 'number') {
+                seed = seed.toString();
+            }
+            if (!this.canReturnFloat() && this.range <= 1) {
+                throw new Error("The difference between " + min + " - " + max + " <= 1 and dtype is not float");
+            }
+            this.random = seedrandom_1(seed);
+        }
+        UniformRandom.prototype.convertValue = function (value) {
+            if (this.canReturnFloat()) {
+                return value;
+            }
+            return Math.round(value);
+        };
+        UniformRandom.prototype.nextValue = function () {
+            return this.convertValue(this.min + this.range * this.random());
+        };
+        return UniformRandom;
+    }());
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a `tf.Tensor` with values sampled from a gamma distribution.
+     *
+     * ```js
+     * tf.randomGamma([2, 2], 1).print();
+     * ```
+     *
+     * @param shape An array of integers defining the output tensor shape.
+     * @param alpha The shape parameter of the gamma distribution.
+     * @param beta The inverse scale parameter of the gamma distribution. Defaults
+     *     to 1.
+     * @param dtype The data type of the output. Defaults to float32.
+     * @param seed The seed for the random number generator.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Random'}
+     */
+    function randomGamma_(shape, alpha, beta, dtype, seed) {
+        if (beta === void 0) { beta = 1; }
+        if (dtype === void 0) { dtype = 'float32'; }
+        if (beta == null) {
+            beta = 1;
+        }
+        if (dtype == null) {
+            dtype = 'float32';
+        }
+        if (dtype !== 'float32' && dtype !== 'int32') {
+            throw new Error("Unsupported data type " + dtype);
+        }
+        var rgamma = new RandGamma(alpha, beta, dtype, seed);
+        var res = buffer(shape, dtype);
+        for (var i = 0; i < res.values.length; i++) {
+            res.values[i] = rgamma.nextValue();
+        }
+        return res.toTensor();
+    }
+    var randomGamma = op({ randomGamma_: randomGamma_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a `tf.Tensor` with values sampled from a normal distribution.
+     *
+     * ```js
+     * tf.randomNormal([2, 2]).print();
+     * ```
+     *
+     * @param shape An array of integers defining the output tensor shape.
+     * @param mean The mean of the normal distribution.
+     * @param stdDev The standard deviation of the normal distribution.
+     * @param dtype The data type of the output.
+     * @param seed The seed for the random number generator.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Random'}
+     */
+    function randomNormal_(shape, mean, stdDev, dtype, seed) {
+        if (mean === void 0) { mean = 0; }
+        if (stdDev === void 0) { stdDev = 1; }
+        if (dtype != null && dtype === 'bool') {
+            throw new Error("Unsupported data type " + dtype);
+        }
+        var randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);
+        var res = buffer(shape, dtype);
+        for (var i = 0; i < res.values.length; i++) {
+            res.values[i] = randGauss.nextValue();
+        }
+        return res.toTensor();
+    }
+    var randomNormal = op({ randomNormal_: randomNormal_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a `tf.Tensor` with values sampled from a uniform distribution.
+     *
+     * The generated values follow a uniform distribution in the range [minval,
+     * maxval). The lower bound minval is included in the range, while the upper
+     * bound maxval is excluded.
+     *
+     * ```js
+     * tf.randomUniform([2, 2]).print();
+     * ```
+     *
+     * @param shape An array of integers defining the output tensor shape.
+     * @param minval The lower bound on the range of random values to generate.
+     *   Defaults to 0.
+     * @param maxval The upper bound on the range of random values to generate.
+     *   Defaults to 1.
+     * @param dtype The data type of the output tensor. Defaults to 'float32'.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Random'}
+     */
+    function randomUniform_(shape, minval, maxval, dtype, seed) {
+        if (minval === void 0) { minval = 0; }
+        if (maxval === void 0) { maxval = 1; }
+        if (dtype === void 0) { dtype = 'float32'; }
+        var res = buffer(shape, dtype);
+        var random = new UniformRandom(minval, maxval, null, seed);
+        for (var i = 0; i < res.values.length; i++) {
+            res.values[i] = random.nextValue();
+        }
+        return res.toTensor();
+    }
+    var randomUniform = op({ randomUniform_: randomUniform_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.
+     *
+     * The tensor is a is half-open interval meaning it includes start, but
+     * excludes stop. Decrementing ranges and negative step values are also
+     * supported.sv
+     *
+     *
+     * ```js
+     * tf.range(0, 9, 2).print();
+     * ```
+     *
+     * @param start An integer start value
+     * @param stop An integer stop value
+     * @param step An integer increment (will default to 1 or -1)
+     * @param dtype The data type of the output tensor. Defaults to 'float32'.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function range(start, stop, step, dtype) {
+        if (step === void 0) { step = 1; }
+        if (dtype === void 0) { dtype = 'float32'; }
+        if (step === 0) {
+            throw new Error('Cannot have a step of zero');
+        }
+        var attrs = { start: start, stop: stop, step: step, dtype: dtype };
+        return ENGINE.runKernel(Range, {} /* inputs */, attrs);
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns the real part of a complex (or real) tensor.
+     *
+     * Given a tensor input, this operation returns a tensor of type float that is
+     * the real part of each element in input considered as a complex number.
+     *
+     * If the input is real, it simply makes a clone.
+     *
+     * ```js
+     * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);
+     * tf.real(x).print();
+     * ```
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function real_(input) {
+        var $input = convertToTensor(input, 'input', 'real');
+        var inputs = { input: $input };
+        return ENGINE.runKernel(Real, inputs);
+    }
+    var real = op({ real_: real_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes reciprocal of x element-wise: `1 / x`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, 1, 2]);
+     *
+     * x.reciprocal().print();  // or tf.reciprocal(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function reciprocal_(x) {
+        var $x = convertToTensor(x, 'x', 'reciprocal');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Reciprocal, inputs);
+    }
+    var reciprocal = op({ reciprocal_: reciprocal_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes rectified linear element-wise: `max(x, 0)`.
+     *
+     * ```js
+     * const x = tf.tensor1d([-1, 2, -3, 4]);
+     *
+     * x.relu().print();  // or tf.relu(x)
+     * ```
+     * @param x The input tensor. If the dtype is `bool`, the output dtype will be
+     *     `int32'.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function relu_(x) {
+        var $x = convertToTensor(x, 'x', 'relu');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Relu, inputs);
+    }
+    var relu = op({ relu_: relu_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.
+     *
+     * ```js
+     * const x = tf.tensor1d([-1, 2, -3, 8]);
+     *
+     * x.relu6().print();  // or tf.relu6(x)
+     * ```
+     * @param x The input tensor. If the dtype is `bool`, the output dtype will be
+     *     `int32'.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function relu6_(x) {
+        var $x = convertToTensor(x, 'x', 'relu6');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Relu6, inputs);
+    }
+    var relu6 = op({ relu6_: relu6_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Reverses a `tf.Tensor` along a specified axis.
+     *
+     * Also available are stricter rank-specific methods that assert that `x` is
+     * of the given rank:
+     *   - `tf.reverse1d`
+     *   - `tf.reverse2d`
+     *   - `tf.reverse3d`
+     *   - `tf.reverse4d`
+     *
+     * Except `tf.reverse1d` (which does not have axis param), all methods have
+     * same signature as this method.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3, 4]);
+     *
+     * x.reverse().print();
+     * ```
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     *
+     * const axis = 1;
+     * x.reverse(axis).print();
+     * ```
+     * @param x The input tensor to be reversed.
+     * @param axis The set of dimensions to reverse. Must be in the
+     *     range [-rank(x), rank(x)). Defaults to all axes.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
+     */
+    function reverse_(x, axis) {
+        var $x = convertToTensor(x, 'x', 'reverse');
+        var inputs = { x: $x };
+        var attrs = { dims: axis };
+        return ENGINE.runKernel(Reverse, inputs, attrs);
+    }
+    var reverse = op({ reverse_: reverse_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Reverses a `tf.Tensor1D`.
+     *
+     * @param x The input tensor.
+     */
+    function reverse1d_(x) {
+        var $x = convertToTensor(x, 'x', 'reverse');
+        assert($x.rank === 1, function () { return "Error in reverse1D: x must be rank 1 but got rank " + $x.rank + "."; });
+        return reverse($x, 0);
+    }
+    var reverse1d = op({ reverse1d_: reverse1d_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Reverses a `tf.Tensor2D` along a specified axis.
+     *
+     * @param x The input tensor.
+     * @param axis The set of dimensions to reverse. Must be in the
+     *     range [-rank(x), rank(x)). Defaults to all axes.
+     */
+    function reverse2d_(x, axis) {
+        var $x = convertToTensor(x, 'x', 'reverse');
+        assert($x.rank === 2, function () { return "Error in reverse2D: x must be rank 2 but got rank " + $x.rank + "."; });
+        return reverse($x, axis);
+    }
+    var reverse2d = op({ reverse2d_: reverse2d_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Reverses a `tf.Tensor3D` along a specified axis.
+     *
+     * @param x The input tensor.
+     * @param axis The set of dimensions to reverse. Must be in the
+     *     range [-rank(x), rank(x)). Defaults to all axes.
+     */
+    function reverse3d_(x, axis) {
+        var $x = convertToTensor(x, 'x', 'reverse');
+        assert($x.rank === 3, function () { return "Error in reverse3D: x must be rank 3 but got rank " + $x.rank + "."; });
+        return reverse($x, axis);
+    }
+    var reverse3d = op({ reverse3d_: reverse3d_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Reverses a `tf.Tensor4D` along a specified axis.
+     *
+     * @param x The input tensor.
+     * @param axis The set of dimensions to reverse. Must be in the
+     *     range [-rank(x), rank(x)). Defaults to all axes.
+     */
+    function reverse4d_(x, axis) {
+        var $x = convertToTensor(x, 'x', 'reverse');
+        assert($x.rank === 4, function () { return "Error in reverse4D: x must be rank 4 but got rank " + $x.rank + "."; });
+        return reverse($x, axis);
+    }
+    var reverse4d = op({ reverse4d_: reverse4d_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes round of input `tf.Tensor` element-wise: `round(x)`.
+     * It implements banker's rounding.
+     *
+     * ```js
+     * const x = tf.tensor1d([.6, 1.1, -3.3]);
+     *
+     * x.round().print();  // or tf.round(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function round_(x) {
+        var $x = convertToTensor(x, 'x', 'round');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Round, inputs);
+    }
+    var round$1 = op({ round_: round_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes reciprocal of square root of the input `tf.Tensor` element-wise:
+     * `y = 1 / sqrt(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 4, -1]);
+     *
+     * x.rsqrt().print();  // or tf.rsqrt(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function rsqrt_(x) {
+        var $x = convertToTensor(x, 'x', 'rsqrt');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Rsqrt, inputs);
+    }
+    var rsqrt = op({ rsqrt_: rsqrt_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.
+     *
+     * The same functionality can be achieved with `tf.tensor`, but in general
+     * we recommend using `tf.scalar` as it makes the code more readable.
+     *
+     * ```js
+     * tf.scalar(3.14).print();
+     * ```
+     *
+     * @param value The value of the scalar.
+     * @param dtype The data type.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function scalar(value, dtype) {
+        if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&
+            dtype !== 'complex64') {
+            throw new Error('Error creating a new Scalar: value must be a primitive ' +
+                '(number|boolean|string)');
+        }
+        if (dtype === 'string' && isTypedArray(value) &&
+            !(value instanceof Uint8Array)) {
+            throw new Error('When making a scalar from encoded string, ' +
+                'the value must be `Uint8Array`.');
+        }
+        var shape = [];
+        var inferredShape = [];
+        return makeTensor(value, shape, inferredShape, dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes scaled exponential linear element-wise.
+     *
+     * `x < 0 ? scale * alpha * (exp(x) - 1) : x`
+     *
+     * ```js
+     * const x = tf.tensor1d([-1, 2, -3, 4]);
+     *
+     * x.selu().print();  // or tf.selu(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function selu_(x) {
+        var $x = convertToTensor(x, 'x', 'selu');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Selu, inputs);
+    }
+    var selu = op({ selu_: selu_ });
+
+    /**
+     * 2-D convolution with separable filters.
+     *
+     * Performs a depthwise convolution that acts separately on channels followed
+     * by a pointwise convolution that mixes channels. Note that this is
+     * separability between dimensions [1, 2] and 3, not spatial separability
+     * between dimensions 1 and 2.
+     *
+     * See
+     * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](
+     *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)
+     * for more details.
+     *
+     * @param x The input tensor, of rank 4 or rank 3, of shape
+     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
+     * assumed.
+     * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape
+     *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is
+     *     the filter used in the first step.
+     * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape
+     *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is
+     *     the filter used in the second step.
+     * @param strides The strides of the convolution: `[strideHeight,
+     * strideWidth]`. If strides is a single number, then `strideHeight ==
+     * strideWidth`.
+     * @param pad The type of padding algorithm.
+     *   - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *   - `valid`: output will be smaller than input if filter is larger
+     *       than 1x1.
+     *   - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
+     *     in which we sample input values across the height and width dimensions
+     *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
+     *     number, then `dilationHeight == dilationWidth`. If it is greater than
+     *     1, then all values of `strides` must be 1.
+     * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
+     *     "NHWC". Specify the data format of the input and output data. With the
+     *     default format "NHWC", the data is stored in the order of: [batch,
+     *     height, width, channels]. Only "NHWC" is currently supported.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {
+        if (dilation === void 0) { dilation = [1, 1]; }
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        var $x = convertToTensor(x, 'x', 'separableConv2d');
+        var $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');
+        var $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+        }
+        if (dataFormat === 'NCHW') {
+            throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +
+                'NHWC is supported');
+        }
+        assert(x4D.rank === 4, function () { return "Error in separableConv2d: input must be rank 4, but got " +
+            ("rank " + x4D.rank + "."); });
+        assert($depthwiseFilter.rank === 4, function () { return "Error in separableConv2d: depthwise filter must be rank 4, but " +
+            ("got rank " + $depthwiseFilter.rank + "."); });
+        assert($pointwiseFilter.rank === 4, function () { return "Error in separableConv2d: pointwise filter must be rank 4, but " +
+            ("got rank " + $depthwiseFilter.rank + "."); });
+        assert($pointwiseFilter.shape[0] === 1, function () {
+            return "Error in separableConv2d: the first dimension of pointwise filter " +
+                (" must be 1, but got " + $pointwiseFilter.shape[0] + ".");
+        });
+        assert($pointwiseFilter.shape[1] === 1, function () { return "Error in separableConv2d: the second dimension of pointwise " +
+            ("filter must be 1, but got " + $pointwiseFilter.shape[1] + "."); });
+        var inChannels = $depthwiseFilter.shape[2];
+        var channelMultiplier = $depthwiseFilter.shape[3];
+        assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, function () {
+            return "Error in separableConv2d: the third dimension of pointwise filter " +
+                ("must be " + inChannels * channelMultiplier + ", ") +
+                ("but got " + $pointwiseFilter.shape[2] + ".");
+        });
+        var depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);
+        var pointwiseStride = 1;
+        var res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);
+        if (reshapedTo4D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+        }
+        return res;
+    }
+    var separableConv2d = op({ separableConv2d_: separableConv2d_ });
+
+    /**
+     * Computes the difference between two lists of numbers.
+     *
+     * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`
+     * that represents all values that are in `x` but not in `y`. The returned
+     * Tensor `out` is sorted in the same order that the numbers appear in `x`
+     * (duplicates are preserved). This operation also returns a Tensor indices that
+     * represents the position of each out element in `x`. In other words:
+     *
+     * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`
+     *
+     * ```js
+     * const x = [1, 2, 3, 4, 5, 6];
+     * const y = [1, 3, 5];
+     *
+     * const [out, indices] = await tf.setdiff1dAsync(x, y);
+     * out.print(); // [2, 4, 6]
+     * indices.print(); // [1, 3, 5]
+     * ```
+     *
+     * @param x 1-D Tensor. Values to keep.
+     * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the
+     *     output.
+     * @returns Promise of Tensor tuple [out, indices].
+     *  out: Tensor with the same type as x.
+     *  indices: A Tensor of type int32.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Transformations'}
+     */
+    function setdiff1dAsync_(x, y) {
+        return __awaiter(this, void 0, void 0, function () {
+            var $x, $y, xVals, yVals, ySet, outputSize, i, buffer, indices, i, p;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        $x = convertToTensor(x, 'x', 'setdiff1d');
+                        $y = convertToTensor(y, 'y', 'setdiff1d');
+                        assert($x.dtype === $y.dtype, function () { return "x and y should have the same dtype, but got x (" + $x.dtype + ") and y (" + $y.dtype + ")."; });
+                        assert($x.rank === 1, function () { return "x should be 1D tensor, but got x (" + $x.shape + ")."; });
+                        assert($y.rank === 1, function () { return "y should be 1D tensor, but got y (" + $y.shape + ")."; });
+                        return [4 /*yield*/, $x.data()];
+                    case 1:
+                        xVals = _a.sent();
+                        return [4 /*yield*/, $y.data()];
+                    case 2:
+                        yVals = _a.sent();
+                        ySet = new Set(yVals);
+                        outputSize = 0;
+                        for (i = 0; i < xVals.length; i++) {
+                            if (!ySet.has(xVals[i])) {
+                                outputSize++;
+                            }
+                        }
+                        buffer = new TensorBuffer([outputSize], $x.dtype);
+                        indices = new TensorBuffer([outputSize], 'int32');
+                        for (i = 0, p = 0; i < xVals.length; i++) {
+                            if (!ySet.has(xVals[i])) {
+                                buffer.values[p] = xVals[i];
+                                indices.values[p] = i;
+                                p++;
+                            }
+                        }
+                        return [2 /*return*/, [buffer.toTensor(), indices.toTensor()]];
+                }
+            });
+        });
+    }
+    var setdiff1dAsync = setdiff1dAsync_;
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns an element-wise indication of the sign of a number.
+     *
+     * ```js
+     * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);
+     *
+     * x.sign().print();  // or tf.sign(x)
+     * ```
+     * @param x The input Tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function sign_(x) {
+        var $x = convertToTensor(x, 'x', 'sign');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Sign, inputs);
+    }
+    var sign = op({ sign_: sign_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes sin of the input Tensor element-wise: `sin(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
+     *
+     * x.sin().print();  // or tf.sin(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function sin_(x) {
+        var $x = convertToTensor(x, 'x', 'sin');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Sin, inputs);
+    }
+    var sin = op({ sin_: sin_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, 1, -1, .7]);
+     *
+     * x.sinh().print();  // or tf.sinh(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function sinh_(x) {
+        var $x = convertToTensor(x, 'x', 'sinh');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Sinh, inputs);
+    }
+    var sinh = op({ sinh_: sinh_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Extracts a 1D slice from 1D array starting at coordinates `begin` and is
+     * of length `size`. See `slice` for details.
+     */
+    function slice1d_(x, begin, size) {
+        var $x = convertToTensor(x, 'x', 'slice1d');
+        assert($x.rank === 1, function () {
+            return "slice1d expects a rank-1 tensor, but got a rank-" + $x.rank + " tensor";
+        });
+        return slice($x, [begin], [size]);
+    }
+    var slice1d = op({ slice1d_: slice1d_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Extracts a 2D slice from a 2D array starting at coordinates `begin` and
+     * is of size `size`. See `slice` for details.
+     */
+    function slice2d_(x, begin, size) {
+        var $x = convertToTensor(x, 'x', 'slice2d');
+        assert($x.rank === 2, function () {
+            return "slice2d expects a rank-2 tensor, but got a rank-" + $x.rank + " tensor";
+        });
+        return slice($x, begin, size);
+    }
+    var slice2d = op({ slice2d_: slice2d_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Extracts a 3D slice from a 3D array starting at coordinates `begin` and
+     * is of size `size`. See `slice` for details.
+     */
+    function slice3d_(x, begin, size) {
+        var $x = convertToTensor(x, 'x', 'slice3d');
+        assert($x.rank === 3, function () {
+            return "slice3d expects a rank-3 tensor, but got a rank-" + $x.rank + " tensor";
+        });
+        return slice($x, begin, size);
+    }
+    var slice3d = op({ slice3d_: slice3d_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Extracts a 4D slice from a 4D array starting at coordinates `begin` and
+     * is of size `size`. See `slice` for details.
+     */
+    function slice4d_(x, begin, size) {
+        var $x = convertToTensor(x, 'x', 'slice4d');
+        assert($x.rank === 4, function () {
+            return "slice4d expects a rank-4 tensor, but got a rank-" + $x.rank + " tensor";
+        });
+        return slice($x, begin, size);
+    }
+    var slice4d = op({ slice4d_: slice4d_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the softmax normalized vector given the logits.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2, 3]);
+     *
+     * a.softmax().print();  // or tf.softmax(a)
+     * ```
+     *
+     * ```js
+     * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);
+     *
+     * a.softmax().print();  // or tf.softmax(a)
+     * ```
+     *
+     * @param logits The logits array.
+     * @param dim The dimension softmax would be performed on. Defaults to `-1`
+     *     which indicates the last dimension.
+     *
+     * @doc {heading: 'Operations', subheading: 'Normalization'}
+     */
+    function softmax_(logits, dim) {
+        if (dim === void 0) { dim = -1; }
+        var $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');
+        if (dim === -1) {
+            dim = $logits.rank - 1;
+        }
+        if (dim !== $logits.rank - 1) {
+            throw Error('Softmax along a non-last dimension is not yet supported. ' +
+                ("Logits was rank " + $logits.rank + " and dim was " + dim));
+        }
+        var inputs = { logits: $logits };
+        var attrs = { dim: dim };
+        return ENGINE.runKernel(Softmax, inputs, attrs);
+    }
+    var softmax = op({ softmax_: softmax_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Fast Fourier transform.
+     *
+     * Computes the 1-dimensional discrete Fourier transform over the inner-most
+     * dimension of input.
+     *
+     * ```js
+     * const real = tf.tensor1d([1, 2, 3]);
+     * const imag = tf.tensor1d([1, 2, 3]);
+     * const x = tf.complex(real, imag);
+     *
+     * x.fft().print();  // tf.spectral.fft(x).print();
+     * ```
+     * @param input The complex input to compute an fft over.
+     *
+     * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
+     */
+    function fft_(input) {
+        assert(input.dtype === 'complex64', function () { return "The dtype for tf.spectral.fft() must be complex64 " +
+            ("but got " + input.dtype + "."); });
+        var inputs = { input: input };
+        return ENGINE.runKernel(FFT, inputs);
+    }
+    var fft = op({ fft_: fft_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Inverse fast Fourier transform.
+     *
+     * Computes the inverse 1-dimensional discrete Fourier transform over the
+     * inner-most dimension of input.
+     *
+     * ```js
+     * const real = tf.tensor1d([1, 2, 3]);
+     * const imag = tf.tensor1d([1, 2, 3]);
+     * const x = tf.complex(real, imag);
+     *
+     * x.ifft().print();  // tf.spectral.ifft(x).print();
+     * ```
+     * @param input The complex input to compute an ifft over.
+     *
+     * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
+     */
+    function ifft_(input) {
+        assert(input.dtype === 'complex64', function () { return "The dtype for tf.spectral.ifft() must be complex64 " +
+            ("but got " + input.dtype + "."); });
+        var inputs = { input: input };
+        return ENGINE.runKernel(IFFT, inputs);
+    }
+    var ifft = op({ ifft_: ifft_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Inversed real value input fast Fourier transform.
+     *
+     * Computes the 1-dimensional inversed discrete Fourier transform over the
+     * inner-most dimension of the real input.
+     *
+     * ```js
+     * const real = tf.tensor1d([1, 2, 3]);
+     * const imag = tf.tensor1d([0, 0, 0]);
+     * const x = tf.complex(real, imag);
+     *
+     * x.irfft().print();
+     * ```
+     * @param input The real value input to compute an irfft over.
+     *
+     * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
+     */
+    function irfft_(input) {
+        var innerDimensionSize = input.shape[input.shape.length - 1];
+        var batch = input.size / innerDimensionSize;
+        var ret;
+        if (innerDimensionSize <= 2) {
+            var complexInput = reshape(input, [batch, innerDimensionSize]);
+            ret = ifft(complexInput);
+        }
+        else {
+            // The length of unique components of the DFT of a real-valued signal
+            // is 2 * (input_len - 1)
+            var outputShape = [batch, 2 * (innerDimensionSize - 1)];
+            var realInput = reshape(real(input), [batch, innerDimensionSize]);
+            var imagInput = reshape(imag(input), [batch, innerDimensionSize]);
+            var realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
+            var imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));
+            var r = concat([realInput, realConjugate], 1);
+            var i = concat([imagInput, imagConjugate], 1);
+            var complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);
+            ret = ifft(complexInput);
+        }
+        ret = real(ret);
+        // reshape the result if the input is 3D tensor.
+        if (input.rank === 3 && input.shape[0] !== 0) {
+            var temp = ret;
+            var batch_1 = input.shape[0];
+            ret = reshape(ret, [batch_1, ret.shape[0] / batch_1, ret.shape[1]]);
+            temp.dispose();
+        }
+        return ret;
+    }
+    var irfft = op({ irfft_: irfft_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Splits a `tf.Tensor` into sub tensors.
+     *
+     * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`
+     * into `numOrSizeSplits` smaller tensors.
+     * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.
+     *
+     * If `numOrSizeSplits` is a number array, splits `x` into
+     * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the
+     * same size as `x` except along dimension `axis` where the size is
+     * `numOrSizeSplits[i]`.
+     *
+     * ```js
+     * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
+     * const [a, b] = tf.split(x, 2, 1);
+     * a.print();
+     * b.print();
+     *
+     * const [c, d, e] = tf.split(x, [1, 2, 1], 1);
+     * c.print();
+     * d.print();
+     * e.print();
+     * ```
+     *
+     * @param x The input tensor to split.
+     * @param numOrSizeSplits Either an integer indicating the number of
+     * splits along the axis or an array of integers containing the sizes of
+     * each output tensor along the axis. If a number then it must evenly divide
+     * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.
+     * Can contain one -1 indicating that dimension is to be inferred.
+     * @param axis The dimension along which to split. Defaults to 0 (the first
+     * dim).
+     *
+     * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
+     */
+    function split_(x, numOrSizeSplits, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $x = convertToTensor(x, 'x', 'split');
+        var inputs = { x: $x };
+        var attr = { numOrSizeSplits: numOrSizeSplits, axis: axis };
+        return ENGINE.runKernel(SplitV, inputs, attr);
+    }
+    var split = op({ split_: split_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Real value input fast Fourier transform.
+     *
+     * Computes the 1-dimensional discrete Fourier transform over the
+     * inner-most dimension of the real input.
+     *
+     * ```js
+     * const real = tf.tensor1d([1, 2, 3]);
+     *
+     * real.rfft().print();
+     * ```
+     * @param input The real value input to compute an rfft over.
+     *
+     * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
+     */
+    function rfft_(input, fftLength) {
+        assert(input.dtype === 'float32', function () { return "The dtype for rfft() must be real value but got " + input.dtype; });
+        var innerDimensionSize = input.shape[input.shape.length - 1];
+        var batch = input.size / innerDimensionSize;
+        var adjustedInput;
+        if (fftLength != null && fftLength < innerDimensionSize) {
+            // Need to crop
+            var begin = input.shape.map(function (v) { return 0; });
+            var size = input.shape.map(function (v) { return v; });
+            size[input.shape.length - 1] = fftLength;
+            adjustedInput = slice(input, begin, size);
+            innerDimensionSize = fftLength;
+        }
+        else if (fftLength != null && fftLength > innerDimensionSize) {
+            // Need to pad with zeros
+            var zerosShape = input.shape.map(function (v) { return v; });
+            zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;
+            adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);
+            innerDimensionSize = fftLength;
+        }
+        else {
+            adjustedInput = input;
+        }
+        // Complement the input with zero imaginary numbers.
+        var zerosInput = zerosLike(adjustedInput);
+        var complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);
+        var ret = fft(complexInput);
+        // Exclude complex conjugations. These conjugations are put symmetrically.
+        var half = Math.floor(innerDimensionSize / 2) + 1;
+        var realValues = real(ret);
+        var imagValues = imag(ret);
+        var realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);
+        var imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);
+        var outputShape = adjustedInput.shape.slice();
+        outputShape[adjustedInput.shape.length - 1] = half;
+        return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);
+    }
+    var rfft = op({ rfft_: rfft_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 4, -1]);
+     *
+     * x.sqrt().print();  // or tf.sqrt(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function sqrt_(x) {
+        var $x = convertToTensor(x, 'x', 'sqrt');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Sqrt, inputs);
+    }
+    var sqrt = op({ sqrt_: sqrt_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns (a - b) * (a - b) element-wise.
+     * Supports broadcasting.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 4, 3, 16]);
+     * const b = tf.tensor1d([1, 2, 9, 4]);
+     *
+     * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)
+     * ```
+     *
+     * ```js
+     * // Broadcast squared difference  a with b.
+     * const a = tf.tensor1d([2, 4, 6, 8]);
+     * const b = tf.scalar(5);
+     *
+     * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)
+     * ```
+     *
+     * @param a The first tensor.
+     * @param b The second tensor. Must have the same type as `a`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Arithmetic'}
+     */
+    function squaredDifference_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'squaredDifference');
+        var $b = convertToTensor(b, 'b', 'squaredDifference');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var inputs = { a: $a, b: $b };
+        var attrs = {};
+        return ENGINE.runKernel(SquaredDifference, inputs, attrs);
+    }
+    var squaredDifference = op({ squaredDifference_: squaredDifference_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Removes dimensions of size 1 from the shape of a `tf.Tensor`.
+     *
+     * ```js
+     * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);
+     * x.squeeze().print();
+     * ```
+     *
+     * @param x The input tensor to be squeezed.
+     * @param axis An optional list of numbers. If specified, only
+     *     squeezes the dimensions listed. The dimension index starts at 0. It
+     * is an error to squeeze a dimension that is not 1.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Transformations'}
+     */
+    function squeeze_(x, axis) {
+        var $x = convertToTensor(x, 'x', 'squeeze');
+        return reshape($x, squeezeShape($x.shape, axis).newShape);
+    }
+    var squeeze = op({ squeeze_: squeeze_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.
+     *
+     * ```js
+     * const a = tf.tensor1d([1, 2]);
+     * const b = tf.tensor1d([3, 4]);
+     * const c = tf.tensor1d([5, 6]);
+     * tf.stack([a, b, c]).print();
+     * ```
+     *
+     * @param tensors A list of tensor objects with the same shape and dtype.
+     * @param axis The axis to stack along. Defaults to 0 (the first dim).
+     *
+     * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
+     */
+    function stack_(tensors, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $tensors = convertToTensorArray(tensors, 'tensors', 'stack', 'string_or_numeric');
+        assert($tensors.length >= 1, function () { return 'Pass at least one tensor to tf.stack'; });
+        if ($tensors.length > 0) {
+            assert(axis <= $tensors[0].rank, function () { return 'Axis must be <= rank of the tensor'; });
+        }
+        var inputs = $tensors;
+        var attrs = { axis: axis };
+        return ENGINE.runKernel(Pack, inputs, attrs);
+    }
+    var stack = op({ stack_: stack_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, 2, -1, -3]);
+     *
+     * x.step(.5).print();  // or tf.step(x, .5)
+     * ```
+     * @param x The input tensor.
+     * @param alpha The gradient when input is negative.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function step_(x, alpha) {
+        if (alpha === void 0) { alpha = 0.0; }
+        var $x = convertToTensor(x, 'x', 'step');
+        var inputs = { x: $x };
+        var attrs = { alpha: alpha };
+        return ENGINE.runKernel(Step, inputs, attrs);
+    }
+    var step = op({ step_: step_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Extracts a strided slice of a tensor.
+     *
+     * Roughly speaking, this op extracts a slice of size (end-begin)/stride from
+     * the given input tensor (x). Starting at the location specified by begin the
+     * slice continues by adding stride to the index until all dimensions are not
+     * less than end. Note that a stride can be negative, which causes a reverse
+     * slice.
+     *
+     * ```js
+     * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
+     *    [3, 2, 3]);
+     * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]
+     * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],
+     *                                                     // [4, 4, 4]]]
+     * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],
+     *                                                     // [3, 3, 3]]]
+     * ```
+     *
+     * @param x The tensor to stride slice.
+     * @param begin The coordinates to start the slice from.
+     * @param end: The coordinates to end the slice at.
+     * @param strides: The size of the slice.
+     * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored
+     *      and the fullest possible range in that dimension is used instead.
+     * @param endMask: If the ith bit of endMask is set, end[i] is ignored
+     *      and the fullest possible range in that dimension is used instead.
+     * @param shrinkAxisMask: a bitmask where bit i implies that
+     * the ith specification should shrink the dimensionality. begin and end must
+     * imply a slice of size 1 in the dimension.
+     *
+     * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
+     */
+    function stridedSlice_(x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+        if (beginMask === void 0) { beginMask = 0; }
+        if (endMask === void 0) { endMask = 0; }
+        if (ellipsisMask === void 0) { ellipsisMask = 0; }
+        if (newAxisMask === void 0) { newAxisMask = 0; }
+        if (shrinkAxisMask === void 0) { shrinkAxisMask = 0; }
+        var $x = convertToTensor(x, 'x', 'stridedSlice', 'string_or_numeric');
+        var inputs = { x: $x };
+        var attrs = {
+            begin: begin,
+            end: end,
+            strides: strides,
+            beginMask: beginMask,
+            endMask: endMask,
+            ellipsisMask: ellipsisMask,
+            newAxisMask: newAxisMask,
+            shrinkAxisMask: shrinkAxisMask
+        };
+        return ENGINE.runKernel(StridedSlice, inputs, attrs);
+    }
+    var stridedSlice = op({ stridedSlice_: stridedSlice_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`
+     *
+     * ```js
+     * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
+     *
+     * x.tan().print();  // or tf.tan(x)
+     * ```
+     * @param x The input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function tan_(x) {
+        var $x = convertToTensor(x, 'x', 'tan');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Tan, inputs);
+    }
+    var tan = op({ tan_: tan_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.
+     *
+     * The same functionality can be achieved with `tf.tensor`, but in general
+     * we recommend using `tf.tensor1d` as it makes the code more readable.
+     *
+     * ```js
+     * tf.tensor1d([1, 2, 3]).print();
+     * ```
+     *
+     * @param values The values of the tensor. Can be array of numbers,
+     *     or a `TypedArray`.
+     * @param dtype The data type.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function tensor1d(values, dtype) {
+        assertNonNull(values);
+        var inferredShape = inferShape(values, dtype);
+        if (inferredShape.length !== 1) {
+            throw new Error('tensor1d() requires values to be a flat/TypedArray');
+        }
+        var shape = null;
+        return makeTensor(values, shape, inferredShape, dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.
+     *
+     * The same functionality can be achieved with `tf.tensor`, but in general
+     * we recommend using `tf.tensor2d` as it makes the code more readable.
+     *
+     *  ```js
+     * // Pass a nested array.
+     * tf.tensor2d([[1, 2], [3, 4]]).print();
+     * ```
+     * ```js
+     * // Pass a flat array and specify a shape.
+     * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();
+     * ```
+     *
+     * @param values The values of the tensor. Can be nested array of numbers,
+     *     or a flat array, or a `TypedArray`.
+     * @param shape The shape of the tensor. If not provided, it is inferred from
+     *     `values`.
+     * @param dtype The data type.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function tensor2d(values, shape, dtype) {
+        assertNonNull(values);
+        if (shape != null && shape.length !== 2) {
+            throw new Error('tensor2d() requires shape to have two numbers');
+        }
+        var inferredShape = inferShape(values, dtype);
+        if (inferredShape.length !== 2 && inferredShape.length !== 1) {
+            throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');
+        }
+        if (inferredShape.length === 1 && shape == null) {
+            throw new Error('tensor2d() requires shape to be provided when `values` ' +
+                'are a flat/TypedArray');
+        }
+        return makeTensor(values, shape, inferredShape, dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.
+     *
+     * The same functionality can be achieved with `tf.tensor`, but in general
+     * we recommend using `tf.tensor4d` as it makes the code more readable.
+     *
+     *  ```js
+     * // Pass a nested array.
+     * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();
+     * ```
+     * ```js
+     * // Pass a flat array and specify a shape.
+     * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();
+     * ```
+     *
+     * @param values The values of the tensor. Can be nested array of numbers,
+     *     or a flat array, or a `TypedArray`.
+     * @param shape The shape of the tensor. Optional. If not provided,
+     *   it is inferred from `values`.
+     * @param dtype The data type.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function tensor4d(values, shape, dtype) {
+        assertNonNull(values);
+        if (shape != null && shape.length !== 4) {
+            throw new Error('tensor4d() requires shape to have four numbers');
+        }
+        var inferredShape = inferShape(values, dtype);
+        if (inferredShape.length !== 4 && inferredShape.length !== 1) {
+            throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');
+        }
+        if (inferredShape.length === 1 && shape == null) {
+            throw new Error('tensor4d() requires shape to be provided when `values` ' +
+                'are a flat array');
+        }
+        return makeTensor(values, shape, inferredShape, dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.
+     *
+     * The same functionality can be achieved with `tf.tensor`, but in general
+     * we recommend using `tf.tensor5d` as it makes the code more readable.
+     *
+     *  ```js
+     * // Pass a nested array.
+     * tf.tensor5d([[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]).print();
+     * ```
+     * ```js
+     * // Pass a flat array and specify a shape.
+     * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();
+     * ```
+     *
+     * @param values The values of the tensor. Can be nested array of numbers,
+     *     or a flat array, or a `TypedArray`.
+     * @param shape The shape of the tensor. Optional. If not provided,
+     *   it is inferred from `values`.
+     * @param dtype The data type.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function tensor5d(values, shape, dtype) {
+        assertNonNull(values);
+        if (shape != null && shape.length !== 5) {
+            throw new Error('tensor5d() requires shape to have five numbers');
+        }
+        var inferredShape = inferShape(values, dtype);
+        if (inferredShape.length !== 5 && inferredShape.length !== 1) {
+            throw new Error('tensor5d() requires values to be ' +
+                'number[][][][][] or flat/TypedArray');
+        }
+        if (inferredShape.length === 1 && shape == null) {
+            throw new Error('tensor5d() requires shape to be provided when `values` ' +
+                'are a flat array');
+        }
+        return makeTensor(values, shape, inferredShape, dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.
+     *
+     * The same functionality can be achieved with `tf.tensor`, but in general
+     * we recommend using `tf.tensor6d` as it makes the code more readable.
+     *
+     *  ```js
+     * // Pass a nested array.
+     * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();
+     * ```
+     * ```js
+     * // Pass a flat array and specify a shape.
+     * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();
+     * ```
+     *
+     * @param values The values of the tensor. Can be nested array of numbers,
+     *     or a flat array, or a `TypedArray`.
+     * @param shape The shape of the tensor. Optional. If not provided,
+     *   it is inferred from `values`.
+     * @param dtype The data type.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function tensor6d(values, shape, dtype) {
+        assertNonNull(values);
+        if (shape != null && shape.length !== 6) {
+            throw new Error('tensor6d() requires shape to have six numbers');
+        }
+        var inferredShape = inferShape(values, dtype);
+        if (inferredShape.length !== 6 && inferredShape.length !== 1) {
+            throw new Error('tensor6d() requires values to be number[][][][][][] or ' +
+                'flat/TypedArray');
+        }
+        if (inferredShape.length === 1 && shape == null) {
+            throw new Error('tensor6d() requires shape to be provided when `values` ' +
+                'are a flat array');
+        }
+        shape = shape ||
+            inferredShape;
+        return makeTensor(values, shape, inferredShape, dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Finds the values and indices of the `k` largest entries along the last
+     * dimension.
+     *
+     * If the input is a vector (rank=1), finds the k largest entries in the vector
+     * and outputs their values and indices as vectors. Thus values[j] is the j-th
+     * largest entry in input, and its index is indices[j].
+     * For higher rank inputs, computes the top k entries along the last dimension.
+     *
+     * If two elements are equal, the lower-index element appears first.
+     *
+     * ```js
+     * const a = tf.tensor2d([[1, 5], [4, 3]]);
+     * const {values, indices} = tf.topk(a);
+     * values.print();
+     * indices.print();
+     * ```
+     * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.
+     * @param k Number of top elements to look for along the last dimension.
+     * @param sorted If true, the resulting `k` elements will be sorted by the
+     *     values in descending order.
+     *
+     * @doc {heading: 'Operations', subheading: 'Evaluation'}
+     */
+    function topk_(x, k, sorted) {
+        if (k === void 0) { k = 1; }
+        if (sorted === void 0) { sorted = true; }
+        var $x = convertToTensor(x, 'x', 'topk');
+        if ($x.rank === 0) {
+            throw new Error('topk() expects the input to be of rank 1 or higher');
+        }
+        var lastDim = $x.shape[$x.shape.length - 1];
+        if (k > lastDim) {
+            throw new Error("'k' passed to topk() must be <= the last dimension (" + lastDim + ") " +
+                ("but got " + k));
+        }
+        var inputs = { x: $x };
+        var attrs = { k: k, sorted: sorted };
+        var _a = ENGINE.runKernel(TopK, inputs, attrs), values = _a[0], indices = _a[1];
+        return { values: values, indices: indices };
+    }
+    var topk = op({ topk_: topk_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a `tf.Tensor` with values sampled from a truncated normal
+     * distribution.
+     *
+     * ```js
+     * tf.truncatedNormal([2, 2]).print();
+     * ```
+     *
+     * The generated values follow a normal distribution with specified mean and
+     * standard deviation, except that values whose magnitude is more than 2
+     * standard deviations from the mean are dropped and re-picked.
+     *
+     * @param shape An array of integers defining the output tensor shape.
+     * @param mean The mean of the normal distribution.
+     * @param stdDev The standard deviation of the normal distribution.
+     * @param dtype The data type of the output tensor.
+     * @param seed The seed for the random number generator.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function truncatedNormal_(shape, mean, stdDev, dtype, seed) {
+        if (mean === void 0) { mean = 0; }
+        if (stdDev === void 0) { stdDev = 1; }
+        if (dtype != null && dtype === 'bool') {
+            throw new Error("Unsupported data type $ { dtype }");
+        }
+        var randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);
+        var res = buffer(shape, dtype);
+        for (var i = 0; i < res.values.length; i++) {
+            res.values[i] = randGauss.nextValue();
+        }
+        return res.toTensor();
+    }
+    var truncatedNormal = op({ truncatedNormal_: truncatedNormal_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Finds unique elements along an axis of a tensor.
+     *
+     * It returns a tensor `values` containing all of the unique elements along the
+     * `axis` of the given tensor `x` in the same order that they occur along the
+     * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor
+     * `indices` the same size as the number of the elements in `x` along the `axis`
+     * dimension. It contains the index in the unique output `values`.
+     *
+     * ```js
+     * // A 1-D tensor
+     * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);
+     * const {values, indices} = tf.unique(a);
+     * values.print();   // [1, 2, 4, 7, 8,]
+     * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]
+     * ```
+     *
+     * ```js
+     * // A 2-D tensor with axis=0
+     * //
+     * // 'a' is: [[1, 0, 0],
+     * //          [1, 0, 0],
+     * //          [2, 0, 0]]
+     * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);
+     * const {values, indices} = tf.unique(a, 0)
+     * values.print();   // [[1, 0, 0],
+     *                   //  [2, 0, 0]]
+     * indices.print();  // [0, 0, 1]
+     * ```
+     *
+     * ```js
+     * // A 2-D tensor with axis=1
+     * //
+     * // 'a' is: [[1, 0, 0],
+     * //          [1, 0, 0],
+     * //          [2, 0, 0]]
+     * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);
+     * const {values, indices} = tf.unique(a, 1)
+     * values.print();   // [[1, 0],
+     *                   //  [1, 0],
+     *                   //  [2, 0]]
+     * indices.print();  // [0, 1, 1]
+     * ```
+     * @param x A tensor (int32, string, bool).
+     * @param axis The axis of the tensor to find the unique elements.
+     * @returns [uniqueElements, indices] (see above for details)
+     *
+     * @doc {heading: 'Operations', subheading: 'Evaluation'}
+     */
+    function unique_(x, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $x = convertToTensor(x, 'x', 'unique', 'string_or_numeric');
+        assert($x.rank > 0, function () { return 'The input tensor must be at least 1D'; });
+        var inputs = { x: $x };
+        var attrs = { axis: axis };
+        var _a = ENGINE.runKernel(Unique, inputs, attrs), values = _a[0], indices = _a[1];
+        return { values: values, indices: indices };
+    }
+    var unique = op({ unique_: unique_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the sum along segments of a `tf.Tensor`.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3, 4]);
+     * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');
+     * const numSegments = 3;
+     *
+     * x.unsortedSegmentSum(segmentIds, numSegments).print()
+     * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)
+     * ```
+     * @param x The `tf.Tensor` that will be summed along its segments.
+     * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s
+     * dimension along the `axis`.  Maps each element of `x` to a segment.
+     * @param numSegments The number of distinct `segmentIds`.
+     *
+     * @doc {heading: 'Operations', subheading: 'Segment'}
+     */
+    function unsortedSegmentSum_(x, segmentIds, numSegments) {
+        var $x = convertToTensor(x, 'x', 'unsortedSegmentSum');
+        var $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');
+        assert(isInt(numSegments), function () { return 'numSegments must be of dtype int'; });
+        var inputs = { x: $x, segmentIds: $segmentIds };
+        var attrs = { numSegments: numSegments };
+        return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs);
+    }
+    var unsortedSegmentSum = op({ unsortedSegmentSum_: unsortedSegmentSum_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.
+     *
+     * ```js
+     * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     *
+     * tf.unstack(a).forEach(tensor => tensor.print());
+     * ```
+     *
+     * @param x A tensor object.
+     * @param axis The axis to unstack along. Defaults to 0 (the first dim).
+     *
+     * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
+     */
+    function unstack_(x, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $x = convertToTensor(x, 'x', 'unstack', 'string_or_numeric');
+        assert(axis >= -$x.shape.length && axis < $x.shape.length, function () {
+            return "Axis = " + axis + " is not in [-" + $x.shape.length + ", " + $x.shape.length + ")";
+        });
+        var inputs = { value: $x };
+        var attrs = { axis: axis };
+        return ENGINE.runKernel(Unpack, inputs, attrs);
+    }
+    var unstack = op({ unstack_: unstack_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a new variable with the provided initial value.
+     * ```js
+     * const x = tf.variable(tf.tensor([1, 2, 3]));
+     * x.assign(tf.tensor([4, 5, 6]));
+     *
+     * x.print();
+     * ```
+     *
+     * @param initialValue Initial value for the tensor.
+     * @param trainable If true, optimizers are allowed to update it.
+     * @param name Name of the variable. Defaults to a unique id.
+     * @param dtype If set, initialValue will be converted to the given type.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    function variable(initialValue, trainable, name, dtype) {
+        if (trainable === void 0) { trainable = true; }
+        return ENGINE.makeVariable(initialValue, trainable, name, dtype);
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function whereImpl(condShape, condVals) {
+        var indices = [];
+        for (var i = 0; i < condVals.length; i++) {
+            if (condVals[i]) {
+                indices.push(i);
+            }
+        }
+        var inBuffer = buffer(condShape, 'int32');
+        var out = buffer([indices.length, condShape.length], 'int32');
+        for (var i = 0; i < indices.length; i++) {
+            var loc = inBuffer.indexToLoc(indices[i]);
+            var offset = i * condShape.length;
+            out.values.set(loc, offset);
+        }
+        return out.toTensor();
+    }
+
+    /**
+     * Returns the coordinates of true elements of condition.
+     *
+     * The coordinates are returned in a 2-D tensor where the first dimension (rows)
+     * represents the number of true elements, and the second dimension (columns)
+     * represents the coordinates of the true elements. Keep in mind, the shape of
+     * the output tensor can vary depending on how many true values there are in
+     * input. Indices are output in row-major order. The resulting tensor has the
+     * shape `[numTrueElems, condition.rank]`.
+     *
+     * This is analogous to calling the python `tf.where(cond)` without an x or y.
+     *
+     * ```js
+     * const cond = tf.tensor1d([false, false, true], 'bool');
+     * const result = await tf.whereAsync(cond);
+     * result.print();
+     * ```
+     *
+     * @doc {heading: 'Operations', subheading: 'Logical'}
+     */
+    function whereAsync_(condition) {
+        return __awaiter(this, void 0, void 0, function () {
+            var $condition, vals, res;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');
+                        return [4 /*yield*/, $condition.data()];
+                    case 1:
+                        vals = _a.sent();
+                        res = whereImpl($condition.shape, vals);
+                        if (condition !== $condition) {
+                            $condition.dispose();
+                        }
+                        return [2 /*return*/, res];
+                }
+            });
+        });
+    }
+    var whereAsync = whereAsync_;
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Apply boolean mask to tensor.
+     *
+     * ```js
+     * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
+     * const mask = tf.tensor1d([1, 0, 1], 'bool');
+     * const result = await tf.booleanMaskAsync(tensor, mask);
+     * result.print();
+     * ```
+     *
+     * @param tensor N-D tensor.
+     * @param mask K-D boolean tensor, K <= N and K must be known statically.
+     * @param axis A 0-D int Tensor representing the axis in tensor to mask from.
+     *     By default, axis is 0 which will mask from the first dimension.
+     *     Otherwise K + axis <= N.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
+     */
+    function booleanMaskAsync_(tensor, mask, axis) {
+        return __awaiter(this, void 0, void 0, function () {
+            var $tensor, $mask, axisFrom, maskDim, tensorShape, leadingSize, i, targetTensorShape, reshapedTensor, reshapedMask, positivePositions, indices, res;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        $tensor = convertToTensor(tensor, 'tensor', 'boolMask');
+                        $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');
+                        axisFrom = axis == null ? 0 : axis;
+                        maskDim = $mask.rank;
+                        tensorShape = $tensor.shape;
+                        assert(maskDim > 0, function () { return 'mask cannot be scalar'; });
+                        assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, "mask's shape must match the first K dimensions of tensor's shape,");
+                        leadingSize = 1;
+                        for (i = axisFrom; i < axisFrom + maskDim; i++) {
+                            leadingSize *= tensorShape[i];
+                        }
+                        targetTensorShape = tensorShape.slice(0, axisFrom)
+                            .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
+                        reshapedTensor = reshape($tensor, targetTensorShape);
+                        reshapedMask = reshape($mask, [-1]);
+                        return [4 /*yield*/, whereAsync(reshapedMask)];
+                    case 1:
+                        positivePositions = _a.sent();
+                        indices = squeeze(positivePositions, [1]);
+                        res = gather(reshapedTensor, indices, axisFrom);
+                        // Ensure no memory leak.
+                        if (tensor !== $tensor) {
+                            $tensor.dispose();
+                        }
+                        if (mask !== $mask) {
+                            $mask.dispose();
+                        }
+                        indices.dispose();
+                        reshapedTensor.dispose();
+                        reshapedMask.dispose();
+                        positivePositions.dispose();
+                        return [2 /*return*/, res];
+                }
+            });
+        });
+    }
+    var booleanMaskAsync = booleanMaskAsync_;
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the norm of scalar, vectors, and matrices.
+     * This function can compute several different vector norms (the 1-norm, the
+     * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)
+     * and matrix norms (Frobenius, 1-norm, and inf-norm).
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 3, 4]);
+     *
+     * x.norm().print();  // or tf.norm(x)
+     * ```
+     *
+     * @param x The input array.
+     * @param ord Optional. Order of the norm. Supported norm types are
+     * following:
+     *
+     *  | ord        | norm for matrices         | norm for vectors
+     *  |------------|---------------------------|---------------------
+     *  |'euclidean' |Frobenius norm             |2-norm
+     *  |'fro'       |Frobenius norm	           |
+     *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))
+     *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))
+     *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))
+     *  |2           |                           |sum(abs(x)^2)^1/2*
+     *
+     * @param axis Optional. If axis is null (the default), the input is
+     * considered a vector and a single vector norm is computed over the entire
+     * set of values in the Tensor, i.e. norm(x, ord) is equivalent
+     * to norm(x.reshape([-1]), ord). If axis is a integer, the input
+     * is considered a batch of vectors, and axis determines the axis in x
+     * over which to compute vector norms. If axis is a 2-tuple of integer it is
+     * considered a batch of matrices and axis determines the axes in NDArray
+     * over which to compute a matrix norm.
+     * @param keepDims Optional. If true, the norm have the same dimensionality
+     * as the input.
+     *
+     * @doc {heading: 'Operations', subheading: 'Matrices'}
+     */
+    function norm_(x, ord, axis, keepDims) {
+        if (ord === void 0) { ord = 'euclidean'; }
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        x = convertToTensor(x, 'x', 'norm');
+        var norm = normImpl(x, ord, axis);
+        var keepDimsShape = norm.shape;
+        if (keepDims) {
+            var axes = parseAxisParam(axis, x.shape);
+            keepDimsShape = expandShapeToKeepDim(norm.shape, axes);
+        }
+        return reshape(norm, keepDimsShape);
+    }
+    function normImpl(x, p, axis) {
+        if (axis === void 0) { axis = null; }
+        if (x.rank === 0) {
+            return abs(x);
+        }
+        // consider vector when no axis is specified
+        if (x.rank !== 1 && axis === null) {
+            return normImpl(reshape(x, [-1]), p, axis);
+        }
+        // vector
+        if (x.rank === 1 || typeof axis === 'number' ||
+            Array.isArray(axis) && axis.length === 1) {
+            if (p === 1) {
+                return sum$1(abs(x), axis);
+            }
+            if (p === Infinity) {
+                return max(abs(x), axis);
+            }
+            if (p === -Infinity) {
+                return min(abs(x), axis);
+            }
+            if (p === 'euclidean' || p === 2) {
+                // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2
+                return sqrt(sum$1(pow(abs(x), scalar(2, 'int32')), axis));
+            }
+            throw new Error("Error in norm: invalid ord value: " + p);
+        }
+        // matrix (assumption axis[0] < axis[1])
+        if (Array.isArray(axis) && axis.length === 2) {
+            if (p === 1) {
+                return max(sum$1(abs(x), axis[0]), axis[1] - 1);
+            }
+            if (p === Infinity) {
+                return max(sum$1(abs(x), axis[1]), axis[0]);
+            }
+            if (p === -Infinity) {
+                return min(sum$1(abs(x), axis[1]), axis[0]);
+            }
+            if (p === 'fro' || p === 'euclidean') {
+                // norm(x) = sqrt(sum(pow(x, 2)))
+                return sqrt(sum$1(square(x), axis));
+            }
+            throw new Error("Error in norm: invalid ord value: " + p);
+        }
+        throw new Error("Error in norm: invalid axis: " + axis);
+    }
+    var norm = op({ norm_: norm_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Compute the moving average of a variable.
+     *
+     * Without zeroDebias, the moving average operation is defined by:
+     *   `v += delta`
+     * where
+     *   `delta = (1 - decay) * (x - v)`
+     *
+     * With zeroDebias (default), the `delta` term is scaled to debias the
+     * effect of the (assumed) zero-initialization of `v`.
+     *   `delta /= (1 - decay ^ step)`
+     *
+     * For more details on the zero-debiasing algorithm, see:
+     *   https://arxiv.org/abs/1412.6980
+     *
+     * Note that this function is completely stateless and does not keep track of
+     * step count. The step count needs to be maintained by the caller and passed
+     * in as `step`.
+     *
+     * @param v The current moving average value.
+     * @param x New input value, must have the same shape and dtype as `v`.
+     * @param decay The decay factor. Typical values are 0.95 and 0.99.
+     * @param step Step count.
+     * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).
+     * @returns The new moving average value.
+     *
+     * @doc {heading: 'Operations', subheading: 'Moving Average'}
+     */
+    function movingAverage_(v, x, decay, step, zeroDebias) {
+        if (zeroDebias === void 0) { zeroDebias = true; }
+        var $v = convertToTensor(v, 'v', 'movingAverage');
+        var $x = convertToTensor(x, 'x', 'movingAverage');
+        var $decay = convertToTensor(decay, 'decay', 'movingAverage');
+        assertTypesMatch($v, $x);
+        assert(arraysEqual($v.shape, $x.shape), function () { return 'Shape mismatch in v and x'; });
+        var one = scalar(1);
+        var oneMinusDecay = sub(one, $decay);
+        var update = mul(sub($x, $v), oneMinusDecay);
+        if (zeroDebias) {
+            assert(step != null, function () { return 'When using zeroDebias: true, step is required.'; });
+            var $step = convertToTensor(step, 'step', 'movingAverage');
+            update = div(update, sub(one, pow($decay, $step)));
+        }
+        return add$1($v, update);
+    }
+    var movingAverage = op({ movingAverage_: movingAverage_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates a new tensor by applying sparse updates to individual
+     * values or slices within a zero tensor of the given shape tensor according to
+     * indices. This operator is the inverse of the `tf.gatherND` operator which
+     * extracts values or slices from a given tensor.
+     *
+     * ```js
+     * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');
+     * const updates = tf.tensor1d([9, 10, 11, 12]);
+     * const shape = [8];
+     * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]
+     * ```
+     *
+     * @param indices The tensor contains the indices into the output tensor.
+     * @param updates The tensor contains the value for the indices.
+     * @param shape: The shape of the output tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
+     */
+    function scatterND_(indices, updates, shape) {
+        var $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');
+        var $updates = convertToTensor(updates, 'updates', 'scatterND');
+        validateInput($updates, $indices, shape);
+        var inputs = { indices: $indices, updates: $updates };
+        var attrs = { shape: shape };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        return ENGINE.runKernel(ScatterNd, inputs, attrs);
+    }
+    var scatterND = op({ scatterND_: scatterND_ });
+
+    /**
+     * Validate sparseToDense inputs.
+     *
+     * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.
+     * sparseIndices[i] contains the complete index where sparseValues[i] will be
+     * placed.
+     * @param sparseValues A 0-D or 1-D Tensor. Values
+     * corresponding to each row of sparseIndices, or a scalar value to be used for
+     * all sparse indices.
+     * @param outputShape number[]. Shape of the dense output tensor.
+     * @param validateIndices boolean. indice validation is not supported, error
+     * will be thrown if it is set.
+     */
+    function validateInput$1(sparseIndices, sparseValues, outputShape, defaultValues) {
+        if (sparseIndices.dtype !== 'int32') {
+            throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +
+                (" but the dtype was " + sparseIndices.dtype + "."));
+        }
+        if (sparseIndices.rank > 2) {
+            throw new Error('sparseIndices should be a scalar, vector, or matrix,' +
+                (" but got shape " + sparseIndices.shape + "."));
+        }
+        var numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
+        var numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
+        if (outputShape.length !== numDims) {
+            throw new Error('outputShape has incorrect number of elements:,' +
+                (" " + outputShape.length + ", should be: " + numDims + "."));
+        }
+        var numValues = sparseValues.size;
+        if (!(sparseValues.rank === 0 ||
+            sparseValues.rank === 1 && numValues === numElems)) {
+            throw new Error('sparseValues has incorrect shape ' +
+                (sparseValues.shape + ", should be [] or [" + numElems + "]"));
+        }
+        if (sparseValues.dtype !== defaultValues.dtype) {
+            throw new Error('sparseValues.dtype must match defaultValues.dtype');
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Converts a sparse representation into a dense tensor.
+     *
+     * Builds an array dense with shape outputShape such that:
+     *
+     * // If sparseIndices is scalar
+     * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)
+     *
+     * // If sparseIndices is a vector, then for each i
+     * dense[sparseIndices[i]] = sparseValues[i]
+     *
+     * // If sparseIndices is an n by d matrix, then for each i in [0, n)
+     * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]
+     * All other values in dense are set to defaultValue. If sparseValues is a
+     * scalar, all sparse indices are set to this single value.
+     *
+     * If indices are repeated the final value is summed over all values for those
+     * indices.
+     *
+     * ```js
+     * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');
+     * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');
+     * const shape = [8];
+     * tf.sparseToDense(indices, values, shape).print();
+     * ```
+     *
+     * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.
+     * sparseIndices[i] contains the complete index where sparseValues[i] will be
+     * placed.
+     * @param sparseValues A 0-D or 1-D Tensor. Values
+     * corresponding to each row of sparseIndices, or a scalar value to be used for
+     * all sparse indices.
+     * @param outputShape Shape of the dense output tensor. the type is inferred.
+     * @param defaultValue Scalar. Value to set for indices not specified in
+     * sparseIndices. Defaults to zero.
+     *
+     * @doc {heading: 'Operations', subheading: 'Normalization'}
+     */
+    function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue) {
+        if (defaultValue === void 0) { defaultValue = 0; }
+        var $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');
+        var $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');
+        var $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);
+        validateInput$1($sparseIndices, $sparseValues, outputShape, $defaultValue);
+        var inputs = {
+            sparseIndices: $sparseIndices,
+            sparseValues: $sparseValues,
+            defaultValue: $defaultValue
+        };
+        var attrs = { outputShape: outputShape };
+        return ENGINE.runKernel(SparseToDense, inputs, attrs);
+    }
+    var sparseToDense = op({ sparseToDense_: sparseToDense_ });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Gather slices from input tensor into a Tensor with shape specified by
+     * `indices`.
+     *
+     * `indices` is an K-dimensional integer tensor, best thought of as a
+     * (K-1)-dimensional tensor of indices into input, where each element defines a
+     * slice of input:
+     * output[\\(i_0, ..., i_{K-2}\\)] = input[indices[\\(i_0, ..., i_{K-2}\\)]]
+     *
+     * Whereas in `tf.gather`, `indices` defines slices into the first dimension of
+     * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions
+     * of input, where N = indices.shape[-1].
+     *
+     * The last dimension of indices can be at most the rank of input:
+     * indices.shape[-1] <= input.rank
+     *
+     * The last dimension of `indices` corresponds to elements
+     * (if indices.shape[-1] == input.rank) or slices
+     * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of
+     * input.
+     * The output tensor has shape
+     * indices.shape[:-1] + input.shape[indices.shape[-1]:]
+     *
+     * Note that on CPU, if an out of bound index is found, an error is returned. On
+     * GPU, if an out of bound index is found, a 0 is stored in the corresponding
+     * output value.
+     *
+     * ```js
+     * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');
+     * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);
+     * tf.gatherND(input, indices).print() // [10, 11]
+     * ```
+     *
+     * @param x The tensor from which to gather values.
+     * @param indices Index tensor, must be of type int32.
+     *
+     * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
+     */
+    function gatherND_(x, indices) {
+        var $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');
+        var $x = convertToTensor(x, 'x', 'gatherND', 'string_or_numeric');
+        var inputs = { params: $x, indices: $indices };
+        return ENGINE.runKernel(GatherNd, inputs);
+    }
+    var gatherND = op({ gatherND_: gatherND_ });
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Normalize noise shape based on provided tensor and noise shape.
+     *
+     * @param x Tensor.
+     * @param noiseShape The shape for the randomly generated keep/drop flags, as
+     *   an array of numbers. Optional.
+     * @returns Normalized noise shape.
+     */
+    function getNoiseShape(x, noiseShape) {
+        if (noiseShape == null) {
+            return x.shape.slice();
+        }
+        if (arraysEqual(x.shape, noiseShape)) {
+            return noiseShape;
+        }
+        if (x.shape.length === noiseShape.length) {
+            var newDimension = [];
+            for (var i = 0; i < x.shape.length; i++) {
+                if (noiseShape[i] == null && x.shape[i] != null) {
+                    newDimension.push(x.shape[i]);
+                }
+                else {
+                    newDimension.push(noiseShape[i]);
+                }
+            }
+            return newDimension;
+        }
+        return noiseShape;
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes dropout.
+     *
+     * ```js
+     * const x = tf.tensor1d([1, 2, 2, 1]);
+     * const rate = 0.75;
+     * const output = tf.dropout(x, rate);
+     * output.print();
+     * ```
+     *
+     * @param x A floating point Tensor or TensorLike.
+     * @param rate A float in the range [0, 1). The probability that each element
+     *   of x is discarded.
+     * @param noiseShape An array of numbers of type int32, representing the
+     * shape for randomly generated keep/drop flags. If the noiseShape has null
+     * value, it will be automatically replaced with the x's relative dimension
+     * size. Optional.
+     * @param seed Used to create random seeds. Optional.
+     * @returns A Tensor of the same shape of x.
+     *
+     * @doc {heading: 'Operations', subheading: 'Dropout'}
+     */
+    function dropout_(x, rate, noiseShape, seed) {
+        var $x = convertToTensor(x, 'x', 'dropout');
+        assert($x.dtype === 'float32', function () { return "x has to be a floating point tensor since it's going to be " +
+            ("scaled, but got a " + $x.dtype + " tensor instead."); });
+        assert(rate >= 0 && rate < 1, function () { return "rate must be a float in the range [0, 1), but got " + rate + "."; });
+        if (rate === 0) {
+            return x instanceof Tensor ? $x.clone() : $x;
+        }
+        var $noiseShape = getNoiseShape($x, noiseShape);
+        var keepProb = 1 - rate;
+        var multiplier = div(floor(add$1(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb);
+        return mul($x, multiplier);
+    }
+    var dropout = op({ dropout_: dropout_ });
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function enclosingPowerOfTwo(value) {
+        // Return 2**N for integer N such that 2**N >= value.
+        return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));
+    }
+    function cosineWindow(windowLength, a, b) {
+        var even = 1 - windowLength % 2;
+        var newValues = new Float32Array(windowLength);
+        for (var i = 0; i < windowLength; ++i) {
+            var cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);
+            newValues[i] = a - b * Math.cos(cosArg);
+        }
+        return tensor1d(newValues, 'float32');
+    }
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Returns whether the targets are in the top K predictions.
+     *
+     * ```js
+     * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);
+     * const targets = tf.tensor1d([2, 0]);
+     * const precision = await tf.inTopKAsync(predictions, targets);
+     * precision.print();
+     * ```
+     * @param predictions 2-D or higher `tf.Tensor` with last dimension being
+     *     at least `k`.
+     * @param targets 1-D or higher `tf.Tensor`.
+     * @param k Optional Number of top elements to look at for computing precision,
+     *     default to 1.
+     *
+     * @doc {heading: 'Operations', subheading: 'Evaluation'}
+     */
+    function inTopKAsync_(predictions, targets, k) {
+        if (k === void 0) { k = 1; }
+        return __awaiter(this, void 0, void 0, function () {
+            var $predictions, $targets, lastDim, predictionsVals, targetsVals, _a, batch, size, precision, b, offset, vals, valAndInd, i, i;
+            return __generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        $predictions = convertToTensor(predictions, 'predictions', 'inTopK');
+                        $targets = convertToTensor(targets, 'targets', 'inTopK');
+                        assert($predictions.rank > 1, function () { return 'inTopK() expects the predictions to be of rank 2 or higher, ' +
+                            ("but got " + $predictions.rank); });
+                        assert($predictions.rank - 1 === $targets.rank, function () { return "predictions rank should be 1 larger than " +
+                            "targets rank, but got predictions rank " +
+                            ($predictions.rank + " and targets rank " + $targets.rank); });
+                        assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, "predictions's shape should be align with the targets' shape, " +
+                            'except the last dimension.');
+                        lastDim = $predictions.shape[$predictions.shape.length - 1];
+                        assert(k > 0 && k <= lastDim, function () { return "'k' passed to inTopK() must be > 0 && <= the predictions last " +
+                            ("dimension (" + lastDim + "), but got " + k); });
+                        return [4 /*yield*/, $predictions.data()];
+                    case 1:
+                        predictionsVals = _b.sent();
+                        return [4 /*yield*/, $targets.data()];
+                    case 2:
+                        targetsVals = _b.sent();
+                        _a = [predictionsVals.length / lastDim, lastDim], batch = _a[0], size = _a[1];
+                        precision = getTypedArrayFromDType('bool', batch);
+                        for (b = 0; b < batch; b++) {
+                            offset = b * size;
+                            vals = predictionsVals.subarray(offset, offset + size);
+                            valAndInd = [];
+                            for (i = 0; i < vals.length; i++) {
+                                valAndInd.push({ value: vals[i], index: i });
+                            }
+                            valAndInd.sort(function (a, b) { return b.value - a.value; });
+                            precision[b] = 0;
+                            for (i = 0; i < k; i++) {
+                                if (valAndInd[i].index === targetsVals[b]) {
+                                    precision[b] = 1;
+                                    break;
+                                }
+                            }
+                        }
+                        if (predictions !== $predictions) {
+                            $predictions.dispose();
+                        }
+                        if (targets !== $targets) {
+                            $targets.dispose();
+                        }
+                        // Output precision has the same shape as targets.
+                        return [2 /*return*/, tensor(precision, $targets.shape, 'bool')];
+                }
+            });
+        });
+    }
+    var inTopKAsync = inTopKAsync_;
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the derivative of the filter of a 2D convolution.
+     *
+     * @param x The input tensor, of rank 4 or rank 3 of shape
+     *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.
+     * @param dy The dy image, of rank 4 or rank 3, of shape
+     *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.
+     * @param filterShape The shape of the filter, length 4,
+     *     [filterHeight, filterWidth, inDepth, outDepth].
+     * @param strides The strides of the convolution: [strideHeight,
+     * strideWidth].
+     * @param pad A string from: 'same', 'valid'. The type of padding algorithm
+     *     used in the forward prop of the op.
+     * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
+     *     "NHWC". Specify the data format of the input and output data. With the
+     *     default format "NHWC", the data is stored in the order of: [batch,
+     *     height, width, channels].
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     */
+    function conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat, dimRoundingMode) {
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        var x4D = x;
+        if (x.rank === 3) {
+            x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
+        }
+        var dy4D = dy;
+        if (dy4D.rank === 3) {
+            dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
+        }
+        assert(x4D.rank === 4, function () { return "Error in conv2dDerFilter: input must be rank 4, but got shape " +
+            (x4D.shape + "."); });
+        assert(dy4D.rank === 4, function () { return "Error in conv2dDerFilter: dy must be rank 4, but got shape " +
+            (dy4D.shape + "."); });
+        assert(filterShape.length === 4, function () { return "Error in conv2dDerFilter: filterShape must be length 4, but got " +
+            (filterShape + "."); });
+        var inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];
+        var outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];
+        assert(inDepth === filterShape[2], function () { return "Error in conv2dDerFilter: depth of input " + inDepth + ") must " +
+            ("match input depth in filter (" + filterShape[2] + "."); });
+        assert(outDepth === filterShape[3], function () { return "Error in conv2dDerFilter: depth of dy (" + outDepth + ") must " +
+            ("match output depth for filter (" + filterShape[3] + ")."); });
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), function () { return "Error in conv2dDerFilter: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+        }
+        var inputs = { x: x4D, dy: dy4D };
+        var attrs = { strides: strides, pad: pad, dataFormat: dataFormat, dimRoundingMode: dimRoundingMode, filterShape: filterShape };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs);
+    }
+    var conv2DBackpropFilter = op({ conv2DBackpropFilter_: conv2DBackpropFilter_ });
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // Returns gradient for fused activation.
+    function getFusedDyActivation(dy, y, activation) {
+        if (activation == null || activation === 'linear') {
+            return dy;
+        }
+        if (activation === 'relu') {
+            return mul(dy, step(y));
+        }
+        throw new Error("Cannot compute gradient for fused activation " + activation + ".");
+    }
+    // Returns gradient for fused bias.
+    function getFusedBiasGradient(bias, dyActivation) {
+        var res = dyActivation;
+        var reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);
+        if (reduceAxes.length > 0) {
+            res = sum$1(res, reduceAxes);
+        }
+        return reshape(res, bias.shape);
+    }
+    function applyActivation(x, activation, preluActivationWeights, leakyreluAlpha) {
+        if (activation === 'linear') {
+            return x;
+        }
+        else if (activation === 'relu') {
+            return relu(x);
+        }
+        else if (activation === 'elu') {
+            return elu(x);
+        }
+        else if (activation === 'relu6') {
+            return relu6(x);
+        }
+        else if (activation === 'prelu') {
+            return prelu(x, preluActivationWeights);
+        }
+        else if (activation === 'leakyrelu') {
+            return leakyRelu(x, leakyreluAlpha);
+        }
+        else if (activation === 'sigmoid') {
+            return sigmoid(x);
+        }
+        throw new Error("Unknown fused activation " + activation + ".");
+    }
+    // Whether we should call fused ops.
+    var shouldFuse = function (gradientDepth, activation) {
+        var gradientMode = gradientDepth > 0;
+        return !gradientMode || activation === 'linear';
+    };
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes a 2D convolution over the input x, optionally fused with adding a
+     * bias and applying an activation.
+     *
+     * ```js
+     * const inputDepth = 2;
+     * const inShape = [2, 2, 2, inputDepth];
+     * const outputDepth = 2;
+     * const fSize = 1;
+     * const pad = 0;
+     * const strides = 1;
+     *
+     * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+     * 16], inShape);
+     * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,
+     * outputDepth]);
+     *
+     * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',
+     * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();
+     * ```
+     *
+     * @param obj An object with the following properties:
+     * @param x The input tensor, of rank 4 or rank 3, of shape
+     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
+     * assumed.
+     * @param filter The filter, rank 4, of shape
+     *     `[filterHeight, filterWidth, inDepth, outDepth]`.
+     * @param strides The strides of the convolution: `[strideHeight,
+     * strideWidth]`.
+     * @param pad The type of padding algorithm.
+     *   - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *   - `valid` output will be smaller than input if filter is larger
+     *       than 1x1.
+     *   - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to
+     *     "NHWC". Specify the data format of the input and output data. With the
+     *     default format "NHWC", the data is stored in the order of: [batch,
+     *     height, width, channels]. Only "NHWC" is currently supported.
+     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
+     *     in which we sample input values across the height and width dimensions
+     *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single
+     *     number, then `dilationHeight == dilationWidth`. If it is greater than
+     *     1, then all values of `strides` must be 1.
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     * @param bias Tensor to be added to the result.
+     * @param activation Name of activation kernel (defaults to `linear`) to be
+     *     applied
+     *      after biasAdd.
+     * @param preluActivationWeights Tensor of prelu weights to be applied as part
+     *     of a `prelu` activation, typically the same shape as `x`.
+     * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`
+     *     activation.
+     */
+    function fusedConv2d_(_a) {
+        var x = _a.x, filter = _a.filter, strides = _a.strides, pad = _a.pad, _b = _a.dataFormat, dataFormat = _b === void 0 ? 'NHWC' : _b, _c = _a.dilations, dilations = _c === void 0 ? [1, 1] : _c, dimRoundingMode = _a.dimRoundingMode, bias = _a.bias, _d = _a.activation, activation = _d === void 0 ? 'linear' : _d, preluActivationWeights = _a.preluActivationWeights, leakyreluAlpha = _a.leakyreluAlpha;
+        activation = activation || 'linear';
+        if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
+            var result = conv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
+            if (bias != null) {
+                result = add$1(result, bias);
+            }
+            return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
+        }
+        var $x = convertToTensor(x, 'x', 'conv2d');
+        var $filter = convertToTensor(filter, 'filter', 'conv2d');
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+        }
+        assert(x4D.rank === 4, function () { return "Error in fused conv2d: input must be rank 4, but got rank " +
+            (x4D.rank + "."); });
+        assert($filter.rank === 4, function () { return "Error in fused conv2d: filter must be rank 4, but got rank " +
+            ($filter.rank + "."); });
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), function () { return "Error in fused conv2d: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+        }
+        assert(x4D.shape[3] === $filter.shape[2], function () { return "Error in conv2d: depth of input (" + x4D.shape[3] + ") must match " +
+            ("input depth for filter " + $filter.shape[2] + "."); });
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in conv2D: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
+        assert(dataFormat === 'NHWC', function () { return "Error in conv2d: got dataFormat of " + dataFormat + " but only NHWC is currently supported."; });
+        var convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);
+        var $bias;
+        if (bias != null) {
+            $bias = convertToTensor(bias, 'bias', 'fused conv2d');
+            $bias = makeTypesMatch($bias, $x)[0];
+            assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
+        }
+        var $preluActivationWeights;
+        if (preluActivationWeights != null) {
+            $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');
+        }
+        var grad = function (dy, saved) {
+            var _a = saved, $filter = _a[0], x4D = _a[1], y = _a[2], $bias = _a[3];
+            var dyActivation = getFusedDyActivation(dy, y, activation);
+            assert(tupleValuesAreOne(dilations), function () { return 'Error in gradient of fused conv2D: ' +
+                "dilation rates greater than 1 " +
+                ("are not yet supported in gradients. Got dilations '" + dilations + "'"); });
+            var xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);
+            var filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);
+            var der = [xDer, filterDer];
+            if ($bias != null) {
+                var biasDer = getFusedBiasGradient($bias, dyActivation);
+                der.push(biasDer);
+            }
+            return der;
+        };
+        var inputs = {
+            x: x4D,
+            filter: $filter,
+            bias: $bias,
+            preluActivationWeights: $preluActivationWeights
+        };
+        var attrs = {
+            strides: strides,
+            pad: pad,
+            dataFormat: dataFormat,
+            dilations: dilations,
+            dimRoundingMode: dimRoundingMode,
+            activation: activation,
+            leakyreluAlpha: leakyreluAlpha
+        };
+        // Depending on the the params passed in we will have different number of
+        // inputs and thus a a different number of elements in the gradient.
+        if (bias == null) {
+            var customOp = customGrad(function (x4D, filter, save) {
+                var res = 
+                // tslint:disable-next-line: no-unnecessary-type-assertion
+                ENGINE.runKernel(FusedConv2D, inputs, attrs);
+                save([filter, x4D, res]);
+                if (reshapedTo4D) {
+                    // tslint:disable-next-line: no-unnecessary-type-assertion
+                    res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+                }
+                return { value: res, gradFunc: grad };
+            });
+            return customOp(x4D, $filter);
+        }
+        else {
+            var customOpWithBias = customGrad(function (x4D, filter, bias, save) {
+                var res = ENGINE.runKernel(FusedConv2D, inputs, attrs);
+                save([filter, x4D, res, bias]);
+                if (reshapedTo4D) {
+                    // tslint:disable-next-line: no-unnecessary-type-assertion
+                    res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+                }
+                return { value: res, gradFunc: grad };
+            });
+            return customOpWithBias(x4D, $filter, $bias);
+        }
+    }
+    var conv2d$1 = op({ fusedConv2d_: fusedConv2d_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad, dilations, dimRoundingMode) {
+        if (dilations === void 0) { dilations = [1, 1]; }
+        var x4D = x;
+        if (x.rank === 3) {
+            x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
+        }
+        var dy4D = dy;
+        if (dy4D.rank === 3) {
+            dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
+        }
+        var inputs = { x: x4D, dy: dy4D };
+        var attrs = { strides: strides, pad: pad, dimRoundingMode: dimRoundingMode, dilations: dilations, filterShape: filterShape };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs);
+    }
+    var depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_: depthwiseConv2dNativeBackpropFilter_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad, dilations, dimRoundingMode) {
+        if (dilations === void 0) { dilations = [1, 1]; }
+        var dy4D = dy;
+        var reshapedTo4D = false;
+        if (dy.rank === 3) {
+            reshapedTo4D = true;
+            dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
+        }
+        var inputs = { dy: dy4D, filter: filter };
+        var attrs = { strides: strides, pad: pad, dimRoundingMode: dimRoundingMode, dilations: dilations, inputShape: xShape };
+        var res = 
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs);
+        if (reshapedTo4D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+        }
+        return res;
+    }
+    var depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_: depthwiseConv2dNativeBackpropInput_ });
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes depthwise 2D convolution, optionally fused with adding a
+     * bias and applying an activation.
+     *
+     * Given a 4D `input` array and a `filter` array of shape
+     * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
+     * `inChannels` convolutional filters of depth 1, this op applies a
+     * different filter to each input channel (expanding from 1 channel to
+     * `channelMultiplier` channels for each), then concatenates the results
+     * together. The output has `inChannels * channelMultiplier` channels.
+     *
+     * See
+     * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
+     *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
+     * for more details.
+     *
+     * @param obj An object with the following properties:
+     * @param x The input tensor, of rank 4 or rank 3, of shape
+     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
+     * assumed.
+     * @param filter The filter tensor, rank 4, of shape
+     *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
+     * @param strides The strides of the convolution: `[strideHeight,
+     * strideWidth]`. If strides is a single number, then `strideHeight ==
+     * strideWidth`.
+     * @param pad The type of padding algorithm.
+     *   - `same` and stride 1: output will be of same size as input,
+     *       regardless of filter size.
+     *   - `valid`: output will be smaller than input if filter is larger
+     *       than 1x1.
+     *   - For more info, see this guide:
+     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
+     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
+     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
+     *     in which we sample input values across the height and width dimensions
+     *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
+     *     number, then `dilationHeight == dilationWidth`. If it is greater than
+     *     1, then all values of `strides` must be 1.
+     * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
+     *     "NHWC". Specify the data format of the input and output data. With the
+     *     default format "NHWC", the data is stored in the order of: [batch,
+     *     height, width, channels]. Only "NHWC" is currently supported.
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     * @param bias Tensor to be added to the result.
+     * @param activation Name of activation kernel (defaults to `linear`).
+     * @param preluActivationWeights Tensor of prelu weights to be applied as part
+     *     of a `prelu` activation, typically the same shape as `x`.
+     * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`
+     *     activation.
+     */
+    function fusedDepthwiseConv2d_(_a) {
+        var x = _a.x, filter = _a.filter, strides = _a.strides, pad = _a.pad, _b = _a.dataFormat, dataFormat = _b === void 0 ? 'NHWC' : _b, _c = _a.dilations, dilations = _c === void 0 ? [1, 1] : _c, dimRoundingMode = _a.dimRoundingMode, bias = _a.bias, _d = _a.activation, activation = _d === void 0 ? 'linear' : _d, preluActivationWeights = _a.preluActivationWeights, leakyreluAlpha = _a.leakyreluAlpha;
+        if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
+            var result = depthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
+            if (bias != null) {
+                result = add$1(result, bias);
+            }
+            return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
+        }
+        var $x = convertToTensor(x, 'x', 'depthwiseConv2d');
+        var $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+        }
+        assert(x4D.rank === 4, function () { return "Error in fused depthwiseConv2d: input must be rank 4, but got " +
+            ("rank " + x4D.rank + "."); });
+        assert($filter.rank === 4, function () { return "Error in fused depthwiseConv2d: filter must be rank 4, " +
+            ("but got rank " + $filter.rank + "."); });
+        assert(x4D.shape[3] === $filter.shape[2], function () { return "Error in fused depthwiseConv2d: number of input channels " +
+            ("(" + x4D.shape[3] + ") must match the inChannels dimension in ") +
+            ("filter " + $filter.shape[2] + "."); });
+        if (dilations == null) {
+            dilations = [1, 1];
+        }
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () {
+            return 'Error in fused depthwiseConv2d: Either strides or dilations must ' +
+                ("be 1. Got strides " + strides + " and dilations '" + dilations + "'");
+        });
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), function () { return "Error in fused depthwiseConv2d: pad must be an integer when " +
+                ("using dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+        }
+        var convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
+        var $bias;
+        if (bias != null) {
+            $bias = convertToTensor(bias, 'bias', 'fused conv2d');
+            $bias = makeTypesMatch($bias, $x)[0];
+            assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
+        }
+        var $preluActivationWeights;
+        if (preluActivationWeights != null) {
+            $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');
+        }
+        var grad = function (dy, saved) {
+            assert(tupleValuesAreOne(dilations), function () { return 'Error in gradient of fused depthwiseConv2d: dilation rates ' +
+                "greater than 1 are not yet supported. Got dilations " +
+                ("'" + dilations + "'"); });
+            var $filter = saved[0], x4D = saved[1], y = saved[2], bias = saved[3];
+            var dyActivation = getFusedDyActivation(dy, y, activation);
+            var xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, strides, pad, dilations, dimRoundingMode);
+            var filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad, dilations, dimRoundingMode);
+            if (bias != null) {
+                var biasDer = getFusedBiasGradient($bias, dyActivation);
+                return [xDer, filterDer, biasDer];
+            }
+            return [xDer, filterDer];
+        };
+        var inputs = {
+            x: x4D,
+            filter: $filter,
+            bias: $bias,
+            preluActivationWeights: $preluActivationWeights
+        };
+        var attrs = {
+            strides: strides,
+            pad: pad,
+            dataFormat: dataFormat,
+            dilations: dilations,
+            dimRoundingMode: dimRoundingMode,
+            activation: activation,
+            leakyreluAlpha: leakyreluAlpha
+        };
+        // Depending on the the params passed in we will have different number of
+        // inputs and thus a a different number of elements in the gradient.
+        if (bias == null) {
+            var customOp = customGrad(function (x4D, filter, save) {
+                // tslint:disable-next-line: no-unnecessary-type-assertion
+                var res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);
+                save([filter, x4D, res]);
+                if (reshapedTo4D) {
+                    // tslint:disable-next-line: no-unnecessary-type-assertion
+                    res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+                }
+                return { value: res, gradFunc: grad };
+            });
+            return customOp(x4D, $filter);
+        }
+        else {
+            var customOpWithBias = customGrad(function (x4D, filter, bias, save) {
+                // tslint:disable-next-line: no-unnecessary-type-assertion
+                var res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);
+                save([filter, x4D, res, bias]);
+                if (reshapedTo4D) {
+                    // tslint:disable-next-line: no-unnecessary-type-assertion
+                    res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+                }
+                return { value: res, gradFunc: grad };
+            });
+            return customOpWithBias(x4D, $filter, $bias);
+        }
+    }
+    var depthwiseConv2d$1 = op({ fusedDepthwiseConv2d_: fusedDepthwiseConv2d_ });
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the dot product of two matrices with optional activation and bias.
+     *
+     * ```js
+     * const a = tf.tensor2d([-1, -2], [1, 2]);
+     * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+     * const bias = tf.tensor2d([1, 2], [1, 2]);
+     *
+     * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();
+     * ```
+     *
+     * @param obj An object with the following properties:
+     * - `a` First matrix in dot product operation.
+     * - `b` Second matrix in dot product operation.
+     * - `transposeA` If true, `a` is transposed before multiplication.
+     * - `transposeB` If true, `b` is transposed before multiplication.
+     * - `bias` Matrix to be added to the result.
+     * - `activation` Name of activation kernel (defaults to `linear`).
+     * - `preluActivationWeights` Tensor of prelu weights.
+     * - `leakyreluAlpha` Alpha of leakyrelu.
+     */
+    function fusedMatMul_(_a) {
+        var _b;
+        var a = _a.a, b = _a.b, _c = _a.transposeA, transposeA = _c === void 0 ? false : _c, _d = _a.transposeB, transposeB = _d === void 0 ? false : _d, bias = _a.bias, _e = _a.activation, activation = _e === void 0 ? 'linear' : _e, preluActivationWeights = _a.preluActivationWeights, leakyreluAlpha = _a.leakyreluAlpha;
+        if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
+            var result = matMul(a, b, transposeA, transposeB);
+            if (bias != null) {
+                result = add$1(result, bias);
+            }
+            return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
+        }
+        var $a = convertToTensor(a, 'a', 'fused matMul');
+        var $b = convertToTensor(b, 'b', 'fused matMul');
+        _b = makeTypesMatch($a, $b), $a = _b[0], $b = _b[1];
+        var innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
+        var innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
+        var outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
+        var outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
+        var outerDimsA = $a.shape.slice(0, -2);
+        var outerDimsB = $b.shape.slice(0, -2);
+        var batchDimA = sizeFromShape(outerDimsA);
+        var batchDimB = sizeFromShape(outerDimsB);
+        assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, function () { return "Error in fused matMul: inputs must have the same rank of at " +
+            ("least 2, got ranks " + $a.rank + " and " + $b.rank + "."); });
+        assert(arraysEqual(outerDimsA, outerDimsB), function () { return "Error in fused matMul: outer dimensions (" + outerDimsA + ") and (" +
+            (outerDimsB + ") of Tensors with shapes " + $a.shape + " and ") +
+            ($b.shape + " must match."); });
+        assert(innerShapeA === innerShapeB, function () { return "Error in fused matMul: inner shapes (" + innerShapeA + ") and (" +
+            (innerShapeB + ") of Tensors with shapes " + $a.shape + " and ") +
+            ($b.shape + " and transposeA=" + transposeA) +
+            (" and transposeB=" + transposeB + " must match."); });
+        var outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
+        var a3D = transposeA ?
+            reshape($a, [batchDimA, innerShapeA, outerShapeA]) :
+            reshape($a, [batchDimA, outerShapeA, innerShapeA]);
+        var b3D = transposeB ?
+            reshape($b, [batchDimB, outerShapeB, innerShapeB]) :
+            reshape($b, [batchDimB, innerShapeB, outerShapeB]);
+        var $bias;
+        if (bias != null) {
+            $bias = convertToTensor(bias, 'bias', 'fused matMul');
+            $bias = makeTypesMatch($bias, $a)[0];
+            assertAndGetBroadcastShape(outShape, $bias.shape);
+        }
+        var $preluActivationWeights;
+        if (preluActivationWeights != null) {
+            $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');
+        }
+        var grad = function (dy, saved) {
+            var a3D = saved[0], b3D = saved[1], y = saved[2], $bias = saved[3];
+            // we reshape dy because the result of the forward is not
+            // necessarily going to be a 3d tensor due to a reshape done at the end of
+            // the customOp.
+            var dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);
+            var aDer;
+            var bDer;
+            if (!transposeA && !transposeB) {
+                aDer = matMul(dyActivation, b3D, false, true);
+                bDer = matMul(a3D, dyActivation, true, false);
+            }
+            else if (!transposeA && transposeB) {
+                aDer = matMul(dyActivation, b3D, false, false);
+                bDer = matMul(dyActivation, a3D, true, false);
+            }
+            else if (transposeA && !transposeB) {
+                aDer = matMul(b3D, dyActivation, false, true);
+                bDer = matMul(a3D, dyActivation, false, false);
+            }
+            else {
+                aDer = matMul(b3D, dyActivation, true, true);
+                bDer = matMul(dyActivation, a3D, true, true);
+            }
+            if (bias != null) {
+                var biasDer = getFusedBiasGradient($bias, dyActivation);
+                return [aDer, bDer, biasDer];
+            }
+            else {
+                return [aDer, bDer];
+            }
+        };
+        var inputs = {
+            a: a3D,
+            b: b3D,
+            bias: $bias,
+            preluActivationWeights: $preluActivationWeights
+        };
+        var attrs = { transposeA: transposeA, transposeB: transposeB, activation: activation, leakyreluAlpha: leakyreluAlpha };
+        // Depending on the the params passed in we will have different number of
+        // inputs and thus a a different number of elements in the gradient.
+        if (bias == null) {
+            var customOp = customGrad(function (a3D, b3D, save) {
+                var res = 
+                // tslint:disable-next-line: no-unnecessary-type-assertion
+                ENGINE.runKernel(_FusedMatMul, inputs, attrs);
+                save([a3D, b3D, res]);
+                return { value: reshape(res, outShape), gradFunc: grad };
+            });
+            return customOp(a3D, b3D);
+        }
+        else {
+            var customOpWithBias = customGrad(function (a3D, b3D, $bias, save) {
+                var res = 
+                // tslint:disable-next-line: no-unnecessary-type-assertion
+                ENGINE.runKernel(_FusedMatMul, inputs, attrs);
+                save([a3D, b3D, res, $bias]);
+                return { value: reshape(res, outShape), gradFunc: grad };
+            });
+            return customOpWithBias(a3D, b3D, $bias);
+        }
+    }
+    var matMul$1 = op({ fusedMatMul_: fusedMatMul_ });
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+
+    var fused_ops = {
+        __proto__: null,
+        conv2d: conv2d$1,
+        depthwiseConv2d: depthwiseConv2d$1,
+        matMul: matMul$1
+    };
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Generate a hamming window.
+     *
+     * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
+     *
+     * ```js
+     * tf.signal.hammingWindow(10).print();
+     * ```
+     * @param The length of window
+     *
+     * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
+     */
+    function hammingWindow_(windowLength) {
+        return cosineWindow(windowLength, 0.54, 0.46);
+    }
+    var hammingWindow = op({ hammingWindow_: hammingWindow_ });
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Generate a Hann window.
+     *
+     * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
+     *
+     * ```js
+     * tf.signal.hannWindow(10).print();
+     * ```
+     * @param The length of window
+     *
+     * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
+     */
+    function hannWindow_(windowLength) {
+        return cosineWindow(windowLength, 0.5, 0.5);
+    }
+    var hannWindow = op({ hannWindow_: hannWindow_ });
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Expands input into frames of frameLength.
+     * Slides a window size with frameStep.
+     *
+     * ```js
+     * tf.signal.frame([1, 2, 3], 2, 1).print();
+     * ```
+     * @param signal The input tensor to be expanded
+     * @param frameLength Length of each frame
+     * @param frameStep The frame hop size in samples.
+     * @param padEnd Whether to pad the end of signal with padValue.
+     * @param padValue An number to use where the input signal does
+     *     not exist when padEnd is True.
+     *
+     * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
+     */
+    function frame_(signal, frameLength, frameStep, padEnd, padValue) {
+        if (padEnd === void 0) { padEnd = false; }
+        if (padValue === void 0) { padValue = 0; }
+        var start = 0;
+        var output = [];
+        while (start + frameLength <= signal.size) {
+            output.push(slice(signal, start, frameLength));
+            start += frameStep;
+        }
+        if (padEnd) {
+            while (start < signal.size) {
+                var padLen = (start + frameLength) - signal.size;
+                var pad = concat([
+                    slice(signal, start, frameLength - padLen), fill([padLen], padValue)
+                ]);
+                output.push(pad);
+                start += frameStep;
+            }
+        }
+        if (output.length === 0) {
+            return tensor2d([], [0, frameLength]);
+        }
+        return reshape(concat(output), [output.length, frameLength]);
+    }
+    var frame = op({ frame_: frame_ });
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the Short-time Fourier Transform of signals
+     * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
+     *
+     * ```js
+     * const input = tf.tensor1d([1, 1, 1, 1, 1])
+     * tf.signal.stft(input, 3, 1).print();
+     * ```
+     * @param signal 1-dimensional real value tensor.
+     * @param frameLength The window length of samples.
+     * @param frameStep The number of samples to step.
+     * @param fftLength The size of the FFT to apply.
+     * @param windowFn A callable that takes a window length and returns 1-d tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
+     */
+    function stft_(signal, frameLength, frameStep, fftLength, windowFn) {
+        if (windowFn === void 0) { windowFn = hannWindow; }
+        if (fftLength == null) {
+            fftLength = enclosingPowerOfTwo(frameLength);
+        }
+        var framedSignal = frame(signal, frameLength, frameStep);
+        var windowedSignal = mul(framedSignal, windowFn(frameLength));
+        return rfft(windowedSignal, fftLength);
+    }
+    var stft = op({ stft_: stft_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Extracts crops from the input image tensor and resizes them using bilinear
+     * sampling or nearest neighbor sampling (possibly with aspect ratio change)
+     * to a common output size specified by cropSize.
+     *
+     * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,
+     *     where imageHeight and imageWidth must be positive, specifying the
+     *     batch of images from which to take crops
+     * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is
+     *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized
+     *     coordinates of the box in the boxInd[i]'th image in the batch
+     * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range
+     *     `[0, batch)` that specifies the image that the `i`-th box refers to.
+     * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`
+     *     specifying the size to which all crops are resized to.
+     * @param method Optional string from `'bilinear' | 'nearest'`,
+     *     defaults to bilinear, which specifies the sampling method for resizing
+     * @param extrapolationValue A threshold for deciding when to remove boxes based
+     *     on score. Defaults to 0.
+     * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {
+        if (method === void 0) { method = 'bilinear'; }
+        if (extrapolationValue === void 0) { extrapolationValue = 0; }
+        var $image = convertToTensor(image, 'image', 'cropAndResize');
+        var $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');
+        var $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');
+        var numBoxes = $boxes.shape[0];
+        assert($image.rank === 4, function () { return 'Error in cropAndResize: image must be rank 4,' +
+            ("but got rank " + $image.rank + "."); });
+        assert($boxes.rank === 2 && $boxes.shape[1] === 4, function () { return "Error in cropAndResize: boxes must be have size [" + numBoxes + ",4] " +
+            ("but had shape " + $boxes.shape + "."); });
+        assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, function () { return "Error in cropAndResize: boxInd must be have size [" + numBoxes + "] " +
+            ("but had shape " + $boxes.shape + "."); });
+        assert(cropSize.length === 2, function () { return "Error in cropAndResize: cropSize must be of length 2, but got " +
+            ("length " + cropSize.length + "."); });
+        assert(cropSize[0] >= 1 && cropSize[1] >= 1, function () { return "cropSize must be atleast [1,1], but was " + cropSize; });
+        assert(method === 'bilinear' || method === 'nearest', function () { return "method must be bilinear or nearest, but was " + method; });
+        var inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };
+        var attrs = { method: method, extrapolationValue: extrapolationValue, cropSize: cropSize };
+        var res = ENGINE.runKernel(CropAndResize, inputs, attrs);
+        return res;
+    }
+    var cropAndResize = op({ cropAndResize_: cropAndResize_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Flips the image left to right. Currently available in the CPU, WebGL, and
+     * WASM backends.
+     *
+     * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.
+     */
+    /** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
+    function flipLeftRight_(image) {
+        var $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');
+        assert($image.rank === 4, function () { return 'Error in flipLeftRight: image must be rank 4,' +
+            ("but got rank " + $image.rank + "."); });
+        var inputs = { image: $image };
+        var res = ENGINE.runKernel(FlipLeftRight, inputs, {});
+        return res;
+    }
+    var flipLeftRight = op({ flipLeftRight_: flipLeftRight_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Rotates the input image tensor counter-clockwise with an optional offset
+     * center of rotation. Currently available in the CPU, WebGL, and WASM backends.
+     *
+     * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.
+     * @param radians The amount of rotation.
+     * @param fillValue The value to fill in the empty space leftover
+     *     after rotation. Can be either a single grayscale value (0-255), or an
+     *     array of three numbers `[red, green, blue]` specifying the red, green,
+     *     and blue channels. Defaults to `0` (black).
+     * @param center The center of rotation. Can be either a single value (0-1), or
+     *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates
+     *     the image around its center).
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function rotateWithOffset_(image, radians, fillValue, center) {
+        if (fillValue === void 0) { fillValue = 0; }
+        if (center === void 0) { center = 0.5; }
+        var $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');
+        assert($image.rank === 4, function () { return 'Error in rotateWithOffset: image must be rank 4,' +
+            ("but got rank " + $image.rank + "."); });
+        var inputs = { image: $image };
+        var attrs = { radians: radians, fillValue: fillValue, center: center };
+        var res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);
+        return res;
+    }
+    var rotateWithOffset = op({ rotateWithOffset_: rotateWithOffset_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
+        if (iouThreshold == null) {
+            iouThreshold = 0.5;
+        }
+        if (scoreThreshold == null) {
+            scoreThreshold = Number.NEGATIVE_INFINITY;
+        }
+        if (softNmsSigma == null) {
+            softNmsSigma = 0.0;
+        }
+        var numBoxes = boxes.shape[0];
+        maxOutputSize = Math.min(maxOutputSize, numBoxes);
+        assert(0 <= iouThreshold && iouThreshold <= 1, function () { return "iouThreshold must be in [0, 1], but was '" + iouThreshold + "'"; });
+        assert(boxes.rank === 2, function () { return "boxes must be a 2D tensor, but was of rank '" + boxes.rank + "'"; });
+        assert(boxes.shape[1] === 4, function () {
+            return "boxes must have 4 columns, but 2nd dimension was " + boxes.shape[1];
+        });
+        assert(scores.rank === 1, function () { return 'scores must be a 1D tensor'; });
+        assert(scores.shape[0] === numBoxes, function () { return "scores has incompatible shape with boxes. Expected " + numBoxes + ", " +
+            ("but was " + scores.shape[0]); });
+        assert(0 <= softNmsSigma && softNmsSigma <= 1, function () { return "softNmsSigma must be in [0, 1], but was '" + softNmsSigma + "'"; });
+        return { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold, softNmsSigma: softNmsSigma };
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Performs non maximum suppression of bounding boxes based on
+     * iou (intersection over union).
+     *
+     * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
+     *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
+     *     the bounding box.
+     * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
+     * @param maxOutputSize The maximum number of boxes to be selected.
+     * @param iouThreshold A float representing the threshold for deciding whether
+     *     boxes overlap too much with respect to IOU. Must be between [0, 1].
+     *     Defaults to 0.5 (50% box overlap).
+     * @param scoreThreshold A threshold for deciding when to remove boxes based
+     *     on score. Defaults to -inf, which means any score is accepted.
+     * @return A 1D tensor with the selected box indices.
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+        if (iouThreshold === void 0) { iouThreshold = 0.5; }
+        if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
+        var $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
+        var $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');
+        var inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
+        maxOutputSize = inputs.maxOutputSize;
+        iouThreshold = inputs.iouThreshold;
+        scoreThreshold = inputs.scoreThreshold;
+        var attrs = { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold };
+        return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs);
+    }
+    var nonMaxSuppression = op({ nonMaxSuppression_: nonMaxSuppression_ });
+
+    /**
+     * @license
+     * Copyright 2019 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Inserts a value into a sorted array. This method allows duplicate, meaning it
+     * allows inserting duplicate value, in which case, the element will be inserted
+     * at the lowest index of the value.
+     * @param arr The array to modify.
+     * @param element The element to insert.
+     * @param comparator Optional. If no comparator is specified, elements are
+     * compared using array_util.defaultComparator, which is suitable for Strings
+     * and Numbers in ascending arrays. If the array contains multiple instances of
+     * the target value, the left-most instance will be returned. To provide a
+     * comparator, it should take 2 arguments to compare and return a negative,
+     * zero, or a positive number.
+     */
+    function binaryInsert(arr, element, comparator) {
+        var index = binarySearch(arr, element, comparator);
+        var insertionPoint = index < 0 ? -(index + 1) : index;
+        arr.splice(insertionPoint, 0, element);
+    }
+    /**
+     * Searches the array for the target using binary search, returns the index
+     * of the found element, or position to insert if element not found. If no
+     * comparator is specified, elements are compared using array_
+     * util.defaultComparator, which is suitable for Strings and Numbers in
+     * ascending arrays. If the array contains multiple instances of the target
+     * value, the left-most instance will be returned.
+     * @param arr The array to be searched in.
+     * @param target The target to be searched for.
+     * @param comparator Should take 2 arguments to compare and return a negative,
+     *    zero, or a positive number.
+     * @return Lowest index of the target value if found, otherwise the insertion
+     *    point where the target should be inserted, in the form of
+     *    (-insertionPoint - 1).
+     */
+    function binarySearch(arr, target, comparator) {
+        return binarySearch_(arr, target, comparator || defaultComparator);
+    }
+    /**
+     * Compares its two arguments for order.
+     * @param a The first element to be compared.
+     * @param b The second element to be compared.
+     * @return A negative number, zero, or a positive number as the first
+     *     argument is less than, equal to, or greater than the second.
+     */
+    function defaultComparator(a, b) {
+        return a > b ? 1 : a < b ? -1 : 0;
+    }
+    function binarySearch_(arr, target, comparator) {
+        var left = 0;
+        var right = arr.length;
+        var middle = 0;
+        var found = false;
+        while (left < right) {
+            middle = left + ((right - left) >>> 1);
+            var compareResult = comparator(target, arr[middle]);
+            if (compareResult > 0) {
+                left = middle + 1;
+            }
+            else {
+                right = middle;
+                // If compareResult is 0, the value is found. We record it is found,
+                // and then keep looking because there may be duplicate.
+                found = !compareResult;
+            }
+        }
+        return found ? left : -left - 1;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+        return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */);
+    }
+    function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {
+        return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true
+        /* returnValidOutputs */ );
+    }
+    function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
+        return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */);
+    }
+    function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor, padToMaxOutputSize, returnValidOutputs) {
+        if (returnScoresTensor === void 0) { returnScoresTensor = false; }
+        if (padToMaxOutputSize === void 0) { padToMaxOutputSize = false; }
+        if (returnValidOutputs === void 0) { returnValidOutputs = false; }
+        // The list is sorted in ascending order, so that we can always pop the
+        // candidate with the largest score in O(1) time.
+        var candidates = [];
+        for (var i = 0; i < scores.length; i++) {
+            if (scores[i] > scoreThreshold) {
+                candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });
+            }
+        }
+        candidates.sort(ascendingComparator);
+        // If softNmsSigma is 0, the outcome of this algorithm is exactly same as
+        // before.
+        var scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;
+        var selectedIndices = [];
+        var selectedScores = [];
+        while (selectedIndices.length < maxOutputSize && candidates.length > 0) {
+            var candidate = candidates.pop();
+            var originalScore = candidate.score, boxIndex = candidate.boxIndex, suppressBeginIndex = candidate.suppressBeginIndex;
+            if (originalScore < scoreThreshold) {
+                break;
+            }
+            // Overlapping boxes are likely to have similar scores, therefore we
+            // iterate through the previously selected boxes backwards in order to
+            // see if candidate's score should be suppressed. We use
+            // suppressBeginIndex to track and ensure a candidate can be suppressed
+            // by a selected box no more than once. Also, if the overlap exceeds
+            // iouThreshold, we simply ignore the candidate.
+            var ignoreCandidate = false;
+            for (var j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {
+                var iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);
+                if (iou >= iouThreshold) {
+                    ignoreCandidate = true;
+                    break;
+                }
+                candidate.score =
+                    candidate.score * suppressWeight(iouThreshold, scale, iou);
+                if (candidate.score <= scoreThreshold) {
+                    break;
+                }
+            }
+            // At this point, if `candidate.score` has not dropped below
+            // `scoreThreshold`, then we know that we went through all of the
+            // previous selections and can safely update `suppressBeginIndex` to the
+            // end of the selected array. Then we can re-insert the candidate with
+            // the updated score and suppressBeginIndex back in the candidate list.
+            // If on the other hand, `candidate.score` has dropped below the score
+            // threshold, we will not add it back to the candidates list.
+            candidate.suppressBeginIndex = selectedIndices.length;
+            if (!ignoreCandidate) {
+                // Candidate has passed all the tests, and is not suppressed, so
+                // select the candidate.
+                if (candidate.score === originalScore) {
+                    selectedIndices.push(boxIndex);
+                    selectedScores.push(candidate.score);
+                }
+                else if (candidate.score > scoreThreshold) {
+                    // Candidate's score is suppressed but is still high enough to be
+                    // considered, so add back to the candidates list.
+                    binaryInsert(candidates, candidate, ascendingComparator);
+                }
+            }
+        }
+        // NonMaxSuppressionV4 feature: padding output to maxOutputSize.
+        var validOutputs = selectedIndices.length;
+        var elemsToPad = maxOutputSize - validOutputs;
+        if (padToMaxOutputSize && elemsToPad > 0) {
+            selectedIndices.push.apply(selectedIndices, new Array(elemsToPad).fill(0));
+            selectedScores.push.apply(selectedScores, new Array(elemsToPad).fill(0.0));
+        }
+        var result = { selectedIndices: selectedIndices };
+        if (returnScoresTensor) {
+            result['selectedScores'] = selectedScores;
+        }
+        if (returnValidOutputs) {
+            result['validOutputs'] = validOutputs;
+        }
+        return result;
+    }
+    function intersectionOverUnion(boxes, i, j) {
+        var iCoord = boxes.subarray(i * 4, i * 4 + 4);
+        var jCoord = boxes.subarray(j * 4, j * 4 + 4);
+        var yminI = Math.min(iCoord[0], iCoord[2]);
+        var xminI = Math.min(iCoord[1], iCoord[3]);
+        var ymaxI = Math.max(iCoord[0], iCoord[2]);
+        var xmaxI = Math.max(iCoord[1], iCoord[3]);
+        var yminJ = Math.min(jCoord[0], jCoord[2]);
+        var xminJ = Math.min(jCoord[1], jCoord[3]);
+        var ymaxJ = Math.max(jCoord[0], jCoord[2]);
+        var xmaxJ = Math.max(jCoord[1], jCoord[3]);
+        var areaI = (ymaxI - yminI) * (xmaxI - xminI);
+        var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
+        if (areaI <= 0 || areaJ <= 0) {
+            return 0.0;
+        }
+        var intersectionYmin = Math.max(yminI, yminJ);
+        var intersectionXmin = Math.max(xminI, xminJ);
+        var intersectionYmax = Math.min(ymaxI, ymaxJ);
+        var intersectionXmax = Math.min(xmaxI, xmaxJ);
+        var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
+            Math.max(intersectionXmax - intersectionXmin, 0.0);
+        return intersectionArea / (areaI + areaJ - intersectionArea);
+    }
+    // A Gaussian penalty function, this method always returns values in [0, 1].
+    // The weight is a function of similarity, the more overlap two boxes are, the
+    // smaller the weight is, meaning highly overlapping boxe will be significantly
+    // penalized. On the other hand, a non-overlapping box will not be penalized.
+    function suppressWeight(iouThreshold, scale, iou) {
+        var weight = Math.exp(scale * iou * iou);
+        return iou <= iouThreshold ? weight : 0.0;
+    }
+    function ascendingComparator(c1, c2) {
+        // For objects with same scores, we make the object with the larger index go
+        // first. In an array that pops from the end, this means that the object with
+        // the smaller index will be popped first. This ensures the same output as
+        // the TensorFlow python version.
+        return (c1.score - c2.score) ||
+            ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Performs non maximum suppression of bounding boxes based on
+     * iou (intersection over union).
+     *
+     * This is the async version of `nonMaxSuppression`
+     *
+     * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
+     *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
+     *     the bounding box.
+     * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
+     * @param maxOutputSize The maximum number of boxes to be selected.
+     * @param iouThreshold A float representing the threshold for deciding whether
+     *     boxes overlap too much with respect to IOU. Must be between [0, 1].
+     *     Defaults to 0.5 (50% box overlap).
+     * @param scoreThreshold A threshold for deciding when to remove boxes based
+     *     on score. Defaults to -inf, which means any score is accepted.
+     * @return A 1D tensor with the selected box indices.
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+        if (iouThreshold === void 0) { iouThreshold = 0.5; }
+        if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
+        return __awaiter(this, void 0, void 0, function () {
+            var $boxes, $scores, inputs, boxesAndScores, boxesVals, scoresVals, selectedIndices;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
+                        $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
+                        inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
+                        maxOutputSize = inputs.maxOutputSize;
+                        iouThreshold = inputs.iouThreshold;
+                        scoreThreshold = inputs.scoreThreshold;
+                        return [4 /*yield*/, Promise.all([$boxes.data(), $scores.data()])];
+                    case 1:
+                        boxesAndScores = _a.sent();
+                        boxesVals = boxesAndScores[0];
+                        scoresVals = boxesAndScores[1];
+                        selectedIndices = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold).selectedIndices;
+                        if ($boxes !== boxes) {
+                            $boxes.dispose();
+                        }
+                        if ($scores !== scores) {
+                            $scores.dispose();
+                        }
+                        return [2 /*return*/, tensor1d(selectedIndices, 'int32')];
+                }
+            });
+        });
+    }
+    var nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Performs non maximum suppression of bounding boxes based on
+     * iou (intersection over union).
+     *
+     * This op also supports a Soft-NMS mode (c.f.
+     * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
+     * of other overlapping boxes, therefore favoring different regions of the image
+     * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`
+     * parameter to be larger than 0.
+     *
+     * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
+     *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
+     *     the bounding box.
+     * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
+     * @param maxOutputSize The maximum number of boxes to be selected.
+     * @param iouThreshold A float representing the threshold for deciding whether
+     *     boxes overlap too much with respect to IOU. Must be between [0, 1].
+     *     Defaults to 0.5 (50% box overlap).
+     * @param scoreThreshold A threshold for deciding when to remove boxes based
+     *     on score. Defaults to -inf, which means any score is accepted.
+     * @param softNmsSigma A float representing the sigma parameter for Soft NMS.
+     *     When sigma is 0, it falls back to nonMaxSuppression.
+     * @return A map with the following properties:
+     *     - selectedIndices: A 1D tensor with the selected box indices.
+     *     - selectedScores: A 1D tensor with the corresponding scores for each
+     *       selected box.
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
+        if (iouThreshold === void 0) { iouThreshold = 0.5; }
+        if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
+        if (softNmsSigma === void 0) { softNmsSigma = 0.0; }
+        var $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
+        var $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');
+        var params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
+        maxOutputSize = params.maxOutputSize;
+        iouThreshold = params.iouThreshold;
+        scoreThreshold = params.scoreThreshold;
+        softNmsSigma = params.softNmsSigma;
+        var inputs = { boxes: $boxes, scores: $scores };
+        var attrs = { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold, softNmsSigma: softNmsSigma };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);
+        return { selectedIndices: result[0], selectedScores: result[1] };
+    }
+    var nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_: nonMaxSuppressionWithScore_ });
+
+    /**
+     * Asynchronously performs non maximum suppression of bounding boxes based on
+     * iou (intersection over union).
+     *
+     * This op also supports a Soft-NMS mode (c.f.
+     * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
+     * of other overlapping boxes, therefore favoring different regions of the image
+     * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`
+     * parameter to be larger than 0.
+     *
+     * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
+     *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
+     *     the bounding box.
+     * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
+     * @param maxOutputSize The maximum number of boxes to be selected.
+     * @param iouThreshold A float representing the threshold for deciding whether
+     *     boxes overlap too much with respect to IOU. Must be between [0, 1].
+     *     Defaults to 0.5 (50% box overlap).
+     * @param scoreThreshold A threshold for deciding when to remove boxes based
+     *     on score. Defaults to -inf, which means any score is accepted.
+     * @param softNmsSigma A float representing the sigma parameter for Soft NMS.
+     *     When sigma is 0, it falls back to nonMaxSuppression.
+     * @return A map with the following properties:
+     *     - selectedIndices: A 1D tensor with the selected box indices.
+     *     - selectedScores: A 1D tensor with the corresponding scores for each
+     *       selected box.
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
+        if (iouThreshold === void 0) { iouThreshold = 0.5; }
+        if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
+        if (softNmsSigma === void 0) { softNmsSigma = 0.0; }
+        return __awaiter(this, void 0, void 0, function () {
+            var $boxes, $scores, params, boxesAndScores, boxesVals, scoresVals, _a, selectedIndices, selectedScores;
+            return __generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
+                        $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
+                        params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
+                        maxOutputSize = params.maxOutputSize;
+                        iouThreshold = params.iouThreshold;
+                        scoreThreshold = params.scoreThreshold;
+                        softNmsSigma = params.softNmsSigma;
+                        return [4 /*yield*/, Promise.all([$boxes.data(), $scores.data()])];
+                    case 1:
+                        boxesAndScores = _b.sent();
+                        boxesVals = boxesAndScores[0];
+                        scoresVals = boxesAndScores[1];
+                        _a = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma), selectedIndices = _a.selectedIndices, selectedScores = _a.selectedScores;
+                        if ($boxes !== boxes) {
+                            $boxes.dispose();
+                        }
+                        if ($scores !== scores) {
+                            $scores.dispose();
+                        }
+                        return [2 /*return*/, {
+                                selectedIndices: tensor1d(selectedIndices, 'int32'),
+                                selectedScores: tensor1d(selectedScores)
+                            }];
+                }
+            });
+        });
+    }
+    var nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Asynchronously performs non maximum suppression of bounding boxes based on
+     * iou (intersection over union), with an option to pad results.
+     *
+     * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
+     *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
+     *     the bounding box.
+     * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
+     * @param maxOutputSize The maximum number of boxes to be selected.
+     * @param iouThreshold A float representing the threshold for deciding whether
+     *     boxes overlap too much with respect to IOU. Must be between [0, 1].
+     *     Defaults to 0.5 (50% box overlap).
+     * @param scoreThreshold A threshold for deciding when to remove boxes based
+     *     on score. Defaults to -inf, which means any score is accepted.
+     * @param padToMaxOutputSize Defalts to false. If true, size of output
+     *     `selectedIndices` is padded to maxOutputSize.
+     * @return A map with the following properties:
+     *     - selectedIndices: A 1D tensor with the selected box indices.
+     *     - validOutputs: A scalar denoting how many elements in `selectedIndices`
+     *       are valid. Valid elements occur first, then padding.
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {
+        if (iouThreshold === void 0) { iouThreshold = 0.5; }
+        if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
+        if (padToMaxOutputSize === void 0) { padToMaxOutputSize = false; }
+        var $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
+        var $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');
+        var params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);
+        var $maxOutputSize = params.maxOutputSize;
+        var $iouThreshold = params.iouThreshold;
+        var $scoreThreshold = params.scoreThreshold;
+        var inputs = { boxes: $boxes, scores: $scores };
+        var attrs = {
+            maxOutputSize: $maxOutputSize,
+            iouThreshold: $iouThreshold,
+            scoreThreshold: $scoreThreshold,
+            padToMaxOutputSize: padToMaxOutputSize
+        };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);
+        return { selectedIndices: result[0], validOutputs: result[1] };
+    }
+    var nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_: nonMaxSuppressionPadded_ });
+
+    /**
+     * Asynchronously performs non maximum suppression of bounding boxes based on
+     * iou (intersection over union), with an option to pad results.
+     *
+     * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
+     *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
+     *     the bounding box.
+     * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
+     * @param maxOutputSize The maximum number of boxes to be selected.
+     * @param iouThreshold A float representing the threshold for deciding whether
+     *     boxes overlap too much with respect to IOU. Must be between [0, 1].
+     *     Defaults to 0.5 (50% box overlap).
+     * @param scoreThreshold A threshold for deciding when to remove boxes based
+     *     on score. Defaults to -inf, which means any score is accepted.
+     * @param padToMaxOutputSize Defalts to false. If true, size of output
+     *     `selectedIndices` is padded to maxOutputSize.
+     * @return A map with the following properties:
+     *     - selectedIndices: A 1D tensor with the selected box indices.
+     *     - validOutputs: A scalar denoting how many elements in `selectedIndices`
+     *       are valid. Valid elements occur first, then padding.
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {
+        if (iouThreshold === void 0) { iouThreshold = 0.5; }
+        if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
+        if (padToMaxOutputSize === void 0) { padToMaxOutputSize = false; }
+        return __awaiter(this, void 0, void 0, function () {
+            var $boxes, $scores, params, $maxOutputSize, $iouThreshold, $scoreThreshold, _a, boxesVals, scoresVals, _b, selectedIndices, validOutputs;
+            return __generator(this, function (_c) {
+                switch (_c.label) {
+                    case 0:
+                        $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
+                        $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
+                        params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);
+                        $maxOutputSize = params.maxOutputSize;
+                        $iouThreshold = params.iouThreshold;
+                        $scoreThreshold = params.scoreThreshold;
+                        return [4 /*yield*/, Promise.all([$boxes.data(), $scores.data()])];
+                    case 1:
+                        _a = _c.sent(), boxesVals = _a[0], scoresVals = _a[1];
+                        _b = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize), selectedIndices = _b.selectedIndices, validOutputs = _b.validOutputs;
+                        if ($boxes !== boxes) {
+                            $boxes.dispose();
+                        }
+                        if ($scores !== scores) {
+                            $scores.dispose();
+                        }
+                        return [2 /*return*/, {
+                                selectedIndices: tensor1d(selectedIndices, 'int32'),
+                                validOutputs: scalar(validOutputs, 'int32')
+                            }];
+                }
+            });
+        });
+    }
+    var nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Bilinear resize a single 3D image or a batch of 3D images to a new shape.
+     *
+     * @param images The images, of rank 4 or rank 3, of shape
+     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
+     * @param size The new shape `[newHeight, newWidth]` to resize the
+     *     images to. Each channel is resized individually.
+     * @param alignCorners Defaults to `false`. If true, rescale
+     *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
+     *     corners of images and resized images. If false, rescale by
+     *     `new_height / height`. Treat similarly the width dimension.
+     * @param halfPixelCenters Defaults to `false`. Whether to assume pixel centers
+     *     are at 0.5, which would make the floating point coordinates of the top
+     *     left pixel 0.5, 0.5.
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function resizeBilinear_(images, size, alignCorners, halfPixelCenters) {
+        if (alignCorners === void 0) { alignCorners = false; }
+        if (halfPixelCenters === void 0) { halfPixelCenters = false; }
+        var $images = convertToTensor(images, 'images', 'resizeBilinear');
+        assert($images.rank === 3 || $images.rank === 4, function () { return "Error in resizeBilinear: x must be rank 3 or 4, but got " +
+            ("rank " + $images.rank + "."); });
+        assert(size.length === 2, function () { return "Error in resizeBilinear: new shape must 2D, but got shape " +
+            (size + "."); });
+        assert(halfPixelCenters === false || alignCorners === false, function () { return "Error in resizeBilinear: If halfPixelCenters is true, " +
+            "alignCorners must be false."; });
+        var batchImages = $images;
+        var reshapedTo4D = false;
+        if ($images.rank === 3) {
+            reshapedTo4D = true;
+            batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
+        }
+        var inputs = { images: batchImages };
+        var attrs = { alignCorners: alignCorners, halfPixelCenters: halfPixelCenters, size: size };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(ResizeBilinear, inputs, attrs);
+        if (reshapedTo4D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+        }
+        return res;
+    }
+    var resizeBilinear = op({ resizeBilinear_: resizeBilinear_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * NearestNeighbor resize a batch of 3D images to a new shape.
+     *
+     * @param images The images, of rank 4 or rank 3, of shape
+     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
+     * @param size The new shape `[newHeight, newWidth]` to resize the
+     *     images to. Each channel is resized individually.
+     * @param alignCorners Defaults to False. If true, rescale
+     *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
+     *     corners of images and resized images. If false, rescale by
+     *     `new_height / height`. Treat similarly the width dimension.
+     * @param halfPixelCenters Defaults to `false`. Whether to assumes pixels are of
+     *      half the actual dimensions, and yields more accurate resizes. This flag
+     *      would also make the floating point coordinates of the top left pixel
+     *      0.5, 0.5.
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function resizeNearestNeighbor_(images, size, alignCorners, halfPixelCenters) {
+        if (alignCorners === void 0) { alignCorners = false; }
+        if (halfPixelCenters === void 0) { halfPixelCenters = false; }
+        var $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');
+        assert($images.rank === 3 || $images.rank === 4, function () { return "Error in resizeNearestNeighbor: x must be rank 3 or 4, but got " +
+            ("rank " + $images.rank + "."); });
+        assert(size.length === 2, function () {
+            return "Error in resizeNearestNeighbor: new shape must 2D, but got shape " +
+                (size + ".");
+        });
+        assert($images.dtype === 'float32' || $images.dtype === 'int32', function () { return '`images` must have `int32` or `float32` as dtype'; });
+        assert(halfPixelCenters === false || alignCorners === false, function () { return "Error in resizeNearestNeighbor: If halfPixelCenters is true, " +
+            "alignCorners must be false."; });
+        var batchImages = $images;
+        var reshapedTo4D = false;
+        if ($images.rank === 3) {
+            reshapedTo4D = true;
+            batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
+        }
+        var inputs = { images: batchImages };
+        var attrs = { alignCorners: alignCorners, halfPixelCenters: halfPixelCenters, size: size };
+        // tslint:disable-next-line: no-unnecessary-type-assertion
+        var res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs);
+        if (reshapedTo4D) {
+            return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+        }
+        return res;
+    }
+    var resizeNearestNeighbor = op({ resizeNearestNeighbor_: resizeNearestNeighbor_ });
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * https://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Performs image binarization with corresponding threshold
+     * (depends on the method)value, which creates a binary image from a grayscale.
+     * @param image 3d tensor of shape [imageHeight,imageWidth, depth],
+     * where imageHeight and imageWidth must be positive.The image color
+     * range should be [0, 255].
+     * @param method Optional string from `'binary' | 'otsu'`
+     * which specifies the method for thresholding. Defaults to 'binary'.
+     * @param inverted Optional boolean whichspecifies
+     * if colours should be inverted. Defaults to false.
+     * @param threshValue Optional number which defines threshold value from 0 to 1.
+     * Defaults to 0.5.
+     * @return A 3d tensor of shape [imageHeight,imageWidth, depth], which
+     * contains binarized image.
+     */
+    function threshold_(image, method, inverted, threshValue) {
+        var _a;
+        if (method === void 0) { method = 'binary'; }
+        if (inverted === void 0) { inverted = false; }
+        if (threshValue === void 0) { threshValue = 0.5; }
+        var $image = convertToTensor(image, 'image', 'threshold');
+        /* 0.2989, 0.5870, 0.1140 are represent luma coefficients in CCIR601.
+        Reference for converting between RGB and grayscale: https://en.wikipedia.org/wiki/Luma_%28video%29  */
+        var RED_INTENCITY_COEF = 0.2989;
+        var GREEN_INTENCITY_COEF = 0.5870;
+        var BLUE_INTENCITY_COEF = 0.1140;
+        var totalPixelsInImage = $image.shape[0] * $image.shape[1];
+        var $threshold = mul(tensor1d([threshValue]), 255);
+        var r, g, b, grayscale;
+        assert($image.rank === 3, function () { return 'Error in threshold: image must be rank 3,' +
+            ("but got rank " + $image.rank + "."); });
+        assert($image.shape[2] === 3 || $image.shape[2] === 1, function () { return 'Error in threshold: ' +
+            'image color channel must be equal to 3 or 1' +
+            ("but got " + $image.shape[2] + "."); });
+        assert($image.dtype === 'int32' || $image.dtype === 'float32', function () { return 'Error in dtype: image dtype must be int32 or float32,' +
+            ("but got dtype " + $image.dtype + "."); });
+        assert(method === 'otsu' || method === 'binary', function () { return "Method must be binary or otsu, but was " + method; });
+        if ($image.shape[2] === 3) {
+            _a = split($image, [1, 1, 1], -1), r = _a[0], g = _a[1], b = _a[2];
+            var $r = mul(r, RED_INTENCITY_COEF);
+            var $g = mul(g, GREEN_INTENCITY_COEF);
+            var $b = mul(b, BLUE_INTENCITY_COEF);
+            grayscale = add$1(add$1($r, $g), $b);
+        }
+        else {
+            grayscale = image;
+        }
+        if (method === 'otsu') {
+            var $histogram = bincount(cast(round$1(grayscale), 'int32'), tensor([]), 256);
+            $threshold = otsu($histogram, totalPixelsInImage);
+        }
+        var invCondition = inverted ?
+            lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);
+        var result = cast(mul(invCondition, 255), 'int32');
+        return result;
+    }
+    function otsu(histogram, total) {
+        var bestThresh = tensor1d([-1]);
+        var bestInBetVar = tensor1d([0]);
+        var cInBetVar = tensor1d([0]);
+        var classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack;
+        for (var index = 0; index < histogram.size - 1; index++) {
+            classFirst = slice(histogram, 0, index + 1);
+            classSecond = slice(histogram, index + 1);
+            weightForeground = div(sum$1(classFirst), total);
+            weightBack = div(sum$1(classSecond), total);
+            var meanFirstDivA = sum$1(mul(classFirst, range(0, classFirst.size)));
+            meanFirst = div(meanFirstDivA, sum$1(classFirst));
+            var meanSecFill = fill(classSecond.shape, classFirst.size);
+            var meanSecAdd = add$1(range(0, classSecond.size), meanSecFill);
+            var meanSecMul = mul(classSecond, (meanSecAdd));
+            meanSec = div(sum$1(meanSecMul), sum$1(classSecond));
+            var cInBetVarSubA = sub(meanFirst, meanSec);
+            var cInBetVarSubB = sub(meanFirst, meanSec);
+            var cInBetVarMul = mul(weightForeground, weightBack);
+            cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB);
+            var condition = greater(cInBetVar, bestInBetVar);
+            bestInBetVar = where(condition, cInBetVar, bestInBetVar);
+            bestThresh = where(condition, tensor1d([index]), bestThresh);
+        }
+        return bestThresh;
+    }
+    var threshold = op({ threshold_: threshold_ });
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Applies the given transform(s) to the image(s).
+     *
+     * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.
+     * @param transforms Projective transform matrix/matrices. A tensor1d of length
+     *     8 or tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0
+     *     b1, b2, c0, c1], then it maps the output point (x, y) to a transformed
+     *     input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k),
+     *     where k = c0 x + c1 y + 1. The transforms are inverted compared to the
+     *     transform mapping input points to output points.
+     * @param interpolation Interpolation mode.
+     *     Supported values: 'nearest', 'bilinear'. Default to 'nearest'.
+     * @param fillMode Points outside the boundaries of the input are filled
+     *     according to the given mode, one of 'constant', 'reflect', 'wrap',
+     *     'nearest'. Default to 'constant'.
+     *     'reflect': (d c b a | a b c d | d c b a ) The input is extended by
+     *     reflecting about the edge of the last pixel.
+     *     'constant': (k k k k | a b c d | k k k k) The input is extended by
+     *     filling all values beyond the edge with the same constant value k.
+     *     'wrap': (a b c d | a b c d | a b c d) The input is extended by
+     *     wrapping around to the opposite edge.
+     *     'nearest': (a a a a | a b c d | d d d d) The input is extended by
+     *     the nearest pixel.
+     * @param fillValue A float represents the value to be filled outside the
+     *     boundaries when fillMode is 'constant'.
+     * @param Output dimension after the transform, [height, width]. If undefined,
+     *     output is the same size as input image.
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function transform_(image, transforms, interpolation, fillMode, fillValue, outputShape) {
+        if (interpolation === void 0) { interpolation = 'nearest'; }
+        if (fillMode === void 0) { fillMode = 'constant'; }
+        if (fillValue === void 0) { fillValue = 0; }
+        var $image = convertToTensor(image, 'image', 'transform', 'float32');
+        var $transforms = convertToTensor(transforms, 'transforms', 'transform', 'float32');
+        assert($image.rank === 4, function () { return 'Error in transform: image must be rank 4,' +
+            ("but got rank " + $image.rank + "."); });
+        assert($transforms.rank === 2 &&
+            ($transforms.shape[0] === $image.shape[0] ||
+                $transforms.shape[0] === 1) &&
+            $transforms.shape[1] === 8, function () { return "Error in transform: Input transform should be batch x 8 or 1 x 8"; });
+        assert(outputShape == null || outputShape.length === 2, function () {
+            return 'Error in transform: outputShape must be [height, width] or null, ' +
+                ("but got " + outputShape + ".");
+        });
+        var inputs = { image: $image, transforms: $transforms };
+        var attrs = { interpolation: interpolation, fillMode: fillMode, fillValue: fillValue, outputShape: outputShape };
+        return ENGINE.runKernel(Transform, inputs, attrs);
+    }
+    var transform = op({ transform_: transform_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Copy a tensor setting everything outside a central band in each innermost
+     * matrix to zero.
+     *
+     * The band part is computed as follows: Assume input has `k` dimensions
+     * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where
+     * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
+     * The indicator function
+     * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`
+     * `&& (num_upper < 0 || (n-m) <= num_upper)`
+     *
+     * ```js
+     * const x = tf.tensor2d([[ 0,  1,  2, 3],
+     *                        [-1,  0,  1, 2],
+     *                        [-2, -1,  0, 1],
+     *                        [-3, -2, -1, 0]]);
+     * let y = tf.linalg.bandPart(x, 1, -1);
+     * y.print(); // [[ 0,  1,  2, 3],
+     *            //  [-1,  0,  1, 2],
+     *            //  [ 0, -1,  0, 1],
+     *            //  [ 0, 0 , -1, 0]]
+     * let z = tf.linalg.bandPart(x, 2, 1);
+     * z.print(); // [[ 0,  1,  0, 0],
+     *            //  [-1,  0,  1, 0],
+     *            //  [-2, -1,  0, 1],
+     *            //  [ 0, -2, -1, 0]]
+     * ```
+     *
+     * @param x Rank `k` tensor
+     * @param numLower Number of subdiagonals to keep.
+     *   If negative, keep entire lower triangle.
+     * @param numUpper Number of subdiagonals to keep.
+     *   If negative, keep entire upper triangle.
+     * @returns Rank `k` tensor of the same shape as input.
+     *   The extracted banded tensor.
+     *
+     * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}
+     */
+    function bandPart_(a, numLower, numUpper) {
+        assert(numLower % 1 === 0, function () { return "bandPart(): numLower must be an integer, got " + numLower + "."; });
+        assert(numUpper % 1 === 0, function () { return "bandPart(): numUpper must be an integer, got " + numUpper + "."; });
+        var $a = convertToTensor(a, 'a', 'bandPart');
+        assert($a.rank >= 2, function () { return "bandPart(): Rank must be at least 2, got " + $a.rank + "."; });
+        var shape = $a.shape;
+        var _a = $a.shape.slice(-2), M = _a[0], N = _a[1];
+        if (!(numLower <= M)) {
+            throw new Error("bandPart(): numLower (" + numLower + ")" +
+                (" must not be greater than the number of rows (" + M + ")."));
+        }
+        if (!(numUpper <= N)) {
+            throw new Error("bandPart(): numUpper (" + numUpper + ")" +
+                (" must not be greater than the number of columns (" + N + ")."));
+        }
+        if (numLower < 0) {
+            numLower = M;
+        }
+        if (numUpper < 0) {
+            numUpper = N;
+        }
+        var i = reshape(range(0, M, 1, 'int32'), [-1, 1]);
+        var j = range(0, N, 1, 'int32');
+        var ij = sub(i, j);
+        var inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32')));
+        var zero = zeros([M, N], $a.dtype);
+        return reshape(stack(unstack(reshape($a, [-1, M, N]))
+            .map(function (mat) { return where(inBand, mat, zero); })), shape);
+    }
+    var bandPart = op({ bandPart_: bandPart_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Gram-Schmidt orthogonalization.
+     *
+     * ```js
+     * const x = tf.tensor2d([[1, 2], [3, 4]]);
+     * let y = tf.linalg.gramSchmidt(x);
+     * y.print();
+     * console.log('Othogonalized:');
+     * y.dot(y.transpose()).print();  // should be nearly the identity matrix.
+     * console.log('First row direction maintained:');
+     * const data = await y.array();
+     * console.log(data[0][1] / data[0][0]);  // should be nearly 2.
+     * ```
+     *
+     * @param xs The vectors to be orthogonalized, in one of the two following
+     *   formats:
+     *   - An Array of `tf.Tensor1D`.
+     *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows
+     *     of `xs`.
+     *   In each case, all the vectors must have the same length and the length
+     *   must be greater than or equal to the number of vectors.
+     * @returns The orthogonalized and normalized vectors or matrix.
+     *   Orthogonalization means that the vectors or the rows of the matrix
+     *   are orthogonal (zero inner products). Normalization means that each
+     *   vector or each row of the matrix has an L2 norm that equals `1`.
+     *
+     * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}
+     */
+    function gramSchmidt_(xs) {
+        var inputIsTensor2D;
+        if (Array.isArray(xs)) {
+            inputIsTensor2D = false;
+            assert(xs != null && xs.length > 0, function () { return 'Gram-Schmidt process: input must not be null, undefined, or ' +
+                'empty'; });
+            var dim_1 = xs[0].shape[0];
+            var _loop_1 = function (i) {
+                assert(xs[i].shape[0] === dim_1, function () {
+                    return 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +
+                        ("(" + xs[i].shape[0] + " vs. " + dim_1 + ")");
+                });
+            };
+            for (var i = 1; i < xs.length; ++i) {
+                _loop_1(i);
+            }
+        }
+        else {
+            inputIsTensor2D = true;
+            xs = split(xs, xs.shape[0], 0).map(function (x) { return squeeze(x, [0]); });
+        }
+        assert(xs.length <= xs[0].shape[0], function () { return "Gram-Schmidt: Number of vectors (" + xs.length + ") exceeds " +
+            ("number of dimensions (" + xs[0].shape[0] + ")."); });
+        var ys = [];
+        var xs1d = xs;
+        var _loop_2 = function (i) {
+            ys.push(ENGINE.tidy(function () {
+                var x = xs1d[i];
+                if (i > 0) {
+                    for (var j = 0; j < i; ++j) {
+                        var proj = mul(sum$1(mul(ys[j], x)), ys[j]);
+                        x = sub(x, proj);
+                    }
+                }
+                return div(x, norm(x, 'euclidean'));
+            }));
+        };
+        for (var i = 0; i < xs.length; ++i) {
+            _loop_2(i);
+        }
+        if (inputIsTensor2D) {
+            return stack(ys, 0);
+        }
+        else {
+            return ys;
+        }
+    }
+    var gramSchmidt = op({ gramSchmidt_: gramSchmidt_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Compute QR decomposition of m-by-n matrix using Householder transformation.
+     *
+     * Implementation based on
+     *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]
+     * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)
+     *
+     * ```js
+     * const a = tf.tensor2d([[1, 2], [3, 4]]);
+     * let [q, r] = tf.linalg.qr(a);
+     * console.log('Q');
+     * q.print();
+     * console.log('R');
+     * r.print();
+     * console.log('Orthogonalized');
+     * q.dot(q.transpose()).print()  // should be nearly the identity matrix.
+     * console.log('Reconstructed');
+     * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];
+     * ```
+     *
+     * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose
+     *   it has the shape `[..., M, N]`.
+     * @param fullMatrices An optional boolean parameter. Defaults to `false`.
+     *   If `true`, compute full-sized `Q`. If `false` (the default),
+     *   compute only the leading N columns of `Q` and `R`.
+     * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,
+     *   i.e., its columns all have unit norm and are mutually orthogonal.
+     *   If `M >= N`,
+     *     If `fullMatrices` is `false` (default),
+     *       - `Q` has a shape of `[..., M, N]`,
+     *       - `R` has a shape of `[..., N, N]`.
+     *     If `fullMatrices` is `true` (default),
+     *       - `Q` has a shape of `[..., M, M]`,
+     *       - `R` has a shape of `[..., M, N]`.
+     *   If `M < N`,
+     *     - `Q` has a shape of `[..., M, M]`,
+     *     - `R` has a shape of `[..., M, N]`.
+     * @throws If the rank of `x` is less than 2.
+     *
+     * @doc {heading:'Operations',
+     *       subheading:'Linear Algebra',
+     *       namespace:'linalg'}
+     */
+    function qr_(x, fullMatrices) {
+        if (fullMatrices === void 0) { fullMatrices = false; }
+        assert(x.rank >= 2, function () { return "qr() requires input tensor to have a rank >= 2, but got rank " + x.rank; });
+        if (x.rank === 2) {
+            return qr2d(x, fullMatrices);
+        }
+        else {
+            // Rank > 2.
+            // TODO(cais): Below we split the input into individual 2D tensors,
+            //   perform QR decomposition on them and then stack the results back
+            //   together. We should explore whether this can be parallelized.
+            var outerDimsProd = x.shape.slice(0, x.shape.length - 2)
+                .reduce(function (value, prev) { return value * prev; });
+            var x2ds = unstack(reshape(x, [
+                outerDimsProd, x.shape[x.shape.length - 2],
+                x.shape[x.shape.length - 1]
+            ]), 0);
+            var q2ds_1 = [];
+            var r2ds_1 = [];
+            x2ds.forEach(function (x2d) {
+                var _a = qr2d(x2d, fullMatrices), q2d = _a[0], r2d = _a[1];
+                q2ds_1.push(q2d);
+                r2ds_1.push(r2d);
+            });
+            var q = reshape(stack(q2ds_1, 0), x.shape);
+            var r = reshape(stack(r2ds_1, 0), x.shape);
+            return [q, r];
+        }
+    }
+    function qr2d(x, fullMatrices) {
+        if (fullMatrices === void 0) { fullMatrices = false; }
+        return ENGINE.tidy(function () {
+            assert(x.shape.length === 2, function () { return "qr2d() requires a 2D Tensor, but got a " + x.shape.length + "D Tensor."; });
+            var m = x.shape[0];
+            var n = x.shape[1];
+            var q = eye(m); // Orthogonal transform so far.
+            var r = clone(x); // Transformed matrix so far.
+            var one2D = tensor2d([[1]], [1, 1]);
+            var w = clone(one2D);
+            var iters = m >= n ? n : m;
+            var _loop_1 = function (j) {
+                var _a;
+                // This tidy within the for-loop ensures we clean up temporary
+                // tensors as soon as they are no longer needed.
+                var rTemp = r;
+                var wTemp = w;
+                var qTemp = q;
+                _a = ENGINE.tidy(function () {
+                    // Find H = I - tau * w * w', to put zeros below R(j, j).
+                    var rjEnd1 = slice(r, [j, j], [m - j, 1]);
+                    var normX = norm(rjEnd1);
+                    var rjj = slice(r, [j, j], [1, 1]);
+                    // The sign() function returns 0 on 0, which causes division by zero.
+                    var s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
+                    var u1 = sub(rjj, mul(s, normX));
+                    var wPre = div(rjEnd1, u1);
+                    if (wPre.shape[0] === 1) {
+                        w = clone(one2D);
+                    }
+                    else {
+                        w = concat([
+                            one2D,
+                            slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])
+                        ], 0);
+                    }
+                    var tau = neg(div(matMul(s, u1), normX));
+                    // -- R := HR, Q := QH.
+                    var rjEndAll = slice(r, [j, 0], [m - j, n]);
+                    var tauTimesW = mul(tau, w);
+                    var wT = transpose(w);
+                    if (j === 0) {
+                        r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
+                    }
+                    else {
+                        var rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
+                        r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);
+                    }
+                    var tawTimesWT = transpose(tauTimesW);
+                    var qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);
+                    if (j === 0) {
+                        q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
+                    }
+                    else {
+                        var qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
+                        q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
+                    }
+                    return [w, r, q];
+                }), w = _a[0], r = _a[1], q = _a[2];
+                dispose([rTemp, wTemp, qTemp]);
+            };
+            for (var j = 0; j < iters; ++j) {
+                _loop_1(j);
+            }
+            if (!fullMatrices && m > n) {
+                q = slice(q, [0, 0], [m, n]);
+                r = slice(r, [0, 0], [n, n]);
+            }
+            return [q, r];
+        });
+    }
+    var qr = op({ qr_: qr_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    (function (Reduction) {
+        Reduction[Reduction["NONE"] = 0] = "NONE";
+        Reduction[Reduction["MEAN"] = 1] = "MEAN";
+        Reduction[Reduction["SUM"] = 2] = "SUM";
+        Reduction[Reduction["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
+    })(exports.Reduction || (exports.Reduction = {}));
+
+    /**
+     * Computes the weighted loss between two tensors.
+     *
+     * @param losses Tensor of shape `[batch_size, d1, ... dN]`.
+     * @param weights Tensor whose rank is either 0, or the same rank as
+     *    `losses`, and must be broadcastable to `losses` (i.e., all
+     *    dimensions must be either `1`, or the same as the corresponding
+     *    `losses` dimension).
+     *
+     * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
+     */
+    function computeWeightedLoss_(losses, weights, reduction) {
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');
+        }
+        var weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);
+        if (reduction === exports.Reduction.NONE) {
+            return weightedLoss;
+        }
+        if (reduction === exports.Reduction.SUM) {
+            return sum$1(weightedLoss);
+        }
+        if (reduction === exports.Reduction.MEAN) {
+            if ($weights == null) {
+                return mean(weightedLoss);
+            }
+            else {
+                var broadcastFactor = $losses.size / $weights.size;
+                var result = div(sum$1(weightedLoss), sum$1($weights));
+                return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :
+                    result;
+            }
+        }
+        if (reduction === exports.Reduction.SUM_BY_NONZERO_WEIGHTS) {
+            if ($weights == null) {
+                return div(sum$1(weightedLoss), scalar($losses.size));
+            }
+            else {
+                var broadcastedWeights = mul($weights, ones$1($losses.shape));
+                var numNonZeros = cast(sum$1(notEqual(broadcastedWeights, scalar(0))), 'float32');
+                return div(sum$1(weightedLoss), numNonZeros);
+            }
+        }
+        throw Error("Unknown reduction: " + reduction);
+    }
+    var computeWeightedLoss = op({ computeWeightedLoss_: computeWeightedLoss_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the absolute difference loss between two tensors.
+     *
+     * @param labels The ground truth output tensor, same dimensions as
+     *    'predictions'.
+     * @param predictions The predicted outputs.
+     * @param weights Tensor whose rank is either 0, or the same rank as
+     *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
+     *    must be either `1`, or the same as the corresponding `losses`
+     *    dimension).
+     * @param reduction Type of reduction to apply to loss. Should be of type
+     *    `Reduction`
+     *
+     * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
+     */
+    function absoluteDifference_(labels, predictions, weights, reduction) {
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $labels = convertToTensor(labels, 'labels', 'absoluteDifference');
+        var $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'absoluteDifference');
+        }
+        assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');
+        var losses = abs(sub($labels, $predictions));
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    var absoluteDifference = op({ absoluteDifference_: absoluteDifference_ });
+
+    /**
+     * Computes the cosine distance loss between two tensors.
+     *
+     * @param labels The ground truth output tensor, same dimensions as
+     *    'predictions'.
+     * @param predictions The predicted outputs.
+     * @param axis The dimension along which the cosine distance is computed.
+     * @param weights Tensor whose rank is either 0, or the same rank as
+     *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
+     *    must be either `1`, or the same as the corresponding `losses`
+     *    dimension).
+     * @param reduction Type of reduction to apply to loss. Should be of type
+     *    `Reduction`
+     *
+     * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
+     */
+    function cosineDistance_(labels, predictions, axis, weights, reduction) {
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $labels = convertToTensor(labels, 'labels', 'cosineDistance');
+        var $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'cosineDistance');
+        }
+        assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');
+        var one = scalar(1);
+        var losses = sub(one, sum$1(mul($labels, $predictions), axis, true));
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    var cosineDistance = op({ cosineDistance_: cosineDistance_ });
+
+    /**
+     * Computes the Hinge loss between two tensors.
+     *
+     * @param labels The ground truth output tensor, same dimensions as
+     *    'predictions'.
+     * @param predictions The predicted outputs.
+     * @param weights Tensor whose rank is either 0, or the same rank as
+     *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
+     *    must be either `1`, or the same as the corresponding `losses`
+     *    dimension).
+     * @param reduction Type of reduction to apply to loss. Should be of type
+     *    `Reduction`
+     *
+     * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
+     */
+    function hingeLoss_(labels, predictions, weights, reduction) {
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $labels = convertToTensor(labels, 'labels', 'hingeLoss');
+        var $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'hingeLoss');
+        }
+        assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');
+        var one = scalar(1);
+        // Convert binary labels to (-1, 1)
+        $labels = sub(mul(scalar(2), $labels), one);
+        var losses = relu(sub(one, mul($labels, $predictions)));
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    var hingeLoss = op({ hingeLoss_: hingeLoss_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the huber loss between two tensors.
+     *
+     * @param labels The ground truth output tensor, same dimensions as
+     *    'predictions'.
+     * @param predictions The predicted outputs.
+     * @param weights Tensor whose rank is either 0, or the same rank as
+     *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
+     *    must be either `1`, or the same as the corresponding `losses`
+     *    dimension).
+     * @param delta Point where huber loss changes from quadratic to linear.
+     * @param reduction Type of reduction to apply to loss. Should be of type
+     *    `Reduction`.
+     *
+     * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
+     */
+    function huberLoss_(labels, predictions, weights, delta, reduction) {
+        if (delta === void 0) { delta = 1.0; }
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $labels = convertToTensor(labels, 'labels', 'huberLoss');
+        var $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'huberLoss');
+        }
+        assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');
+        var deltaScalar = scalar(delta);
+        var error = abs(sub($predictions, $labels));
+        var quadratic = minimum(error, deltaScalar);
+        var linear = sub(error, quadratic);
+        var losses = add$1(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    var huberLoss = op({ huberLoss_: huberLoss_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the log loss between two tensors.
+     *
+     * @param labels The ground truth output tensor, same dimensions as
+     *    'predictions'.
+     * @param predictions The predicted outputs.
+     * @param weights Tensor whose rank is either 0, or the same rank as
+     *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
+     *    must be either `1`, or the same as the corresponding `losses`
+     *    dimension).
+     * @param epsilon A small increment to avoid taking log of zero
+     * @param reduction Type of reduction to apply to loss. Should be of type
+     *    `Reduction`
+     *
+     * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
+     */
+    function logLoss_(labels, predictions, weights, epsilon, reduction) {
+        if (epsilon === void 0) { epsilon = 1e-7; }
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $labels = convertToTensor(labels, 'labels', 'logLoss');
+        var $predictions = convertToTensor(predictions, 'predictions', 'logLoss');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'logLoss');
+        }
+        assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');
+        var one = scalar(1);
+        var epsilonScalar = scalar(epsilon);
+        var l1 = neg(mul($labels, log(add$1($predictions, epsilonScalar))));
+        var l2 = mul(sub(one, $labels), log(add$1(sub(one, $predictions), epsilonScalar)));
+        var losses = sub(l1, l2);
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    var logLoss = op({ logLoss_: logLoss_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the mean squared error between two tensors.
+     *
+     * @param labels The ground truth output tensor, same dimensions as
+     *    'predictions'.
+     * @param predictions The predicted outputs.
+     * @param weights Tensor whose rank is either 0, or the same rank as
+     *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
+     *    must be either `1`, or the same as the corresponding `losses`
+     *    dimension).
+     * @param reduction Type of reduction to apply to loss. Should be of type
+     *    `Reduction`
+     *
+     * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
+     */
+    function meanSquaredError_(labels, predictions, weights, reduction) {
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $labels = convertToTensor(labels, 'labels', 'meanSquaredError');
+        var $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'meanSquaredError');
+        }
+        assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');
+        var losses = squaredDifference($labels, $predictions);
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    var meanSquaredError = op({ meanSquaredError_: meanSquaredError_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function sigmoidCrossEntropyWithLogits_(labels, logits) {
+        var $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');
+        var $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');
+        assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');
+        /**
+         * Implementation Details:
+         *
+         * For brevity, let `x = logits`, `z = labels`.  The logistic loss is
+         *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
+         *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
+         *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
+         *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
+         *   = (1 - z) * x + log(1 + exp(-x))
+         *   = x - x * z + log(1 + exp(-x))
+         *
+         *   For x < 0, to avoid overflow in exp(-x), we reformulate the above
+         *     x - x * z + log(1 + exp(-x))
+         *   = log(exp(x)) - x * z + log(1 + exp(-x))
+         *   = - x * z + log(1 + exp(x))
+         *
+         * Hence, to ensure stability and avoid overflow, the implementation uses
+         * this equivalent formulation:
+         *     max(x, 0) - x * z + log(1 + exp(-abs(x)))
+         */
+        var maxOutput = relu($logits);
+        var outputXTarget = mul($logits, $labels);
+        var sigmoidOutput = log1p(exp(neg(abs($logits))));
+        return add$1(sub(maxOutput, outputXTarget), sigmoidOutput);
+    }
+    /**
+     * Computes the sigmoid cross entropy loss between two tensors.
+     *
+     * If labelSmoothing is nonzero, smooth the labels towards 1/2:
+     *
+     *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)
+     *                         + 0.5 * labelSmoothing
+     *
+     * @param multiClassLabels The ground truth output tensor of shape
+     * [batch_size, num_classes], same dimensions as 'predictions'.
+     * @param logits The predicted outputs.
+     * @param weights Tensor whose rank is either 0, or the same rank as
+     *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
+     *    must be either `1`, or the same as the corresponding `losses`
+     *    dimension).
+     * @param labelSmoothing If greater than 0, then smooth the labels.
+     * @param reduction Type of reduction to apply to loss. Should be of type
+     *    `Reduction`
+     *
+     * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }
+     */
+    function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing, reduction) {
+        if (labelSmoothing === void 0) { labelSmoothing = 0; }
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');
+        var $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');
+        }
+        assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');
+        if (labelSmoothing > 0) {
+            var labelSmoothingScalar = scalar(labelSmoothing);
+            var one = scalar(1);
+            var half = scalar(0.5);
+            $multiClassLabels =
+                add$1(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));
+        }
+        var losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    var sigmoidCrossEntropy = op({ sigmoidCrossEntropy_: sigmoidCrossEntropy_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes softmax cross entropy between logits and labels.
+     *
+     * Measures the probability error in discrete classification tasks in which
+     * the classes are mutually exclusive (each entry is in exactly one class).
+     * For example, each CIFAR-10 image is labeled with one and only one label: an
+     * image can be a dog or a truck, but not both.
+     *
+     * `NOTE`: While the classes are mutually exclusive, their probabilities need
+     * not be. All that is required is that each row of labels is a valid
+     * probability distribution. If they are not, the computation of the gradient
+     * will be incorrect.
+     *
+     * `WARNING`: This op expects unscaled logits, since it performs a softmax on
+     * logits internally for efficiency. Do not call this op with the output of
+     * softmax, as it will produce incorrect results.
+     *
+     * logits and labels must have the same shape, e.g. [batch_size, num_classes]
+     * and the same dtype.
+     * @param labels The labels array.
+     * @param logits The logits array.
+     * @param dim The dimension softmax would be performed on. Defaults to `-1`
+     *     which indicates the last dimension.
+     */
+    function softmaxCrossEntropyWithLogits_(labels, logits, dim) {
+        if (dim === void 0) { dim = -1; }
+        if (dim === -1) {
+            dim = logits.rank - 1;
+        }
+        if (dim !== logits.rank - 1) {
+            throw Error("Softmax cross entropy along a non-last dimension is not yet " +
+                ("supported. Labels / logits was rank " + logits.rank + " ") +
+                ("and dim was " + dim));
+        }
+        // Use a custom gradient for numerical stability.
+        var customOp = customGrad(function (labels, logits, save) {
+            // Reference:
+            //   1. http://cs231n.github.io/linear-classify/#softmax
+            //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/
+            var keepDims = true;
+            var lse = logSumExp(logits, [dim], keepDims);
+            var logResult = sub(cast(logits, 'float32'), lse);
+            save([labels, logResult]);
+            var costVector = neg(mul(logResult, labels));
+            var value = sum$1(costVector, [dim]);
+            var gradFunc = function (dy, saved) {
+                var labels = saved[0], logResult = saved[1];
+                var dyShape = expandShapeToKeepDim(dy.shape, [dim]);
+                return [
+                    mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))),
+                    mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))),
+                ];
+            };
+            return { value: value, gradFunc: gradFunc };
+        });
+        return customOp(labels, logits);
+    }
+    /**
+     * Computes the softmax cross entropy loss between two tensors.
+     *
+     * If labelSmoothing is nonzero, smooth the labels towards 1/2:
+     *
+     *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)
+     *                         + labelSmoothing / numClasses
+     *
+     * @param onehotLabels One hot encoded labels
+     *    [batch_size, num_classes], same dimensions as 'predictions'.
+     * @param logits The predicted outputs.
+     * @param weights Tensor whose rank is either 0, or 1, and must be
+     *    broadcastable to `loss`  of shape [batch_size]
+     * @param labelSmoothing If greater than 0, then smooth the labels.
+     * @param reduction Type of reduction to apply to loss. Should be of type
+     *    `Reduction`
+     *
+     * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }
+     */
+    function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing, reduction) {
+        if (labelSmoothing === void 0) { labelSmoothing = 0; }
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');
+        var $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');
+        }
+        assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');
+        if (labelSmoothing > 0) {
+            var labelSmoothingScalar = scalar(labelSmoothing);
+            var one = scalar(1);
+            var numClasses = scalar($onehotLabels.shape[1]);
+            $onehotLabels =
+                add$1(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));
+        }
+        var losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    var softmaxCrossEntropy = op({ softmaxCrossEntropy_: softmaxCrossEntropy_ });
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * The input SparseTensor is represented via the map of inputs {`indices`,
+     * `values`, `denseShape`}. The output SparseTensor has the same `denseShape`
+     * but with indices `outputIndices` and values `outputValues`. This op inserts a
+     * single entry for every row that doesn't have any values. The index is created
+     * as `[row, 0, ..., 0]` and the inserted value is `defaultValue`.
+     *
+     * For example, suppose `spInput` has shape [5, 6] and non-empty values:
+     * [0, 1]: a
+     * [0, 3]: b
+     * [2, 0]: c
+     * [3, 1]: d
+     *
+     * Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values:
+     * [0, 1]: a
+     * [0, 3]: b
+     * [1, 0]: `defaultValue`
+     * [2, 0]: c
+     * [3, 1]: d
+     * [4, 0]: `defaultValue`
+     *
+     * The output SparseTensor will be in row-major order and will have the same
+     * shape as the input.
+     *
+     * This op also returns an indicator vector shaped [dense_shape[0]] such that
+     * emptyRowIndicator[i] = True iff row i was an empty row.
+     *
+     * And a reverse index map vector shaped [indices.shape[0]] that is used during
+     * backpropagation, reverseIndexMap[i] = outi s.t. indices[i, j] ==
+     * outputIndices[outi, j] for all j
+     *
+     * ```js
+     * const result = tf.sparse.sparseFillEmptyRows(
+     *   [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]],
+     *   [0, 10, 13, 14, 32, 33], [5, 6], -1);
+     * console.log(result);
+     * result['outputIndices'].print(); // [[0, 0], [1, 0], [1, 3], [1, 4],
+     *                                  //  [2, 0], [3, 2], [3, 3], [4, 0]]
+     * result['outputValues'].print(); // [0, 10, 13, 14,-1, 32, 33, -1]
+     * result['emptyRowIndicator'].print(); // [false, false, true, false, true]
+     * result['reverseIndexMap'].print(); // [0, 1, 2, 3, 5, 6]
+     * ```
+     * @param indices: 2-D. the indices of the sparse tensor.
+     * @param values: 1-D. the values of the sparse tensor.
+     * @param denseShape: 1-D. the shape of the sparse tensor.
+     * @param defaultValue: 0-D. default value to insert into location [row, 0, ...,
+     *     0] for rows missing from the input sparse tensor.
+     * @return A map with the following properties:
+     *     - outputIndices
+     *     - outputValues: 1-D. the values of the filled sparse tensor.
+     *     - emptyRowIndicator: 1-D. whether the dense row was missing in the input
+     * sparse tensor.
+     *     - reverseIndexMap: 1-D. a map from the input indices to the output
+     * indices.
+     * @doc {heading: 'Operations', subheading: 'Sparse'}
+     */
+    function sparseFillEmptyRows_(indices, values, denseShape, defaultValue) {
+        var $indices = convertToTensor(indices, 'indices', 'sparseFillEmptyRows');
+        var $values = convertToTensor(values, 'values', 'sparseFillEmptyRows');
+        var $denseShape = convertToTensor(denseShape, 'denseShape', 'sparseFillEmptyRows');
+        var $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseFillEmptyRows', $values.dtype);
+        if ($indices.rank !== 2) {
+            throw new Error("Indices should be Tensor2D but received shape\n        " + $indices.shape);
+        }
+        if ($values.rank !== 1) {
+            throw new Error("Values should be Tensor1D but received shape " + $values.shape);
+        }
+        if ($denseShape.rank !== 1) {
+            throw new Error("Dense shape should be Tensor1D but received shape " + $denseShape.shape);
+        }
+        if ($defaultValue.rank !== 0) {
+            throw new Error("Default value should be a scalar but received shape " + $defaultValue.shape);
+        }
+        var inputs = {
+            indices: $indices,
+            values: $values,
+            denseShape: $denseShape,
+            defaultValue: $defaultValue
+        };
+        var result = ENGINE.runKernel(SparseFillEmptyRows, inputs);
+        return {
+            outputIndices: result[0],
+            outputValues: result[1],
+            emptyRowIndicator: result[2],
+            reverseIndexMap: result[3]
+        };
+    }
+    var sparseFillEmptyRows = op({ sparseFillEmptyRows_: sparseFillEmptyRows_ });
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * This operation has the same semantics as reshape on the represented dense
+     * tensor. The `inputIndices` are recomputed based on the requested `newShape`.
+     * If one component of `newShape` is the special value -1, the size of that
+     * dimension is computed so that the total dense size remains constant. At most
+     * one component of `newShape` can be -1. The number of dense elements implied
+     * by `newShape` must be the same as the number of dense elements originally
+     * implied by `inputShape`. Reshaping does not affect the order of values in the
+     * SparseTensor. If the input tensor has rank R_in and N non-empty values, and
+     * `newShape` has length R_out, then `inputIndices` has shape [N, R_in],
+     * `inputShape` has length R_in, `outputIndices` has shape [N, R_out], and
+     * `outputShape` has length R_out.
+     *
+     * ```js
+     * const result = tf.sparse.sparseReshape(
+     *   [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]],
+     *   [2, 3, 6], [9, -1]);
+     * console.log(result);
+     * result['outputIndices'].print(); //[[0, 0], [0, 1], [1, 2], [4, 2], [8, 1]]
+     * result['outputShape'].print(); // [9, 4]
+     * ```
+     * @param inputIndices: 2-D. N x R_in matrix with the indices of non-empty
+     * values in a SparseTensor.
+     * @param inputShape: 1-D. R_in Tensor1D with the input SparseTensor's dense
+     * shape.
+     * @param newShape: 1-D. R_out Tensor1D with the requested new dense shape.
+     * @return A map with the following properties:
+     *     - outputIndices: 2-D. N x R_out matrix with the updated indices of
+     *       non-empty values in the output SparseTensor.
+     *     - outputShape: 1-D. R_out vector with the full dense shape of the output
+     *       SparseTensor. This is the same as newShape but with any -1 dimensions
+     *        filled in.
+     * @doc {heading: 'Operations', subheading: 'Sparse'}
+     */
+    function sparseReshape_(inputIndices, inputShape, newShape) {
+        var $inputIndices = convertToTensor(inputIndices, 'inputIndices', 'sparseReshape');
+        var $inputShape = convertToTensor(inputShape, 'inputShape', 'sparseReshape');
+        var $newShape = convertToTensor(newShape, 'newShape', 'sparseReshape');
+        if ($inputIndices.rank !== 2) {
+            throw new Error("Input indices should be Tensor2D but received shape\n        " + $inputIndices.shape);
+        }
+        if ($inputShape.rank !== 1) {
+            throw new Error("Input shape should be Tensor1D but received shape " + $inputShape.shape);
+        }
+        if ($newShape.rank !== 1) {
+            throw new Error("New shape should be Tensor1D but received shape " + $newShape.shape);
+        }
+        var inputs = {
+            inputIndices: $inputIndices,
+            inputShape: $inputShape,
+            newShape: $newShape
+        };
+        var result = ENGINE.runKernel(SparseReshape, inputs);
+        return { outputIndices: result[0], outputShape: result[1] };
+    }
+    var sparseReshape = op({ sparseReshape_: sparseReshape_ });
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the mean along sparse segments of a tensor.
+     *
+     * ```js
+     * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [6,7,8,9]]);
+     * // Select two rows, one segment.
+     * const result1 = tf.sparse.sparseSegmentMean(c,
+     *                                           tf.tensor1d([0, 1], 'int32'),
+     *                                           tf.tensor1d([0, 0], 'int32'));
+     * result1.print(); // [[0, 0, 0, 0]]
+     *
+     * // Select two rows, two segments.
+     * const result2 = tf.sparse.sparseSegmentMean(c,
+     *                                             tf.tensor1d([0, 1], 'int32'),
+     *                                             tf.tensor1d([0, 1], 'int32'));
+     * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]
+     *
+     * // Select all rows, two segments.
+     * const result3 = tf.sparse.sparseSegmentMean(c,
+     *                                             tf.tensor1d([0, 1, 2], 'int32'),
+     *                                             tf.tensor1d([0, 1, 1], 'int32'));
+     * result3.print(); // [[1.0, 2.0, 3.0, 4.0], [2.5, 2.5, 2.5, 2.5]]
+     * ```
+     * @param data: A Tensor of at least one dimension with data that will be
+     *     assembled in the output.
+     * @param indices: A 1-D Tensor with indices into data. Has same rank as
+     *     segmentIds.
+     * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values
+     *     should be sorted and can be repeated.
+     * @return Has same shape as data, except for dimension 0 which has equal to
+     *         the number of segments.
+     *
+     * @doc {heading: 'Operations', subheading: 'Sparse'}
+     */
+    function sparseSegmentMean_(data, indices, segmentIds) {
+        var $data = convertToTensor(data, 'data', 'sparseSegmentMean');
+        var $indices = convertToTensor(indices, 'indices', 'sparseSegmentMean');
+        var $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentMean');
+        if ($data.rank < 1) {
+            throw new Error("Data should be at least 1 dimensional but received scalar");
+        }
+        if ($indices.rank !== 1) {
+            throw new Error("Indices should be Tensor1D but received shape\n          " + $indices.shape);
+        }
+        if ($segmentIds.rank !== 1) {
+            throw new Error("Segment ids should be Tensor1D but received shape\n          " + $segmentIds.shape);
+        }
+        var inputs = {
+            data: $data,
+            indices: $indices,
+            segmentIds: $segmentIds
+        };
+        return ENGINE.runKernel(SparseSegmentMean, inputs);
+    }
+    var sparseSegmentMean = op({ sparseSegmentMean_: sparseSegmentMean_ });
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Computes the sum along sparse segments of a tensor.
+     *
+     * ```js
+     * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]);
+     * // Select two rows, one segment.
+     * const result1 = tf.sparse.sparseSegmentSum(c,
+     *                                           tf.tensor1d([0, 1], 'int32'),
+     *                                           tf.tensor1d([0, 0], 'int32'));
+     * result1.print(); // [[0, 0, 0, 0]]
+     *
+     * // Select two rows, two segment.
+     * const result2 = tf.sparse.sparseSegmentSum(c,
+     *                                           tf.tensor1d([0, 1], 'int32'),
+     *                                           tf.tensor1d([0, 1], 'int32'));
+     * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]
+     *
+     * // Select all rows, two segments.
+     * const result3 = tf.sparse.sparseSegmentSum(c,
+     *                                           tf.tensor1d([0, 1, 2], 'int32'),
+     *                                           tf.tensor1d([0, 0, 1], 'int32'));
+     * result3.print(); // [[0, 0, 0, 0], [5, 6, 7, 8]]
+     * ```
+     * @param data: A Tensor of at least one dimension with data that will be
+     *     assembled in the output.
+     * @param indices: A 1-D Tensor with indices into data. Has same rank as
+     *     segmentIds.
+     * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values
+     *     should be sorted and can be repeated.
+     * @return Has same shape as data, except for dimension 0 which has equal to
+     *         the number of segments.
+     *
+     * @doc {heading: 'Operations', subheading: 'Sparse'}
+     */
+    function sparseSegmentSum_(data, indices, segmentIds) {
+        var $data = convertToTensor(data, 'data', 'sparseSegmentSum');
+        var $indices = convertToTensor(indices, 'indices', 'sparseSegmentSum');
+        var $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentSum');
+        if ($data.rank < 1) {
+            throw new Error("Data should be at least 1 dimensional but received scalar");
+        }
+        if ($indices.rank !== 1) {
+            throw new Error("Indices should be Tensor1D but received shape\n         " + $indices.shape);
+        }
+        if ($segmentIds.rank !== 1) {
+            throw new Error("Segment ids should be Tensor1D but received shape\n         " + $segmentIds.shape);
+        }
+        var inputs = {
+            data: $data,
+            indices: $indices,
+            segmentIds: $segmentIds
+        };
+        return ENGINE.runKernel(SparseSegmentSum, inputs);
+    }
+    var sparseSegmentSum = op({ sparseSegmentSum_: sparseSegmentSum_ });
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Creates ngrams from ragged string data.
+     *
+     * This op accepts a ragged tensor with 1 ragged dimension containing only
+     * strings and outputs a ragged tensor with 1 ragged dimension containing ngrams
+     * of that string, joined along the innermost axis.
+     *
+     * ```js
+     * const result = tf.string.stringNGrams(
+     *   ['a', 'b', 'c', 'd'], tf.tensor1d([0, 2, 4], 'int32'),
+     *   '|', [1, 2], 'LP', 'RP', -1, false);
+     * result['nGrams'].print(); // ['a', 'b', 'LP|a', 'a|b', 'b|RP',
+     *                           //  'c', 'd', 'LP|c', 'c|d', 'd|RP']
+     * result['nGramsSplits'].print(); // [0, 5, 10]
+     * ```
+     * @param data: The values tensor of the ragged string tensor to make ngrams out
+     *     of. Must be a 1D string tensor.
+     * @param dataSplits: The splits tensor of the ragged string tensor to make
+     *     ngrams out of.
+     * @param separator: The string to append between elements of the token. Use ""
+     *     for no separator.
+     * @param nGramWidths: The sizes of the ngrams to create.
+     * @param leftPad: The string to use to pad the left side of the ngram sequence.
+     *     Only used if pad_width !== 0.
+     * @param rightPad: The string to use to pad the right side of the ngram
+     *     sequence. Only used if pad_width !== 0.
+     * @param padWidth: The number of padding elements to add to each side of each
+     *     sequence. Note that padding will never be greater than `nGramWidths`-1
+     *     regardless of this value. If `padWidth`=-1 , then add max(`nGramWidths)-1
+     *     elements.
+     * @param preserveShortSequences: If true, then ensure that at least one ngram
+     *     is generated for each input sequence. In particular, if an input sequence
+     *     is shorter than min(ngramWidth) + 2*padWidth, then generate a single
+     *     ngram containing the entire sequence. If false, then no ngrams are
+     *     generated for these short input sequences.
+     * @return A map with the following properties:
+     *     - nGrams: The values tensor of the output ngrams ragged tensor.
+     *     - nGramsSplits: The splits tensor of the output ngrams ragged tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'String'}
+     */
+    function stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) {
+        var $data = convertToTensor(data, 'data', 'stringNGrams', 'string');
+        if ($data.dtype !== 'string') {
+            throw new Error('Data must be of datatype string');
+        }
+        if ($data.shape.length !== 1) {
+            throw new Error("Data must be a vector, saw: " + $data.shape);
+        }
+        var $dataSplits = convertToTensor(dataSplits, 'dataSplits', 'stringNGrams');
+        if ($dataSplits.dtype !== 'int32') {
+            throw new Error('Data splits must be of datatype int32');
+        }
+        var attrs = {
+            separator: separator,
+            nGramWidths: nGramWidths,
+            leftPad: leftPad,
+            rightPad: rightPad,
+            padWidth: padWidth,
+            preserveShortSequences: preserveShortSequences
+        };
+        var inputs = { data: $data, dataSplits: $dataSplits };
+        var result = ENGINE.runKernel(StringNGrams, inputs, attrs);
+        return { nGrams: result[0], nGramsSplits: result[1] };
+    }
+    var stringNGrams = op({ stringNGrams_: stringNGrams_ });
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Split elements of `input` based on `delimiter` into a SparseTensor .
+     *
+     * Let N be the size of source (typically N will be the batch size). Split each
+     * element of `input` based on `delimiter` and return a SparseTensor containing
+     * the splitted tokens. Empty tokens are ignored if `skipEmpty` is set to True.
+     *
+     * `delimiter` can be empty, or a string of split characters. If `delimiter` is
+     * an empty string, each element of `input` is split into individual
+     * character strings. Otherwise every character of `delimiter` is a potential
+     * split point.
+     *
+     * ```js
+     * const result = tf.string.stringSplit(['hello world',  'a b c'], ' ');
+     * result['indices'].print(); // [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2]]
+     * result['values'].print(); // ['hello', 'world', 'a', 'b', 'c']
+     * result['shape'].print(); // [2, 3]
+     * ```
+     * @param input: 1-D. Strings to split.
+     * @param delimiter: 0-D. Delimiter characters, or empty string.
+     * @param skipEmpty: Optional. If true, skip the empty strings from the result.
+     *     Defaults to true.
+     * @return A map with the following properties:
+     *     - indices: A dense matrix of int32 representing the indices of the sparse
+     *       tensor.
+     *     - values: A vector of strings corresponding to the splited values.
+     *     - shape: a length-2 vector of int32 representing the shape of the sparse
+     * tensor, where the first value is N and the second value is the maximum number
+     * of tokens in a single input entry.
+     *
+     * @doc {heading: 'Operations', subheading: 'String'}
+     */
+    function stringSplit_(input, delimiter, skipEmpty) {
+        if (skipEmpty === void 0) { skipEmpty = true; }
+        var $input = convertToTensor(input, 'input', 'stringSplit', 'string');
+        var $delimiter = convertToTensor(delimiter, 'delimiter', 'stringSplit', 'string');
+        if ($input.rank !== 1) {
+            throw new Error("Input should be Tensor1D but received shape " + $input.shape);
+        }
+        if ($delimiter.rank !== 0) {
+            throw new Error("Delimiter should be a scalar but received shape " + $delimiter.shape);
+        }
+        var attrs = { skipEmpty: skipEmpty };
+        var inputs = { input: $input, delimiter: $delimiter };
+        var result = ENGINE.runKernel(StringSplit, inputs, attrs);
+        return { indices: result[0], values: result[1], shape: result[2] };
+    }
+    var stringSplit = op({ stringSplit_: stringSplit_ });
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Converts each string in the input Tensor to its hash mod by a number of
+     * buckets.
+     *
+     * The hash function is deterministic on the content of the string within the
+     * process and will never change. However, it is not suitable for cryptography.
+     * This function may be used when CPU time is scarce and inputs are trusted or
+     * unimportant. There is a risk of adversaries constructing inputs that all hash
+     * to the same bucket.
+     *
+     * ```js
+     * const result = tf.string.stringToHashBucketFast(
+     *   ['Hello', 'TensorFlow', '2.x'], 3);
+     * result.print(); // [0, 2, 2]
+     * ```
+     * @param input: The strings to assign a hash bucket.
+     * @param numBuckets: The number of buckets.
+     * @return A Tensor of the same shape as the input tensor.
+     *
+     * @doc {heading: 'Operations', subheading: 'String'}
+     */
+    function stringToHashBucketFast_(input, numBuckets) {
+        var $input = convertToTensor(input, 'input', 'stringToHashBucketFast', 'string');
+        var attrs = { numBuckets: numBuckets };
+        if (numBuckets <= 0) {
+            throw new Error("Number of buckets must be at least 1");
+        }
+        var inputs = { input: $input };
+        return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs);
+    }
+    var stringToHashBucketFast = op({ stringToHashBucketFast_: stringToHashBucketFast_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var spectral = {
+        fft: fft,
+        ifft: ifft,
+        rfft: rfft,
+        irfft: irfft
+    };
+    var signal = {
+        hammingWindow: hammingWindow,
+        hannWindow: hannWindow,
+        frame: frame,
+        stft: stft,
+    };
+    var image = {
+        flipLeftRight: flipLeftRight,
+        resizeNearestNeighbor: resizeNearestNeighbor,
+        resizeBilinear: resizeBilinear,
+        rotateWithOffset: rotateWithOffset,
+        cropAndResize: cropAndResize,
+        nonMaxSuppression: nonMaxSuppression,
+        nonMaxSuppressionAsync: nonMaxSuppressionAsync,
+        nonMaxSuppressionWithScore: nonMaxSuppressionWithScore,
+        nonMaxSuppressionWithScoreAsync: nonMaxSuppressionWithScoreAsync,
+        nonMaxSuppressionPadded: nonMaxSuppressionPadded,
+        nonMaxSuppressionPaddedAsync: nonMaxSuppressionPaddedAsync,
+        threshold: threshold,
+        transform: transform
+    };
+    var linalg = {
+        bandPart: bandPart,
+        gramSchmidt: gramSchmidt,
+        qr: qr
+    };
+    var losses = {
+        absoluteDifference: absoluteDifference,
+        computeWeightedLoss: computeWeightedLoss,
+        cosineDistance: cosineDistance,
+        hingeLoss: hingeLoss,
+        huberLoss: huberLoss,
+        logLoss: logLoss,
+        meanSquaredError: meanSquaredError,
+        sigmoidCrossEntropy: sigmoidCrossEntropy,
+        softmaxCrossEntropy: softmaxCrossEntropy
+    };
+    var sparse = {
+        sparseFillEmptyRows: sparseFillEmptyRows,
+        sparseReshape: sparseReshape,
+        sparseSegmentMean: sparseSegmentMean,
+        sparseSegmentSum: sparseSegmentSum
+    };
+    // tslint:disable-next-line:variable-name
+    var string = {
+        stringNGrams: stringNGrams,
+        stringSplit: stringSplit,
+        stringToHashBucketFast: stringToHashBucketFast
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */
+    var Optimizer = /** @class */ (function (_super) {
+        __extends(Optimizer, _super);
+        function Optimizer() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        /**
+         * Executes `f()` and minimizes the scalar output of `f()` by computing
+         * gradients of y with respect to the list of trainable variables provided by
+         * `varList`. If no list is provided, it defaults to all trainable variables.
+         *
+         * @param f The function to execute and whose output to minimize.
+         * @param returnCost Whether to return the scalar cost value produced by
+         * executing `f()`.
+         * @param varList An optional list of variables to update. If specified, only
+         * the trainable variables in varList will be updated by minimize. Defaults to
+         * all trainable variables.
+         *
+         * @doc {heading: 'Training', subheading: 'Optimizers'}
+         */
+        Optimizer.prototype.minimize = function (f, returnCost, varList) {
+            if (returnCost === void 0) { returnCost = false; }
+            var _a = this.computeGradients(f, varList), value = _a.value, grads = _a.grads;
+            if (varList != null) {
+                var gradArray = varList.map(function (v) { return ({ name: v.name, tensor: grads[v.name] }); });
+                this.applyGradients(gradArray);
+            }
+            else {
+                this.applyGradients(grads);
+            }
+            // Dispose gradients.
+            dispose(grads);
+            if (returnCost) {
+                return value;
+            }
+            else {
+                value.dispose();
+                return null;
+            }
+        };
+        Object.defineProperty(Optimizer.prototype, "iterations", {
+            /**
+             * The number of iterations that this optimizer instance has been invoked for.
+             */
+            get: function () {
+                if (this.iterations_ == null) {
+                    this.iterations_ = 0;
+                }
+                return this.iterations_;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Optimizer.prototype.incrementIterations = function () {
+            this.iterations_ = this.iterations + 1;
+        };
+        /**
+         * Executes f() and computes the gradient of the scalar output of f() with
+         * respect to the list of trainable variables provided by `varList`. If no
+         * list is provided, it defaults to all trainable variables.
+         *
+         * @param f The function to execute and whose output to use for computing
+         * gradients with respect to variables.
+         * @param varList An optional list of variables to compute gradients with
+         * respect to. If specified, only the trainable variables in varList will have
+         * gradients computed with respect to. Defaults to all trainable variables.
+         *
+         * @doc {heading: 'Training', subheading: 'Optimizers'}
+         */
+        Optimizer.prototype.computeGradients = function (f, varList) {
+            return variableGrads(f, varList);
+        };
+        /**
+         * Dispose the variables (if any) owned by this optimizer instance.
+         */
+        Optimizer.prototype.dispose = function () {
+            if (this.iterations_ != null) {
+                dispose(this.iterations_);
+            }
+        };
+        Optimizer.prototype.saveIterations = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    if (this.iterations_ == null) {
+                        this.iterations_ = 0;
+                    }
+                    return [2 /*return*/, {
+                            name: 'iter',
+                            // TODO(cais): Use 'int64' type when available.
+                            tensor: scalar(this.iterations_, 'int32')
+                        }];
+                });
+            });
+        };
+        Optimizer.prototype.getWeights = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    throw new Error('getWeights() is not implemented for this optimizer yet.');
+                });
+            });
+        };
+        Optimizer.prototype.setWeights = function (weightValues) {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    throw new Error("setWeights() is not implemented for this optimizer class " +
+                        ("" + this.getClassName()));
+                });
+            });
+        };
+        /**
+         * Extract the first element of the weight values and set it
+         * as the iterations counter variable of this instance of optimizer.
+         *
+         * @param weightValues
+         * @returns Weight values with the first element consumed and excluded.
+         */
+        Optimizer.prototype.extractIterations = function (weightValues) {
+            return __awaiter(this, void 0, void 0, function () {
+                var _a;
+                return __generator(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = this;
+                            return [4 /*yield*/, weightValues[0].tensor.data()];
+                        case 1:
+                            _a.iterations_ = (_b.sent())[0];
+                            return [2 /*return*/, weightValues.slice(1)];
+                    }
+                });
+            });
+        };
+        return Optimizer;
+    }(Serializable));
+    Object.defineProperty(Optimizer, Symbol.hasInstance, {
+        value: function (instance) {
+            return instance.minimize != null && instance.computeGradients != null &&
+                instance.applyGradients != null;
+        }
+    });
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /** @doclink Optimizer */
+    var AdadeltaOptimizer = /** @class */ (function (_super) {
+        __extends(AdadeltaOptimizer, _super);
+        function AdadeltaOptimizer(learningRate, rho, epsilon) {
+            if (epsilon === void 0) { epsilon = null; }
+            var _this = _super.call(this) || this;
+            _this.learningRate = learningRate;
+            _this.rho = rho;
+            _this.epsilon = epsilon;
+            _this.accumulatedGrads = [];
+            _this.accumulatedUpdates = [];
+            if (epsilon == null) {
+                _this.epsilon = ENGINE.backend.epsilon();
+            }
+            return _this;
+        }
+        AdadeltaOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            var variableNames = Array.isArray(variableGradients) ?
+                variableGradients.map(function (item) { return item.name; }) :
+                Object.keys(variableGradients);
+            variableNames.forEach(function (name, i) {
+                var value = ENGINE.registeredVariables[name];
+                var trainable = false;
+                if (_this.accumulatedGrads[i] == null) {
+                    _this.accumulatedGrads[i] = {
+                        originalName: name + "/accum_grad",
+                        variable: tidy(function () { return zerosLike(value).variable(trainable); })
+                    };
+                }
+                if (_this.accumulatedUpdates[i] == null) {
+                    _this.accumulatedUpdates[i] = {
+                        originalName: name + "/accum_var",
+                        variable: tidy(function () { return zerosLike(value).variable(trainable); })
+                    };
+                }
+                var gradient = Array.isArray(variableGradients) ?
+                    variableGradients[i].tensor :
+                    variableGradients[name];
+                if (gradient == null) {
+                    return;
+                }
+                var accumulatedGrad = _this.accumulatedGrads[i].variable;
+                var accumulatedUpdate = _this.accumulatedUpdates[i].variable;
+                tidy(function () {
+                    var newAccumulatedGrad = add$1(mul(accumulatedGrad, _this.rho), mul(square(gradient), 1 - _this.rho));
+                    var updates = mul(div(sqrt(add$1(accumulatedUpdate, _this.epsilon)), sqrt(add$1(accumulatedGrad, _this.epsilon))), gradient);
+                    var newAccumulatedUpdate = add$1(mul(accumulatedUpdate, _this.rho), mul(square(updates), 1 - _this.rho));
+                    accumulatedGrad.assign(newAccumulatedGrad);
+                    accumulatedUpdate.assign(newAccumulatedUpdate);
+                    var newValue = add$1(mul(updates, -_this.learningRate), value);
+                    value.assign(newValue);
+                });
+            });
+            this.incrementIterations();
+        };
+        AdadeltaOptimizer.prototype.dispose = function () {
+            if (this.accumulatedUpdates != null) {
+                dispose(this.accumulatedGrads.map(function (v) { return v.variable; }));
+                dispose(this.accumulatedUpdates.map(function (v) { return v.variable; }));
+            }
+        };
+        AdadeltaOptimizer.prototype.getWeights = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var variables;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            variables = this.accumulatedGrads.concat(this.accumulatedUpdates);
+                            return [4 /*yield*/, this.saveIterations()];
+                        case 1: return [2 /*return*/, [_a.sent()].concat(variables.map(function (v) { return ({ name: v.originalName, tensor: v.variable }); }))];
+                    }
+                });
+            });
+        };
+        AdadeltaOptimizer.prototype.setWeights = function (weightValues) {
+            return __awaiter(this, void 0, void 0, function () {
+                var variableCount, trainable;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
+                        case 1:
+                            weightValues = _a.sent();
+                            variableCount = weightValues.length / 2;
+                            trainable = false;
+                            this.accumulatedGrads =
+                                weightValues.slice(0, variableCount).map(function (v) { return ({
+                                    originalName: v.name,
+                                    variable: v.tensor.variable(trainable)
+                                }); });
+                            this.accumulatedUpdates =
+                                weightValues.slice(variableCount, variableCount * 2)
+                                    .map(function (v) { return ({
+                                    originalName: v.name,
+                                    variable: v.tensor.variable(trainable)
+                                }); });
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        AdadeltaOptimizer.prototype.getConfig = function () {
+            return {
+                'learningRate': this.learningRate,
+                'rho': this.rho,
+                'epsilon': this.epsilon
+            };
+        };
+        /** @nocollapse */
+        AdadeltaOptimizer.fromConfig = function (cls, config) {
+            return new cls(config['learningRate'], config['rho'], config['epsilon']);
+        };
+        /** @nocollapse */
+        AdadeltaOptimizer.className = 'Adadelta'; // Name matters for Python compatibility.
+        return AdadeltaOptimizer;
+    }(Optimizer));
+    registerClass(AdadeltaOptimizer);
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /** @doclink Optimizer */
+    var AdagradOptimizer = /** @class */ (function (_super) {
+        __extends(AdagradOptimizer, _super);
+        function AdagradOptimizer(learningRate, initialAccumulatorValue) {
+            if (initialAccumulatorValue === void 0) { initialAccumulatorValue = 0.1; }
+            var _this = _super.call(this) || this;
+            _this.learningRate = learningRate;
+            _this.initialAccumulatorValue = initialAccumulatorValue;
+            _this.accumulatedGrads = [];
+            return _this;
+        }
+        AdagradOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            var variableNames = Array.isArray(variableGradients) ?
+                variableGradients.map(function (item) { return item.name; }) :
+                Object.keys(variableGradients);
+            variableNames.forEach(function (name, i) {
+                var value = ENGINE.registeredVariables[name];
+                if (_this.accumulatedGrads[i] == null) {
+                    var trainable_1 = false;
+                    _this.accumulatedGrads[i] = {
+                        originalName: name + "/accumulator",
+                        variable: tidy(function () { return fill(value.shape, _this.initialAccumulatorValue)
+                            .variable(trainable_1); })
+                    };
+                }
+                var gradient = Array.isArray(variableGradients) ?
+                    variableGradients[i].tensor :
+                    variableGradients[name];
+                if (gradient == null) {
+                    return;
+                }
+                var accumulatedGrad = _this.accumulatedGrads[i].variable;
+                tidy(function () {
+                    var newAccumulatedGrad = add$1(accumulatedGrad, square(gradient));
+                    accumulatedGrad.assign(newAccumulatedGrad);
+                    var newValue = add$1(mul(div(gradient, sqrt(add$1(newAccumulatedGrad, ENGINE.backend.epsilon()))), -_this.learningRate), value);
+                    value.assign(newValue);
+                });
+            });
+            this.incrementIterations();
+        };
+        AdagradOptimizer.prototype.dispose = function () {
+            if (this.accumulatedGrads != null) {
+                dispose(this.accumulatedGrads.map(function (v) { return v.variable; }));
+            }
+        };
+        AdagradOptimizer.prototype.getWeights = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.saveIterations()];
+                        case 1: 
+                        // Order matters for Python compatibility.
+                        return [2 /*return*/, [_a.sent()].concat(this.accumulatedGrads.map(function (v) { return ({ name: v.originalName, tensor: v.variable }); }))];
+                    }
+                });
+            });
+        };
+        AdagradOptimizer.prototype.setWeights = function (weightValues) {
+            return __awaiter(this, void 0, void 0, function () {
+                var trainable;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
+                        case 1:
+                            weightValues = _a.sent();
+                            trainable = false;
+                            this.accumulatedGrads = weightValues.map(function (v) { return ({ originalName: v.name, variable: v.tensor.variable(trainable) }); });
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        AdagradOptimizer.prototype.getConfig = function () {
+            return {
+                'learningRate': this.learningRate,
+                'initialAccumulatorValue': this.initialAccumulatorValue,
+            };
+        };
+        /** @nocollapse */
+        AdagradOptimizer.fromConfig = function (cls, config) {
+            return new cls(config['learningRate'], config['initialAccumulatorValue']);
+        };
+        /** @nocollapse */
+        AdagradOptimizer.className = 'Adagrad'; // Note: Name matters for Python compatibility.
+        return AdagradOptimizer;
+    }(Optimizer));
+    registerClass(AdagradOptimizer);
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var AdamOptimizer = /** @class */ (function (_super) {
+        __extends(AdamOptimizer, _super);
+        function AdamOptimizer(learningRate, beta1, beta2, epsilon) {
+            if (epsilon === void 0) { epsilon = null; }
+            var _this = _super.call(this) || this;
+            _this.learningRate = learningRate;
+            _this.beta1 = beta1;
+            _this.beta2 = beta2;
+            _this.epsilon = epsilon;
+            _this.accumulatedFirstMoment = [];
+            _this.accumulatedSecondMoment = [];
+            tidy(function () {
+                // accB* will be updated by batch.
+                _this.accBeta1 = scalar(beta1).variable();
+                _this.accBeta2 = scalar(beta2).variable();
+            });
+            if (epsilon == null) {
+                _this.epsilon = ENGINE.backend.epsilon();
+            }
+            return _this;
+        }
+        AdamOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            var varNames = Array.isArray(variableGradients) ?
+                variableGradients.map(function (v) { return v.name; }) :
+                Object.keys(variableGradients);
+            tidy(function () {
+                var oneMinusAccBeta1 = sub(1, _this.accBeta1);
+                var oneMinusAccBeta2 = sub(1, _this.accBeta2);
+                varNames.forEach(function (name, i) {
+                    var value = ENGINE.registeredVariables[name];
+                    var trainable = false;
+                    if (_this.accumulatedFirstMoment[i] == null) {
+                        _this.accumulatedFirstMoment[i] = {
+                            originalName: name + "/m",
+                            variable: tidy(function () { return zerosLike(value).variable(trainable); })
+                        };
+                    }
+                    if (_this.accumulatedSecondMoment[i] == null) {
+                        _this.accumulatedSecondMoment[i] = {
+                            originalName: name + "/v",
+                            variable: tidy(function () { return zerosLike(value).variable(trainable); })
+                        };
+                    }
+                    var gradient = Array.isArray(variableGradients) ?
+                        variableGradients[i].tensor :
+                        variableGradients[name];
+                    if (gradient == null) {
+                        return;
+                    }
+                    var firstMoment = _this.accumulatedFirstMoment[i].variable;
+                    var secondMoment = _this.accumulatedSecondMoment[i].variable;
+                    var newFirstMoment = add$1(mul(firstMoment, _this.beta1), mul(gradient, 1 - _this.beta1));
+                    var newSecondMoment = add$1(mul(secondMoment, _this.beta2), mul(square(gradient), 1 - _this.beta2));
+                    var biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
+                    var biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);
+                    firstMoment.assign(newFirstMoment);
+                    secondMoment.assign(newSecondMoment);
+                    var newValue = add$1(mul(div(biasCorrectedFirstMoment, add$1(sqrt(biasCorrectedSecondMoment), _this.epsilon)), -_this.learningRate), value);
+                    value.assign(newValue);
+                });
+                _this.accBeta1.assign(mul(_this.accBeta1, _this.beta1));
+                _this.accBeta2.assign(mul(_this.accBeta2, _this.beta2));
+            });
+            this.incrementIterations();
+        };
+        AdamOptimizer.prototype.dispose = function () {
+            this.accBeta1.dispose();
+            this.accBeta2.dispose();
+            if (this.accumulatedFirstMoment != null) {
+                dispose(this.accumulatedFirstMoment.map(function (v) { return v.variable; }));
+            }
+            if (this.accumulatedSecondMoment != null) {
+                dispose(this.accumulatedSecondMoment.map(function (v) { return v.variable; }));
+            }
+        };
+        AdamOptimizer.prototype.getWeights = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var variables;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            variables = this.accumulatedFirstMoment.concat(this.accumulatedSecondMoment);
+                            return [4 /*yield*/, this.saveIterations()];
+                        case 1: return [2 /*return*/, [_a.sent()].concat(variables.map(function (v) { return ({ name: v.originalName, tensor: v.variable }); }))];
+                    }
+                });
+            });
+        };
+        AdamOptimizer.prototype.setWeights = function (weightValues) {
+            return __awaiter(this, void 0, void 0, function () {
+                var variableCount, trainable;
+                var _this = this;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
+                        case 1:
+                            weightValues = _a.sent();
+                            tidy(function () {
+                                _this.accBeta1.assign(pow(_this.beta1, _this.iterations_ + 1));
+                                _this.accBeta2.assign(pow(_this.beta2, _this.iterations_ + 1));
+                            });
+                            variableCount = weightValues.length / 2;
+                            trainable = false;
+                            this.accumulatedFirstMoment =
+                                weightValues.slice(0, variableCount).map(function (v) { return ({
+                                    originalName: v.name,
+                                    variable: v.tensor.variable(trainable)
+                                }); });
+                            this.accumulatedSecondMoment =
+                                weightValues.slice(variableCount, variableCount * 2)
+                                    .map(function (v) { return ({
+                                    originalName: v.name,
+                                    variable: v.tensor.variable(trainable)
+                                }); });
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        AdamOptimizer.prototype.getConfig = function () {
+            return {
+                'learningRate': this.learningRate,
+                'beta1': this.beta1,
+                'beta2': this.beta2,
+                'epsilon': this.epsilon,
+            };
+        };
+        /** @nocollapse */
+        AdamOptimizer.fromConfig = function (cls, config) {
+            return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon']);
+        };
+        /** @nocollapse */
+        AdamOptimizer.className = 'Adam'; // Note: Name matters for Python compatibility.
+        return AdamOptimizer;
+    }(Optimizer));
+    registerClass(AdamOptimizer);
+
+    /**
+    * @license
+    * Copyright 2018 Google LLC. All Rights Reserved.
+    * Licensed under the Apache License, Version 2.0 (the "License");
+    * you may not use this file except in compliance with the License.
+    * You may obtain a copy of the License at
+    *
+    * http://www.apache.org/licenses/LICENSE-2.0
+    *
+    * Unless required by applicable law or agreed to in writing, software
+    * distributed under the License is distributed on an "AS IS" BASIS,
+    * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+    * See the License for the specific language governing permissions and
+    * limitations under the License.
+    * =============================================================================
+    */
+    var AdamaxOptimizer = /** @class */ (function (_super) {
+        __extends(AdamaxOptimizer, _super);
+        function AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay) {
+            if (epsilon === void 0) { epsilon = null; }
+            if (decay === void 0) { decay = 0.0; }
+            var _this = _super.call(this) || this;
+            _this.learningRate = learningRate;
+            _this.beta1 = beta1;
+            _this.beta2 = beta2;
+            _this.epsilon = epsilon;
+            _this.decay = decay;
+            _this.accumulatedFirstMoment = [];
+            _this.accumulatedWeightedInfNorm = [];
+            tidy(function () {
+                _this.iteration = scalar(0).variable();
+                _this.accBeta1 = scalar(beta1).variable();
+            });
+            if (epsilon == null) {
+                _this.epsilon = ENGINE.backend.epsilon();
+            }
+            return _this;
+        }
+        AdamaxOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            var variableNames = Array.isArray(variableGradients) ?
+                variableGradients.map(function (item) { return item.name; }) :
+                Object.keys(variableGradients);
+            tidy(function () {
+                var oneMinusAccBeta1 = sub(1, _this.accBeta1);
+                var lr = div(-_this.learningRate, add$1(mul(_this.iteration, _this.decay), 1));
+                variableNames.forEach(function (name, i) {
+                    var value = ENGINE.registeredVariables[name];
+                    var trainable = false;
+                    if (_this.accumulatedFirstMoment[i] == null) {
+                        _this.accumulatedFirstMoment[i] = {
+                            originalName: name + "/m",
+                            variable: zerosLike(value).variable(trainable)
+                        };
+                    }
+                    if (_this.accumulatedWeightedInfNorm[i] == null) {
+                        _this.accumulatedWeightedInfNorm[i] = {
+                            originalName: name + "/v",
+                            variable: zerosLike(value).variable(trainable)
+                        };
+                    }
+                    var gradient = Array.isArray(variableGradients) ?
+                        variableGradients[i].tensor :
+                        variableGradients[name];
+                    if (gradient == null) {
+                        return;
+                    }
+                    var firstMoment = _this.accumulatedFirstMoment[i].variable;
+                    var weightedInfNorm = _this.accumulatedWeightedInfNorm[i].variable;
+                    var newFirstMoment = add$1(mul(firstMoment, _this.beta1), mul(gradient, 1 - _this.beta1));
+                    var ut0 = mul(weightedInfNorm, _this.beta2);
+                    var ut1 = abs(gradient);
+                    var newWeightedInfNorm = maximum(ut0, ut1);
+                    firstMoment.assign(newFirstMoment);
+                    weightedInfNorm.assign(newWeightedInfNorm);
+                    var newValue = add$1(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add$1(newWeightedInfNorm, _this.epsilon))), value);
+                    value.assign(newValue);
+                });
+                _this.iteration.assign(add$1(_this.iteration, 1));
+                _this.accBeta1.assign(mul(_this.accBeta1, _this.beta1));
+            });
+            this.incrementIterations();
+        };
+        AdamaxOptimizer.prototype.dispose = function () {
+            this.accBeta1.dispose();
+            this.iteration.dispose();
+            if (this.accumulatedFirstMoment != null) {
+                dispose(this.accumulatedFirstMoment.map(function (v) { return v.variable; }));
+            }
+            if (this.accumulatedWeightedInfNorm != null) {
+                dispose(this.accumulatedWeightedInfNorm.map(function (v) { return v.variable; }));
+            }
+        };
+        AdamaxOptimizer.prototype.getWeights = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    throw new Error('getWeights() is not implemented for Adamax yet.');
+                });
+            });
+        };
+        AdamaxOptimizer.prototype.setWeights = function (weightValues) {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    throw new Error('setWeights() is not implemented for Adamax yet.');
+                });
+            });
+        };
+        AdamaxOptimizer.prototype.getConfig = function () {
+            return {
+                'learningRate': this.learningRate,
+                'beta1': this.beta1,
+                'beta2': this.beta2,
+                'epsilon': this.epsilon,
+                'decay': this.decay
+            };
+        };
+        /** @nocollapse */
+        AdamaxOptimizer.fromConfig = function (cls, config) {
+            return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon'], config['decay']);
+        };
+        /** @nocollapse */
+        AdamaxOptimizer.className = 'Adamax'; // Note: Name matters for Python compatbility.
+        return AdamaxOptimizer;
+    }(Optimizer));
+    registerClass(AdamaxOptimizer);
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /** @doclink Optimizer */
+    var SGDOptimizer = /** @class */ (function (_super) {
+        __extends(SGDOptimizer, _super);
+        function SGDOptimizer(learningRate) {
+            var _this = _super.call(this) || this;
+            _this.learningRate = learningRate;
+            _this.setLearningRate(learningRate);
+            return _this;
+        }
+        SGDOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            var varNames = Array.isArray(variableGradients) ?
+                variableGradients.map(function (v) { return v.name; }) :
+                Object.keys(variableGradients);
+            varNames.forEach(function (name, i) {
+                var gradient = Array.isArray(variableGradients) ?
+                    variableGradients[i].tensor :
+                    variableGradients[name];
+                if (gradient == null) {
+                    return;
+                }
+                var value = ENGINE.registeredVariables[name];
+                tidy(function () {
+                    var newValue = add$1(mul(_this.c, gradient), value);
+                    value.assign(newValue);
+                });
+            });
+            this.incrementIterations();
+        };
+        /**
+         * Sets the learning rate of the optimizer.
+         */
+        SGDOptimizer.prototype.setLearningRate = function (learningRate) {
+            this.learningRate = learningRate;
+            if (this.c != null) {
+                this.c.dispose();
+            }
+            this.c = keep(scalar(-learningRate));
+        };
+        SGDOptimizer.prototype.dispose = function () {
+            this.c.dispose();
+        };
+        SGDOptimizer.prototype.getWeights = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.saveIterations()];
+                        case 1: return [2 /*return*/, [_a.sent()]];
+                    }
+                });
+            });
+        };
+        SGDOptimizer.prototype.setWeights = function (weightValues) {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
+                        case 1:
+                            weightValues = _a.sent();
+                            if (weightValues.length !== 0) {
+                                throw new Error('SGD optimizer does not have settable weights.');
+                            }
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        SGDOptimizer.prototype.getConfig = function () {
+            return { 'learningRate': this.learningRate };
+        };
+        /** @nocollapse */
+        SGDOptimizer.fromConfig = function (cls, config) {
+            return new cls(config['learningRate']);
+        };
+        /** @nocollapse */
+        SGDOptimizer.className = 'SGD'; // Note: Name matters for Python compatibility.
+        return SGDOptimizer;
+    }(Optimizer));
+    registerClass(SGDOptimizer);
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /** @doclink Optimizer */
+    var MomentumOptimizer = /** @class */ (function (_super) {
+        __extends(MomentumOptimizer, _super);
+        function MomentumOptimizer(learningRate, momentum, useNesterov) {
+            if (useNesterov === void 0) { useNesterov = false; }
+            var _this = _super.call(this, learningRate) || this;
+            _this.learningRate = learningRate;
+            _this.momentum = momentum;
+            _this.useNesterov = useNesterov;
+            _this.accumulations = [];
+            _this.m = scalar(_this.momentum);
+            return _this;
+        }
+        MomentumOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            var variableNames = Array.isArray(variableGradients) ?
+                variableGradients.map(function (item) { return item.name; }) :
+                Object.keys(variableGradients);
+            variableNames.forEach(function (name, i) {
+                var value = ENGINE.registeredVariables[name];
+                if (_this.accumulations[i] == null) {
+                    var trainable_1 = false;
+                    _this.accumulations[i] = {
+                        originalName: name + "/momentum",
+                        variable: tidy(function () { return zerosLike(value).variable(trainable_1); })
+                    };
+                }
+                var accumulation = _this.accumulations[i].variable;
+                var gradient = Array.isArray(variableGradients) ?
+                    variableGradients[i].tensor :
+                    variableGradients[name];
+                if (gradient == null) {
+                    return;
+                }
+                tidy(function () {
+                    var newValue;
+                    var newAccumulation = add$1(mul(_this.m, accumulation), gradient);
+                    if (_this.useNesterov) {
+                        newValue = add$1(mul(_this.c, add$1(gradient, mul(newAccumulation, _this.m))), value);
+                    }
+                    else {
+                        newValue = add$1(mul(_this.c, newAccumulation), value);
+                    }
+                    accumulation.assign(newAccumulation);
+                    value.assign(newValue);
+                });
+            });
+            this.incrementIterations();
+        };
+        MomentumOptimizer.prototype.dispose = function () {
+            this.m.dispose();
+            if (this.accumulations != null) {
+                dispose(this.accumulations.map(function (v) { return v.variable; }));
+            }
+        };
+        /**
+         * Sets the momentum of the optimizer.
+         *
+         * @param momentum
+         */
+        MomentumOptimizer.prototype.setMomentum = function (momentum) {
+            this.momentum = momentum;
+        };
+        MomentumOptimizer.prototype.getWeights = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.saveIterations()];
+                        case 1: 
+                        // Order matters for Python compatibility.
+                        return [2 /*return*/, [_a.sent()].concat(this.accumulations.map(function (v) { return ({ name: v.originalName, tensor: v.variable }); }))];
+                    }
+                });
+            });
+        };
+        MomentumOptimizer.prototype.setWeights = function (weightValues) {
+            return __awaiter(this, void 0, void 0, function () {
+                var trainable;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
+                        case 1:
+                            weightValues = _a.sent();
+                            trainable = false;
+                            this.accumulations = weightValues.map(function (v) { return ({ originalName: v.name, variable: v.tensor.variable(trainable) }); });
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        MomentumOptimizer.prototype.getConfig = function () {
+            return {
+                'learningRate': this.learningRate,
+                'momentum': this.momentum,
+                'useNesterov': this.useNesterov
+            };
+        };
+        /** @nocollapse */
+        MomentumOptimizer.fromConfig = function (cls, config) {
+            return new cls(config['learningRate'], config['momentum'], config['useNesterov']);
+        };
+        /** @nocollapse */
+        MomentumOptimizer.className = 'Momentum'; // Name matters for Python compatibility.
+        return MomentumOptimizer;
+    }(SGDOptimizer));
+    registerClass(MomentumOptimizer);
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /** @doclink Optimizer */
+    var RMSPropOptimizer = /** @class */ (function (_super) {
+        __extends(RMSPropOptimizer, _super);
+        function RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered) {
+            if (decay === void 0) { decay = 0.9; }
+            if (momentum === void 0) { momentum = 0.0; }
+            if (epsilon === void 0) { epsilon = null; }
+            if (centered === void 0) { centered = false; }
+            var _this = _super.call(this) || this;
+            _this.learningRate = learningRate;
+            _this.decay = decay;
+            _this.momentum = momentum;
+            _this.epsilon = epsilon;
+            _this.accumulatedMeanSquares = [];
+            _this.accumulatedMoments = [];
+            _this.accumulatedMeanGrads = [];
+            _this.centered = centered;
+            if (epsilon == null) {
+                _this.epsilon = ENGINE.backend.epsilon();
+            }
+            if (learningRate == null) {
+                throw new Error("learningRate for RMSPropOptimizer must be defined.");
+            }
+            return _this;
+        }
+        RMSPropOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            var variableNames = Array.isArray(variableGradients) ?
+                variableGradients.map(function (item) { return item.name; }) :
+                Object.keys(variableGradients);
+            variableNames.forEach(function (name, i) {
+                var value = ENGINE.registeredVariables[name];
+                var trainable = false;
+                if (_this.accumulatedMeanSquares[i] == null) {
+                    _this.accumulatedMeanSquares[i] = {
+                        originalName: name + "/rms",
+                        variable: tidy(function () { return zerosLike(value).variable(trainable); })
+                    };
+                }
+                if (_this.accumulatedMoments[i] == null) {
+                    _this.accumulatedMoments[i] = {
+                        originalName: name + "/momentum",
+                        variable: tidy(function () { return zerosLike(value).variable(trainable); })
+                    };
+                }
+                if (_this.accumulatedMeanGrads[i] == null && _this.centered) {
+                    _this.accumulatedMeanGrads[i] = {
+                        originalName: name + "/mg",
+                        variable: tidy(function () { return zerosLike(value).variable(trainable); })
+                    };
+                }
+                var gradient = Array.isArray(variableGradients) ?
+                    variableGradients[i].tensor :
+                    variableGradients[name];
+                if (gradient == null) {
+                    return;
+                }
+                var accumulatedMeanSquare = _this.accumulatedMeanSquares[i].variable;
+                var accumulatedMoments = _this.accumulatedMoments[i].variable;
+                tidy(function () {
+                    var newAccumulatedMeanSquare = add$1(mul(accumulatedMeanSquare, _this.decay), mul(square(gradient), 1 - _this.decay));
+                    if (_this.centered) {
+                        var accumulatedMeanGrad = _this.accumulatedMeanGrads[i].variable;
+                        // Centered gradient
+                        var newAccumulatedMeanGrad = add$1(mul(accumulatedMeanGrad, _this.decay), mul(gradient, 1 - _this.decay));
+                        var gradContribution = div(mul(gradient, _this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add$1(square(newAccumulatedMeanGrad), _this.epsilon))));
+                        var newAccumulatedMoments = add$1(mul(accumulatedMoments, _this.momentum), gradContribution);
+                        accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
+                        accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
+                        accumulatedMoments.assign(newAccumulatedMoments);
+                        var newValue = sub(value, newAccumulatedMoments);
+                        value.assign(newValue);
+                    }
+                    else {
+                        // Plain gradient
+                        var newAccumulatedMeanSquare_1 = add$1(mul(accumulatedMeanSquare, _this.decay), mul(square(gradient), 1 - _this.decay));
+                        var newAccumulatedMoments = add$1(mul(accumulatedMoments, _this.momentum), div(mul(gradient, _this.learningRate), sqrt(add$1(newAccumulatedMeanSquare_1, _this.epsilon))));
+                        accumulatedMeanSquare.assign(newAccumulatedMeanSquare_1);
+                        accumulatedMoments.assign(newAccumulatedMoments);
+                        var newValue = sub(value, newAccumulatedMoments);
+                        value.assign(newValue);
+                    }
+                });
+            });
+            this.incrementIterations();
+        };
+        RMSPropOptimizer.prototype.dispose = function () {
+            if (this.accumulatedMeanSquares != null) {
+                dispose(this.accumulatedMeanSquares.map(function (v) { return v.variable; }));
+            }
+            if (this.accumulatedMeanGrads != null && this.centered) {
+                dispose(this.accumulatedMeanGrads.map(function (v) { return v.variable; }));
+            }
+            if (this.accumulatedMoments != null) {
+                dispose(this.accumulatedMoments.map(function (v) { return v.variable; }));
+            }
+        };
+        RMSPropOptimizer.prototype.getWeights = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var variables;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            variables = this.accumulatedMeanSquares.concat(this.accumulatedMoments);
+                            if (this.centered) {
+                                variables.push.apply(variables, this.accumulatedMeanGrads);
+                            }
+                            return [4 /*yield*/, this.saveIterations()];
+                        case 1: return [2 /*return*/, [_a.sent()].concat(variables.map(function (v) { return ({ name: v.originalName, tensor: v.variable }); }))];
+                    }
+                });
+            });
+        };
+        RMSPropOptimizer.prototype.setWeights = function (weightValues) {
+            return __awaiter(this, void 0, void 0, function () {
+                var variableCount, trainable;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
+                        case 1:
+                            weightValues = _a.sent();
+                            variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
+                            trainable = false;
+                            this.accumulatedMeanSquares =
+                                weightValues.slice(0, variableCount).map(function (v) { return ({
+                                    originalName: v.name,
+                                    variable: v.tensor.variable(trainable)
+                                }); });
+                            this.accumulatedMoments =
+                                weightValues.slice(variableCount, variableCount * 2)
+                                    .map(function (v) { return ({
+                                    originalName: v.name,
+                                    variable: v.tensor.variable(trainable)
+                                }); });
+                            if (this.centered) {
+                                this.accumulatedMeanGrads =
+                                    weightValues.slice(variableCount * 2, variableCount * 3)
+                                        .map(function (v) { return ({
+                                        originalName: v.name,
+                                        variable: v.tensor.variable(trainable)
+                                    }); });
+                            }
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        RMSPropOptimizer.prototype.getConfig = function () {
+            return {
+                'learningRate': this.learningRate,
+                'decay': this.decay,
+                'momentum': this.momentum,
+                'epsilon': this.epsilon,
+                'centered': this.centered
+            };
+        };
+        /** @nocollapse */
+        RMSPropOptimizer.fromConfig = function (cls, config) {
+            return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);
+        };
+        /** @nocollapse */
+        RMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.
+        return RMSPropOptimizer;
+    }(Optimizer));
+    registerClass(RMSPropOptimizer);
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var OptimizerConstructors = /** @class */ (function () {
+        function OptimizerConstructors() {
+        }
+        /**
+         * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.
+         *
+         * ```js
+         * // Fit a quadratic function by learning the coefficients a, b, c.
+         * const xs = tf.tensor1d([0, 1, 2, 3]);
+         * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);
+         *
+         * const a = tf.scalar(Math.random()).variable();
+         * const b = tf.scalar(Math.random()).variable();
+         * const c = tf.scalar(Math.random()).variable();
+         *
+         * // y = a * x^2 + b * x + c.
+         * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);
+         * const loss = (pred, label) => pred.sub(label).square().mean();
+         *
+         * const learningRate = 0.01;
+         * const optimizer = tf.train.sgd(learningRate);
+         *
+         * // Train the model.
+         * for (let i = 0; i < 10; i++) {
+         *   optimizer.minimize(() => loss(f(xs), ys));
+         * }
+         *
+         * // Make predictions.
+         * console.log(
+         *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);
+         * const preds = f(xs).dataSync();
+         * preds.forEach((pred, i) => {
+         *   console.log(`x: ${i}, pred: ${pred}`);
+         * });
+         * ```
+         *
+         * @param learningRate The learning rate to use for the SGD algorithm.
+         *
+         * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
+         */
+        OptimizerConstructors.sgd = function (learningRate) {
+            return new SGDOptimizer(learningRate);
+        };
+        /**
+         * Constructs a `tf.MomentumOptimizer` that uses momentum gradient
+         * descent.
+         *
+         * See
+         * [http://proceedings.mlr.press/v28/sutskever13.pdf](
+         * http://proceedings.mlr.press/v28/sutskever13.pdf)
+         *
+         * @param learningRate The learning rate to use for the Momentum gradient
+         * descent algorithm.
+         * @param momentum The momentum to use for the momentum gradient descent
+         * algorithm.
+         *
+         * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
+         */
+        OptimizerConstructors.momentum = function (learningRate, momentum, useNesterov) {
+            if (useNesterov === void 0) { useNesterov = false; }
+            return new MomentumOptimizer(learningRate, momentum, useNesterov);
+        };
+        /**
+         * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient
+         * descent. This implementation uses plain momentum and is not centered
+         * version of RMSProp.
+         *
+         * See
+         * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](
+         * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
+         *
+         * @param learningRate The learning rate to use for the RMSProp gradient
+         * descent algorithm.
+         * @param decay The discounting factor for the history/coming gradient.
+         * @param momentum The momentum to use for the RMSProp gradient descent
+         * algorithm.
+         * @param epsilon Small value to avoid zero denominator.
+         * @param centered If true, gradients are normalized by the estimated
+         * variance of the gradient.
+         *
+         * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
+         */
+        OptimizerConstructors.rmsprop = function (learningRate, decay, momentum, epsilon, centered) {
+            if (decay === void 0) { decay = .9; }
+            if (momentum === void 0) { momentum = 0.0; }
+            if (epsilon === void 0) { epsilon = null; }
+            if (centered === void 0) { centered = false; }
+            return new RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);
+        };
+        /**
+         * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.
+         * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)
+         *
+         * @param learningRate The learning rate to use for the Adam gradient
+         * descent algorithm.
+         * @param beta1 The exponential decay rate for the 1st moment estimates.
+         * @param beta2 The exponential decay rate for the 2nd moment estimates.
+         * @param epsilon A small constant for numerical stability.
+         *
+         * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
+         */
+        OptimizerConstructors.adam = function (learningRate, beta1, beta2, epsilon) {
+            if (learningRate === void 0) { learningRate = 0.001; }
+            if (beta1 === void 0) { beta1 = 0.9; }
+            if (beta2 === void 0) { beta2 = 0.999; }
+            if (epsilon === void 0) { epsilon = null; }
+            return new AdamOptimizer(learningRate, beta1, beta2, epsilon);
+        };
+        /**
+         * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.
+         * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)
+         *
+         * @param learningRate The learning rate to use for the Adadelta gradient
+         * descent algorithm.
+         * @param rho The learning rate decay over each update.
+         * @param epsilon A constant epsilon used to better condition the grad
+         * update.
+         *
+         * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
+         */
+        OptimizerConstructors.adadelta = function (learningRate, rho, epsilon) {
+            if (learningRate === void 0) { learningRate = .001; }
+            if (rho === void 0) { rho = .95; }
+            if (epsilon === void 0) { epsilon = null; }
+            return new AdadeltaOptimizer(learningRate, rho, epsilon);
+        };
+        /**
+         * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.
+         * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)
+         *
+         * @param learningRate The learning rate to use for the Adamax gradient
+         * descent algorithm.
+         * @param beta1 The exponential decay rate for the 1st moment estimates.
+         * @param beta2 The exponential decay rate for the 2nd moment estimates.
+         * @param epsilon A small constant for numerical stability.
+         * @param decay The learning rate decay over each update.
+         *
+         * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
+         */
+        OptimizerConstructors.adamax = function (learningRate, beta1, beta2, epsilon, decay) {
+            if (learningRate === void 0) { learningRate = 0.002; }
+            if (beta1 === void 0) { beta1 = 0.9; }
+            if (beta2 === void 0) { beta2 = 0.999; }
+            if (epsilon === void 0) { epsilon = null; }
+            if (decay === void 0) { decay = 0.0; }
+            return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);
+        };
+        /**
+         * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.
+         * See
+         * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](
+         * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
+         * or
+         * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](
+         * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)
+         *
+         * @param learningRate The learning rate to use for the Adagrad gradient
+         * descent algorithm.
+         * @param initialAccumulatorValue Starting value for the accumulators, must be
+         * positive.
+         *
+         * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
+         */
+        OptimizerConstructors.adagrad = function (learningRate, initialAccumulatorValue) {
+            if (initialAccumulatorValue === void 0) { initialAccumulatorValue = 0.1; }
+            return new AdagradOptimizer(learningRate, initialAccumulatorValue);
+        };
+        return OptimizerConstructors;
+    }());
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var train = {
+        sgd: OptimizerConstructors.sgd,
+        momentum: OptimizerConstructors.momentum,
+        adadelta: OptimizerConstructors.adadelta,
+        adagrad: OptimizerConstructors.adagrad,
+        rmsprop: OptimizerConstructors.rmsprop,
+        adamax: OptimizerConstructors.adamax,
+        adam: OptimizerConstructors.adam
+    };
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var delayCallback = (function () {
+        if (typeof requestAnimationFrame !== 'undefined') {
+            return requestAnimationFrame;
+        }
+        else if (typeof setImmediate !== 'undefined') {
+            return setImmediate;
+        }
+        return function (f) { return f(); }; // no delays
+    })();
+    /**
+     * Returns a promise that resolve when a requestAnimationFrame has completed.
+     *
+     * On Node.js this uses setImmediate instead of requestAnimationFrame.
+     *
+     * This is simply a sugar method so that users can do the following:
+     * `await tf.nextFrame();`
+     *
+     * @doc {heading: 'Performance', subheading: 'Timing'}
+     */
+    function nextFrame() {
+        return new Promise(function (resolve) { return delayCallback(function () { return resolve(); }); });
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function assertParamsConsistent(shapes, axis) {
+        var rank = shapes[0].length;
+        shapes.forEach(function (shape, i) {
+            assert(shape.length === rank, function () {
+                return "Error in concat" + rank + "D: rank of tensors[" + i + "] must be the same " +
+                    ("as the rank of the rest (" + rank + ")");
+            });
+        });
+        assert(axis >= 0 && axis < rank, function () { return "Error in concat" + rank + "D: axis must be between 0 and " + (rank - 1) + "."; });
+        var firstShape = shapes[0];
+        shapes.forEach(function (shape, i) {
+            for (var r = 0; r < rank; r++) {
+                assert((r === axis) || (shape[r] === firstShape[r]), function () { return "Error in concat" + rank + "D: Shape of tensors[" + i + "] (" + shape + ") " +
+                    ("does not match the shape of the rest (" + firstShape + ") ") +
+                    ("along the non-concatenated axis " + i + "."); });
+            }
+        });
+    }
+    function computeOutShape$1(shapes, axis) {
+        var outputShape = shapes[0].slice();
+        for (var i = 1; i < shapes.length; i++) {
+            outputShape[axis] += shapes[i][axis];
+        }
+        return outputShape;
+    }
+
+    /**
+     * @license
+     * Copyright 2017 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var PARALLELIZE_THRESHOLD = 30;
+    function computeOptimalWindowSize(inSize) {
+        if (inSize <= PARALLELIZE_THRESHOLD) {
+            return inSize;
+        }
+        return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    // Returns the image center in pixels.
+    function getImageCenter(center, imageHeight, imageWidth) {
+        var centerX = imageWidth * (typeof center === 'number' ? center : center[0]);
+        var centerY = imageHeight * (typeof center === 'number' ? center : center[1]);
+        return [centerX, centerY];
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Gets the new shape of the input Tensor after it's been reshaped
+     * to:
+     * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),
+     * inputShape[1], ..., inputShape[N-1]]
+     *
+     * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
+     */
+    function getReshaped(inputShape, blockShape, prod, batchToSpace) {
+        if (batchToSpace === void 0) { batchToSpace = true; }
+        var reshaped = [];
+        if (batchToSpace) {
+            reshaped = reshaped.concat(blockShape.slice(0));
+            reshaped.push(inputShape[0] / prod);
+            reshaped = reshaped.concat(inputShape.slice(1));
+        }
+        else {
+            reshaped = reshaped.concat(inputShape[0]);
+            var spatialLength = blockShape.length;
+            for (var i = 0; i < spatialLength; ++i) {
+                reshaped =
+                    reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
+            }
+            reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
+        }
+        return reshaped;
+    }
+    /**
+     * Gets the permutation that will transpose the dimensions of the
+     * reshaped tensor to shape:
+     *
+     * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,
+     * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]
+     *
+     * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
+     */
+    function getPermuted(reshapedRank, blockShapeRank, batchToSpace) {
+        if (batchToSpace === void 0) { batchToSpace = true; }
+        var permuted = [];
+        if (batchToSpace) {
+            permuted.push(blockShapeRank);
+            for (var i = blockShapeRank + 1; i < reshapedRank; ++i) {
+                if (i <= 2 * blockShapeRank) {
+                    permuted.push(i);
+                    permuted.push(i - (blockShapeRank + 1));
+                }
+                else {
+                    permuted.push(i);
+                }
+            }
+        }
+        else {
+            var permutedBeforeBatch = [];
+            var permutedAfterBatch = [];
+            for (var i = 1; i < reshapedRank; ++i) {
+                if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
+                    permutedAfterBatch.push(i);
+                }
+                else {
+                    permutedBeforeBatch.push(i);
+                }
+            }
+            permuted.push.apply(permuted, permutedBeforeBatch);
+            permuted.push(0);
+            permuted.push.apply(permuted, permutedAfterBatch);
+        }
+        return permuted;
+    }
+    /**
+     * Gets the shape of the reshaped and permuted input Tensor before any cropping
+     * is applied.  The new shape will be:
+     *
+     * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,
+     * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]
+     *
+     * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
+     */
+    function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace) {
+        if (batchToSpace === void 0) { batchToSpace = true; }
+        var reshapedPermuted = [];
+        if (batchToSpace) {
+            reshapedPermuted.push(inputShape[0] / prod);
+        }
+        else {
+            reshapedPermuted.push(inputShape[0] * prod);
+        }
+        for (var i = 1; i < inputShape.length; ++i) {
+            if (i <= blockShape.length) {
+                if (batchToSpace) {
+                    reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
+                }
+                else {
+                    reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
+                }
+            }
+            else {
+                reshapedPermuted.push(inputShape[i]);
+            }
+        }
+        return reshapedPermuted;
+    }
+    /**
+     * Converts the crops argument into the beginning coordinates of a slice
+     * operation.
+     */
+    function getSliceBeginCoords(crops, blockShape) {
+        var sliceBeginCoords = [0];
+        for (var i = 0; i < blockShape; ++i) {
+            sliceBeginCoords.push(crops[i][0]);
+        }
+        return sliceBeginCoords;
+    }
+    /**
+     * Converts the crops argument into the size of a slice operation.  When
+     * combined with getSliceBeginCoords this function allows the reshaped and
+     * permuted Tensor to be cropped to its final output shape of:
+     *
+     * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,
+     * inputShape[M] * blockShape[M-1] -crops[M-1,0] -
+     * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]
+     *
+     * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
+     */
+    function getSliceSize(uncroppedShape, crops, blockShape) {
+        var sliceSize = uncroppedShape.slice(0, 1);
+        for (var i = 0; i < blockShape; ++i) {
+            sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
+        }
+        return sliceSize;
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var SELU_SCALEALPHA = 1.7580993408473768599402175208123;
+    var SELU_SCALE = 1.0507009873554804934193349852946;
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ERF_P = 0.3275911;
+    var ERF_A1 = 0.254829592;
+    var ERF_A2 = -0.284496736;
+    var ERF_A3 = 1.421413741;
+    var ERF_A4 = -1.453152027;
+    var ERF_A5 = 1.061405429;
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function warn() {
+        var msg = [];
+        for (var _i = 0; _i < arguments.length; _i++) {
+            msg[_i] = arguments[_i];
+        }
+        if (!env().getBool('IS_TEST')) {
+            console.warn.apply(console, msg);
+        }
+    }
+    function log$1() {
+        var msg = [];
+        for (var _i = 0; _i < arguments.length; _i++) {
+            msg[_i] = arguments[_i];
+        }
+        if (!env().getBool('IS_TEST')) {
+            console.log.apply(console, msg);
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    /**
+     * Merges real and imaginary Float32Arrays into a single complex Float32Array.
+     *
+     * The memory layout is interleaved as follows:
+     * real: [r0, r1, r2]
+     * imag: [i0, i1, i2]
+     * complex: [r0, i0, r1, i1, r2, i2]
+     *
+     * This is the inverse of splitRealAndImagArrays.
+     *
+     * @param real The real values of the complex tensor values.
+     * @param imag The imag values of the complex tensor values.
+     * @returns A complex tensor as a Float32Array with merged values.
+     */
+    function mergeRealAndImagArrays(real, imag) {
+        if (real.length !== imag.length) {
+            throw new Error("Cannot merge real and imag arrays of different lengths. real:" +
+                (real.length + ", imag: " + imag.length + "."));
+        }
+        var result = new Float32Array(real.length * 2);
+        for (var i = 0; i < result.length; i += 2) {
+            result[i] = real[i / 2];
+            result[i + 1] = imag[i / 2];
+        }
+        return result;
+    }
+    /**
+     * Splits a complex Float32Array into real and imag parts.
+     *
+     * The memory layout is interleaved as follows:
+     * complex: [r0, i0, r1, i1, r2, i2]
+     * real: [r0, r1, r2]
+     * imag: [i0, i1, i2]
+     *
+     * This is the inverse of mergeRealAndImagArrays.
+     *
+     * @param complex The complex tensor values.
+     * @returns An object with real and imag Float32Array components of the complex
+     *     tensor.
+     */
+    function splitRealAndImagArrays(complex) {
+        var real = new Float32Array(complex.length / 2);
+        var imag = new Float32Array(complex.length / 2);
+        for (var i = 0; i < complex.length; i += 2) {
+            real[i / 2] = complex[i];
+            imag[i / 2] = complex[i + 1];
+        }
+        return { real: real, imag: imag };
+    }
+    /**
+     * Extracts even indexed complex values in the given array.
+     * @param complex The complex tensor values
+     */
+    function complexWithEvenIndex(complex) {
+        var len = Math.ceil(complex.length / 4);
+        var real = new Float32Array(len);
+        var imag = new Float32Array(len);
+        for (var i = 0; i < complex.length; i += 4) {
+            real[Math.floor(i / 4)] = complex[i];
+            imag[Math.floor(i / 4)] = complex[i + 1];
+        }
+        return { real: real, imag: imag };
+    }
+    /**
+     * Extracts odd indexed comple values in the given array.
+     * @param complex The complex tensor values
+     */
+    function complexWithOddIndex(complex) {
+        var len = Math.floor(complex.length / 4);
+        var real = new Float32Array(len);
+        var imag = new Float32Array(len);
+        for (var i = 2; i < complex.length; i += 4) {
+            real[Math.floor(i / 4)] = complex[i];
+            imag[Math.floor(i / 4)] = complex[i + 1];
+        }
+        return { real: real, imag: imag };
+    }
+    /**
+     * Get the map representing a complex value in the given array.
+     * @param complex The complex tensor values.
+     * @param index An index of the target complex value.
+     */
+    function getComplexWithIndex(complex, index) {
+        var real = complex[index * 2];
+        var imag = complex[index * 2 + 1];
+        return { real: real, imag: imag };
+    }
+    /**
+     * Insert a given complex value into the TypedArray.
+     * @param data The array in which the complex value is inserted.
+     * @param c The complex value to be inserted.
+     * @param index An index of the target complex value.
+     */
+    function assignToTypedArray(data, real, imag, index) {
+        data[index * 2] = real;
+        data[index * 2 + 1] = imag;
+    }
+    /**
+     * Make the list of exponent terms used by FFT.
+     */
+    function exponents(n, inverse) {
+        var real = new Float32Array(n / 2);
+        var imag = new Float32Array(n / 2);
+        for (var i = 0; i < Math.ceil(n / 2); i++) {
+            var x = (inverse ? 2 : -2) * Math.PI * (i / n);
+            real[i] = Math.cos(x);
+            imag[i] = Math.sin(x);
+        }
+        return { real: real, imag: imag };
+    }
+    /**
+     * Make the exponent term used by FFT.
+     */
+    function exponent(k, n, inverse) {
+        var x = (inverse ? 2 : -2) * Math.PI * (k / n);
+        var real = Math.cos(x);
+        var imag = Math.sin(x);
+        return { real: real, imag: imag };
+    }
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var ARROW = '->';
+    var ARROW_REGEX = /->/g;
+    var COMMA = ',';
+    var ELLIPSIS = '...';
+    /**
+     * Parse an equation for einsum.
+     *
+     * @param equation The einsum equation (e.g., "ij,jk->ik").
+     * @param numTensors Number of tensors provided along with `equation`. Used to
+     *   check matching number of input tensors.
+     * @returns An object consisting of the following fields:
+     *   - allDims: all dimension names as strings.
+     *   - summedDims: a list of all dimensions being summed over, as indices to
+     *     the elements of `allDims`.
+     *   - idDims: indices of the dimensions in each input tensor, as indices to
+     *     the elements of `allDims.
+     */
+    function decodeEinsumEquation(equation, numTensors) {
+        equation = equation.replace(/\s/g, ''); // Remove witespace in equation.
+        var numArrows = (equation.length - equation.replace(ARROW_REGEX, '').length) /
+            ARROW.length;
+        if (numArrows < 1) {
+            throw new Error('Equations without an arrow are not supported.');
+        }
+        else if (numArrows > 1) {
+            throw new Error("Equation must contain exactly one arrow (\"" + ARROW + "\").");
+        }
+        var _a = equation.split(ARROW), inputString = _a[0], outputString = _a[1];
+        assert(inputString.indexOf(ELLIPSIS) === -1, function () { return "The ellipsis notation (\"" + ELLIPSIS + "\") is not supported yet."; });
+        var inputTerms = inputString.split(COMMA);
+        var numInputs = inputTerms.length;
+        if (numTensors !== numInputs) {
+            throw new Error("Expected " + numInputs + " input tensors, received " + numTensors);
+        }
+        if (numInputs > 2) {
+            throw new Error('Support for more than 2 input tensors is not implemented yet.');
+        }
+        var allDims = [];
+        var _loop_1 = function (i) {
+            var dimName = outputString[i];
+            if (!inputTerms.some(function (inputTerm) { return inputTerm.indexOf(dimName) !== -1; })) {
+                throw new Error("Output subscripts contain the label " + dimName + " " +
+                    "not present in the input subscripts.");
+            }
+            if (allDims.indexOf(dimName) === -1) {
+                allDims.push(dimName);
+            }
+        };
+        for (var i = 0; i < outputString.length; ++i) {
+            _loop_1(i);
+        }
+        for (var i = 0; i < inputString.length; ++i) {
+            var dimName = inputString[i];
+            if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {
+                allDims.push(dimName);
+            }
+        }
+        var idDims = new Array(inputTerms.length);
+        for (var i = 0; i < numInputs; ++i) {
+            if (new Set(inputTerms[i].split('')).size !== inputTerms[i].length) {
+                throw new Error("Found duplicate axes in input component " + inputTerms[i] + ". " +
+                    "Support for duplicate axes in input is not implemented yet.");
+            }
+            idDims[i] = [];
+            for (var j = 0; j < inputTerms[i].length; ++j) {
+                idDims[i].push(allDims.indexOf(inputTerms[i][j]));
+            }
+        }
+        var numDims = allDims.length; // Number of unique dimensions.
+        var numOutDims = outputString.length; // Number of output dimensions.
+        var summedDims = []; // Dimensions being summed over.
+        for (var i = numOutDims; i < numDims; ++i) {
+            summedDims.push(i);
+        }
+        return { allDims: allDims, summedDims: summedDims, idDims: idDims };
+    }
+    /**
+     * Get the permutation for a given input tensor.
+     *
+     * @param nDims Total number of dimension of all tensors involved in the einsum
+     *   operation.
+     * @param idDims Dimension indices involve in the tensor in question.
+     * @returns An object consisting of the following fields:
+     *   - permutationIndices: Indices to permute the axes of the tensor with.
+     *   - expandDims: Indices to the dimension that need to be expanded from the
+     *     tensor after permutation.
+     */
+    function getEinsumPermutation(nDims, idDims) {
+        var permutationIndices = new Array(nDims);
+        permutationIndices.fill(-1);
+        for (var i = 0; i < idDims.length; ++i) {
+            permutationIndices[idDims[i]] = i;
+        }
+        var expandDims = [];
+        for (var i = 0; i < nDims; ++i) {
+            if (permutationIndices[i] === -1) {
+                expandDims.push(i);
+            }
+        }
+        permutationIndices = permutationIndices.filter(function (d) { return d !== -1; });
+        return { permutationIndices: permutationIndices, expandDims: expandDims };
+    }
+    /**
+     * Checks that the dimension sizes from different input tensors match the
+     * equation.
+     */
+    function checkEinsumDimSizes(nDims, idDims, tensors) {
+        var dimSizes = new Array(nDims);
+        var _loop_2 = function (i) {
+            var shape = tensors[i].shape;
+            var _loop_3 = function (j) {
+                if (dimSizes[idDims[i][j]] === undefined) {
+                    dimSizes[idDims[i][j]] = shape[j];
+                }
+                else {
+                    assert(dimSizes[idDims[i][j]] === shape[j], function () { return "Expected dimension " + dimSizes[idDims[i][j]] + " at axis " + j + " " +
+                        ("of input shaped " + JSON.stringify(shape) + ", ") +
+                        ("but got dimension " + shape[j]); });
+                }
+            };
+            for (var j = 0; j < idDims[i].length; ++j) {
+                _loop_3(j);
+            }
+        };
+        for (var i = 0; i < tensors.length; ++i) {
+            _loop_2(i);
+        }
+    }
+    /**
+     * Gets path of computation for einsum.
+     *
+     * @param summedDims indices to the dimensions being summed over.
+     * @param idDims A look up table for the dimensions present in each input
+     *     tensor. Each consituent array contains indices for the dimensions in the
+     *     corresponding input tensor.
+     *
+     * @return A map with two fields:
+     *   - path: The path of computation, with each element indicating the dimension
+     *     being summed over after the element-wise multiplication in that step.
+     *   - steps: With the same length as `path`. Each element contains the indices
+     *     to the input tensors being used for element-wise multiplication in the
+     *     corresponding step.
+     */
+    function getEinsumComputePath(summedDims, idDims) {
+        var path = summedDims;
+        var steps = [];
+        var nSteps = 0;
+        if (summedDims.length === 0) {
+            // Einsum that involes no summing: e.g., transpose and outer product.
+            path.push(-1);
+        }
+        nSteps = summedDims.length + 1;
+        for (var i = 0; i < nSteps; ++i) {
+            steps.push([]);
+        }
+        var computedTermIndices = [];
+        for (var i = 0; i < path.length; ++i) {
+            var summedDim = path[i];
+            var termIndices = findTermsWithDim(idDims, summedDim);
+            for (var _i = 0, termIndices_1 = termIndices; _i < termIndices_1.length; _i++) {
+                var termIndex = termIndices_1[_i];
+                if (computedTermIndices.indexOf(termIndex) === -1) {
+                    steps[i].push(termIndex);
+                    computedTermIndices.push(termIndex);
+                }
+            }
+        }
+        return { path: path, steps: steps };
+    }
+    /** Determines if an axes permutation is the identity permutation. */
+    function isIdentityPermutation(perm) {
+        return perm.every(function (dim, index) { return dim === index; });
+    }
+    function findTermsWithDim(idDims, dim) {
+        var termIndices = [];
+        for (var i = 0; i < idDims.length; ++i) {
+            if (idDims[i].length === 0 || idDims[i].indexOf(dim) !== -1 || dim === -1) {
+                termIndices.push(i);
+            }
+        }
+        return termIndices;
+    }
+
+    /**
+     * Prepare the split size array. When the input is a number, the axis is evenly
+     * divided among the split size. When the input contains the negative value, the
+     * rest of the axis is allocated toward that.
+     */
+    function prepareSplitSize(x, numOrSizeSplits, axis) {
+        if (axis === void 0) { axis = 0; }
+        var splitSizes = [];
+        if (typeof (numOrSizeSplits) === 'number') {
+            assert(x.shape[axis] % numOrSizeSplits === 0, function () { return 'Number of splits must evenly divide the axis.'; });
+            splitSizes =
+                new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);
+        }
+        else {
+            var numOfNegs = numOrSizeSplits.reduce(function (count, value) {
+                if (value === -1) {
+                    count += 1;
+                }
+                return count;
+            }, 0);
+            assert(numOfNegs <= 1, function () { return 'There should be only one negative value in split array.'; });
+            var negIndex = numOrSizeSplits.indexOf(-1);
+            // Allow the number of split array to be -1, which indicates the rest
+            // of dimension is allocated to that split.
+            if (negIndex !== -1) {
+                var total = numOrSizeSplits.reduce(function (a, b) { return b > 0 ? a + b : a; });
+                numOrSizeSplits[negIndex] = x.shape[axis] - total;
+            }
+            assert(x.shape[axis] === numOrSizeSplits.reduce(function (a, b) { return a + b; }), function () { return 'The sum of sizes must match the size of the axis dimension.'; });
+            splitSizes = numOrSizeSplits;
+        }
+        return splitSizes;
+    }
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function segOpComputeOptimalWindowSize(inSize, numSegments) {
+        var done = false;
+        var res;
+        if (inSize <= PARALLELIZE_THRESHOLD) {
+            res = inSize;
+            done = true;
+        }
+        else {
+            res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
+        }
+        while (!done) {
+            if (res > numSegments || res === inSize) {
+                done = true;
+            }
+            else {
+                res = nearestDivisor(inSize, res + 1);
+            }
+        }
+        return res;
+    }
+    function computeOutShape$2(aShape, axis, numSegments) {
+        var outShape = [];
+        var rank = aShape.length;
+        for (var dim = 0; dim < rank; dim++) {
+            if (dim !== axis) {
+                outShape.push(aShape[dim]);
+            }
+            else {
+                outShape.push(numSegments);
+            }
+        }
+        return outShape;
+    }
+    function collectGatherOpShapeInfo(x, indices, axis, batchDims) {
+        var indicesRank = indices.shape.length;
+        var xRank = x.shape.length;
+        if (batchDims !== 0) {
+            if (batchDims < -indicesRank || batchDims > indicesRank) {
+                throw new Error("Expect batchDims in the range of [-" + indicesRank + ", " + indicesRank + "], but got " + batchDims);
+            }
+        }
+        if (batchDims < 0) {
+            batchDims += indicesRank;
+        }
+        if (batchDims > xRank) {
+            throw new Error("batchDims (" + batchDims + ") must be less than rank(x) (\n    " + xRank + ").");
+        }
+        if (axis < batchDims) {
+            throw new Error("batchDims (" + batchDims + ") must be less than or equal to axis (" + axis + ").");
+        }
+        for (var i = 0; i < batchDims; ++i) {
+            if (x.shape[i] !== indices.shape[i]) {
+                throw new Error("x.shape[" + i + "]: " + x.shape[i] + " should be equal to indices.shape[" + i + "]: " + indices.shape[i] + ".");
+            }
+        }
+        var dimSize = x.shape[axis];
+        var outputShape = [];
+        var batchSize = 1;
+        var outerSize = 1;
+        var sliceSize = 1;
+        for (var i = 0; i < batchDims; ++i) {
+            outputShape.push(x.shape[i]);
+            batchSize *= x.shape[i];
+        }
+        for (var i = batchDims; i < axis; i++) {
+            outputShape.push(x.shape[i]);
+            outerSize *= x.shape[i];
+        }
+        for (var i = batchDims; i < indicesRank; i++) {
+            outputShape.push(indices.shape[i]);
+        }
+        for (var i = axis + 1; i < xRank; i++) {
+            outputShape.push(x.shape[i]);
+            sliceSize *= x.shape[i];
+        }
+        return { batchSize: batchSize, sliceSize: sliceSize, outerSize: outerSize, dimSize: dimSize, outputShape: outputShape };
+    }
+
+    var segment_util = {
+        __proto__: null,
+        segOpComputeOptimalWindowSize: segOpComputeOptimalWindowSize,
+        computeOutShape: computeOutShape$2,
+        collectGatherOpShapeInfo: collectGatherOpShapeInfo
+    };
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    function fromUint8ToStringArray(vals) {
+        try {
+            // Decode the bytes into string.
+            return vals.map(function (val) { return decodeString(val); });
+        }
+        catch (err) {
+            throw new Error("Failed to decode encoded string bytes into utf-8, error: " + err);
+        }
+    }
+    function fromStringArrayToUint8(strings) {
+        return strings.map(function (s) { return encodeString(s); });
+    }
+
+    var backend_util = {
+        __proto__: null,
+        slice_util: slice_util,
+        segment_util: segment_util,
+        fromUint8ToStringArray: fromUint8ToStringArray,
+        fromStringArrayToUint8: fromStringArrayToUint8,
+        upcastType: upcastType,
+        axesAreInnerMostDims: axesAreInnerMostDims,
+        combineLocations: combineLocations,
+        computeOutAndReduceShapes: computeOutAndReduceShapes,
+        expandShapeToKeepDim: expandShapeToKeepDim,
+        assertAxesAreInnerMostDims: assertAxesAreInnerMostDims,
+        getAxesPermutation: getAxesPermutation,
+        getUndoAxesPermutation: getUndoAxesPermutation,
+        getInnerMostAxes: getInnerMostAxes,
+        getBroadcastDims: getBroadcastDims,
+        getReductionAxes: getReductionAxes,
+        assertAndGetBroadcastShape: assertAndGetBroadcastShape,
+        assertParamsConsistent: assertParamsConsistent,
+        computeOutShape: computeOutShape$1,
+        computeDilation2DInfo: computeDilation2DInfo,
+        computePool2DInfo: computePool2DInfo,
+        computePool3DInfo: computePool3DInfo,
+        computeConv2DInfo: computeConv2DInfo,
+        computeConv3DInfo: computeConv3DInfo,
+        computeDefaultPad: computeDefaultPad,
+        tupleValuesAreOne: tupleValuesAreOne,
+        eitherStridesOrDilationsAreOne: eitherStridesOrDilationsAreOne,
+        convertConv2DDataFormat: convertConv2DDataFormat,
+        getFusedDyActivation: getFusedDyActivation,
+        getFusedBiasGradient: getFusedBiasGradient,
+        applyActivation: applyActivation,
+        shouldFuse: shouldFuse,
+        PARALLELIZE_THRESHOLD: PARALLELIZE_THRESHOLD,
+        computeOptimalWindowSize: computeOptimalWindowSize,
+        getImageCenter: getImageCenter,
+        getReshaped: getReshaped,
+        getPermuted: getPermuted,
+        getReshapedPermuted: getReshapedPermuted,
+        getSliceBeginCoords: getSliceBeginCoords,
+        getSliceSize: getSliceSize,
+        prepareAndValidate: prepareAndValidate,
+        validateUpdateShape: validateUpdateShape,
+        validateInput: validateInput,
+        calculateShapes: calculateShapes,
+        SELU_SCALEALPHA: SELU_SCALEALPHA,
+        SELU_SCALE: SELU_SCALE,
+        ERF_P: ERF_P,
+        ERF_A1: ERF_A1,
+        ERF_A2: ERF_A2,
+        ERF_A3: ERF_A3,
+        ERF_A4: ERF_A4,
+        ERF_A5: ERF_A5,
+        warn: warn,
+        log: log$1,
+        mergeRealAndImagArrays: mergeRealAndImagArrays,
+        splitRealAndImagArrays: splitRealAndImagArrays,
+        complexWithEvenIndex: complexWithEvenIndex,
+        complexWithOddIndex: complexWithOddIndex,
+        getComplexWithIndex: getComplexWithIndex,
+        assignToTypedArray: assignToTypedArray,
+        exponents: exponents,
+        exponent: exponent,
+        decodeEinsumEquation: decodeEinsumEquation,
+        getEinsumPermutation: getEinsumPermutation,
+        checkEinsumDimSizes: checkEinsumDimSizes,
+        getEinsumComputePath: getEinsumComputePath,
+        isIdentityPermutation: isIdentityPermutation,
+        prepareSplitSize: prepareSplitSize
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+
+    var kernel_impls = {
+        __proto__: null,
+        nonMaxSuppressionV3Impl: nonMaxSuppressionV3Impl,
+        nonMaxSuppressionV4Impl: nonMaxSuppressionV4Impl,
+        nonMaxSuppressionV5Impl: nonMaxSuppressionV5Impl,
+        whereImpl: whereImpl
+    };
+
+    exports.Abs = Abs;
+    exports.Acos = Acos;
+    exports.Acosh = Acosh;
+    exports.AdadeltaOptimizer = AdadeltaOptimizer;
+    exports.AdagradOptimizer = AdagradOptimizer;
+    exports.AdamOptimizer = AdamOptimizer;
+    exports.AdamaxOptimizer = AdamaxOptimizer;
+    exports.Add = Add;
+    exports.AddN = AddN;
+    exports.All = All;
+    exports.Any = Any;
+    exports.ArgMax = ArgMax;
+    exports.ArgMin = ArgMin;
+    exports.Asin = Asin;
+    exports.Asinh = Asinh;
+    exports.Atan = Atan;
+    exports.Atan2 = Atan2;
+    exports.Atanh = Atanh;
+    exports.AvgPool = AvgPool;
+    exports.AvgPool3D = AvgPool3D;
+    exports.AvgPool3DGrad = AvgPool3DGrad;
+    exports.AvgPoolGrad = AvgPoolGrad;
+    exports.BatchMatMul = BatchMatMul;
+    exports.BatchToSpaceND = BatchToSpaceND;
+    exports.Bincount = Bincount;
+    exports.BroadcastTo = BroadcastTo;
+    exports.Cast = Cast;
+    exports.Ceil = Ceil;
+    exports.ClipByValue = ClipByValue;
+    exports.Complex = Complex;
+    exports.ComplexAbs = ComplexAbs;
+    exports.Concat = Concat;
+    exports.Conv2D = Conv2D;
+    exports.Conv2DBackpropFilter = Conv2DBackpropFilter;
+    exports.Conv2DBackpropInput = Conv2DBackpropInput;
+    exports.Conv3D = Conv3D;
+    exports.Conv3DBackpropFilterV2 = Conv3DBackpropFilterV2;
+    exports.Conv3DBackpropInputV2 = Conv3DBackpropInputV2;
+    exports.Cos = Cos;
+    exports.Cosh = Cosh;
+    exports.CropAndResize = CropAndResize;
+    exports.Cumsum = Cumsum;
+    exports.DataStorage = DataStorage;
+    exports.DenseBincount = DenseBincount;
+    exports.DepthToSpace = DepthToSpace;
+    exports.DepthwiseConv2dNative = DepthwiseConv2dNative;
+    exports.DepthwiseConv2dNativeBackpropFilter = DepthwiseConv2dNativeBackpropFilter;
+    exports.DepthwiseConv2dNativeBackpropInput = DepthwiseConv2dNativeBackpropInput;
+    exports.Diag = Diag;
+    exports.Dilation2D = Dilation2D;
+    exports.Dilation2DBackpropFilter = Dilation2DBackpropFilter;
+    exports.Dilation2DBackpropInput = Dilation2DBackpropInput;
+    exports.Einsum = Einsum;
+    exports.Elu = Elu;
+    exports.EluGrad = EluGrad;
+    exports.Environment = Environment;
+    exports.Equal = Equal;
+    exports.Erf = Erf;
+    exports.Exp = Exp;
+    exports.ExpandDims = ExpandDims;
+    exports.Expm1 = Expm1;
+    exports.FFT = FFT;
+    exports.Fill = Fill;
+    exports.FlipLeftRight = FlipLeftRight;
+    exports.Floor = Floor;
+    exports.FloorDiv = FloorDiv;
+    exports.FromPixels = FromPixels;
+    exports.FusedBatchNorm = FusedBatchNorm;
+    exports.FusedConv2D = FusedConv2D;
+    exports.FusedDepthwiseConv2D = FusedDepthwiseConv2D;
+    exports.GatherNd = GatherNd;
+    exports.GatherV2 = GatherV2;
+    exports.Greater = Greater;
+    exports.GreaterEqual = GreaterEqual;
+    exports.IFFT = IFFT;
+    exports.Identity = Identity;
+    exports.Imag = Imag;
+    exports.IsFinite = IsFinite;
+    exports.IsInf = IsInf;
+    exports.IsNan = IsNan;
+    exports.KernelBackend = KernelBackend;
+    exports.LRN = LRN;
+    exports.LRNGrad = LRNGrad;
+    exports.LeakyRelu = LeakyRelu;
+    exports.Less = Less;
+    exports.LessEqual = LessEqual;
+    exports.LinSpace = LinSpace;
+    exports.Log = Log;
+    exports.Log1p = Log1p;
+    exports.LogSoftmax = LogSoftmax;
+    exports.LogicalAnd = LogicalAnd;
+    exports.LogicalNot = LogicalNot;
+    exports.LogicalOr = LogicalOr;
+    exports.Max = Max;
+    exports.MaxPool = MaxPool;
+    exports.MaxPool3D = MaxPool3D;
+    exports.MaxPool3DGrad = MaxPool3DGrad;
+    exports.MaxPoolGrad = MaxPoolGrad;
+    exports.MaxPoolWithArgmax = MaxPoolWithArgmax;
+    exports.Maximum = Maximum;
+    exports.Mean = Mean;
+    exports.Min = Min;
+    exports.Minimum = Minimum;
+    exports.MirrorPad = MirrorPad;
+    exports.Mod = Mod;
+    exports.MomentumOptimizer = MomentumOptimizer;
+    exports.Multinomial = Multinomial;
+    exports.Multiply = Multiply;
+    exports.Neg = Neg;
+    exports.NonMaxSuppressionV3 = NonMaxSuppressionV3;
+    exports.NonMaxSuppressionV4 = NonMaxSuppressionV4;
+    exports.NonMaxSuppressionV5 = NonMaxSuppressionV5;
+    exports.NotEqual = NotEqual;
+    exports.OP_SCOPE_SUFFIX = OP_SCOPE_SUFFIX;
+    exports.OneHot = OneHot;
+    exports.OnesLike = OnesLike;
+    exports.Optimizer = Optimizer;
+    exports.Pack = Pack;
+    exports.PadV2 = PadV2;
+    exports.Pool = Pool;
+    exports.Pow = Pow;
+    exports.Prelu = Prelu;
+    exports.Prod = Prod;
+    exports.RMSPropOptimizer = RMSPropOptimizer;
+    exports.Range = Range;
+    exports.Real = Real;
+    exports.RealDiv = RealDiv;
+    exports.Reciprocal = Reciprocal;
+    exports.Relu = Relu;
+    exports.Relu6 = Relu6;
+    exports.Reshape = Reshape;
+    exports.ResizeBilinear = ResizeBilinear;
+    exports.ResizeBilinearGrad = ResizeBilinearGrad;
+    exports.ResizeNearestNeighbor = ResizeNearestNeighbor;
+    exports.ResizeNearestNeighborGrad = ResizeNearestNeighborGrad;
+    exports.Reverse = Reverse;
+    exports.RotateWithOffset = RotateWithOffset;
+    exports.Round = Round;
+    exports.Rsqrt = Rsqrt;
+    exports.SGDOptimizer = SGDOptimizer;
+    exports.ScatterNd = ScatterNd;
+    exports.Select = Select;
+    exports.Selu = Selu;
+    exports.Sigmoid = Sigmoid;
+    exports.Sign = Sign;
+    exports.Sin = Sin;
+    exports.Sinh = Sinh;
+    exports.Slice = Slice;
+    exports.Softmax = Softmax;
+    exports.Softplus = Softplus;
+    exports.SpaceToBatchND = SpaceToBatchND;
+    exports.SparseFillEmptyRows = SparseFillEmptyRows;
+    exports.SparseReshape = SparseReshape;
+    exports.SparseSegmentMean = SparseSegmentMean;
+    exports.SparseSegmentSum = SparseSegmentSum;
+    exports.SparseToDense = SparseToDense;
+    exports.SplitV = SplitV;
+    exports.Sqrt = Sqrt;
+    exports.Square = Square;
+    exports.SquaredDifference = SquaredDifference;
+    exports.Step = Step;
+    exports.StridedSlice = StridedSlice;
+    exports.StringNGrams = StringNGrams;
+    exports.StringSplit = StringSplit;
+    exports.StringToHashBucketFast = StringToHashBucketFast;
+    exports.Sub = Sub;
+    exports.Sum = Sum;
+    exports.Tan = Tan;
+    exports.Tanh = Tanh;
+    exports.Tensor = Tensor;
+    exports.TensorBuffer = TensorBuffer;
+    exports.Tile = Tile;
+    exports.TopK = TopK;
+    exports.Transform = Transform;
+    exports.Transpose = Transpose;
+    exports.Unique = Unique;
+    exports.Unpack = Unpack;
+    exports.UnsortedSegmentSum = UnsortedSegmentSum;
+    exports.Variable = Variable;
+    exports.ZerosLike = ZerosLike;
+    exports._FusedMatMul = _FusedMatMul;
+    exports.abs = abs;
+    exports.acos = acos;
+    exports.acosh = acosh;
+    exports.add = add$1;
+    exports.addN = addN;
+    exports.all = all;
+    exports.any = any;
+    exports.argMax = argMax;
+    exports.argMin = argMin;
+    exports.asin = asin;
+    exports.asinh = asinh;
+    exports.atan = atan;
+    exports.atan2 = atan2;
+    exports.atanh = atanh;
+    exports.avgPool = avgPool;
+    exports.avgPool3d = avgPool3d;
+    exports.backend = backend;
+    exports.backend_util = backend_util;
+    exports.basicLSTMCell = basicLSTMCell;
+    exports.batchNorm = batchNorm;
+    exports.batchNorm2d = batchNorm2d;
+    exports.batchNorm3d = batchNorm3d;
+    exports.batchNorm4d = batchNorm4d;
+    exports.batchToSpaceND = batchToSpaceND;
+    exports.bincount = bincount;
+    exports.booleanMaskAsync = booleanMaskAsync;
+    exports.broadcastTo = broadcastTo;
+    exports.browser = browser;
+    exports.buffer = buffer;
+    exports.cast = cast;
+    exports.ceil = ceil;
+    exports.clipByValue = clipByValue;
+    exports.clone = clone;
+    exports.complex = complex;
+    exports.concat = concat;
+    exports.concat1d = concat1d;
+    exports.concat2d = concat2d;
+    exports.concat3d = concat3d;
+    exports.concat4d = concat4d;
+    exports.conv1d = conv1d;
+    exports.conv2d = conv2d;
+    exports.conv2dTranspose = conv2dTranspose;
+    exports.conv3d = conv3d;
+    exports.conv3dTranspose = conv3dTranspose;
+    exports.copyRegisteredKernels = copyRegisteredKernels;
+    exports.cos = cos;
+    exports.cosh = cosh;
+    exports.cosineWindow = cosineWindow;
+    exports.cumsum = cumsum;
+    exports.customGrad = customGrad;
+    exports.denseBincount = denseBincount;
+    exports.deprecationWarn = deprecationWarn;
+    exports.depthToSpace = depthToSpace;
+    exports.depthwiseConv2d = depthwiseConv2d;
+    exports.device_util = device_util;
+    exports.diag = diag;
+    exports.dilation2d = dilation2d;
+    exports.disableDeprecationWarnings = disableDeprecationWarnings;
+    exports.dispose = dispose;
+    exports.disposeVariables = disposeVariables;
+    exports.div = div;
+    exports.divNoNan = divNoNan;
+    exports.dot = dot;
+    exports.dropout = dropout;
+    exports.einsum = einsum;
+    exports.elu = elu;
+    exports.enableDebugMode = enableDebugMode;
+    exports.enableProdMode = enableProdMode;
+    exports.enclosingPowerOfTwo = enclosingPowerOfTwo;
+    exports.engine = engine;
+    exports.env = env;
+    exports.equal = equal;
+    exports.erf = erf;
+    exports.exp = exp;
+    exports.expandDims = expandDims;
+    exports.expm1 = expm1;
+    exports.eye = eye;
+    exports.fft = fft;
+    exports.fill = fill;
+    exports.findBackend = findBackend;
+    exports.findBackendFactory = findBackendFactory;
+    exports.floor = floor;
+    exports.floorDiv = floorDiv;
+    exports.fused = fused_ops;
+    exports.gather = gather;
+    exports.gatherND = gatherND;
+    exports.gather_util = gather_nd_util;
+    exports.getBackend = getBackend;
+    exports.getGradient = getGradient;
+    exports.getKernel = getKernel;
+    exports.getKernelsForBackend = getKernelsForBackend;
+    exports.grad = grad;
+    exports.grads = grads;
+    exports.greater = greater;
+    exports.greaterEqual = greaterEqual;
+    exports.ifft = ifft;
+    exports.imag = imag;
+    exports.image = image;
+    exports.inTopKAsync = inTopKAsync;
+    exports.io = io;
+    exports.irfft = irfft;
+    exports.isFinite = isFinite$1;
+    exports.isInf = isInf;
+    exports.isNaN = isNaN$1;
+    exports.keep = keep;
+    exports.kernel_impls = kernel_impls;
+    exports.leakyRelu = leakyRelu;
+    exports.less = less;
+    exports.lessEqual = lessEqual;
+    exports.linalg = linalg;
+    exports.linspace = linspace;
+    exports.localResponseNormalization = localResponseNormalization;
+    exports.log = log;
+    exports.log1p = log1p;
+    exports.logSigmoid = logSigmoid;
+    exports.logSoftmax = logSoftmax;
+    exports.logSumExp = logSumExp;
+    exports.logicalAnd = logicalAnd;
+    exports.logicalNot = logicalNot;
+    exports.logicalOr = logicalOr;
+    exports.logicalXor = logicalXor;
+    exports.losses = losses;
+    exports.matMul = matMul;
+    exports.math = math;
+    exports.max = max;
+    exports.maxPool = maxPool;
+    exports.maxPool3d = maxPool3d;
+    exports.maxPoolWithArgmax = maxPoolWithArgmax;
+    exports.maximum = maximum;
+    exports.mean = mean;
+    exports.memory = memory;
+    exports.meshgrid = meshgrid;
+    exports.min = min;
+    exports.minimum = minimum;
+    exports.mirrorPad = mirrorPad;
+    exports.mod = mod;
+    exports.moments = moments;
+    exports.movingAverage = movingAverage;
+    exports.mul = mul;
+    exports.multiRNNCell = multiRNNCell;
+    exports.multinomial = multinomial;
+    exports.neg = neg;
+    exports.nextFrame = nextFrame;
+    exports.norm = norm;
+    exports.notEqual = notEqual;
+    exports.oneHot = oneHot;
+    exports.ones = ones$1;
+    exports.onesLike = onesLike;
+    exports.op = op;
+    exports.outerProduct = outerProduct;
+    exports.pad = pad;
+    exports.pad1d = pad1d;
+    exports.pad2d = pad2d;
+    exports.pad3d = pad3d;
+    exports.pad4d = pad4d;
+    exports.pool = pool;
+    exports.pow = pow;
+    exports.prelu = prelu;
+    exports.print = print;
+    exports.prod = prod;
+    exports.profile = profile;
+    exports.rand = rand;
+    exports.randomGamma = randomGamma;
+    exports.randomNormal = randomNormal;
+    exports.randomUniform = randomUniform;
+    exports.range = range;
+    exports.ready = ready;
+    exports.real = real;
+    exports.reciprocal = reciprocal;
+    exports.registerBackend = registerBackend;
+    exports.registerGradient = registerGradient;
+    exports.registerKernel = registerKernel;
+    exports.relu = relu;
+    exports.relu6 = relu6;
+    exports.removeBackend = removeBackend;
+    exports.reshape = reshape;
+    exports.reverse = reverse;
+    exports.reverse1d = reverse1d;
+    exports.reverse2d = reverse2d;
+    exports.reverse3d = reverse3d;
+    exports.reverse4d = reverse4d;
+    exports.rfft = rfft;
+    exports.round = round$1;
+    exports.rsqrt = rsqrt;
+    exports.scalar = scalar;
+    exports.scatterND = scatterND;
+    exports.scatter_util = scatter_nd_util;
+    exports.selu = selu;
+    exports.separableConv2d = separableConv2d;
+    exports.serialization = serialization;
+    exports.setBackend = setBackend;
+    exports.setPlatform = setPlatform;
+    exports.setdiff1dAsync = setdiff1dAsync;
+    exports.sigmoid = sigmoid;
+    exports.sign = sign;
+    exports.signal = signal;
+    exports.sin = sin;
+    exports.sinh = sinh;
+    exports.slice = slice;
+    exports.slice1d = slice1d;
+    exports.slice2d = slice2d;
+    exports.slice3d = slice3d;
+    exports.slice4d = slice4d;
+    exports.slice_util = slice_util;
+    exports.softmax = softmax;
+    exports.softplus = softplus;
+    exports.spaceToBatchND = spaceToBatchND;
+    exports.sparse = sparse;
+    exports.sparseToDense = sparseToDense;
+    exports.spectral = spectral;
+    exports.split = split;
+    exports.sqrt = sqrt;
+    exports.square = square;
+    exports.squaredDifference = squaredDifference;
+    exports.squeeze = squeeze;
+    exports.stack = stack;
+    exports.step = step;
+    exports.stridedSlice = stridedSlice;
+    exports.string = string;
+    exports.sub = sub;
+    exports.sum = sum$1;
+    exports.sumOutType = sumOutType;
+    exports.tan = tan;
+    exports.tanh = tanh$1;
+    exports.tensor = tensor;
+    exports.tensor1d = tensor1d;
+    exports.tensor2d = tensor2d;
+    exports.tensor3d = tensor3d;
+    exports.tensor4d = tensor4d;
+    exports.tensor5d = tensor5d;
+    exports.tensor6d = tensor6d;
+    exports.tensor_util = tensor_util;
+    exports.test_util = test_util;
+    exports.tidy = tidy;
+    exports.tile = tile;
+    exports.time = time;
+    exports.topk = topk;
+    exports.train = train;
+    exports.transpose = transpose;
+    exports.truncatedNormal = truncatedNormal;
+    exports.unique = unique;
+    exports.unregisterGradient = unregisterGradient;
+    exports.unregisterKernel = unregisterKernel;
+    exports.unsortedSegmentSum = unsortedSegmentSum;
+    exports.unstack = unstack;
+    exports.upcastType = upcastType;
+    exports.util = util;
+    exports.valueAndGrad = valueAndGrad;
+    exports.valueAndGrads = valueAndGrads;
+    exports.variable = variable;
+    exports.variableGrads = variableGrads;
+    exports.version_core = version;
+    exports.where = where;
+    exports.whereAsync = whereAsync;
+    exports.zeros = zeros;
+    exports.zerosLike = zerosLike;
+
+    Object.defineProperty(exports, '__esModule', { value: true });
+
+})));
+//# sourceMappingURL=tf-core.js.map
diff --git a/thirdparty/tfjs/tf-core.js.map b/thirdparty/tfjs/tf-core.js.map
new file mode 100644
index 0000000..f48f4fb
--- /dev/null
+++ b/thirdparty/tfjs/tf-core.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tf-core.js","sources":["../src/backends/backend.ts","../src/util_base.ts","../src/environment.ts","../src/global_util.ts","../src/kernel_names.ts","../src/kernel_registry.ts","../node_modules/long/src/long.js","../src/hash_util.ts","../src/util.ts","../src/profiler.ts","../src/tape.ts","../src/tensor_format.ts","../src/tensor.ts","../src/types.ts","../src/tensor_util.ts","../src/engine.ts","../src/device_util.ts","../src/flags.ts","../src/tensor_util_env.ts","../src/ops/operation.ts","../src/ops/complex.ts","../src/ops/tensor_ops_util.ts","../src/ops/tensor.ts","../src/io/types.ts","../src/io/io_utils.ts","../src/io/router_registry.ts","../src/io/indexed_db.ts","../src/io/local_storage.ts","../src/io/model_management.ts","../src/platforms/platform_browser.ts","../src/platforms/platform_node.ts","../src/ops/buffer.ts","../src/ops/cast.ts","../src/ops/clone.ts","../src/ops/print.ts","../src/base_side_effects.ts","../src/io/browser_files.ts","../src/io/progress.ts","../src/io/weights_loader.ts","../src/io/http.ts","../src/io/passthrough.ts","../src/io/io.ts","../src/ops/mat_mul.ts","../src/ops/one_hot.ts","../src/ops/transpose.ts","../src/ops/confusion_matrix.ts","../src/math.ts","../src/ops/tensor3d.ts","../src/ops/browser.ts","../src/ops/gather_nd_util.ts","../src/ops/scatter_nd_util.ts","../src/ops/slice_util.ts","../src/serialization.ts","../src/test_util.ts","../src/version.ts","../src/globals.ts","../src/ops/add.ts","../src/ops/floorDiv.ts","../src/ops/div.ts","../src/ops/mul.ts","../src/ops/abs.ts","../src/ops/acos.ts","../src/ops/acosh.ts","../src/ops/add_n.ts","../src/ops/all.ts","../src/ops/any.ts","../src/ops/arg_max.ts","../src/ops/arg_min.ts","../src/ops/asin.ts","../src/ops/asinh.ts","../src/ops/atan.ts","../src/ops/atan2.ts","../src/ops/atanh.ts","../src/ops/conv_util.ts","../src/ops/reshape.ts","../src/ops/avg_pool.ts","../src/ops/avg_pool_3d.ts","../src/ops/concat.ts","../src/ops/sigmoid.ts","../src/ops/slice.ts","../src/ops/tanh.ts","../src/ops/basic_lstm_cell.ts","../src/ops/batch_to_space_nd.ts","../src/ops/batchnorm_util.ts","../src/ops/batchnorm.ts","../src/ops/batchnorm2d.ts","../src/ops/batchnorm3d.ts","../src/ops/batchnorm4d.ts","../src/ops/bincount.ts","../src/ops/broadcast_to.ts","../src/ops/ceil.ts","../src/ops/clip_by_value.ts","../src/ops/concat_1d.ts","../src/ops/concat_2d.ts","../src/ops/concat_3d.ts","../src/ops/concat_4d.ts","../src/ops/conv2d.ts","../src/ops/conv1d.ts","../src/ops/conv2d_backprop_input.ts","../src/ops/conv2d_transpose.ts","../src/ops/conv3d.ts","../src/ops/conv3d_backprop_input.ts","../src/ops/conv3d_transpose.ts","../src/ops/cos.ts","../src/ops/cosh.ts","../src/ops/cumsum.ts","../src/ops/dense_bincount.ts","../src/ops/depth_to_space.ts","../src/ops/depthwise_conv2d.ts","../src/ops/diag.ts","../src/ops/dilation2d.ts","../src/ops/broadcast_util.ts","../src/ops/equal.ts","../src/ops/where.ts","../src/ops/zeros_like.ts","../src/ops/div_no_nan.ts","../src/ops/dot.ts","../src/ops/einsum.ts","../src/ops/elu.ts","../src/ops/erf.ts","../src/ops/exp.ts","../src/ops/expand_dims.ts","../src/ops/expm1.ts","../src/ops/tile.ts","../src/ops/eye.ts","../src/ops/fill.ts","../src/ops/floor.ts","../src/ops/gather.ts","../src/ops/greater.ts","../src/ops/greater_equal.ts","../src/ops/imag.ts","../src/ops/is_finite.ts","../src/ops/is_inf.ts","../src/ops/is_nan.ts","../src/ops/leaky_relu.ts","../src/ops/less.ts","../src/ops/less_equal.ts","../src/ops/linspace.ts","../src/ops/local_response_normalization.ts","../src/ops/log.ts","../src/ops/log1p.ts","../src/gradients.ts","../src/ops/neg.ts","../src/ops/softplus.ts","../src/ops/log_sigmoid.ts","../src/ops/max.ts","../src/ops/sub.ts","../src/ops/sum.ts","../src/ops/log_softmax.ts","../src/ops/axis_util.ts","../src/ops/log_sum_exp.ts","../src/ops/logical_and.ts","../src/ops/logical_not.ts","../src/ops/logical_or.ts","../src/ops/logical_xor.ts","../src/ops/max_pool.ts","../src/ops/max_pool_3d.ts","../src/ops/max_pool_with_argmax.ts","../src/ops/maximum.ts","../src/ops/mean.ts","../src/ops/zeros.ts","../src/ops/ones.ts","../src/ops/meshgrid.ts","../src/ops/min.ts","../src/ops/minimum.ts","../src/ops/mirror_pad.ts","../src/ops/mod.ts","../src/ops/square.ts","../src/ops/moments.ts","../src/ops/multi_rnn_cell.ts","../src/ops/multinomial.ts","../src/ops/not_equal.ts","../src/ops/ones_like.ts","../src/ops/outer_product.ts","../src/ops/pad.ts","../src/ops/pad1d.ts","../src/ops/pad2d.ts","../src/ops/pad3d.ts","../src/ops/pad4d.ts","../src/ops/space_to_batch_nd.ts","../src/ops/pool.ts","../src/ops/pow.ts","../src/ops/prelu.ts","../src/ops/prod.ts","../src/ops/rand.ts","../node_modules/seedrandom/lib/alea.js","../node_modules/seedrandom/lib/xor128.js","../node_modules/seedrandom/lib/xorwow.js","../node_modules/seedrandom/lib/xorshift7.js","../node_modules/seedrandom/lib/xor4096.js","../node_modules/seedrandom/lib/tychei.js","../node_modules/seedrandom/seedrandom.js","../node_modules/seedrandom/index.js","../src/ops/rand_util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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Backend, DataId} from '../tensor';\nimport {BackendValues, DataType} from '../types';\n\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n\n// Required information for all backends.\nexport interface BackendTimingInfo {\n  kernelMs: number|{error: string};\n  getExtraProfileInfo?(): string;  // a field for additional timing information\n                                   // e.g. packing / unpacking for WebGL backend\n}\n\nexport interface TensorStorage {\n  read(dataId: DataId): Promise<BackendValues>;\n  readSync(dataId: DataId): BackendValues;\n  disposeData(dataId: DataId, force?: boolean): boolean;\n  write(values: BackendValues, shape: number[], dtype: DataType): DataId;\n  move(\n      dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,\n      refCount: number): void;\n  memory(): {unreliable: boolean;};  // Backend-specific information.\n  /** Returns number of data ids currently in the storage. */\n  numDataIds(): number;\n  refCount(dataId: DataId): number;\n}\n\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage<T> {\n  private data = new WeakMap<DataId, T>();\n  private dataIdsCount = 0;\n\n  constructor(private backend: KernelBackend, private dataMover: DataMover) {}\n\n  get(dataId: DataId) {\n    if (!this.data.has(dataId)) {\n      this.dataMover.moveData(this.backend, dataId);\n    }\n    return this.data.get(dataId);\n  }\n\n  set(dataId: DataId, value: T): void {\n    this.dataIdsCount++;\n    this.data.set(dataId, value);\n  }\n\n  has(dataId: DataId): boolean {\n    return this.data.has(dataId);\n  }\n\n  delete(dataId: DataId): boolean {\n    this.dataIdsCount--;\n    return this.data.delete(dataId);\n  }\n\n  numDataIds(): number {\n    return this.dataIdsCount;\n  }\n}\n\nexport interface DataMover {\n  /**\n   * To be called by backends whenever they see a dataId that they don't own.\n   * Upon calling this method, the mover will fetch the tensor from another\n   * backend and register it with the current active backend.\n   */\n  moveData(backend: KernelBackend, dataId: DataId): void;\n}\n\nexport interface BackendTimer {\n  // check if backend timer is available\n  timerAvailable(): boolean;\n  time(f: () => void): Promise<BackendTimingInfo>;\n}\n\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend implements TensorStorage, Backend, BackendTimer {\n  refCount(dataId: DataId): number {\n    return notYetImplemented('refCount');\n  }\n  incRef(dataId: DataId): void {\n    return notYetImplemented('incRef');\n  }\n  timerAvailable(): boolean {\n    return true;\n  }\n  time(f: () => void): Promise<BackendTimingInfo> {\n    return notYetImplemented('time');\n  }\n  read(dataId: object): Promise<BackendValues> {\n    return notYetImplemented('read');\n  }\n  readSync(dataId: object): BackendValues {\n    return notYetImplemented('readSync');\n  }\n  numDataIds(): number {\n    return notYetImplemented('numDataIds');\n  }\n  disposeData(dataId: object, force?: boolean): boolean {\n    return notYetImplemented('disposeData');\n  }\n  write(values: BackendValues, shape: number[], dtype: DataType): DataId {\n    return notYetImplemented('write');\n  }\n  move(\n      dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,\n      refCount: number): void {\n    return notYetImplemented('move');\n  }\n  memory(): {unreliable: boolean; reasons?: string[]} {\n    return notYetImplemented('memory');\n  }\n  /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n  floatPrecision(): 16|32 {\n    return notYetImplemented('floatPrecision');\n  }\n  /** Returns the smallest representable number.  */\n  epsilon(): number {\n    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n  }\n  dispose(): void {\n    return notYetImplemented('dispose');\n  }\n}\n\nfunction notYetImplemented(kernelName: string): never {\n  throw new Error(\n      `'${kernelName}' not yet implemented or not found in the registry. ` +\n      `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, DataTypeMap, FlatVector, NumericDataType, RecursiveArray, TensorLike, TypedArray} from './types';\n\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array: any[]|Uint32Array|Int32Array|\n                        Float32Array): void {\n  let counter = array.length;\n  let temp = 0;\n  let index = 0;\n  // While there are elements in the array\n  while (counter > 0) {\n    // Pick a random index\n    index = (Math.random() * counter) | 0;\n    // Decrease counter by 1\n    counter--;\n    // And swap the last element with it\n    temp = array[counter];\n    array[counter] = array[index];\n    array[index] = temp;\n  }\n}\n\n/**\n * Shuffles two arrays in-place the same way using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1,2,3,4,5];\n * const b = [11,22,33,44,55];\n * tf.util.shuffleCombo(a, b);\n * console.log(a, b);\n * ```\n *\n * @param array The first array to shuffle in-place.\n * @param array2 The second array to shuffle in-place with the same permutation\n *     as the first array.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function shuffleCombo(\n    // tslint:disable-next-line:no-any\n    array: any[]|Uint32Array|Int32Array|Float32Array,\n    // tslint:disable-next-line:no-any\n    array2: any[]|Uint32Array|Int32Array|Float32Array): void {\n  if (array.length !== array2.length) {\n    throw new Error(\n        `Array sizes must match to be shuffled together ` +\n        `First array length was ${array.length}` +\n        `Second array length was ${array2.length}`);\n  }\n  let counter = array.length;\n  let temp, temp2;\n  let index = 0;\n  // While there are elements in the array\n  while (counter > 0) {\n    // Pick a random index\n    index = (Math.random() * counter) | 0;\n    // Decrease counter by 1\n    counter--;\n    // And swap the last element of each array with it\n    temp = array[counter];\n    temp2 = array2[counter];\n    array[counter] = array[index];\n    array2[counter] = array2[index];\n    array[index] = temp;\n    array2[index] = temp2;\n  }\n}\n\n/** Clamps a value to a specified range. */\nexport function clamp(min: number, x: number, max: number): number {\n  return Math.max(min, Math.min(x, max));\n}\n\nexport function nearestLargerEven(val: number): number {\n  return val % 2 === 0 ? val : val + 1;\n}\n\nexport function sum(arr: number[]): number {\n  let sum = 0;\n  for (let i = 0; i < arr.length; i++) {\n    sum += arr[i];\n  }\n  return sum;\n}\n\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a: number, b: number) {\n  const r = Math.random();\n  return (b * r) + (1 - r) * a;\n}\n\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a: FlatVector, b: FlatVector): number {\n  let result = 0;\n  for (let i = 0; i < a.length; i++) {\n    const diff = Number(a[i]) - Number(b[i]);\n    result += diff * diff;\n  }\n  return result;\n}\n\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n *     error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr: boolean, msg: () => string) {\n  if (!expr) {\n    throw new Error(typeof msg === 'string' ? msg : msg());\n  }\n}\n\nexport function assertShapesMatch(\n    shapeA: number[], shapeB: number[], errorMessagePrefix = ''): void {\n  assert(\n      arraysEqual(shapeA, shapeB),\n      () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\n\nexport function assertNonNull(a: TensorLike): void {\n  assert(\n      a != null,\n      () => `The input to the tensor constructor must be a non-null value.`);\n}\n\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\n/**\n *  Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n *  @param arr The nested array to flatten.\n *  @param result The destination array which holds the elements.\n *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n *      to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function\nflatten<T extends number|boolean|string|Promise<number>|TypedArray>(\n    arr: T|RecursiveArray<T>, result: T[] = [], skipTypedArray = false): T[] {\n  if (result == null) {\n    result = [];\n  }\n  if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n    for (let i = 0; i < arr.length; ++i) {\n      flatten(arr[i], result, skipTypedArray);\n    }\n  } else {\n    result.push(arr as T);\n  }\n  return result;\n}\n\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape: number[]): number {\n  if (shape.length === 0) {\n    // Scalar.\n    return 1;\n  }\n  let size = shape[0];\n  for (let i = 1; i < shape.length; i++) {\n    size *= shape[i];\n  }\n  return size;\n}\n\nexport function isScalarShape(shape: number[]): boolean {\n  return shape.length === 0;\n}\n\nexport function arraysEqual(n1: FlatVector, n2: FlatVector) {\n  if (n1 === n2) {\n    return true;\n  }\n  if (n1 == null || n2 == null) {\n    return false;\n  }\n\n  if (n1.length !== n2.length) {\n    return false;\n  }\n  for (let i = 0; i < n1.length; i++) {\n    if (n1[i] !== n2[i]) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function isInt(a: number): boolean {\n  return a % 1 === 0;\n}\n\nexport function tanh(x: number): number {\n  // tslint:disable-next-line:no-any\n  if ((Math as any).tanh != null) {\n    // tslint:disable-next-line:no-any\n    return (Math as any).tanh(x);\n  }\n  if (x === Infinity) {\n    return 1;\n  } else if (x === -Infinity) {\n    return -1;\n  } else {\n    const e2x = Math.exp(2 * x);\n    return (e2x - 1) / (e2x + 1);\n  }\n}\n\nexport function sizeToSquarishShape(size: number): [number, number] {\n  const width = Math.ceil(Math.sqrt(size));\n  return [width, Math.ceil(size / width)];\n}\n\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n: number): Uint32Array {\n  const shuffledIndices = new Uint32Array(n);\n  for (let i = 0; i < n; ++i) {\n    shuffledIndices[i] = i;\n  }\n  shuffle(shuffledIndices);\n  return shuffledIndices;\n}\n\nexport function rightPad(a: string, size: number): string {\n  if (size <= a.length) {\n    return a;\n  }\n  return a + ' '.repeat(size - a.length);\n}\n\nexport function repeatedTry(\n    checkFn: () => boolean, delayFn = (counter: number) => 0,\n    maxCounter?: number): Promise<void> {\n  return new Promise<void>((resolve, reject) => {\n    let tryCount = 0;\n\n    const tryFn = () => {\n      if (checkFn()) {\n        resolve();\n        return;\n      }\n\n      tryCount++;\n\n      const nextBackoff = delayFn(tryCount);\n\n      if (maxCounter != null && tryCount >= maxCounter) {\n        reject();\n        return;\n      }\n      setTimeout(tryFn, nextBackoff);\n    };\n\n    tryFn();\n  });\n}\n\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(\n    shape: number[], size: number): number[] {\n  let shapeProd = 1;\n  let implicitIdx = -1;\n\n  for (let i = 0; i < shape.length; ++i) {\n    if (shape[i] >= 0) {\n      shapeProd *= shape[i];\n    } else if (shape[i] === -1) {\n      if (implicitIdx !== -1) {\n        throw Error(\n            `Shapes can only have 1 implicit size. ` +\n            `Found -1 at dim ${implicitIdx} and dim ${i}`);\n      }\n      implicitIdx = i;\n    } else if (shape[i] < 0) {\n      throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n    }\n  }\n\n  if (implicitIdx === -1) {\n    if (size > 0 && size !== shapeProd) {\n      throw Error(`Size(${size}) must match the product of shape ${shape}`);\n    }\n    return shape;\n  }\n\n  if (shapeProd === 0) {\n    throw Error(\n        `Cannot infer the missing size in [${shape}] when ` +\n        `there are 0 elements`);\n  }\n  if (size % shapeProd !== 0) {\n    throw Error(\n        `The implicit shape can't be a fractional number. ` +\n        `Got ${size} / ${shapeProd}`);\n  }\n\n  const newShape = shape.slice();\n  newShape[implicitIdx] = size / shapeProd;\n  return newShape;\n}\n\nexport function parseAxisParam(\n    axis: number|number[], shape: number[]): number[] {\n  const rank = shape.length;\n\n  // Normalize input\n  axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n\n  // Check for valid range\n  assert(\n      axis.every(ax => ax >= -rank && ax < rank),\n      () =>\n          `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n          `got axis ${axis}`);\n\n  // Check for only integers\n  assert(\n      axis.every(ax => isInt(ax)),\n      () => `All values in axis param must be integers but ` +\n          `got axis ${axis}`);\n\n  // Handle negative axis.\n  return axis.map(a => a < 0 ? rank + a : a);\n}\n\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape: number[], axis?: number[]):\n    {newShape: number[], keptDims: number[]} {\n  const newShape: number[] = [];\n  const keptDims: number[] = [];\n  const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n  const axes = (axis == null || isEmptyArray) ?\n      null :\n      parseAxisParam(axis, shape).sort();\n  let j = 0;\n  for (let i = 0; i < shape.length; ++i) {\n    if (axes != null) {\n      if (axes[j] === i && shape[i] !== 1) {\n        throw new Error(\n            `Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n      }\n      if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n        newShape.push(shape[i]);\n        keptDims.push(i);\n      }\n      if (axes[j] <= i) {\n        j++;\n      }\n    }\n    if (shape[i] !== 1) {\n      newShape.push(shape[i]);\n      keptDims.push(i);\n    }\n  }\n  return {newShape, keptDims};\n}\n\nexport function getTypedArrayFromDType<D extends NumericDataType>(\n    dtype: D, size: number): DataTypeMap[D] {\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values as DataTypeMap[D];\n}\n\nexport function getArrayFromDType<D extends DataType>(\n    dtype: D, size: number): DataTypeMap[D] {\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else if (dtype === 'string') {\n    values = new Array<'string'>(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values as DataTypeMap[D];\n}\n\nexport function checkConversionForErrors<D extends DataType>(\n    vals: DataTypeMap[D]|number[], dtype: D): void {\n  for (let i = 0; i < vals.length; i++) {\n    const num = vals[i] as number;\n    if (isNaN(num) || !isFinite(num)) {\n      throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n    }\n  }\n}\n\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype: DataType): boolean {\n  return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n      dtype === 'int32' || dtype === 'string';\n}\n\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType: DataType, newType: DataType): boolean {\n  if (newType === 'complex64') {\n    return false;\n  }\n  if (newType === 'float32' && oldType !== 'complex64') {\n    return false;\n  }\n  if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n    return false;\n  }\n  if (newType === 'bool' && oldType === 'bool') {\n    return false;\n  }\n  return true;\n}\n\nexport function isTypedArray(a: {}): a is Float32Array|Int32Array|Uint8Array {\n  return a instanceof Float32Array || a instanceof Int32Array ||\n      a instanceof Uint8Array;\n}\n\nexport function bytesPerElement(dtype: DataType): number {\n  if (dtype === 'float32' || dtype === 'int32') {\n    return 4;\n  } else if (dtype === 'complex64') {\n    return 8;\n  } else if (dtype === 'bool') {\n    return 1;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\n\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr: Uint8Array[]): number {\n  if (arr == null) {\n    return 0;\n  }\n  let bytes = 0;\n  arr.forEach(x => bytes += x.length);\n  return bytes;\n}\n\n/** Returns true if the value is a string. */\nexport function isString(value: {}): value is string {\n  return typeof value === 'string' || value instanceof String;\n}\n\nexport function isBoolean(value: {}): boolean {\n  return typeof value === 'boolean';\n}\n\nexport function isNumber(value: {}): boolean {\n  return typeof value === 'number';\n}\n\nexport function inferDtype(values: TensorLike): DataType {\n  if (Array.isArray(values)) {\n    return inferDtype(values[0]);\n  }\n  if (values instanceof Float32Array) {\n    return 'float32';\n  } else if (values instanceof Int32Array || values instanceof Uint8Array) {\n    return 'int32';\n  } else if (isNumber(values)) {\n    return 'float32';\n  } else if (isString(values)) {\n    return 'string';\n  } else if (isBoolean(values)) {\n    return 'bool';\n  }\n  return 'float32';\n}\n\nexport function isFunction(f: Function) {\n  return !!(f && f.constructor && f.call && f.apply);\n}\n\nexport function nearestDivisor(size: number, start: number): number {\n  for (let i = start; i < size; ++i) {\n    if (size % i === 0) {\n      return i;\n    }\n  }\n  return size;\n}\n\nexport function computeStrides(shape: number[]): number[] {\n  const rank = shape.length;\n  if (rank < 2) {\n    return [];\n  }\n\n  // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n  // strides.\n  const strides = new Array(rank - 1);\n  strides[rank - 2] = shape[rank - 1];\n  for (let i = rank - 3; i >= 0; --i) {\n    strides[i] = strides[i + 1] * shape[i + 1];\n  }\n  return strides;\n}\n\nfunction createNestedArray(\n    offset: number, shape: number[], a: TypedArray, isComplex = false) {\n  const ret = new Array();\n  if (shape.length === 1) {\n    const d = shape[0] * (isComplex ? 2 : 1);\n    for (let i = 0; i < d; i++) {\n      ret[i] = a[offset + i];\n    }\n  } else {\n    const d = shape[0];\n    const rest = shape.slice(1);\n    const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n    for (let i = 0; i < d; i++) {\n      ret[i] = createNestedArray(offset + i * len, rest, a, isComplex);\n    }\n  }\n  return ret;\n}\n\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(\n    shape: number[], a: TypedArray, isComplex = false) {\n  if (shape.length === 0) {\n    // Scalar type should return a single number.\n    return a[0];\n  }\n  const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n  if (size === 0) {\n    // A tensor with shape zero should be turned into empty list.\n    return [];\n  }\n  if (size !== a.length) {\n    throw new Error(`[${shape}] does not match the input size ${a.length}${\n        isComplex ? ' for a complex tensor' : ''}.`);\n  }\n\n  return createNestedArray(0, shape, a, isComplex);\n}\n\nexport function makeOnesTypedArray<D extends DataType>(\n    size: number, dtype: D): DataTypeMap[D] {\n  const array = makeZerosTypedArray(size, dtype);\n  for (let i = 0; i < array.length; i++) {\n    array[i] = 1;\n  }\n  return array;\n}\n\nexport function makeZerosTypedArray<D extends DataType>(\n    size: number, dtype: D): DataTypeMap[D] {\n  if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n    return new Float32Array(size) as DataTypeMap[D];\n  } else if (dtype === 'int32') {\n    return new Int32Array(size) as DataTypeMap[D];\n  } else if (dtype === 'bool') {\n    return new Uint8Array(size) as DataTypeMap[D];\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\n\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray<D extends DataType>(\n    shape: number[], dtype: D) {\n  const size = shape.reduce((prev, curr) => prev * curr, 1);\n  if (dtype == null || dtype === 'float32') {\n    return toNestedArray(shape, new Float32Array(size));\n  } else if (dtype === 'int32') {\n    return toNestedArray(shape, new Int32Array(size));\n  } else if (dtype === 'bool') {\n    return toNestedArray(shape, new Uint8Array(size));\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\n\nexport function assertNonNegativeIntegerDimensions(shape: number[]) {\n  shape.forEach(dimSize => {\n    assert(\n        Number.isInteger(dimSize) && dimSize >= 0,\n        () =>\n            `Tensor must have a shape comprised of positive integers but got ` +\n            `shape [${shape}].`);\n  });\n}\n\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(\n    locs: number[], rank: number, strides: number[]): number {\n  if (rank === 0) {\n    return 0;\n  } else if (rank === 1) {\n    return locs[0];\n  }\n  let index = locs[locs.length - 1];\n  for (let i = 0; i < locs.length - 1; ++i) {\n    index += strides[i] * locs[i];\n  }\n  return index;\n}\n\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(\n    index: number, rank: number, strides: number[]): number[] {\n  if (rank === 0) {\n    return [];\n  } else if (rank === 1) {\n    return [index];\n  }\n  const locs: number[] = new Array(rank);\n  for (let i = 0; i < locs.length - 1; ++i) {\n    locs[i] = Math.floor(index / strides[i]);\n    index -= locs[i] * strides[i];\n  }\n  locs[locs.length - 1] = index;\n  return locs;\n}\n\n/**\n * This method asserts whether an object is a Promise instance.\n * @param object\n */\n// tslint:disable-next-line: no-any\nexport function isPromise(object: any) {\n  //  We chose to not use 'obj instanceOf Promise' for two reasons:\n  //  1. It only reliably works for es6 Promise, not other Promise\n  //  implementations.\n  //  2. It doesn't work with framework that uses zone.js. zone.js monkey patch\n  //  the async calls, so it is possible the obj (patched) is comparing to a\n  //  pre-patched Promise.\n  return object && object.then && typeof object.then === 'function';\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Platform} from './platforms/platform';\nimport {isPromise} from './util_base';\n\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n\ntype FlagValue = number|boolean;\ntype FlagEvaluationFn = (() => FlagValue)|(() => Promise<FlagValue>);\nexport type Flags = {\n  [featureName: string]: FlagValue\n};\nexport type FlagRegistryEntry = {\n  evaluationFn: FlagEvaluationFn;\n  setHook?: (value: FlagValue) => void;\n};\n\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n  private flags: Flags = {};\n  private flagRegistry: {[flagName: string]: FlagRegistryEntry} = {};\n\n  private urlFlags: Flags = {};\n\n  platformName: string;\n  platform: Platform;\n\n  // Jasmine spies on this in 'environment_test.ts'\n  getQueryParams = getQueryParams;\n\n  // tslint:disable-next-line: no-any\n  constructor(public global: any) {\n    this.populateURLFlags();\n  }\n\n  setPlatform(platformName: string, platform: Platform) {\n    if (this.platform != null) {\n      console.warn(\n          `Platform ${this.platformName} has already been set. ` +\n          `Overwriting the platform with ${platform}.`);\n    }\n    this.platformName = platformName;\n    this.platform = platform;\n  }\n\n  registerFlag(\n      flagName: string, evaluationFn: FlagEvaluationFn,\n      setHook?: (value: FlagValue) => void) {\n    this.flagRegistry[flagName] = {evaluationFn, setHook};\n\n    // Override the flag value from the URL. This has to happen here because the\n    // environment is initialized before flags get registered.\n    if (this.urlFlags[flagName] != null) {\n      const flagValue = this.urlFlags[flagName];\n      console.warn(\n          `Setting feature override from URL ${flagName}: ${flagValue}.`);\n      this.set(flagName, flagValue);\n    }\n  }\n\n  async getAsync(flagName: string): Promise<FlagValue> {\n    if (flagName in this.flags) {\n      return this.flags[flagName];\n    }\n\n    this.flags[flagName] = await this.evaluateFlag(flagName);\n    return this.flags[flagName];\n  }\n\n  get(flagName: string): FlagValue {\n    if (flagName in this.flags) {\n      return this.flags[flagName];\n    }\n\n    const flagValue = this.evaluateFlag(flagName);\n    if (isPromise(flagValue)) {\n      throw new Error(\n          `Flag ${flagName} cannot be synchronously evaluated. ` +\n          `Please use getAsync() instead.`);\n    }\n\n    this.flags[flagName] = flagValue as number | boolean;\n\n    return this.flags[flagName];\n  }\n\n  getNumber(flagName: string): number {\n    return this.get(flagName) as number;\n  }\n\n  getBool(flagName: string): boolean {\n    return this.get(flagName) as boolean;\n  }\n\n  getFlags(): Flags {\n    return this.flags;\n  }\n  // For backwards compatibility.\n  get features(): Flags {\n    return this.flags;\n  }\n\n  set(flagName: string, value: FlagValue): void {\n    if (this.flagRegistry[flagName] == null) {\n      throw new Error(\n          `Cannot set flag ${flagName} as it has not been registered.`);\n    }\n    this.flags[flagName] = value;\n    if (this.flagRegistry[flagName].setHook != null) {\n      this.flagRegistry[flagName].setHook(value);\n    }\n  }\n\n  private evaluateFlag(flagName: string): FlagValue|Promise<FlagValue> {\n    if (this.flagRegistry[flagName] == null) {\n      throw new Error(\n          `Cannot evaluate flag '${flagName}': no evaluation function found.`);\n    }\n    return this.flagRegistry[flagName].evaluationFn();\n  }\n\n  setFlags(flags: Flags) {\n    this.flags = Object.assign({}, flags);\n  }\n\n  reset() {\n    this.flags = {};\n    this.urlFlags = {};\n    this.populateURLFlags();\n  }\n\n  private populateURLFlags(): void {\n    if (typeof this.global === 'undefined' ||\n        typeof this.global.location === 'undefined' ||\n        typeof this.global.location.search === 'undefined') {\n      return;\n    }\n\n    const urlParams = this.getQueryParams(this.global.location.search);\n    if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n      const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n      keyValues.forEach(keyValue => {\n        const [key, value] = keyValue.split(':') as [string, string];\n        this.urlFlags[key] = parseValue(key, value);\n      });\n    }\n  }\n}\n\nexport function getQueryParams(queryString: string): {[key: string]: string} {\n  const params = {};\n  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n    decodeParam(params, t[0], t[1]);\n    return t.join('=');\n  });\n  return params;\n}\n\nfunction decodeParam(\n    params: {[key: string]: string}, name: string, value?: string) {\n  params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\n\nfunction parseValue(flagName: string, value: string): FlagValue {\n  value = value.toLowerCase();\n  if (value === 'true' || value === 'false') {\n    return value === 'true';\n  } else if (`${+ value}` === value) {\n    return +value;\n  }\n  throw new Error(\n      `Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n  return ENV;\n}\n\nexport let ENV: Environment = null;\nexport function setEnvironmentGlobal(environment: Environment) {\n  ENV = environment;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace: {_tfGlobals: Map<string, any>};\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace(): {_tfGlobals: Map<string, any>} {\n  if (globalNameSpace == null) {\n    // tslint:disable-next-line:no-any\n    let ns: any;\n    if (typeof (window) !== 'undefined') {\n      ns = window;\n    } else if (typeof (global) !== 'undefined') {\n      ns = global;\n    } else if (typeof (process) !== 'undefined') {\n      ns = process;\n    } else if (typeof (self) !== 'undefined') {\n      ns = self;\n    } else {\n      throw new Error('Could not find a global object');\n    }\n    globalNameSpace = ns;\n  }\n  return globalNameSpace;\n}\n\n// tslint:disable-next-line:no-any\nfunction getGlobalMap(): Map<string, any> {\n  const ns = getGlobalNamespace();\n  if (ns._tfGlobals == null) {\n    ns._tfGlobals = new Map();\n  }\n  return ns._tfGlobals;\n}\n\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n *             the first time it is fetched.\n */\nexport function getGlobal<T>(key: string, init: () => T): T {\n  const globalMap = getGlobalMap();\n  if (globalMap.has(key)) {\n    return globalMap.get(key);\n  } else {\n    const singleton = init();\n    globalMap.set(key, singleton);\n    return globalMap.get(key);\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Allow UpperCamelCase variable names\n// tslint:disable: variable-name\n// Unfortunately just enabling PascalCase per file (tslint:enable:\n// allow-pascal-case) doesn't work.\nimport {NamedTensorInfoMap, TensorInfo} from './kernel_registry';\nimport {ExplicitPadding} from './ops/conv_util';\nimport {Activation} from './ops/fused_types';\nimport {DataType, PixelData} from './types';\n\nexport const Abs = 'Abs';\nexport type AbsInputs = UnaryInputs;\n\nexport const Acos = 'Acos';\nexport type AcosInputs = UnaryInputs;\n\nexport const Acosh = 'Acosh';\nexport type AcoshInputs = UnaryInputs;\n\nexport const Add = 'Add';\nexport type AddInputs = BinaryInputs;\n\nexport const AddN = 'AddN';\nexport type AddNInputs = TensorInfo[];\n\nexport const All = 'All';\nexport type AllInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface AllAttrs {\n  axis: number|number[];\n  keepDims: boolean;\n}\n\nexport const Any = 'Any';\nexport type AnyInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface AnyAttrs {\n  axis: number|number[];\n  keepDims: boolean;\n}\n\nexport const ArgMax = 'ArgMax';\nexport type ArgMaxInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface ArgMaxAttrs {\n  axis: number;\n}\n\nexport const ArgMin = 'ArgMin';\nexport type ArgMinInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface ArgMinAttrs {\n  axis: number;\n}\n\nexport const Asin = 'Asin';\nexport type AsinInputs = UnaryInputs;\n\nexport const Asinh = 'Asinh';\nexport type AsinhInputs = UnaryInputs;\n\nexport const Atan = 'Atan';\nexport type AtanInputs = UnaryInputs;\n\nexport const Atanh = 'Atanh';\nexport type AtanhInputs = UnaryInputs;\n\nexport const Atan2 = 'Atan2';\nexport type Atan2Inputs = BinaryInputs;\n\nexport const AvgPool = 'AvgPool';\nexport type AvgPoolInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface AvgPoolAttrs {\n  filterSize: [number, number]|number;\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const AvgPoolGrad = 'AvgPoolGrad';\nexport type AvgPoolGradInputs = Pick<NamedTensorInfoMap, 'dy'|'input'>;\nexport interface AvgPoolGradAttrs {\n  filterSize: [number, number]|number;\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n}\n\nexport const AvgPool3D = 'AvgPool3D';\nexport type AvgPool3DInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface AvgPool3DAttrs {\n  filterSize: [number, number, number]|number;\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same'|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n  dataFormat: 'NDHWC'|'NCDHW';\n}\n\nexport const AvgPool3DGrad = 'AvgPool3DGrad';\nexport type AvgPool3DGradInputs = Pick<NamedTensorInfoMap, 'dy'|'input'>;\nexport interface AvgPool3DGradAttrs {\n  filterSize: [number, number, number]|number;\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same'|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const BatchMatMul = 'BatchMatMul';\nexport type BatchMatMulInputs = Pick<NamedTensorInfoMap, 'a'|'b'>;\nexport interface BatchMatMulAttrs {\n  transposeA: boolean;\n  transposeB: boolean;\n}\n\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport type BatchToSpaceNDInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface BatchToSpaceNDAttrs {\n  blockShape: number[];\n  crops: number[][];\n}\n\nexport type BinaryInputs = Pick<NamedTensorInfoMap, 'a'|'b'>;\n\nexport const Bincount = 'Bincount';\nexport type BincountInputs = Pick<NamedTensorInfoMap, 'x'|'weights'>;\nexport interface BincountAttrs {\n  size: number;\n}\n\nexport const BroadcastTo = 'BroadcastTo';\nexport type BroadcastToInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface BroadCastToAttrs {\n  shape: number[];\n  inputShape: number[];  // for gradient\n}\n\nexport const Cast = 'Cast';\nexport type CastInputs = UnaryInputs;\nexport interface CastAttrs {\n  dtype: DataType;\n}\n\nexport const Ceil = 'Ceil';\nexport type CeilInputs = UnaryInputs;\n\nexport const ClipByValue = 'ClipByValue';\nexport type ClipByValueInputs = UnaryInputs;\nexport interface ClipByValueAttrs {\n  clipValueMin: number;\n  clipValueMax: number;\n}\n\nexport const Complex = 'Complex';\nexport type ComplexInputs = Pick<NamedTensorInfoMap, 'real'|'imag'>;\n\nexport const ComplexAbs = 'ComplexAbs';\nexport type ComplexAbsInputs = UnaryInputs;\n\nexport const Concat = 'Concat';\nexport type ConcatInputs = TensorInfo[];\nexport interface ConcatAttrs {\n  axis: number;\n}\n\nexport const Conv2D = 'Conv2D';\nexport type Conv2DInputs = Pick<NamedTensorInfoMap, 'x'|'filter'>;\nexport interface Conv2DAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number|ExplicitPadding;\n  dataFormat: 'NHWC'|'NCHW';\n  dilations: [number, number]|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport type Conv2DBackpropFilterInputs = Pick<NamedTensorInfoMap, 'x'|'dy'>;\nexport interface Conv2DBackpropFilterAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number|ExplicitPadding;\n  dataFormat: 'NHWC'|'NCHW';\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n  filterShape: [number, number, number, number];\n}\n\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport type Conv2DBackpropInputInputs = Pick<NamedTensorInfoMap, 'dy'|'filter'>;\nexport interface Conv2DBackpropInputAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number|ExplicitPadding;\n  dataFormat: 'NHWC'|'NCHW';\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n  inputShape: [number, number, number, number];\n}\n\nexport const Conv3D = 'Conv3D';\nexport type Conv3DInputs = Pick<NamedTensorInfoMap, 'x'|'filter'>;\nexport interface Conv3DAttrs {\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same';\n  dataFormat: 'NDHWC'|'NCDHW';\n  dilations: [number, number, number]|number;\n}\n\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport type Conv3DBackpropFilterV2Inputs = Pick<NamedTensorInfoMap, 'x'|'dy'>;\n\nexport interface Conv3DBackpropFilterV2Attrs {\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same';\n  filterShape: [number, number, number, number, number];\n}\n\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport type Conv3DBackpropInputV2Inputs =\n    Pick<NamedTensorInfoMap, 'dy'|'filter'>;\nexport interface Conv3DBackpropInputV2Attrs {\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same';\n  inputShape: [number, number, number, number, number];\n}\n\nexport const Cos = 'Cos';\nexport type CosInputs = UnaryInputs;\n\nexport const Cosh = 'Cosh';\nexport type CoshInputs = UnaryInputs;\n\nexport const Cumsum = 'Cumsum';\nexport type CumsumInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface CumsumAttrs {\n  axis: number;\n  exclusive: boolean;\n  reverse: boolean;\n}\n\nexport const CropAndResize = 'CropAndResize';\nexport type CropAndResizeInputs =\n    Pick<NamedTensorInfoMap, 'image'|'boxes'|'boxInd'>;\nexport interface CropAndResizeAttrs {\n  cropSize: [number, number];\n  method: 'bilinear'|'nearest';\n  extrapolationValue: number;\n}\n\nexport const DenseBincount = 'DenseBincount';\nexport type DenseBincountInputs = Pick<NamedTensorInfoMap, 'x'|'weights'>;\nexport interface DenseBincountAttrs {\n  size: number;\n  binaryOutput?: boolean;\n}\n\nexport const DepthToSpace = 'DepthToSpace';\nexport type DepthToSpaceInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface DepthToSpaceAttrs {\n  blockSize: number;\n  dataFormat: 'NHWC'|'NCHW';\n}\n\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport type DepthwiseConv2dNativeInputs =\n    Pick<NamedTensorInfoMap, 'x'|'filter'>;\nexport interface DepthwiseConv2dNativeAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number|ExplicitPadding;\n  dataFormat: 'NHWC'|'NCHW';\n  dilations: [number, number]|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const DepthwiseConv2dNativeBackpropFilter =\n    'DepthwiseConv2dNativeBackpropFilter';\nexport type DepthwiseConv2dNativeBackpropFilterInputs =\n    Pick<NamedTensorInfoMap, 'x'|'dy'>;\nexport interface DepthwiseConv2dNativeBackpropFilterAttrs {\n  strides: [number, number]|number;\n  dilations: [number, number]|number;\n  pad: 'valid'|'same'|number|ExplicitPadding;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n  filterShape: [number, number, number, number];\n}\n\nexport const DepthwiseConv2dNativeBackpropInput =\n    'DepthwiseConv2dNativeBackpropInput';\nexport type DepthwiseConv2dNativeBackpropInputInputs =\n    Pick<NamedTensorInfoMap, 'dy'|'filter'>;\nexport interface DepthwiseConv2dNativeBackpropInputAttrs {\n  strides: [number, number]|number;\n  dilations: [number, number]|number;\n  pad: 'valid'|'same'|number|ExplicitPadding;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n  inputShape: [number, number, number, number];\n}\n\nexport const Diag = 'Diag';\nexport type DiagInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const Dilation2D = 'Dilation2D';\nexport type Dilation2DInputs = Pick<NamedTensorInfoMap, 'x'|'filter'>;\nexport interface Dilation2DAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  dilations: [number, number]|number;\n}\n\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport type Dilation2DBackpropInputInputs =\n    Pick<NamedTensorInfoMap, 'x'|'filter'|'dy'>;\n\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport type Dilation2DBackpropFilterInputs =\n    Pick<NamedTensorInfoMap, 'x'|'filter'|'dy'>;\n\nexport const RealDiv = 'RealDiv';\nexport type RealDivInputs = BinaryInputs;\n\nexport const Einsum = 'Einsum';\nexport type EinsumInputs = TensorInfo[];\nexport interface EinsumAttrs {\n  equation: string;\n}\n\nexport const Elu = 'Elu';\nexport type EluInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const EluGrad = 'EluGrad';\nexport type EluGradInputs = Pick<NamedTensorInfoMap, 'dy'|'y'>;\n\nexport const Erf = 'Erf';\nexport type ErfInputs = UnaryInputs;\n\nexport const Equal = 'Equal';\nexport type EqualInputs = BinaryInputs;\n\nexport const Exp = 'Exp';\nexport type ExpInputs = UnaryInputs;\n\nexport const ExpandDims = 'ExpandDims';\nexport type ExpandDimsInputs = Pick<NamedTensorInfoMap, 'input'>;\nexport interface ExpandDimsAttrs {\n  dim: number;\n}\n\nexport const Expm1 = 'Expm1';\nexport type Expm1Inputs = UnaryInputs;\n\nexport const FFT = 'FFT';\nexport type FFTInputs = Pick<NamedTensorInfoMap, 'input'>;\n\nexport const Fill = 'Fill';\nexport interface FillAttrs {\n  shape: number[];\n  value: number|string;\n  dtype: DataType;\n}\n\nexport const FlipLeftRight = 'FlipLeftRight';\nexport type FlipLeftRightInputs = Pick<NamedTensorInfoMap, 'image'>;\n\nexport const Floor = 'Floor';\nexport type FloorInputs = UnaryInputs;\n\nexport const FloorDiv = 'FloorDiv';\nexport type FloorDivInputs = BinaryInputs;\n\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport type FusedBatchNormInputs =\n    Pick<NamedTensorInfoMap, 'x'|'scale'|'offset'|'mean'|'variance'>;\nexport interface FusedBatchNormAttrs {\n  varianceEpsilon: number;\n}\n\nexport const GatherV2 = 'GatherV2';\nexport type GatherV2Inputs = Pick<NamedTensorInfoMap, 'x'|'indices'>;\nexport interface GatherV2Attrs {\n  axis: number;\n  batchDims: number;\n}\n\nexport const GatherNd = 'GatherNd';\nexport type GatherNdInputs = Pick<NamedTensorInfoMap, 'params'|'indices'>;\n\nexport const Greater = 'Greater';\nexport type GreaterInputs = BinaryInputs;\n\nexport const GreaterEqual = 'GreaterEqual';\nexport type GreaterEqualInputs = BinaryInputs;\n\nexport const Identity = 'Identity';\nexport type IdentityInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const IFFT = 'IFFT';\nexport type IFFTInputs = Pick<NamedTensorInfoMap, 'input'>;\n\nexport const Imag = 'Imag';\nexport type ImagInputs = Pick<NamedTensorInfoMap, 'input'>;\n\nexport const IsFinite = 'IsFinite';\nexport type IsFiniteInputs = UnaryInputs;\n\nexport const IsInf = 'IsInf';\nexport type IsInfInputs = UnaryInputs;\n\nexport const IsNan = 'IsNan';\nexport type IsNanInputs = UnaryInputs;\n\nexport const LeakyRelu = 'LeakyRelu';\nexport type LeakyReluInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface LeakyReluAttrs {\n  alpha: number;\n}\n\nexport const Less = 'Less';\nexport type LessInputs = BinaryInputs;\n\nexport const LessEqual = 'LessEqual';\nexport type LessEqualInputs = BinaryInputs;\n\nexport const LinSpace = 'LinSpace';\nexport interface LinSpaceAttrs {\n  start: number;\n  stop: number;\n  num: number;\n}\nexport const Log = 'Log';\nexport type LogInputs = UnaryInputs;\n\nexport const Log1p = 'Log1p';\nexport type Log1pInputs = UnaryInputs;\n\nexport const LogicalAnd = 'LogicalAnd';\nexport type LogicalAndInputs = BinaryInputs;\n\nexport const LogicalNot = 'LogicalNot';\nexport type LogicalNotInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const LogicalOr = 'LogicalOr';\nexport type LogicalOrInputs = BinaryInputs;\n\nexport const LogSoftmax = 'LogSoftmax';\nexport type LogSoftmaxInputs = Pick<NamedTensorInfoMap, 'logits'>;\nexport interface LogSoftmaxAttrs {\n  axis: number;\n}\n\nexport const LRN = 'LRN';\nexport type LRNInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface LRNAttrs {\n  depthRadius: number;\n  bias: number;\n  alpha: number;\n  beta: number;\n}\n\nexport const LRNGrad = 'LRNGrad';\nexport type LRNGradInputs = Pick<NamedTensorInfoMap, 'x'|'y'|'dy'>;\nexport interface LRNGradAttrs {\n  depthRadius: number;\n  bias: number;\n  alpha: number;\n  beta: number;\n}\n\nexport const Max = 'Max';\nexport type MaxInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MaxAttrs {\n  reductionIndices: number|number[];\n  keepDims: boolean;\n}\n\nexport const Maximum = 'Maximum';\nexport type MaximumInputs = BinaryInputs;\n\nexport const MaxPool = 'MaxPool';\nexport type MaxPoolInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MaxPoolAttrs {\n  filterSize: [number, number]|number;\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPoolGrad = 'MaxPoolGrad';\nexport type MaxPoolGradInputs = Pick<NamedTensorInfoMap, 'dy'|'input'|'output'>;\nexport interface MaxPoolGradAttrs {\n  filterSize: [number, number]|number;\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPool3D = 'MaxPool3D';\nexport type MaxPool3DInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MaxPool3DAttrs {\n  filterSize: [number, number, number]|number;\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same'|number;\n  dataFormat: 'NDHWC'|'NCDHW';\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPool3DGrad = 'MaxPool3DGrad';\nexport type MaxPool3DGradInputs =\n    Pick<NamedTensorInfoMap, 'dy'|'input'|'output'>;\nexport interface MaxPool3DGradAttrs {\n  filterSize: [number, number, number]|number;\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same'|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport type MaxPoolWithArgmaxInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MaxPoolWithArgmaxAttrs {\n  filterSize: [number, number]|number;\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  includeBatchInIndex: boolean;\n}\n\nexport const Mean = 'Mean';\nexport type MeanInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MeanAttrs {\n  axis: number|number[];\n  keepDims: boolean;\n}\n\nexport const Min = 'Min';\nexport type MinInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MinAttrs {\n  axis: number|number[];\n  keepDims: boolean;\n}\n\nexport const Minimum = 'Minimum';\nexport type MinimumInputs = BinaryInputs;\n\nexport const MirrorPad = 'MirrorPad';\nexport type MirrorPadInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MirrorPadAttrs {\n  paddings: Array<[number, number]>;\n  mode: 'reflect'|'symmetric';\n}\n\nexport const Mod = 'Mod';\nexport type ModInputs = BinaryInputs;\n\nexport const Multinomial = 'Multinomial';\nexport type MultinomialInputs = Pick<NamedTensorInfoMap, 'logits'>;\nexport interface MultinomialAttrs {\n  numSamples: number;\n  seed: number;\n  normalized: boolean;\n}\n\nexport const Multiply = 'Multiply';\nexport type MultiplyInputs = BinaryInputs;\n\nexport const Neg = 'Neg';\nexport type NegInputs = UnaryInputs;\n\nexport const NotEqual = 'NotEqual';\nexport type NotEqualInputs = BinaryInputs;\n\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport type NonMaxSuppressionV3Inputs =\n    Pick<NamedTensorInfoMap, 'boxes'|'scores'>;\nexport interface NonMaxSuppressionV3Attrs {\n  maxOutputSize: number;\n  iouThreshold: number;\n  scoreThreshold: number;\n}\n\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport type NonMaxSuppressionV4Inputs =\n    Pick<NamedTensorInfoMap, 'boxes'|'scores'>;\nexport interface NonMaxSuppressionV4Attrs {\n  maxOutputSize: number;\n  iouThreshold: number;\n  scoreThreshold: number;\n  padToMaxOutputSize: boolean;\n}\n\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport type NonMaxSuppressionV5Inputs =\n    Pick<NamedTensorInfoMap, 'boxes'|'scores'>;\nexport interface NonMaxSuppressionV5Attrs {\n  maxOutputSize: number;\n  iouThreshold: number;\n  scoreThreshold: number;\n  softNmsSigma: number;\n}\n\nexport const OnesLike = 'OnesLike';\nexport type OnesLikeInputs = UnaryInputs;\n\nexport const OneHot = 'OneHot';\nexport type OneHotInputs = Pick<NamedTensorInfoMap, 'indices'>;\nexport interface OneHotAttrs {\n  depth: number;\n  onValue: number;\n  offValue: number;\n}\n\nexport const Pack = 'Pack';\nexport type PackInputs = TensorInfo[];\nexport interface PackAttrs {\n  axis: number;\n}\n\nexport const PadV2 = 'PadV2';\nexport type PadV2Inputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface PadV2Attrs {\n  paddings: Array<[number, number]>;\n  constantValue: number;\n}\n\nexport const Pool = 'Pool';\nexport type PoolInputs = Pick<NamedTensorInfoMap, 'input'>;\n\nexport const Pow = 'Pow';\nexport type PowInputs = BinaryInputs;\n\nexport const Prelu = 'Prelu';\nexport type PreluInputs = Pick<NamedTensorInfoMap, 'x'|'alpha'>;\n\nexport const Prod = 'Prod';\nexport type ProdInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface ProdAttrs {\n  axis: number|number[];\n  keepDims: boolean;\n}\n\nexport const Range = 'Range';\nexport interface RangeAttrs {\n  start: number;\n  stop: number;\n  step: number;\n  dtype: 'float32'|'int32';\n}\n\nexport const Real = 'Real';\nexport type RealInputs = Pick<NamedTensorInfoMap, 'input'>;\n\nexport const Reciprocal = 'Reciprocal';\nexport type ReciprocalInputs = UnaryInputs;\n\nexport const Relu = 'Relu';\nexport type ReluInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const Reshape = 'Reshape';\nexport type ReshapeInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface ReshapeAttrs {\n  shape: number[];\n}\n\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport type ResizeNearestNeighborInputs = Pick<NamedTensorInfoMap, 'images'>;\nexport interface ResizeNearestNeighborAttrs {\n  alignCorners: boolean;\n  halfPixelCenters: boolean;\n  size: [number, number];\n}\n\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport type ResizeNearestNeighborGradInputs =\n    Pick<NamedTensorInfoMap, 'images'|'dy'>;\nexport type ResizeNearestNeighborGradAttrs = ResizeNearestNeighborAttrs;\n\nexport const ResizeBilinear = 'ResizeBilinear';\nexport type ResizeBilinearInputs = Pick<NamedTensorInfoMap, 'images'>;\nexport interface ResizeBilinearAttrs {\n  alignCorners: boolean;\n  halfPixelCenters: boolean;\n  size: [number, number];\n}\n\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport type ResizeBilinearGradInputs = Pick<NamedTensorInfoMap, 'images'|'dy'>;\nexport type ResizeBilinearGradAttrs = ResizeBilinearAttrs;\n\nexport const Relu6 = 'Relu6';\nexport type Relu6Inputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const Reverse = 'Reverse';\nexport type ReverseInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface ReverseAttrs {\n  dims: number|number[];\n}\n\nexport const Round = 'Round';\nexport type RoundInputs = UnaryInputs;\n\nexport const Rsqrt = 'Rsqrt';\nexport type RsqrtInputs = UnaryInputs;\n\nexport const ScatterNd = 'ScatterNd';\nexport type ScatterNdInputs = Pick<NamedTensorInfoMap, 'indices'|'updates'>;\nexport interface ScatterNdAttrs {\n  shape: number[];\n}\n\nexport const Select = 'Select';\nexport type SelectInputs = Pick<NamedTensorInfoMap, 'condition'|'t'|'e'>;\n\nexport const Selu = 'Selu';\nexport type SeluInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const Slice = 'Slice';\nexport type SliceInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface SliceAttrs {\n  begin: number|number[];\n  size: number|number[];\n}\nexport const Sin = 'Sin';\nexport type SinInputs = UnaryInputs;\n\nexport const Sinh = 'Sinh';\nexport type SinhInputs = UnaryInputs;\n\nexport const Sign = 'Sign';\nexport type SignInputs = UnaryInputs;\n\nexport const Sigmoid = 'Sigmoid';\nexport type SigmoidInputs = UnaryInputs;\n\nexport const Softplus = 'Softplus';\nexport type SoftplusInputs = UnaryInputs;\n\nexport const Sqrt = 'Sqrt';\nexport type SqrtInputs = UnaryInputs;\n\nexport const Sum = 'Sum';\nexport type SumInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface SumAttrs {\n  axis: number|number[];\n  keepDims: boolean;\n}\n\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport type SpaceToBatchNDInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface SpaceToBatchNDAttrs {\n  blockShape: number[];\n  paddings: number[][];\n}\n\nexport const SplitV = 'SplitV';\nexport type SplitVInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface SplitVAttrs {\n  numOrSizeSplits: number[]|number;\n  axis: number;\n}\n\nexport const Softmax = 'Softmax';\nexport type SoftmaxInputs = Pick<NamedTensorInfoMap, 'logits'>;\nexport interface SoftmaxAttrs {\n  dim: number;\n}\n\nexport const SparseFillEmptyRows = 'SparseFillEmptyRows';\nexport type SparseFillEmptyRowsInputs =\n    Pick<NamedTensorInfoMap, 'indices'|'values'|'denseShape'|'defaultValue'>;\n\nexport const SparseReshape = 'SparseReshape';\nexport type SparseReshapeInputs =\n    Pick<NamedTensorInfoMap, 'inputIndices'|'inputShape'|'newShape'>;\n\nexport const SparseSegmentMean = 'SparseSegmentMean';\nexport type SparseSegmentMeanInputs =\n    Pick<NamedTensorInfoMap, 'data'|'indices'|'segmentIds'>;\n\nexport const SparseSegmentSum = 'SparseSegmentSum';\nexport type SparseSegmentSumInputs =\n    Pick<NamedTensorInfoMap, 'data'|'indices'|'segmentIds'>;\n\nexport const SparseToDense = 'SparseToDense';\nexport type SparseToDenseInputs =\n    Pick<NamedTensorInfoMap, 'sparseIndices'|'sparseValues'|'defaultValue'>;\nexport interface SparseToDenseAttrs {\n  outputShape: number[];\n}\n\nexport const SquaredDifference = 'SquaredDifference';\nexport type SquaredDifferenceInputs = BinaryInputs;\n\nexport const Square = 'Square';\nexport type SquareInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const StridedSlice = 'StridedSlice';\nexport type StridedSliceInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface StridedSliceAttrs {\n  begin: number[];\n  end: number[];\n  strides: number[];\n  beginMask: number;\n  endMask: number;\n  ellipsisMask: number;\n  newAxisMask: number;\n  shrinkAxisMask: number;\n}\n\nexport const StringNGrams = 'StringNGrams';\nexport type StringNGramsInputs = Pick<NamedTensorInfoMap, 'data'|'dataSplits'>;\nexport interface StringNGramsAttrs {\n  separator: string;\n  nGramWidths: number[];\n  leftPad: string;\n  rightPad: string;\n  padWidth: number;\n  preserveShortSequences: boolean;\n}\n\nexport const StringSplit = 'StringSplit';\nexport type StringSplitInputs = Pick<NamedTensorInfoMap, 'input'|'delimiter'>;\nexport interface StringSplitAttrs {\n  skipEmpty: boolean;\n}\n\nexport const StringToHashBucketFast = 'StringToHashBucketFast';\nexport type StringToHashBucketFastInputs = Pick<NamedTensorInfoMap, 'input'>;\nexport interface StringToHashBucketFastAttrs {\n  numBuckets: number;\n}\n\nexport const Sub = 'Sub';\nexport type SubInputs = BinaryInputs;\n\nexport const Tan = 'Tan';\nexport type TanInputs = UnaryInputs;\n\nexport const Tanh = 'Tanh';\nexport type TanhInputs = UnaryInputs;\n\nexport const Tile = 'Tile';\nexport type TileInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface TileAttrs {\n  reps: number[];\n}\n\nexport const TopK = 'TopK';\nexport type TopKInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface TopKAttrs {\n  k: number;\n  sorted: boolean;\n}\n\nexport const Transform = 'Transform';\nexport type TransformInputs = Pick<NamedTensorInfoMap, 'image'|'transforms'>;\nexport interface TransformAttrs {\n  interpolation: 'nearest'|'bilinear';\n  fillMode: 'constant'|'reflect'|'wrap'|'nearest';\n  fillValue: number;\n  outputShape?: [number, number];\n}\n\nexport const Transpose = 'Transpose';\nexport type TransposeInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface TransposeAttrs {\n  perm: number[];\n}\n\nexport const Unique = 'Unique';\nexport type UniqueInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface UniqueAttrs {\n  axis: number;\n}\n\nexport type UnaryInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const Unpack = 'Unpack';\nexport type UnpackInputs = Pick<NamedTensorInfoMap, 'value'>;\nexport interface UnpackAttrs {\n  axis: number;\n}\n\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport type UnsortedSegmentSumInputs =\n    Pick<NamedTensorInfoMap, 'x'|'segmentIds'>;\nexport interface UnsortedSegmentSumAttrs {\n  numSegments: number;\n}\n\nexport const ZerosLike = 'ZerosLike';\nexport type ZerosLikeInputs = UnaryInputs;\n\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport type StepInputs = UnaryInputs;\nexport interface StepAttrs {\n  alpha: number;\n}\n\nexport const FromPixels = 'FromPixels';\nexport interface FromPixelsInputs {\n  pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|\n      HTMLVideoElement|ImageBitmap;\n}\nexport interface FromPixelsAttrs {\n  numChannels: number;\n}\n\nexport const RotateWithOffset = 'RotateWithOffset';\nexport type RotateWithOffsetInputs = Pick<NamedTensorInfoMap, 'image'>;\nexport interface RotateWithOffsetAttrs {\n  radians: number;\n  fillValue: number|[number, number, number];\n  center: number|[number, number];\n}\n\nexport const _FusedMatMul = '_FusedMatMul';\n// tslint:disable-next-line: class-name\nexport interface _FusedMatMulInputs extends NamedTensorInfoMap {\n  a: TensorInfo;\n  b: TensorInfo;\n  bias?: TensorInfo;\n  preluActivationWeights?: TensorInfo;\n}\n// tslint:disable-next-line: class-name\nexport interface _FusedMatMulAttrs {\n  transposeA: boolean;\n  transposeB: boolean;\n  activation: Activation;\n  leakyreluAlpha?: number;\n}\n\nexport const FusedConv2D = 'FusedConv2D';\nexport interface FusedConv2DInputs extends NamedTensorInfoMap {\n  x: TensorInfo;\n  filter: TensorInfo;\n  bias?: TensorInfo;\n  preluActivationWeights?: TensorInfo;\n}\nexport interface FusedConv2DAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number|ExplicitPadding;\n  dataFormat: 'NHWC'|'NCHW';\n  dilations: [number, number]|number;\n  dimRoundingMode: 'floor'|'round'|'ceil';\n  activation: Activation;\n  leakyreluAlpha?: number;\n}\n\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\nexport interface FusedDepthwiseConv2DInputs extends NamedTensorInfoMap {\n  x: TensorInfo;\n  filter: TensorInfo;\n  bias?: TensorInfo;\n  preluActivationWeights?: TensorInfo;\n}\nexport interface FusedDepthwiseConv2DAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number|ExplicitPadding;\n  dataFormat: 'NHWC'|'NCHW';\n  dilations: [number, number]|number;\n  dimRoundingMode: 'floor'|'round'|'ceil';\n  activation: Activation;\n  leakyreluAlpha?: number;\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {env} from './environment';\n\nimport {getGlobal} from './global_util';\nimport {NamedGradientMap} from './tape';\nimport {Tensor} from './tensor';\nimport {DataType, RecursiveArray} from './types';\n\nconst kernelRegistry =\n    getGlobal('kernelRegistry', () => new Map<string, KernelConfig>());\nconst gradRegistry =\n    getGlobal('gradRegistry', () => new Map<string, GradConfig>());\n\nexport type DataId = object;\n\ntype AttributeValue =\n    number|number[]|boolean|boolean[]|string|string[]|NamedAttrMap;\n\n/** These are extra non-tensor/primitive params passed to kernel functions. */\nexport type Attribute = AttributeValue|RecursiveArray<AttributeValue>;\n\n/** Specifies the code to run when executing a kernel. */\nexport type KernelFunc = (params: {\n  inputs: NamedTensorInfoMap,\n  backend: {},\n  attrs?: NamedAttrMap,\n}) => TensorInfo|TensorInfo[];\n\n/** The function to run when computing a gradient during backprop. */\nexport type GradFunc =\n    (dy: Tensor|Tensor[], saved: Tensor[], attrs: NamedAttrMap) =>\n        NamedGradientMap;\n\n/** Function that gets called after the backend initializes. */\nexport type KernelSetupFunc = (backend: {}) => void;\n/** Function that gets called right before the backend is disposed. */\nexport type KernelDisposeFunc = KernelSetupFunc;\n\n/** Config object for registering a kernel in the global registry. */\nexport interface KernelConfig {\n  kernelName: string;\n  backendName: string;\n  kernelFunc: KernelFunc;\n  setupFunc?: KernelSetupFunc;\n  disposeFunc?: KernelDisposeFunc;\n}\n\n/** Config object for registering a gradient in the global registry. */\nexport interface GradConfig {\n  kernelName: string;\n  inputsToSave?: string[];\n  // When saveAllInputs is true, all inputs will be saved. Only use this flag\n  // if inputs is an array of Tensors.\n  saveAllInputs?: boolean;\n  outputsToSave?: boolean[];\n  gradFunc: GradFunc;\n}\n\n/** Holds metadata for a given tensor. */\nexport interface TensorInfo {\n  dataId: DataId;\n  shape: number[];\n  dtype: DataType;\n}\n\nexport interface NamedTensorInfoMap {\n  [name: string]: TensorInfo;\n}\n\nexport interface NamedAttrMap {\n  [name: string]: Attribute;\n}\n\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(\n    kernelName: string, backendName: string): KernelConfig {\n  const key = makeKey(kernelName, backendName);\n  return kernelRegistry.get(key);\n}\n\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName: string): GradConfig {\n  return gradRegistry.get(kernelName);\n}\n\nexport function getKernelsForBackend(backendName: string): KernelConfig[] {\n  const it = kernelRegistry.entries();\n  const result: KernelConfig[] = [];\n\n  while (true) {\n    const {done, value} = it.next();\n    if (done) {\n      break;\n    }\n    const [key, config] = value;\n    const [backend, ] = key.split('_');\n    if (backend === backendName) {\n      result.push(config);\n    }\n  }\n  return result;\n}\n\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config: KernelConfig) {\n  const {kernelName, backendName} = config;\n  const key = makeKey(kernelName, backendName);\n  if (kernelRegistry.has(key)) {\n    console.warn(\n        `The kernel '${kernelName}' for backend ` +\n        `'${backendName}' is already registered`);\n  }\n  kernelRegistry.set(key, config);\n}\n\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config: GradConfig) {\n  const {kernelName} = config;\n\n  if (gradRegistry.has(kernelName)) {\n    // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n    // to debug mode.\n    if (env().getBool('DEBUG')) {\n      console.warn(`Overriding the gradient for '${kernelName}'`);\n    }\n  }\n  gradRegistry.set(kernelName, config);\n}\n\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(\n    kernelName: string, backendName: string): void {\n  const key = makeKey(kernelName, backendName);\n  if (!kernelRegistry.has(key)) {\n    throw new Error(\n        `The kernel '${kernelName}' for backend ` +\n        `'${backendName}' is not registered`);\n  }\n  kernelRegistry.delete(key);\n}\n\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName: string): void {\n  if (!gradRegistry.has(kernelName)) {\n    throw new Error(\n        `The gradient '${kernelName}' for backend is not registered`);\n  }\n  gradRegistry.delete(kernelName);\n}\n\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(\n    registeredBackendName: string, newBackendName: string): void {\n  const kernels = getKernelsForBackend(registeredBackendName);\n  kernels.forEach(kernelConfig => {\n    const newKernelConfig =\n        Object.assign({}, kernelConfig, {backendName: newBackendName});\n    registerKernel(newKernelConfig);\n  });\n}\n\nfunction makeKey(kernelName: string, backendName: string) {\n  return `${backendName}_${kernelName}`;\n}\n","module.exports = Long;\r\n\r\n/**\r\n * wasm optimizations, to do native i64 multiplication and divide\r\n */\r\nvar wasm = null;\r\n\r\ntry {\r\n  wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([\r\n    0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11\r\n  ])), {}).exports;\r\n} catch (e) {\r\n  // no wasm support :(\r\n}\r\n\r\n/**\r\n * Constructs a 64 bit two's-complement integer, given its low and high 32 bit values as *signed* integers.\r\n *  See the from* functions below for more convenient ways of constructing Longs.\r\n * @exports Long\r\n * @class A Long class for representing a 64 bit two's-complement integer value.\r\n * @param {number} low The low (signed) 32 bits of the long\r\n * @param {number} high The high (signed) 32 bits of the long\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @constructor\r\n */\r\nfunction Long(low, high, unsigned) {\r\n\r\n    /**\r\n     * The low 32 bits as a signed value.\r\n     * @type {number}\r\n     */\r\n    this.low = low | 0;\r\n\r\n    /**\r\n     * The high 32 bits as a signed value.\r\n     * @type {number}\r\n     */\r\n    this.high = high | 0;\r\n\r\n    /**\r\n     * Whether unsigned or not.\r\n     * @type {boolean}\r\n     */\r\n    this.unsigned = !!unsigned;\r\n}\r\n\r\n// The internal representation of a long is the two given signed, 32-bit values.\r\n// We use 32-bit pieces because these are the size of integers on which\r\n// Javascript performs bit-operations.  For operations like addition and\r\n// multiplication, we split each number into 16 bit pieces, which can easily be\r\n// multiplied within Javascript's floating-point representation without overflow\r\n// or change in sign.\r\n//\r\n// In the algorithms below, we frequently reduce the negative case to the\r\n// positive case by negating the input(s) and then post-processing the result.\r\n// Note that we must ALWAYS check specially whether those values are MIN_VALUE\r\n// (-2^63) because -MIN_VALUE == MIN_VALUE (since 2^63 cannot be represented as\r\n// a positive number, it overflows back into a negative).  Not handling this\r\n// case would often result in infinite recursion.\r\n//\r\n// Common constant values ZERO, ONE, NEG_ONE, etc. are defined below the from*\r\n// methods on which they depend.\r\n\r\n/**\r\n * An indicator used to reliably determine if an object is a Long or not.\r\n * @type {boolean}\r\n * @const\r\n * @private\r\n */\r\nLong.prototype.__isLong__;\r\n\r\nObject.defineProperty(Long.prototype, \"__isLong__\", { value: true });\r\n\r\n/**\r\n * @function\r\n * @param {*} obj Object\r\n * @returns {boolean}\r\n * @inner\r\n */\r\nfunction isLong(obj) {\r\n    return (obj && obj[\"__isLong__\"]) === true;\r\n}\r\n\r\n/**\r\n * Tests if the specified object is a Long.\r\n * @function\r\n * @param {*} obj Object\r\n * @returns {boolean}\r\n */\r\nLong.isLong = isLong;\r\n\r\n/**\r\n * A cache of the Long representations of small integer values.\r\n * @type {!Object}\r\n * @inner\r\n */\r\nvar INT_CACHE = {};\r\n\r\n/**\r\n * A cache of the Long representations of small unsigned integer values.\r\n * @type {!Object}\r\n * @inner\r\n */\r\nvar UINT_CACHE = {};\r\n\r\n/**\r\n * @param {number} value\r\n * @param {boolean=} unsigned\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromInt(value, unsigned) {\r\n    var obj, cachedObj, cache;\r\n    if (unsigned) {\r\n        value >>>= 0;\r\n        if (cache = (0 <= value && value < 256)) {\r\n            cachedObj = UINT_CACHE[value];\r\n            if (cachedObj)\r\n                return cachedObj;\r\n        }\r\n        obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true);\r\n        if (cache)\r\n            UINT_CACHE[value] = obj;\r\n        return obj;\r\n    } else {\r\n        value |= 0;\r\n        if (cache = (-128 <= value && value < 128)) {\r\n            cachedObj = INT_CACHE[value];\r\n            if (cachedObj)\r\n                return cachedObj;\r\n        }\r\n        obj = fromBits(value, value < 0 ? -1 : 0, false);\r\n        if (cache)\r\n            INT_CACHE[value] = obj;\r\n        return obj;\r\n    }\r\n}\r\n\r\n/**\r\n * Returns a Long representing the given 32 bit integer value.\r\n * @function\r\n * @param {number} value The 32 bit integer in question\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {!Long} The corresponding Long value\r\n */\r\nLong.fromInt = fromInt;\r\n\r\n/**\r\n * @param {number} value\r\n * @param {boolean=} unsigned\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromNumber(value, unsigned) {\r\n    if (isNaN(value))\r\n        return unsigned ? UZERO : ZERO;\r\n    if (unsigned) {\r\n        if (value < 0)\r\n            return UZERO;\r\n        if (value >= TWO_PWR_64_DBL)\r\n            return MAX_UNSIGNED_VALUE;\r\n    } else {\r\n        if (value <= -TWO_PWR_63_DBL)\r\n            return MIN_VALUE;\r\n        if (value + 1 >= TWO_PWR_63_DBL)\r\n            return MAX_VALUE;\r\n    }\r\n    if (value < 0)\r\n        return fromNumber(-value, unsigned).neg();\r\n    return fromBits((value % TWO_PWR_32_DBL) | 0, (value / TWO_PWR_32_DBL) | 0, unsigned);\r\n}\r\n\r\n/**\r\n * Returns a Long representing the given value, provided that it is a finite number. Otherwise, zero is returned.\r\n * @function\r\n * @param {number} value The number in question\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {!Long} The corresponding Long value\r\n */\r\nLong.fromNumber = fromNumber;\r\n\r\n/**\r\n * @param {number} lowBits\r\n * @param {number} highBits\r\n * @param {boolean=} unsigned\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromBits(lowBits, highBits, unsigned) {\r\n    return new Long(lowBits, highBits, unsigned);\r\n}\r\n\r\n/**\r\n * Returns a Long representing the 64 bit integer that comes by concatenating the given low and high bits. Each is\r\n *  assumed to use 32 bits.\r\n * @function\r\n * @param {number} lowBits The low 32 bits\r\n * @param {number} highBits The high 32 bits\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {!Long} The corresponding Long value\r\n */\r\nLong.fromBits = fromBits;\r\n\r\n/**\r\n * @function\r\n * @param {number} base\r\n * @param {number} exponent\r\n * @returns {number}\r\n * @inner\r\n */\r\nvar pow_dbl = Math.pow; // Used 4 times (4*8 to 15+4)\r\n\r\n/**\r\n * @param {string} str\r\n * @param {(boolean|number)=} unsigned\r\n * @param {number=} radix\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromString(str, unsigned, radix) {\r\n    if (str.length === 0)\r\n        throw Error('empty string');\r\n    if (str === \"NaN\" || str === \"Infinity\" || str === \"+Infinity\" || str === \"-Infinity\")\r\n        return ZERO;\r\n    if (typeof unsigned === 'number') {\r\n        // For goog.math.long compatibility\r\n        radix = unsigned,\r\n        unsigned = false;\r\n    } else {\r\n        unsigned = !! unsigned;\r\n    }\r\n    radix = radix || 10;\r\n    if (radix < 2 || 36 < radix)\r\n        throw RangeError('radix');\r\n\r\n    var p;\r\n    if ((p = str.indexOf('-')) > 0)\r\n        throw Error('interior hyphen');\r\n    else if (p === 0) {\r\n        return fromString(str.substring(1), unsigned, radix).neg();\r\n    }\r\n\r\n    // Do several (8) digits each time through the loop, so as to\r\n    // minimize the calls to the very expensive emulated div.\r\n    var radixToPower = fromNumber(pow_dbl(radix, 8));\r\n\r\n    var result = ZERO;\r\n    for (var i = 0; i < str.length; i += 8) {\r\n        var size = Math.min(8, str.length - i),\r\n            value = parseInt(str.substring(i, i + size), radix);\r\n        if (size < 8) {\r\n            var power = fromNumber(pow_dbl(radix, size));\r\n            result = result.mul(power).add(fromNumber(value));\r\n        } else {\r\n            result = result.mul(radixToPower);\r\n            result = result.add(fromNumber(value));\r\n        }\r\n    }\r\n    result.unsigned = unsigned;\r\n    return result;\r\n}\r\n\r\n/**\r\n * Returns a Long representation of the given string, written using the specified radix.\r\n * @function\r\n * @param {string} str The textual representation of the Long\r\n * @param {(boolean|number)=} unsigned Whether unsigned or not, defaults to signed\r\n * @param {number=} radix The radix in which the text is written (2-36), defaults to 10\r\n * @returns {!Long} The corresponding Long value\r\n */\r\nLong.fromString = fromString;\r\n\r\n/**\r\n * @function\r\n * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val\r\n * @param {boolean=} unsigned\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromValue(val, unsigned) {\r\n    if (typeof val === 'number')\r\n        return fromNumber(val, unsigned);\r\n    if (typeof val === 'string')\r\n        return fromString(val, unsigned);\r\n    // Throws for non-objects, converts non-instanceof Long:\r\n    return fromBits(val.low, val.high, typeof unsigned === 'boolean' ? unsigned : val.unsigned);\r\n}\r\n\r\n/**\r\n * Converts the specified value to a Long using the appropriate from* function for its type.\r\n * @function\r\n * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val Value\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {!Long}\r\n */\r\nLong.fromValue = fromValue;\r\n\r\n// NOTE: the compiler should inline these constant values below and then remove these variables, so there should be\r\n// no runtime penalty for these.\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_16_DBL = 1 << 16;\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_24_DBL = 1 << 24;\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;\r\n\r\n/**\r\n * @type {!Long}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar ZERO = fromInt(0);\r\n\r\n/**\r\n * Signed zero.\r\n * @type {!Long}\r\n */\r\nLong.ZERO = ZERO;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar UZERO = fromInt(0, true);\r\n\r\n/**\r\n * Unsigned zero.\r\n * @type {!Long}\r\n */\r\nLong.UZERO = UZERO;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar ONE = fromInt(1);\r\n\r\n/**\r\n * Signed one.\r\n * @type {!Long}\r\n */\r\nLong.ONE = ONE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar UONE = fromInt(1, true);\r\n\r\n/**\r\n * Unsigned one.\r\n * @type {!Long}\r\n */\r\nLong.UONE = UONE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar NEG_ONE = fromInt(-1);\r\n\r\n/**\r\n * Signed negative one.\r\n * @type {!Long}\r\n */\r\nLong.NEG_ONE = NEG_ONE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar MAX_VALUE = fromBits(0xFFFFFFFF|0, 0x7FFFFFFF|0, false);\r\n\r\n/**\r\n * Maximum signed value.\r\n * @type {!Long}\r\n */\r\nLong.MAX_VALUE = MAX_VALUE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar MAX_UNSIGNED_VALUE = fromBits(0xFFFFFFFF|0, 0xFFFFFFFF|0, true);\r\n\r\n/**\r\n * Maximum unsigned value.\r\n * @type {!Long}\r\n */\r\nLong.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar MIN_VALUE = fromBits(0, 0x80000000|0, false);\r\n\r\n/**\r\n * Minimum signed value.\r\n * @type {!Long}\r\n */\r\nLong.MIN_VALUE = MIN_VALUE;\r\n\r\n/**\r\n * @alias Long.prototype\r\n * @inner\r\n */\r\nvar LongPrototype = Long.prototype;\r\n\r\n/**\r\n * Converts the Long to a 32 bit integer, assuming it is a 32 bit integer.\r\n * @returns {number}\r\n */\r\nLongPrototype.toInt = function toInt() {\r\n    return this.unsigned ? this.low >>> 0 : this.low;\r\n};\r\n\r\n/**\r\n * Converts the Long to a the nearest floating-point representation of this value (double, 53 bit mantissa).\r\n * @returns {number}\r\n */\r\nLongPrototype.toNumber = function toNumber() {\r\n    if (this.unsigned)\r\n        return ((this.high >>> 0) * TWO_PWR_32_DBL) + (this.low >>> 0);\r\n    return this.high * TWO_PWR_32_DBL + (this.low >>> 0);\r\n};\r\n\r\n/**\r\n * Converts the Long to a string written in the specified radix.\r\n * @param {number=} radix Radix (2-36), defaults to 10\r\n * @returns {string}\r\n * @override\r\n * @throws {RangeError} If `radix` is out of range\r\n */\r\nLongPrototype.toString = function toString(radix) {\r\n    radix = radix || 10;\r\n    if (radix < 2 || 36 < radix)\r\n        throw RangeError('radix');\r\n    if (this.isZero())\r\n        return '0';\r\n    if (this.isNegative()) { // Unsigned Longs are never negative\r\n        if (this.eq(MIN_VALUE)) {\r\n            // We need to change the Long value before it can be negated, so we remove\r\n            // the bottom-most digit in this base and then recurse to do the rest.\r\n            var radixLong = fromNumber(radix),\r\n                div = this.div(radixLong),\r\n                rem1 = div.mul(radixLong).sub(this);\r\n            return div.toString(radix) + rem1.toInt().toString(radix);\r\n        } else\r\n            return '-' + this.neg().toString(radix);\r\n    }\r\n\r\n    // Do several (6) digits each time through the loop, so as to\r\n    // minimize the calls to the very expensive emulated div.\r\n    var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned),\r\n        rem = this;\r\n    var result = '';\r\n    while (true) {\r\n        var remDiv = rem.div(radixToPower),\r\n            intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0,\r\n            digits = intval.toString(radix);\r\n        rem = remDiv;\r\n        if (rem.isZero())\r\n            return digits + result;\r\n        else {\r\n            while (digits.length < 6)\r\n                digits = '0' + digits;\r\n            result = '' + digits + result;\r\n        }\r\n    }\r\n};\r\n\r\n/**\r\n * Gets the high 32 bits as a signed integer.\r\n * @returns {number} Signed high bits\r\n */\r\nLongPrototype.getHighBits = function getHighBits() {\r\n    return this.high;\r\n};\r\n\r\n/**\r\n * Gets the high 32 bits as an unsigned integer.\r\n * @returns {number} Unsigned high bits\r\n */\r\nLongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {\r\n    return this.high >>> 0;\r\n};\r\n\r\n/**\r\n * Gets the low 32 bits as a signed integer.\r\n * @returns {number} Signed low bits\r\n */\r\nLongPrototype.getLowBits = function getLowBits() {\r\n    return this.low;\r\n};\r\n\r\n/**\r\n * Gets the low 32 bits as an unsigned integer.\r\n * @returns {number} Unsigned low bits\r\n */\r\nLongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {\r\n    return this.low >>> 0;\r\n};\r\n\r\n/**\r\n * Gets the number of bits needed to represent the absolute value of this Long.\r\n * @returns {number}\r\n */\r\nLongPrototype.getNumBitsAbs = function getNumBitsAbs() {\r\n    if (this.isNegative()) // Unsigned Longs are never negative\r\n        return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();\r\n    var val = this.high != 0 ? this.high : this.low;\r\n    for (var bit = 31; bit > 0; bit--)\r\n        if ((val & (1 << bit)) != 0)\r\n            break;\r\n    return this.high != 0 ? bit + 33 : bit + 1;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value equals zero.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isZero = function isZero() {\r\n    return this.high === 0 && this.low === 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value equals zero. This is an alias of {@link Long#isZero}.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.eqz = LongPrototype.isZero;\r\n\r\n/**\r\n * Tests if this Long's value is negative.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isNegative = function isNegative() {\r\n    return !this.unsigned && this.high < 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is positive.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isPositive = function isPositive() {\r\n    return this.unsigned || this.high >= 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is odd.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isOdd = function isOdd() {\r\n    return (this.low & 1) === 1;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is even.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isEven = function isEven() {\r\n    return (this.low & 1) === 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value equals the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.equals = function equals(other) {\r\n    if (!isLong(other))\r\n        other = fromValue(other);\r\n    if (this.unsigned !== other.unsigned && (this.high >>> 31) === 1 && (other.high >>> 31) === 1)\r\n        return false;\r\n    return this.high === other.high && this.low === other.low;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value equals the specified's. This is an alias of {@link Long#equals}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.eq = LongPrototype.equals;\r\n\r\n/**\r\n * Tests if this Long's value differs from the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.notEquals = function notEquals(other) {\r\n    return !this.eq(/* validates */ other);\r\n};\r\n\r\n/**\r\n * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.neq = LongPrototype.notEquals;\r\n\r\n/**\r\n * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.ne = LongPrototype.notEquals;\r\n\r\n/**\r\n * Tests if this Long's value is less than the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.lessThan = function lessThan(other) {\r\n    return this.comp(/* validates */ other) < 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is less than the specified's. This is an alias of {@link Long#lessThan}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.lt = LongPrototype.lessThan;\r\n\r\n/**\r\n * Tests if this Long's value is less than or equal the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {\r\n    return this.comp(/* validates */ other) <= 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.lte = LongPrototype.lessThanOrEqual;\r\n\r\n/**\r\n * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.le = LongPrototype.lessThanOrEqual;\r\n\r\n/**\r\n * Tests if this Long's value is greater than the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.greaterThan = function greaterThan(other) {\r\n    return this.comp(/* validates */ other) > 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is greater than the specified's. This is an alias of {@link Long#greaterThan}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.gt = LongPrototype.greaterThan;\r\n\r\n/**\r\n * Tests if this Long's value is greater than or equal the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {\r\n    return this.comp(/* validates */ other) >= 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.gte = LongPrototype.greaterThanOrEqual;\r\n\r\n/**\r\n * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.ge = LongPrototype.greaterThanOrEqual;\r\n\r\n/**\r\n * Compares this Long's value with the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {number} 0 if they are the same, 1 if the this is greater and -1\r\n *  if the given one is greater\r\n */\r\nLongPrototype.compare = function compare(other) {\r\n    if (!isLong(other))\r\n        other = fromValue(other);\r\n    if (this.eq(other))\r\n        return 0;\r\n    var thisNeg = this.isNegative(),\r\n        otherNeg = other.isNegative();\r\n    if (thisNeg && !otherNeg)\r\n        return -1;\r\n    if (!thisNeg && otherNeg)\r\n        return 1;\r\n    // At this point the sign bits are the same\r\n    if (!this.unsigned)\r\n        return this.sub(other).isNegative() ? -1 : 1;\r\n    // Both are positive if at least one is unsigned\r\n    return (other.high >>> 0) > (this.high >>> 0) || (other.high === this.high && (other.low >>> 0) > (this.low >>> 0)) ? -1 : 1;\r\n};\r\n\r\n/**\r\n * Compares this Long's value with the specified's. This is an alias of {@link Long#compare}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {number} 0 if they are the same, 1 if the this is greater and -1\r\n *  if the given one is greater\r\n */\r\nLongPrototype.comp = LongPrototype.compare;\r\n\r\n/**\r\n * Negates this Long's value.\r\n * @returns {!Long} Negated Long\r\n */\r\nLongPrototype.negate = function negate() {\r\n    if (!this.unsigned && this.eq(MIN_VALUE))\r\n        return MIN_VALUE;\r\n    return this.not().add(ONE);\r\n};\r\n\r\n/**\r\n * Negates this Long's value. This is an alias of {@link Long#negate}.\r\n * @function\r\n * @returns {!Long} Negated Long\r\n */\r\nLongPrototype.neg = LongPrototype.negate;\r\n\r\n/**\r\n * Returns the sum of this and the specified Long.\r\n * @param {!Long|number|string} addend Addend\r\n * @returns {!Long} Sum\r\n */\r\nLongPrototype.add = function add(addend) {\r\n    if (!isLong(addend))\r\n        addend = fromValue(addend);\r\n\r\n    // Divide each number into 4 chunks of 16 bits, and then sum the chunks.\r\n\r\n    var a48 = this.high >>> 16;\r\n    var a32 = this.high & 0xFFFF;\r\n    var a16 = this.low >>> 16;\r\n    var a00 = this.low & 0xFFFF;\r\n\r\n    var b48 = addend.high >>> 16;\r\n    var b32 = addend.high & 0xFFFF;\r\n    var b16 = addend.low >>> 16;\r\n    var b00 = addend.low & 0xFFFF;\r\n\r\n    var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\r\n    c00 += a00 + b00;\r\n    c16 += c00 >>> 16;\r\n    c00 &= 0xFFFF;\r\n    c16 += a16 + b16;\r\n    c32 += c16 >>> 16;\r\n    c16 &= 0xFFFF;\r\n    c32 += a32 + b32;\r\n    c48 += c32 >>> 16;\r\n    c32 &= 0xFFFF;\r\n    c48 += a48 + b48;\r\n    c48 &= 0xFFFF;\r\n    return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the difference of this and the specified Long.\r\n * @param {!Long|number|string} subtrahend Subtrahend\r\n * @returns {!Long} Difference\r\n */\r\nLongPrototype.subtract = function subtract(subtrahend) {\r\n    if (!isLong(subtrahend))\r\n        subtrahend = fromValue(subtrahend);\r\n    return this.add(subtrahend.neg());\r\n};\r\n\r\n/**\r\n * Returns the difference of this and the specified Long. This is an alias of {@link Long#subtract}.\r\n * @function\r\n * @param {!Long|number|string} subtrahend Subtrahend\r\n * @returns {!Long} Difference\r\n */\r\nLongPrototype.sub = LongPrototype.subtract;\r\n\r\n/**\r\n * Returns the product of this and the specified Long.\r\n * @param {!Long|number|string} multiplier Multiplier\r\n * @returns {!Long} Product\r\n */\r\nLongPrototype.multiply = function multiply(multiplier) {\r\n    if (this.isZero())\r\n        return ZERO;\r\n    if (!isLong(multiplier))\r\n        multiplier = fromValue(multiplier);\r\n\r\n    // use wasm support if present\r\n    if (wasm) {\r\n        var low = wasm.mul(this.low,\r\n                           this.high,\r\n                           multiplier.low,\r\n                           multiplier.high);\r\n        return fromBits(low, wasm.get_high(), this.unsigned);\r\n    }\r\n\r\n    if (multiplier.isZero())\r\n        return ZERO;\r\n    if (this.eq(MIN_VALUE))\r\n        return multiplier.isOdd() ? MIN_VALUE : ZERO;\r\n    if (multiplier.eq(MIN_VALUE))\r\n        return this.isOdd() ? MIN_VALUE : ZERO;\r\n\r\n    if (this.isNegative()) {\r\n        if (multiplier.isNegative())\r\n            return this.neg().mul(multiplier.neg());\r\n        else\r\n            return this.neg().mul(multiplier).neg();\r\n    } else if (multiplier.isNegative())\r\n        return this.mul(multiplier.neg()).neg();\r\n\r\n    // If both longs are small, use float multiplication\r\n    if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24))\r\n        return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);\r\n\r\n    // Divide each long into 4 chunks of 16 bits, and then add up 4x4 products.\r\n    // We can skip products that would overflow.\r\n\r\n    var a48 = this.high >>> 16;\r\n    var a32 = this.high & 0xFFFF;\r\n    var a16 = this.low >>> 16;\r\n    var a00 = this.low & 0xFFFF;\r\n\r\n    var b48 = multiplier.high >>> 16;\r\n    var b32 = multiplier.high & 0xFFFF;\r\n    var b16 = multiplier.low >>> 16;\r\n    var b00 = multiplier.low & 0xFFFF;\r\n\r\n    var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\r\n    c00 += a00 * b00;\r\n    c16 += c00 >>> 16;\r\n    c00 &= 0xFFFF;\r\n    c16 += a16 * b00;\r\n    c32 += c16 >>> 16;\r\n    c16 &= 0xFFFF;\r\n    c16 += a00 * b16;\r\n    c32 += c16 >>> 16;\r\n    c16 &= 0xFFFF;\r\n    c32 += a32 * b00;\r\n    c48 += c32 >>> 16;\r\n    c32 &= 0xFFFF;\r\n    c32 += a16 * b16;\r\n    c48 += c32 >>> 16;\r\n    c32 &= 0xFFFF;\r\n    c32 += a00 * b32;\r\n    c48 += c32 >>> 16;\r\n    c32 &= 0xFFFF;\r\n    c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;\r\n    c48 &= 0xFFFF;\r\n    return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the product of this and the specified Long. This is an alias of {@link Long#multiply}.\r\n * @function\r\n * @param {!Long|number|string} multiplier Multiplier\r\n * @returns {!Long} Product\r\n */\r\nLongPrototype.mul = LongPrototype.multiply;\r\n\r\n/**\r\n * Returns this Long divided by the specified. The result is signed if this Long is signed or\r\n *  unsigned if this Long is unsigned.\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Quotient\r\n */\r\nLongPrototype.divide = function divide(divisor) {\r\n    if (!isLong(divisor))\r\n        divisor = fromValue(divisor);\r\n    if (divisor.isZero())\r\n        throw Error('division by zero');\r\n\r\n    // use wasm support if present\r\n    if (wasm) {\r\n        // guard against signed division overflow: the largest\r\n        // negative number / -1 would be 1 larger than the largest\r\n        // positive number, due to two's complement.\r\n        if (!this.unsigned &&\r\n            this.high === -0x80000000 &&\r\n            divisor.low === -1 && divisor.high === -1) {\r\n            // be consistent with non-wasm code path\r\n            return this;\r\n        }\r\n        var low = (this.unsigned ? wasm.div_u : wasm.div_s)(\r\n            this.low,\r\n            this.high,\r\n            divisor.low,\r\n            divisor.high\r\n        );\r\n        return fromBits(low, wasm.get_high(), this.unsigned);\r\n    }\r\n\r\n    if (this.isZero())\r\n        return this.unsigned ? UZERO : ZERO;\r\n    var approx, rem, res;\r\n    if (!this.unsigned) {\r\n        // This section is only relevant for signed longs and is derived from the\r\n        // closure library as a whole.\r\n        if (this.eq(MIN_VALUE)) {\r\n            if (divisor.eq(ONE) || divisor.eq(NEG_ONE))\r\n                return MIN_VALUE;  // recall that -MIN_VALUE == MIN_VALUE\r\n            else if (divisor.eq(MIN_VALUE))\r\n                return ONE;\r\n            else {\r\n                // At this point, we have |other| >= 2, so |this/other| < |MIN_VALUE|.\r\n                var halfThis = this.shr(1);\r\n                approx = halfThis.div(divisor).shl(1);\r\n                if (approx.eq(ZERO)) {\r\n                    return divisor.isNegative() ? ONE : NEG_ONE;\r\n                } else {\r\n                    rem = this.sub(divisor.mul(approx));\r\n                    res = approx.add(rem.div(divisor));\r\n                    return res;\r\n                }\r\n            }\r\n        } else if (divisor.eq(MIN_VALUE))\r\n            return this.unsigned ? UZERO : ZERO;\r\n        if (this.isNegative()) {\r\n            if (divisor.isNegative())\r\n                return this.neg().div(divisor.neg());\r\n            return this.neg().div(divisor).neg();\r\n        } else if (divisor.isNegative())\r\n            return this.div(divisor.neg()).neg();\r\n        res = ZERO;\r\n    } else {\r\n        // The algorithm below has not been made for unsigned longs. It's therefore\r\n        // required to take special care of the MSB prior to running it.\r\n        if (!divisor.unsigned)\r\n            divisor = divisor.toUnsigned();\r\n        if (divisor.gt(this))\r\n            return UZERO;\r\n        if (divisor.gt(this.shru(1))) // 15 >>> 1 = 7 ; with divisor = 8 ; true\r\n            return UONE;\r\n        res = UZERO;\r\n    }\r\n\r\n    // Repeat the following until the remainder is less than other:  find a\r\n    // floating-point that approximates remainder / other *from below*, add this\r\n    // into the result, and subtract it from the remainder.  It is critical that\r\n    // the approximate value is less than or equal to the real value so that the\r\n    // remainder never becomes negative.\r\n    rem = this;\r\n    while (rem.gte(divisor)) {\r\n        // Approximate the result of division. This may be a little greater or\r\n        // smaller than the actual value.\r\n        approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));\r\n\r\n        // We will tweak the approximate result by changing it in the 48-th digit or\r\n        // the smallest non-fractional digit, whichever is larger.\r\n        var log2 = Math.ceil(Math.log(approx) / Math.LN2),\r\n            delta = (log2 <= 48) ? 1 : pow_dbl(2, log2 - 48),\r\n\r\n        // Decrease the approximation until it is smaller than the remainder.  Note\r\n        // that if it is too large, the product overflows and is negative.\r\n            approxRes = fromNumber(approx),\r\n            approxRem = approxRes.mul(divisor);\r\n        while (approxRem.isNegative() || approxRem.gt(rem)) {\r\n            approx -= delta;\r\n            approxRes = fromNumber(approx, this.unsigned);\r\n            approxRem = approxRes.mul(divisor);\r\n        }\r\n\r\n        // We know the answer can't be zero... and actually, zero would cause\r\n        // infinite recursion since we would make no progress.\r\n        if (approxRes.isZero())\r\n            approxRes = ONE;\r\n\r\n        res = res.add(approxRes);\r\n        rem = rem.sub(approxRem);\r\n    }\r\n    return res;\r\n};\r\n\r\n/**\r\n * Returns this Long divided by the specified. This is an alias of {@link Long#divide}.\r\n * @function\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Quotient\r\n */\r\nLongPrototype.div = LongPrototype.divide;\r\n\r\n/**\r\n * Returns this Long modulo the specified.\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Remainder\r\n */\r\nLongPrototype.modulo = function modulo(divisor) {\r\n    if (!isLong(divisor))\r\n        divisor = fromValue(divisor);\r\n\r\n    // use wasm support if present\r\n    if (wasm) {\r\n        var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(\r\n            this.low,\r\n            this.high,\r\n            divisor.low,\r\n            divisor.high\r\n        );\r\n        return fromBits(low, wasm.get_high(), this.unsigned);\r\n    }\r\n\r\n    return this.sub(this.div(divisor).mul(divisor));\r\n};\r\n\r\n/**\r\n * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}.\r\n * @function\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Remainder\r\n */\r\nLongPrototype.mod = LongPrototype.modulo;\r\n\r\n/**\r\n * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}.\r\n * @function\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Remainder\r\n */\r\nLongPrototype.rem = LongPrototype.modulo;\r\n\r\n/**\r\n * Returns the bitwise NOT of this Long.\r\n * @returns {!Long}\r\n */\r\nLongPrototype.not = function not() {\r\n    return fromBits(~this.low, ~this.high, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the bitwise AND of this Long and the specified.\r\n * @param {!Long|number|string} other Other Long\r\n * @returns {!Long}\r\n */\r\nLongPrototype.and = function and(other) {\r\n    if (!isLong(other))\r\n        other = fromValue(other);\r\n    return fromBits(this.low & other.low, this.high & other.high, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the bitwise OR of this Long and the specified.\r\n * @param {!Long|number|string} other Other Long\r\n * @returns {!Long}\r\n */\r\nLongPrototype.or = function or(other) {\r\n    if (!isLong(other))\r\n        other = fromValue(other);\r\n    return fromBits(this.low | other.low, this.high | other.high, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the bitwise XOR of this Long and the given one.\r\n * @param {!Long|number|string} other Other Long\r\n * @returns {!Long}\r\n */\r\nLongPrototype.xor = function xor(other) {\r\n    if (!isLong(other))\r\n        other = fromValue(other);\r\n    return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns this Long with bits shifted to the left by the given amount.\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shiftLeft = function shiftLeft(numBits) {\r\n    if (isLong(numBits))\r\n        numBits = numBits.toInt();\r\n    if ((numBits &= 63) === 0)\r\n        return this;\r\n    else if (numBits < 32)\r\n        return fromBits(this.low << numBits, (this.high << numBits) | (this.low >>> (32 - numBits)), this.unsigned);\r\n    else\r\n        return fromBits(0, this.low << (numBits - 32), this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns this Long with bits shifted to the left by the given amount. This is an alias of {@link Long#shiftLeft}.\r\n * @function\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shl = LongPrototype.shiftLeft;\r\n\r\n/**\r\n * Returns this Long with bits arithmetically shifted to the right by the given amount.\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shiftRight = function shiftRight(numBits) {\r\n    if (isLong(numBits))\r\n        numBits = numBits.toInt();\r\n    if ((numBits &= 63) === 0)\r\n        return this;\r\n    else if (numBits < 32)\r\n        return fromBits((this.low >>> numBits) | (this.high << (32 - numBits)), this.high >> numBits, this.unsigned);\r\n    else\r\n        return fromBits(this.high >> (numBits - 32), this.high >= 0 ? 0 : -1, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns this Long with bits arithmetically shifted to the right by the given amount. This is an alias of {@link Long#shiftRight}.\r\n * @function\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shr = LongPrototype.shiftRight;\r\n\r\n/**\r\n * Returns this Long with bits logically shifted to the right by the given amount.\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {\r\n    if (isLong(numBits))\r\n        numBits = numBits.toInt();\r\n    numBits &= 63;\r\n    if (numBits === 0)\r\n        return this;\r\n    else {\r\n        var high = this.high;\r\n        if (numBits < 32) {\r\n            var low = this.low;\r\n            return fromBits((low >>> numBits) | (high << (32 - numBits)), high >>> numBits, this.unsigned);\r\n        } else if (numBits === 32)\r\n            return fromBits(high, 0, this.unsigned);\r\n        else\r\n            return fromBits(high >>> (numBits - 32), 0, this.unsigned);\r\n    }\r\n};\r\n\r\n/**\r\n * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}.\r\n * @function\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shru = LongPrototype.shiftRightUnsigned;\r\n\r\n/**\r\n * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}.\r\n * @function\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shr_u = LongPrototype.shiftRightUnsigned;\r\n\r\n/**\r\n * Converts this Long to signed.\r\n * @returns {!Long} Signed long\r\n */\r\nLongPrototype.toSigned = function toSigned() {\r\n    if (!this.unsigned)\r\n        return this;\r\n    return fromBits(this.low, this.high, false);\r\n};\r\n\r\n/**\r\n * Converts this Long to unsigned.\r\n * @returns {!Long} Unsigned long\r\n */\r\nLongPrototype.toUnsigned = function toUnsigned() {\r\n    if (this.unsigned)\r\n        return this;\r\n    return fromBits(this.low, this.high, true);\r\n};\r\n\r\n/**\r\n * Converts this Long to its byte representation.\r\n * @param {boolean=} le Whether little or big endian, defaults to big endian\r\n * @returns {!Array.<number>} Byte representation\r\n */\r\nLongPrototype.toBytes = function toBytes(le) {\r\n    return le ? this.toBytesLE() : this.toBytesBE();\r\n};\r\n\r\n/**\r\n * Converts this Long to its little endian byte representation.\r\n * @returns {!Array.<number>} Little endian byte representation\r\n */\r\nLongPrototype.toBytesLE = function toBytesLE() {\r\n    var hi = this.high,\r\n        lo = this.low;\r\n    return [\r\n        lo        & 0xff,\r\n        lo >>>  8 & 0xff,\r\n        lo >>> 16 & 0xff,\r\n        lo >>> 24       ,\r\n        hi        & 0xff,\r\n        hi >>>  8 & 0xff,\r\n        hi >>> 16 & 0xff,\r\n        hi >>> 24\r\n    ];\r\n};\r\n\r\n/**\r\n * Converts this Long to its big endian byte representation.\r\n * @returns {!Array.<number>} Big endian byte representation\r\n */\r\nLongPrototype.toBytesBE = function toBytesBE() {\r\n    var hi = this.high,\r\n        lo = this.low;\r\n    return [\r\n        hi >>> 24       ,\r\n        hi >>> 16 & 0xff,\r\n        hi >>>  8 & 0xff,\r\n        hi        & 0xff,\r\n        lo >>> 24       ,\r\n        lo >>> 16 & 0xff,\r\n        lo >>>  8 & 0xff,\r\n        lo        & 0xff\r\n    ];\r\n};\r\n\r\n/**\r\n * Creates a Long from its byte representation.\r\n * @param {!Array.<number>} bytes Byte representation\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @param {boolean=} le Whether little or big endian, defaults to big endian\r\n * @returns {Long} The corresponding Long value\r\n */\r\nLong.fromBytes = function fromBytes(bytes, unsigned, le) {\r\n    return le ? Long.fromBytesLE(bytes, unsigned) : Long.fromBytesBE(bytes, unsigned);\r\n};\r\n\r\n/**\r\n * Creates a Long from its little endian byte representation.\r\n * @param {!Array.<number>} bytes Little endian byte representation\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {Long} The corresponding Long value\r\n */\r\nLong.fromBytesLE = function fromBytesLE(bytes, unsigned) {\r\n    return new Long(\r\n        bytes[0]       |\r\n        bytes[1] <<  8 |\r\n        bytes[2] << 16 |\r\n        bytes[3] << 24,\r\n        bytes[4]       |\r\n        bytes[5] <<  8 |\r\n        bytes[6] << 16 |\r\n        bytes[7] << 24,\r\n        unsigned\r\n    );\r\n};\r\n\r\n/**\r\n * Creates a Long from its big endian byte representation.\r\n * @param {!Array.<number>} bytes Big endian byte representation\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {Long} The corresponding Long value\r\n */\r\nLong.fromBytesBE = function fromBytesBE(bytes, unsigned) {\r\n    return new Long(\r\n        bytes[4] << 24 |\r\n        bytes[5] << 16 |\r\n        bytes[6] <<  8 |\r\n        bytes[7],\r\n        bytes[0] << 24 |\r\n        bytes[1] << 16 |\r\n        bytes[2] <<  8 |\r\n        bytes[3],\r\n        unsigned\r\n    );\r\n};\r\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Workaround for allowing cjs module to be included in bundle created by\n// rollup.\nimport * as LongExports from 'long';\n// tslint:disable-next-line\nconst Long: LongExports.LongConstructor =\n    // tslint:disable-next-line\n    (LongExports as any).default || LongExports;\n\nexport function hexToLong(hex: string): Long {\n  return Long.fromString(hex, true, 16);\n}\n\n// Some primes between 2^63 and 2^64 for various uses.\n// Hex 0xc3a5c85c97cb3127\nconst k0: Long = hexToLong('c3a5c85c97cb3127');\n// Hex 0xb492b66fbe98f273\nconst k1: Long = hexToLong('b492b66fbe98f273');\n// Hex 0x9ae16a3b2f90404f\nconst k2: Long = hexToLong('9ae16a3b2f90404f');\n\nfunction shiftMix(val: Long): Long {\n  return val.xor(val.shru(47));\n}\n\nfunction fetch(s: Uint8Array, offset: number, numBytes: number): Long {\n  const bytes = s.slice(offset, offset + numBytes);\n  return Long.fromBytes(Array.from(bytes), true, true);\n}\n\nfunction fetch64(s: Uint8Array, offset: number): Long {\n  return fetch(s, offset, 8);\n}\n\nfunction fetch32(s: Uint8Array, offset: number): Long {\n  return fetch(s, offset, 4);\n}\n\nfunction rotate64(val: Long, shift: number): Long {\n  // Avoid shifting by 64: doing so yields an undefined result.\n  return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));\n}\n\nfunction hashLen16(u: Long, v: Long, mul = hexToLong('9ddfea08eb382d69')) {\n  // Murmur-inspired hashing.\n  let a = u.xor(v).mul(mul);\n  a = a.xor(a.shru(47));\n  let b = v.xor(a).mul(mul);\n  b = b.xor(b.shru(47));\n  b = b.mul(mul);\n  return b;\n}\n\n// Return a 16-byte hash for 48 bytes.  Quick and dirty.\n// Callers do best to use \"random-looking\" values for a and b.\nfunction weakHashLen32WithSeeds(\n    w: Long, x: Long, y: Long, z: Long, a: Long, b: Long) {\n  a = a.add(w);\n  b = rotate64(b.add(a).add(z), 21);\n  const c = a;\n  a = a.add(x);\n  a = a.add(y);\n  b = b.add(rotate64(a, 44));\n  return [a.add(z), b.add(c)];\n}\n\nfunction weakHashLen32WithSeedsStr(\n    s: Uint8Array, offset: number, a: Long, b: Long) {\n  return weakHashLen32WithSeeds(\n      fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16),\n      fetch64(s, offset + 24), a, b);\n}\n\nfunction hashLen0to16(s: Uint8Array, len = s.length): Long {\n  if (len >= 8) {\n    const mul = k2.add(len * 2);\n    const a = fetch64(s, 0).add(k2);\n    const b = fetch64(s, len - 8);\n    const c = rotate64(b, 37).mul(mul).add(a);\n    const d = rotate64(a, 25).add(b).mul(mul);\n    return hashLen16(c, d, mul);\n  }\n  if (len >= 4) {\n    const mul = k2.add(len * 2);\n    const a = fetch32(s, 0);\n    return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul);\n  }\n  if (len > 0) {\n    const a = s[0];\n    const b = s[len >> 1];\n    const c = s[len - 1];\n    const y = a + (b << 8);\n    const z = len + (c << 2);\n    return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);\n  }\n  return k2;\n}\n\nfunction hashLen17to32(s: Uint8Array, len = s.length): Long {\n  const mul = k2.add(len * 2);\n  const a = fetch64(s, 0).mul(k1);\n  const b = fetch64(s, 8);\n  const c = fetch64(s, len - 8).mul(mul);\n  const d = fetch64(s, len - 16).mul(k2);\n  return hashLen16(\n      rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d),\n      a.add(rotate64(b.add(k2), 18)).add(c), mul);\n}\n\nfunction hashLen33to64(s: Uint8Array, len = s.length): Long {\n  const mul = k2.add(len * 2);\n  const a = fetch64(s, 0).mul(k2);\n  const b = fetch64(s, 8);\n  const c = fetch64(s, len - 8).mul(mul);\n  const d = fetch64(s, len - 16).mul(k2);\n  const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d);\n  const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul);\n  const e = fetch64(s, 16).mul(mul);\n  const f = fetch64(s, 24);\n  const g = y.add(fetch64(s, len - 32)).mul(mul);\n  const h = z.add(fetch64(s, len - 24)).mul(mul);\n  return hashLen16(\n      rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h),\n      e.add(rotate64(f.add(a), 18)).add(g), mul);\n}\n\nexport function fingerPrint64(s: Uint8Array, len = s.length): Long {\n  const seed: Long = Long.fromNumber(81, true);\n  if (len <= 32) {\n    if (len <= 16) {\n      return hashLen0to16(s, len);\n    } else {\n      return hashLen17to32(s, len);\n    }\n  } else if (len <= 64) {\n    return hashLen33to64(s, len);\n  }\n\n  // For strings over 64 bytes we loop.  Internal state consists of\n  // 56 bytes: v, w, x, y, and z.\n  let x = seed;\n  let y = seed.mul(k1).add(113);\n\n  let z = shiftMix(y.mul(k2).add(113)).mul(k2);\n  let v = [Long.UZERO, Long.UZERO];\n  let w = [Long.UZERO, Long.UZERO];\n  x = x.mul(k2).add(fetch64(s, 0));\n\n  let offset = 0;\n  // Set end so that after the loop we have 1 to 64 bytes left to process.\n  const end = ((len - 1) >> 6) * 64;\n  const last64 = end + ((len - 1) & 63) - 63;\n\n  do {\n    x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1);\n    y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1);\n    x = x.xor(w[1]);\n    y = y.add(v[0]).add(fetch64(s, offset + 40));\n    z = rotate64(z.add(w[0]), 33).mul(k1);\n    v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0]));\n    w = weakHashLen32WithSeedsStr(\n        s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));\n\n    [z, x] = [x, z];\n    offset += 64;\n  } while (offset !== end);\n  const mul = k1.add(z.and(0xff).shl(1));\n  // Point to the last 64 bytes of input.\n  offset = last64;\n\n  w[0] = w[0].add((len - 1) & 63);\n  v[0] = v[0].add(w[0]);\n  w[0] = w[0].add(v[0]);\n\n  x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul);\n  y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul);\n  x = x.xor(w[1].mul(9));\n  y = y.add(v[0].mul(9).add(fetch64(s, offset + 40)));\n  z = rotate64(z.add(w[0]), 33).mul(mul);\n  v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul), x.add(w[0]));\n  w = weakHashLen32WithSeedsStr(\n      s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));\n\n  [z, x] = [x, z];\n\n  return hashLen16(\n      hashLen16(v[0], w[0], mul).add(shiftMix(y).mul(k0)).add(z),\n      hashLen16(v[1], w[1], mul).add(x), mul);\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from './environment';\nimport {BackendValues, DataType, TensorLike, TypedArray} from './types';\nimport * as base from './util_base';\nexport * from './util_base';\nexport * from './hash_util';\n\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(\n    value: DataType, dtype: DataType): BackendValues {\n  if (dtype === 'string') {\n    return encodeString(value);\n  }\n\n  return toTypedArray([value], dtype);\n}\n\nfunction noConversionNeeded(a: TensorLike, dtype: DataType): boolean {\n  return (a instanceof Float32Array && dtype === 'float32') ||\n      (a instanceof Int32Array && dtype === 'int32') ||\n      (a instanceof Uint8Array && dtype === 'bool');\n}\n\nexport function toTypedArray(a: TensorLike, dtype: DataType): TypedArray {\n  if (dtype === 'string') {\n    throw new Error('Cannot convert a string[] to a TypedArray');\n  }\n  if (Array.isArray(a)) {\n    a = base.flatten(a);\n  }\n\n  if (env().getBool('DEBUG')) {\n    base.checkConversionForErrors(a as number[], dtype);\n  }\n  if (noConversionNeeded(a, dtype)) {\n    return a as TypedArray;\n  }\n  if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n    return new Float32Array(a as number[]);\n  } else if (dtype === 'int32') {\n    return new Int32Array(a as number[]);\n  } else if (dtype === 'bool') {\n    const bool = new Uint8Array((a as number[]).length);\n    for (let i = 0; i < bool.length; ++i) {\n      if (Math.round((a as number[])[i]) !== 0) {\n        bool[i] = 1;\n      }\n    }\n    return bool;\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\n\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now(): number {\n  return env().platform.now();\n}\n\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(\n    path: string, requestInits?: RequestInit): Promise<Response> {\n  return env().platform.fetch(path, requestInits);\n}\n\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s: string, encoding = 'utf-8'): Uint8Array {\n  encoding = encoding || 'utf-8';\n  return env().platform.encode(s, encoding);\n}\n\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes: Uint8Array, encoding = 'utf-8'): string {\n  encoding = encoding || 'utf-8';\n  return env().platform.decode(bytes, encoding);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimer, BackendTimingInfo} from './backends/backend';\nimport {env} from './environment';\nimport {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport {DataType, DataTypeMap, TypedArray} from './types';\nimport * as util from './util';\n\nexport type KernelProfile = {\n  kernelName: string,\n  outputs: Tensor[],\n  inputs: NamedTensorMap,\n  timeMs: Promise<number|{error: string}>,\n  extraInfo: Promise<string>\n};\n\nexport class Profiler {\n  constructor(private backendTimer: BackendTimer, private logger?: Logger) {\n    if (logger == null) {\n      this.logger = new Logger();\n    }\n  }\n\n  profileKernel(kernelName: string, inputs: NamedTensorMap, f: () => Tensor[]):\n      KernelProfile {\n    let outputs: Tensor[];\n    const holdResultWrapperFn = () => {\n      outputs = f();\n    };\n    let timer: Promise<BackendTimingInfo>;\n    const start = util.now();\n    if (this.backendTimer.timerAvailable()) {\n      timer = this.backendTimer.time(holdResultWrapperFn);\n    } else {\n      holdResultWrapperFn();\n      for (const output of outputs) {\n        output.dataSync();\n      }\n      timer = Promise.resolve({kernelMs: util.now() - start});\n    }\n    if (env().getBool('CHECK_COMPUTATION_FOR_ERRORS')) {\n      for (let i = 0; i < outputs.length; i++) {\n        const output = outputs[i];\n        // Dangling promise here because we don't want to propagate up\n        // asynchronicity.\n        output.data().then(tensorVals => {\n          checkComputationForErrors(tensorVals, output.dtype, kernelName);\n        });\n      }\n    }\n\n    const kernelProfile = {\n      kernelName,\n      outputs,\n      inputs,\n      timeMs: timer.then(timing => timing.kernelMs),\n      extraInfo: timer.then(\n          timing => timing.getExtraProfileInfo != null ?\n              timing.getExtraProfileInfo() :\n              '')\n    };\n    return kernelProfile;\n  }\n\n  logKernelProfile(kernelProfile: KernelProfile): void {\n    const {kernelName, outputs, timeMs, inputs, extraInfo} = kernelProfile;\n\n    outputs.forEach(result => {\n      Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n        this.logger.logKernelProfile(\n            kernelName, result, valueContainer[0], valueContainer[1], inputs,\n            valueContainer[2]);\n      });\n    });\n  }\n}\n\nexport function checkComputationForErrors<D extends DataType>(\n    vals: DataTypeMap[D], dtype: D, kernelName: string): boolean {\n  if (dtype !== 'float32') {\n    // Only floating point computations will generate NaN values\n    return false;\n  }\n  for (let i = 0; i < vals.length; i++) {\n    const num = vals[i] as number;\n    if (isNaN(num) || !isFinite(num)) {\n      // Throwing custom exception so behavior is testable.\n      console.warn(`Found ${num} in the result of '${kernelName}'`);\n      return true;\n    }\n  }\n  return false;\n}\n\nexport class Logger {\n  logKernelProfile(\n      name: string, result: Tensor, vals: TypedArray,\n      timeMs: number|{error: string}, inputs: NamedTensorMap,\n      extraInfo?: string) {\n    const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n                                              timeMs['error'];\n    const paddedName = util.rightPad(name, 25);\n    const rank = result.rank;\n    const size = result.size;\n    const shape = util.rightPad(result.shape.toString(), 14);\n    let inputShapesDescription = '';\n\n    for (const name in inputs) {\n      const input = inputs[name];\n      if (input != null) {\n        // The input might be a non-tensor (e.g HTMLImageElement), in which case\n        // we claim the output shape as input shape.\n        const inputShape = input.shape || result.shape;\n        const inputRank = inputShape.length;\n        inputShapesDescription +=\n            `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n      }\n    }\n\n    console.log(\n        `%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${\n            inputShapesDescription}\\t%c${extraInfo}`,\n        'font-weight:bold', 'color:red', 'color:blue', 'color: orange',\n        'color: green', 'color: steelblue');\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport * as util from './util';\n\nexport interface TapeNode {\n  id: number;\n  kernelName: string;\n  outputs: Tensor[];\n  inputs: NamedTensorMap;\n  // Optional params, defined only for ops with gradient impl.\n  gradient?: (dys: Tensor[]) => NamedGradientMap;\n  saved?: Tensor[];\n}\n\nexport type NamedGradientMap = {\n  [inputName: string]: () => Tensor;\n};\n\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(\n    tape: TapeNode[], xs: Tensor[], y: Tensor): TapeNode[] {\n  // Forward pass to compute all the nodes and Tensors that are transitively a\n  // function of x.\n  const tensorsFromX: {[tensorId: number]: boolean} = {};\n  const nodesFromX: {[nodeId: number]: boolean} = {};\n  for (let i = 0; i < xs.length; i++) {\n    tensorsFromX[xs[i].id] = true;\n  }\n\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n    for (const inputName in nodeInputs) {\n      const input = nodeInputs[inputName];\n\n      let anyInputFromX = false;\n      for (let j = 0; j < xs.length; j++) {\n        if (tensorsFromX[input.id]) {\n          node.outputs.forEach(output => tensorsFromX[output.id] = true);\n          anyInputFromX = true;\n          nodesFromX[node.id] = true;\n          break;\n        }\n      }\n\n      if (anyInputFromX) {\n        break;\n      }\n    }\n  }\n\n  // Backward pass to find all of the nodes and Tensors that lead to y.\n  const tensorsLeadToY: {[tensorId: number]: boolean} = {};\n  tensorsLeadToY[y.id] = true;\n  const nodesToY: {[nodeId: number]: boolean} = {};\n\n  for (let i = tape.length - 1; i >= 0; i--) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n\n    // If any of the outputs lead to y, mark all of the inputs as leading to y.\n    for (let j = 0; j < node.outputs.length; j++) {\n      if (tensorsLeadToY[node.outputs[j].id]) {\n        for (const inputName in nodeInputs) {\n          tensorsLeadToY[nodeInputs[inputName].id] = true;\n          nodesToY[node.id] = true;\n        }\n        break;\n      }\n    }\n  }\n\n  // Return the paths that come from x and lead to y.\n  const filteredTape: TapeNode[] = [];\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n\n    if (nodesFromX[node.id] && nodesToY[node.id]) {\n      // Prune the inputs from the node that aren't a function of x.\n      const prunedInputs: {[inputName: string]: Tensor} = {};\n      for (const inputName in node.inputs) {\n        const nodeInput = node.inputs[inputName];\n        if (tensorsFromX[nodeInput.id]) {\n          prunedInputs[inputName] = nodeInput;\n        }\n      }\n\n      // Copy the node and overwrite inputsAndArgs to the pruned version.\n      const prunedNode = Object.assign({}, node);\n      prunedNode.inputs = prunedInputs;\n      prunedNode.outputs = node.outputs;\n\n      filteredTape.push(prunedNode);\n    }\n  }\n\n  return filteredTape;\n}\n\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(\n    tensorAccumulatedGradientMap: {[tensorId: number]: Tensor},\n    filteredTape: TapeNode[], tidy: (f: Function) => Tensor,\n    add: (a: Tensor, b: Tensor) => Tensor) {\n  // Walk the tape backward and keep a map of Tensor to its gradient.\n  for (let i = filteredTape.length - 1; i >= 0; i--) {\n    const node = filteredTape[i];\n\n    const dys: Tensor[] = [];\n    node.outputs.forEach(o => {\n      const gradTensor = tensorAccumulatedGradientMap[o.id];\n      if (gradTensor != null) {\n        dys.push(gradTensor);\n      } else {\n        // This particular output is not in the back-propagation subgraph, so it\n        // does not affect the final output, thus we put null for its dy.\n        dys.push(null);\n      }\n    });\n\n    if (node.gradient == null) {\n      throw new Error(\n          `Cannot compute gradient: gradient function not found ` +\n          `for ${node.kernelName}.`);\n    }\n\n    // Backprop dy through this node and accumulate gradients over the inputs.\n    const inputGradients = node.gradient(dys);\n\n    for (const inputName in node.inputs) {\n      if (!(inputName in inputGradients)) {\n        throw new Error(\n            `Cannot backprop through input ${inputName}. ` +\n            `Available gradients found: ${Object.keys(inputGradients)}.`);\n      }\n\n      // Call the gradient function.\n      const dx = tidy(() => inputGradients[inputName]());\n      if (dx.dtype !== 'float32') {\n        throw new Error(\n            `Error in gradient for op ${\n                node.kernelName}. The gradient of input ` +\n            `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n      }\n      const x = node.inputs[inputName];\n      if (!util.arraysEqual(dx.shape, x.shape)) {\n        throw new Error(\n            `Error in gradient for op ${\n                node.kernelName}. The gradient of input ` +\n            `'${inputName}' has shape '${dx.shape}', which does not match ` +\n            `the shape of the input '${x.shape}'`);\n      }\n\n      if (tensorAccumulatedGradientMap[x.id] == null) {\n        tensorAccumulatedGradientMap[x.id] = dx;\n      } else {\n        const curGradient = tensorAccumulatedGradientMap[x.id];\n        tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n        curGradient.dispose();\n      }\n    }\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, TypedArray} from './types';\nimport {computeStrides, isString, rightPad, sizeFromShape} from './util';\n\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\n\nexport function tensorToString(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    verbose: boolean) {\n  const strides = computeStrides(shape);\n  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n  const rank = shape.length;\n  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n  const lines = ['Tensor'];\n  if (verbose) {\n    lines.push(`  dtype: ${dtype}`);\n    lines.push(`  rank: ${rank}`);\n    lines.push(`  shape: [${shape}]`);\n    lines.push(`  values:`);\n  }\n  lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n  return lines.join('\\n');\n}\n\nfunction computeMaxSizePerColumn(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    strides: number[]): number[] {\n  const n = sizeFromShape(shape);\n  const numCols = strides[strides.length - 1];\n  const padPerCol = new Array(numCols).fill(0);\n  const rank = shape.length;\n  const valuesOrTuples =\n      dtype === 'complex64' ? createComplexTuples(vals) : vals;\n\n  if (rank > 1) {\n    for (let row = 0; row < n / numCols; row++) {\n      const offset = row * numCols;\n      for (let j = 0; j < numCols; j++) {\n        padPerCol[j] = Math.max(\n            padPerCol[j],\n            valToString(valuesOrTuples[offset + j], 0, dtype).length);\n      }\n    }\n  }\n  return padPerCol;\n}\n\nfunction valToString(\n    val: number|string|[number, number], pad: number, dtype: DataType) {\n  let valStr: string;\n  if (Array.isArray(val)) {\n    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n        `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n  } else if (isString(val)) {\n    valStr = `'${val}'`;\n  } else if (dtype === 'bool') {\n    valStr = boolNumToString(val);\n  } else {\n    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n  }\n\n  return rightPad(valStr, pad);\n}\n\nfunction boolNumToString(v: number): string {\n  return v === 0 ? 'false' : 'true';\n}\n\nfunction subTensorToString(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    strides: number[], padPerCol: number[], isLast = true): string[] {\n  const storagePerElement = dtype === 'complex64' ? 2 : 1;\n\n  const size = shape[0];\n  const rank = shape.length;\n  if (rank === 0) {\n    if (dtype === 'complex64') {\n      const complexTuple = createComplexTuples(vals);\n      return [valToString(complexTuple[0], 0, dtype)];\n    }\n    if (dtype === 'bool') {\n      return [boolNumToString(vals[0] as number)];\n    }\n    return [vals[0].toString()];\n  }\n\n  if (rank === 1) {\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n\n      let firstVals = Array.from<number|string|[number, number]>(\n          vals.slice(0, firstValsSize));\n      let lastVals = Array.from<number|string|[number, number]>(vals.slice(\n          (size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement,\n          size * storagePerElement));\n      if (dtype === 'complex64') {\n        firstVals = createComplexTuples(firstVals);\n        lastVals = createComplexTuples(lastVals);\n      }\n      return [\n        '[' +\n        firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n            .join(', ') +\n        ', ..., ' +\n        lastVals\n            .map(\n                (x, i) => valToString(\n                    x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n            .join(', ') +\n        ']'\n      ];\n    }\n    const displayVals: Array<number|string|[number, number]> =\n        dtype === 'complex64' ? createComplexTuples(vals) :\n                                Array.from<number|string>(vals);\n\n    return [\n      '[' +\n      displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n          .join(', ') +\n      ']'\n    ];\n  }\n\n  // The array is rank 2 or more.\n  const subshape = shape.slice(1);\n  const substrides = strides.slice(1);\n  const stride = strides[0] * storagePerElement;\n  const lines: string[] = [];\n  if (size > FORMAT_LIMIT_NUM_VALS) {\n    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          false /* isLast */));\n    }\n    lines.push('...');\n    for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          i === size - 1 /* isLast */));\n    }\n  } else {\n    for (let i = 0; i < size; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          i === size - 1 /* isLast */));\n    }\n  }\n  const sep = rank === 2 ? ',' : '';\n  lines[0] = '[' + lines[0] + sep;\n  for (let i = 1; i < lines.length - 1; i++) {\n    lines[i] = ' ' + lines[i] + sep;\n  }\n  let newLineSep = ',\\n';\n  for (let i = 2; i < rank; i++) {\n    newLineSep += '\\n';\n  }\n  lines[lines.length - 1] =\n      ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n  return lines;\n}\n\nfunction createComplexTuples(vals: Array<{}>|\n                             TypedArray): Array<[number, number]> {\n  const complexTuples: Array<[number, number]> = [];\n  for (let i = 0; i < vals.length; i += 2) {\n    complexTuples.push([vals[i], vals[i + 1]] as [number, number]);\n  }\n  return complexTuples;\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getGlobal} from './global_util';\nimport {tensorToString} from './tensor_format';\nimport {ArrayMap, BackendValues, DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, SingleValueMap, TypedArray} from './types';\nimport * as util from './util';\nimport {computeStrides, toNestedArray} from './util';\n\nexport interface TensorData<D extends DataType> {\n  dataId?: DataId;\n  values?: DataTypeMap[D];\n}\n\n// This interface mimics KernelBackend (in backend.ts), which would create a\n// circular dependency if imported.\nexport interface Backend {}\n\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {\n  size: number;\n  shape: ShapeMap[R];\n  strides: number[];\n  values: DataTypeMap[D];\n\n  constructor(shape: ShapeMap[R], public dtype: D, values?: DataTypeMap[D]) {\n    this.shape = shape.slice() as ShapeMap[R];\n    this.size = util.sizeFromShape(shape);\n\n    if (values != null) {\n      const n = values.length;\n      util.assert(\n          n === this.size,\n          () => `Length of values '${n}' does not match the size ` +\n              `inferred by the shape '${this.size}'.`);\n    }\n    if (dtype === 'complex64') {\n      throw new Error(\n          `complex64 dtype TensorBuffers are not supported. Please create ` +\n          `a TensorBuffer for the real and imaginary parts separately and ` +\n          `call tf.complex(real, imag).`);\n    }\n    this.values = values || util.getArrayFromDType(dtype, this.size);\n    this.strides = computeStrides(shape);\n  }\n\n  /**\n   * Sets a value in the buffer at a given location.\n   *\n   * @param value The value to set.\n   * @param locs  The location indices.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Creation'}\n   */\n  set(value: SingleValueMap[D], ...locs: number[]): void {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    util.assert(\n        locs.length === this.rank,\n        () => `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n\n    const index = this.locToIndex(locs);\n    this.values[index] = value as number;\n  }\n\n  /**\n   * Returns the value in the buffer at the provided location.\n   *\n   * @param locs The location indices.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Creation'}\n   */\n  get(...locs: number[]): SingleValueMap[D] {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    let i = 0;\n    for (const loc of locs) {\n      if (loc < 0 || loc >= this.shape[i]) {\n        const msg = `Requested out of range element at ${locs}. ` +\n            `  Buffer shape=${this.shape}`;\n        throw new Error(msg);\n      }\n      i++;\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return this.values[index] as SingleValueMap[D];\n  }\n\n  locToIndex(locs: number[]): number {\n    if (this.rank === 0) {\n      return 0;\n    } else if (this.rank === 1) {\n      return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return index;\n  }\n\n  indexToLoc(index: number): number[] {\n    if (this.rank === 0) {\n      return [];\n    } else if (this.rank === 1) {\n      return [index];\n    }\n    const locs: number[] = new Array(this.shape.length);\n    for (let i = 0; i < locs.length - 1; ++i) {\n      locs[i] = Math.floor(index / this.strides[i]);\n      index -= locs[i] * this.strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n  }\n\n  get rank() {\n    return this.shape.length;\n  }\n\n  /**\n   * Creates an immutable `tf.Tensor` object from the buffer.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Creation'}\n   */\n  toTensor(): Tensor<R> {\n    return trackerFn().makeTensor(this.values, this.shape, this.dtype) as\n        Tensor<R>;\n  }\n}\n\nexport interface TensorTracker {\n  makeTensor(\n      values: DataValues, shape: number[], dtype: DataType,\n      backend?: Backend): Tensor;\n  makeVariable(\n      initialValue: Tensor, trainable?: boolean, name?: string,\n      dtype?: DataType): Variable;\n  incRef(a: Tensor, backend: Backend): void;\n  disposeTensor(t: Tensor): void;\n  disposeVariable(v: Variable): void;\n  read(dataId: DataId): Promise<BackendValues>;\n  readSync(dataId: DataId): BackendValues;\n}\n\n/**\n * The Tensor class calls into this handler to delegate chaining operations.\n */\nexport interface OpHandler {\n  cast<T extends Tensor>(x: T, dtype: DataType): T;\n  buffer<R extends Rank, D extends DataType>(\n      shape: ShapeMap[R], dtype: D,\n      values?: DataTypeMap[D]): TensorBuffer<R, D>;\n  print<T extends Tensor>(x: T, verbose: boolean): void;\n  clone<T extends Tensor>(x: T): T;\n  // TODO(yassogba) bring reshape back?\n}\n\n// For tracking tensor creation and disposal.\nlet trackerFn: () => TensorTracker = null;\n// Used by chaining methods to call into ops.\nlet opHandler: OpHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn: (msg: string) => void = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn: () => TensorTracker) {\n  trackerFn = fn;\n}\n\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler: OpHandler) {\n  opHandler = handler;\n}\n\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn: (msg: string) => void) {\n  deprecationWarningFn = fn;\n}\n\n/**\n * We wrap data id since we use weak map to avoid memory leaks.\n * Since we have our own memory management, we have a reference counter\n * mapping a tensor to its data, so there is always a pointer (even if that\n * data is otherwise garbage collectable).\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/\n * Global_Objects/WeakMap\n */\nexport type DataId = object;  // object instead of {} to force non-primitive.\n\n// Declare this namespace to make Tensor class augmentation work in google3.\nexport declare namespace Tensor {}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * For performance reasons, functions that create tensors do not necessarily\n * perform a copy of the data passed to them (e.g. if the data is passed as a\n * `Float32Array`), and changes to the data will change the tensor. This is not\n * a feature and is not supported. To avoid this behavior, use the tensor before\n * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor<R extends Rank = Rank> {\n  /** Unique id of this tensor. */\n  readonly id: number;\n  /**\n   * Id of the bucket holding the data for this tensor. Multiple arrays can\n   * point to the same bucket (e.g. when calling array.reshape()).\n   */\n  dataId: DataId;\n  /** The shape of the tensor. */\n  readonly shape: ShapeMap[R];\n  /** Number of elements in the tensor. */\n  readonly size: number;\n  /** The data type for the array. */\n  readonly dtype: DataType;\n  /** The rank type for the array (see `Rank` enum). */\n  readonly rankType: R;\n\n  /** Whether this tensor has been globally kept. */\n  kept = false;\n  /** The id of the scope this tensor is being tracked in. */\n  scopeId: number;\n\n  /**\n   * Number of elements to skip in each dimension when indexing. See\n   * https://docs.scipy.org/doc/numpy/reference/generated/\\\n   * numpy.ndarray.strides.html\n   */\n  readonly strides: number[];\n\n  constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number) {\n    this.shape = shape.slice() as ShapeMap[R];\n    this.dtype = dtype || 'float32';\n    this.size = util.sizeFromShape(shape);\n    this.strides = computeStrides(shape);\n    this.dataId = dataId;\n    this.id = id;\n    this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher') as R;\n  }\n\n  get rank(): number {\n    return this.shape.length;\n  }\n\n  /**\n   * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  async buffer<D extends DataType = 'float32'>(): Promise<TensorBuffer<R, D>> {\n    const vals = await this.data<D>();\n    return opHandler.buffer(this.shape, this.dtype as D, vals);\n  }\n\n  /**\n   * Returns a `tf.TensorBuffer` that holds the underlying data.\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  bufferSync<D extends DataType = 'float32'>(): TensorBuffer<R, D> {\n    return opHandler.buffer(this.shape, this.dtype as D, this.dataSync());\n  }\n\n  /**\n   * Returns the tensor data as a nested array. The transfer of data is done\n   * asynchronously.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  async array(): Promise<ArrayMap[R]> {\n    const vals = await this.data();\n    return toNestedArray(this.shape, vals, this.dtype === 'complex64') as\n        ArrayMap[R];\n  }\n\n  /**\n   * Returns the tensor data as a nested array. The transfer of data is done\n   * synchronously.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  arraySync(): ArrayMap[R] {\n    return toNestedArray(\n               this.shape, this.dataSync(), this.dtype === 'complex64') as\n        ArrayMap[R];\n  }\n\n  /**\n   * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n   * promise of `TypedArray` that resolves when the computation has finished.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  async data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]> {\n    this.throwIfDisposed();\n    const data = trackerFn().read(this.dataId);\n    if (this.dtype === 'string') {\n      const bytes = await data as Uint8Array[];\n      try {\n        return bytes.map(b => util.decodeString(b)) as DataTypeMap[D];\n      } catch {\n        throw new Error(\n            'Failed to decode the string bytes into utf-8. ' +\n            'To get the original bytes, call tensor.bytes().');\n      }\n    }\n    return data as Promise<DataTypeMap[D]>;\n  }\n\n  /**\n   * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n   * UI thread until the values are ready, which can cause performance issues.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D] {\n    this.throwIfDisposed();\n    const data = trackerFn().readSync(this.dataId);\n    if (this.dtype === 'string') {\n      try {\n        return (data as Uint8Array[]).map(b => util.decodeString(b)) as\n            DataTypeMap[D];\n      } catch {\n        throw new Error(\n            'Failed to decode the string bytes into utf-8. ' +\n            'To get the original bytes, call tensor.bytes().');\n      }\n    }\n    return data as DataTypeMap[D];\n  }\n\n  /** Returns the underlying bytes of the tensor's data. */\n  async bytes(): Promise<Uint8Array[]|Uint8Array> {\n    this.throwIfDisposed();\n    const data = await trackerFn().read(this.dataId);\n    if (this.dtype === 'string') {\n      return data as Uint8Array[];\n    } else {\n      return new Uint8Array((data as TypedArray).buffer);\n    }\n  }\n\n  /**\n   * Disposes `tf.Tensor` from memory.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  dispose(): void {\n    if (this.isDisposed) {\n      return;\n    }\n    trackerFn().disposeTensor(this);\n    this.isDisposedInternal = true;\n  }\n\n  protected isDisposedInternal = false;\n  get isDisposed(): boolean {\n    return this.isDisposedInternal;\n  }\n\n  throwIfDisposed() {\n    if (this.isDisposed) {\n      throw new Error(`Tensor is disposed.`);\n    }\n  }\n\n  /**\n   * Prints the `tf.Tensor`. See `tf.print` for details.\n   *\n   * @param verbose Whether to print verbose information about the tensor,\n   *    including dtype and size.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  print(verbose = false): void {\n    return opHandler.print(this, verbose);\n  }\n\n  /**\n   * Returns a copy of the tensor. See `tf.clone` for details.\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  clone<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.clone(this);\n  }\n\n  /**\n   * Returns a human-readable description of the tensor. Useful for logging.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  toString(verbose = false): string {\n    const vals = this.dataSync();\n    return tensorToString(vals, this.shape, this.dtype, verbose);\n  }\n\n  cast<T extends this>(dtype: DataType): T {\n    this.throwIfDisposed();\n    return opHandler.cast(this as T, dtype);\n  }\n  variable(trainable = true, name?: string, dtype?: DataType): Variable<R> {\n    this.throwIfDisposed();\n    return trackerFn().makeVariable(this, trainable, name, dtype) as\n        Variable<R>;\n  }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n  value: (instance: Tensor) => {\n    // Implementation note: we should use properties of the object that will be\n    // defined before the constructor body has finished executing (methods).\n    // This is because when this code is transpiled by babel, babel will call\n    // classCallCheck before the constructor body is run.\n    // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n    return !!instance && instance.data != null && instance.dataSync != null &&\n        instance.throwIfDisposed != null;\n  }\n});\n\nexport function getGlobalTensorClass() {\n  // Use getGlobal so that we can augment the Tensor class across package\n  // boundaries becase the node resolution alg may result in different modules\n  // being returned for this file depending on the path they are loaded from.\n  return getGlobal('Tensor', () => {\n    return Tensor;\n  });\n}\n\n// Global side effect. Cache global reference to Tensor class\ngetGlobalTensorClass();\n\nexport interface NumericTensor<R extends Rank = Rank> extends Tensor<R> {\n  dtype: NumericDataType;\n  dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];\n  data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;\n}\n\nexport interface StringTensor<R extends Rank = Rank> extends Tensor<R> {\n  dtype: 'string';\n  dataSync<D extends DataType = 'string'>(): DataTypeMap[D];\n  data<D extends DataType = 'string'>(): Promise<DataTypeMap[D]>;\n}\n\n/** @doclink Tensor */\nexport type Scalar = Tensor<Rank.R0>;\n/** @doclink Tensor */\nexport type Tensor1D = Tensor<Rank.R1>;\n/** @doclink Tensor */\nexport type Tensor2D = Tensor<Rank.R2>;\n/** @doclink Tensor */\nexport type Tensor3D = Tensor<Rank.R3>;\n/** @doclink Tensor */\nexport type Tensor4D = Tensor<Rank.R4>;\n/** @doclink Tensor */\nexport type Tensor5D = Tensor<Rank.R5>;\n/** @doclink Tensor */\nexport type Tensor6D = Tensor<Rank.R6>;\n\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable<R extends Rank = Rank> extends Tensor<R> {\n  name: string;\n\n  constructor(\n      initialValue: Tensor<R>, public trainable: boolean, name: string,\n      tensorId: number) {\n    super(\n        initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n    this.name = name;\n  }\n\n  /**\n   * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n   * the same shape and dtype as the old `tf.Tensor`.\n   *\n   * @param newValue New tensor to be assigned to this variable.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  assign(newValue: Tensor<R>): void {\n    if (newValue.dtype !== this.dtype) {\n      throw new Error(\n          `dtype of the new value (${newValue.dtype}) and ` +\n          `previous value (${this.dtype}) must match`);\n    }\n    if (!util.arraysEqual(newValue.shape, this.shape)) {\n      throw new Error(\n          `shape of the new value (${newValue.shape}) and ` +\n          `previous value (${this.shape}) must match`);\n    }\n    trackerFn().disposeTensor(this);\n    this.dataId = newValue.dataId;\n    trackerFn().incRef(this, null /* backend */);\n  }\n\n  dispose(): void {\n    trackerFn().disposeVariable(this);\n    this.isDisposedInternal = true;\n  }\n}\n\nObject.defineProperty(Variable, Symbol.hasInstance, {\n  value: (instance: Variable) => {\n    return instance instanceof Tensor && instance.assign != null &&\n        instance.assign instanceof Function;\n  }\n});\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** @docalias number[] */\nexport interface ShapeMap {\n  R0: number[];\n  R1: [number];\n  R2: [number, number];\n  R3: [number, number, number];\n  R4: [number, number, number, number];\n  R5: [number, number, number, number, number];\n  R6: [number, number, number, number, number, number];\n}\n\n/** @docalias number[] */\nexport interface ArrayMap {\n  R0: number;\n  R1: number[];\n  R2: number[][];\n  R3: number[][][];\n  R4: number[][][][];\n  R5: number[][][][][];\n  R6: number[][][][][][];\n}\n\nexport interface DataTypeMap {\n  float32: Float32Array;\n  int32: Int32Array;\n  bool: Uint8Array;\n  complex64: Float32Array;\n  string: string[];\n}\n\nexport interface SingleValueMap {\n  bool: boolean;\n  int32: number;\n  float32: number;\n  complex64: number;\n  string: string;\n}\n\n/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */\nexport type DataType = keyof DataTypeMap;\nexport type NumericDataType = 'float32'|'int32'|'bool'|'complex64';\nexport type TypedArray = Float32Array|Int32Array|Uint8Array;\n/** Tensor data used in tensor creation and user-facing API. */\nexport type DataValues = DataTypeMap[DataType];\n/** The underlying tensor data that gets stored in a backend. */\nexport type BackendValues = Float32Array|Int32Array|Uint8Array|Uint8Array[];\n\nexport enum Rank {\n  R0 = 'R0',\n  R1 = 'R1',\n  R2 = 'R2',\n  R3 = 'R3',\n  R4 = 'R4',\n  R5 = 'R5',\n  R6 = 'R6'\n}\n\nexport type FlatVector = boolean[]|number[]|TypedArray;\nexport type RegularArray<T> =\n    T[]|T[][]|T[][][]|T[][][][]|T[][][][][]|T[][][][][][];\n\n// tslint:disable-next-line:no-any\nexport interface RecursiveArray<T extends any> {\n  [index: number]: T|RecursiveArray<T>;\n}\n\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nenum UpcastInt32AndMap {\n  'float32' = 'float32',\n  'int32' = 'int32',\n  'bool' = 'int32',\n  'complex64' = 'complex64'\n}\n\nenum UpcastBoolAndMap {\n  'float32' = 'float32',\n  'int32' = 'int32',\n  'bool' = 'bool',\n  'complex64' = 'complex64'\n}\n\nenum UpcastFloat32AndMap {\n  'float32' = 'float32',\n  'int32' = 'float32',\n  'bool' = 'float32',\n  'complex64' = 'complex64'\n}\n\nenum UpcastComplex64AndMap {\n  'float32' = 'complex64',\n  'int32' = 'complex64',\n  'bool' = 'complex64',\n  'complex64' = 'complex64'\n}\n\nconst upcastTypeMap = {\n  'float32': UpcastFloat32AndMap,\n  'int32': UpcastInt32AndMap,\n  'bool': UpcastBoolAndMap,\n  'complex64': UpcastComplex64AndMap\n};\n\nexport function upcastType(typeA: DataType, typeB: DataType): DataType {\n  if (typeA === 'string' || typeB === 'string') {\n    if (typeA === 'string' && typeB === 'string') {\n      return 'string';\n    }\n    throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n  }\n  return upcastTypeMap[typeA][typeB];\n}\n\n/** Returns the output type after summation. */\nexport function sumOutType(type: DataType): DataType {\n  return upcastType(type, 'int32');\n}\n\n/** @docalias TypedArray|Array */\nexport type TensorLike =\n    TypedArray|number|boolean|string|RecursiveArray<number|number[]|TypedArray>|\n    RecursiveArray<boolean>|RecursiveArray<string>|Uint8Array[];\nexport type ScalarLike = number|boolean|string|Uint8Array;\n/** @docalias TypedArray|Array */\nexport type TensorLike1D = TypedArray|number[]|boolean[]|string[]|Uint8Array[];\n/** @docalias TypedArray|Array */\nexport type TensorLike2D = TypedArray|number[]|number[][]|boolean[]|boolean[][]|\n    string[]|string[][]|Uint8Array[]|Uint8Array[][];\n/** @docalias TypedArray|Array */\nexport type TensorLike3D = TypedArray|number[]|number[][][]|boolean[]|\n    boolean[][][]|string[]|string[][][]|Uint8Array[]|Uint8Array[][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike4D = TypedArray|number[]|number[][][][]|boolean[]|\n    boolean[][][][]|string[]|string[][][][]|Uint8Array[]|Uint8Array[][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike5D =\n    TypedArray|number[]|number[][][][][]|boolean[]|boolean[][][][][]|string[]|\n    string[][][][][]|Uint8Array[]|Uint8Array[][][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike6D =\n    TypedArray|number[]|number[][][][][][]|boolean[]|boolean[][][][][][]|\n    string[]|string[][][][][][]|Uint8Array[]|Uint8Array[][][][][];\n\n/** Type for representing image data in Uint8Array type. */\nexport interface PixelData {\n  width: number;\n  height: number;\n  data: Uint8Array;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {TensorContainer, TensorContainerArray} from './tensor_types';\nimport {upcastType} from './types';\nimport {assert} from './util';\n\nexport function makeTypesMatch<T extends Tensor>(a: T, b: T): [T, T] {\n  if (a.dtype === b.dtype) {\n    return [a, b];\n  }\n  const dtype = upcastType(a.dtype, b.dtype);\n  return [a.cast(dtype), b.cast(dtype)];\n}\n\nexport function assertTypesMatch(a: Tensor, b: Tensor): void {\n  assert(\n      a.dtype === b.dtype,\n      () => `The dtypes of the first(${a.dtype}) and` +\n          ` second(${b.dtype}) input must match`);\n}\n\nexport function isTensorInList(tensor: Tensor, tensorList: Tensor[]): boolean {\n  return tensorList.some(x => x.id === tensor.id);\n}\n\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result: TensorContainer): Tensor[] {\n  const list: Tensor[] = [];\n  const seen = new Set<{}|void>();\n  walkTensorContainer(result, list, seen);\n  return list;\n}\n\nfunction walkTensorContainer(\n    container: TensorContainer, list: Tensor[], seen: Set<{}|void>): void {\n  if (container == null) {\n    return;\n  }\n  if (container instanceof Tensor) {\n    list.push(container);\n    return;\n  }\n  if (!isIterable(container)) {\n    return;\n  }\n  // Iteration over keys works also for arrays.\n  const iterable = container as TensorContainerArray;\n  for (const k in iterable) {\n    const val = iterable[k];\n    if (!seen.has(val)) {\n      seen.add(val);\n      walkTensorContainer(val, list, seen);\n    }\n  }\n}\n\n// tslint:disable-next-line:no-any\nfunction isIterable(obj: any): boolean {\n  return Array.isArray(obj) || typeof obj === 'object';\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimingInfo, DataMover, KernelBackend} from './backends/backend';\nimport {Environment, setEnvironmentGlobal} from './environment';\nimport {getGlobalNamespace} from './global_util';\nimport {Add, Cast, Identity} from './kernel_names';\nimport {getGradient, getKernel, getKernelsForBackend, GradFunc, NamedAttrMap, TensorInfo} from './kernel_registry';\nimport {KernelProfile, Profiler} from './profiler';\nimport {backpropagateGradients, getFilteredNodesXToY, TapeNode} from './tape';\nimport {DataId, setTensorTracker, Tensor, TensorTracker, Variable} from './tensor';\nimport {GradSaveFunc, NamedTensorMap, NamedVariableMap, TensorContainer} from './tensor_types';\nimport {getTensorsInContainer} from './tensor_util';\nimport {BackendValues, DataType, DataValues} from './types';\nimport * as util from './util';\nimport {bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape} from './util';\n\n/**\n * A function that computes an output. The save function is for saving tensors\n * computed in the forward pass, that we need in the backward pass.\n */\nexport type ForwardFunc<T> = (backend: KernelBackend, save?: GradSaveFunc) => T;\n\n/**\n * @docalias (a: Tensor, b: Tensor,..., save?: Function) => {\n *   value: Tensor,\n *   gradFunc: (dy: Tensor, saved?: NamedTensorMap) => Tensor | Tensor[]\n * }\n */\nexport type CustomGradientFunc<T extends Tensor> =\n    (...inputs: Array<Tensor|GradSaveFunc>) => {\n      value: T;\n      gradFunc: (dy: T, saved: Tensor[]) => Tensor | Tensor[];\n    };\n\nexport type MemoryInfo = {\n  numTensors: number; numDataBuffers: number; numBytes: number;\n  unreliable?: boolean; reasons: string[];\n};\n\ntype KernelInfo = {\n  name: string; bytesAdded: number; totalBytesSnapshot: number;\n  tensorsAdded: number;\n  totalTensorsSnapshot: number;\n  inputShapes: number[][];\n  outputShapes: number[][];\n  kernelTimeMs: number | {error: string} | Promise<number|{error: string}>;\n  extraInfo: string | Promise<string>;\n};\n\nexport type ProfileInfo = {\n  newBytes: number; newTensors: number; peakBytes: number;\n  kernels: KernelInfo[];\n  result: TensorContainer;\n  kernelNames: string[];\n};\n\nexport interface TimingInfo extends BackendTimingInfo {\n  wallMs: number;\n}\n\n/** @docalias Function */\nexport type ScopeFn<T extends TensorContainer> = () => T;\n\ninterface ScopeState {\n  track: Tensor[];\n  name: string;\n  id: number;\n}\n\ninterface RegisteredKernelInvocation<I extends NamedTensorMap> {\n  kernelName: string;\n  inputs: I;\n  attrs?: NamedAttrMap;\n}\n\ninterface CustomGradKernelInvocation<T extends Tensor|Tensor[],\n                                               I extends NamedTensorMap> {\n  forwardFunc: ForwardFunc<T>;\n  backwardsFunc: (dy: T, saved: Tensor[]) => {\n    [P in keyof I]: () => I[P]\n  };\n  inputs: I;\n  attrs?: NamedAttrMap;\n}\n\nfunction isRegisteredKernelInvocation<T extends Tensor|Tensor[],\n                                                I extends NamedTensorMap>(\n    kernelInvocation: RegisteredKernelInvocation<I>|\n    CustomGradKernelInvocation<T, I>):\n    kernelInvocation is RegisteredKernelInvocation<I> {\n  return (kernelInvocation as RegisteredKernelInvocation<I>).kernelName != null;\n}\n\nclass EngineState {\n  // Public since optimizers will use it.\n  registeredVariables: NamedVariableMap = {};\n\n  nextTapeNodeId = 0;\n  numBytes = 0;\n  numTensors = 0;\n  numStringTensors = 0;\n  numDataBuffers = 0;\n\n  activeTape: TapeNode[];\n  // Number of nested tf.grad() statements when computing higher-order\n  // gradients. E.g. `1` for first-order gradients and `2` for second-order\n  // gradients. Used to track if the tape should be removed after a backprop.\n  gradientDepth = 0;\n  // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n  // off the tape.\n  kernelDepth = 0;\n\n  // Keep Tensors that parallel the tapes.\n  activeScope: ScopeState;\n  scopeStack: ScopeState[] = [];\n  /**\n   * Keeps track of the number of data moves during a kernel execution. We\n   * maintain a stack since kernels can call other kernels, recursively.\n   */\n  numDataMovesStack: number[] = [];\n  nextScopeId = 0;\n\n  tensorInfo = new WeakMap<DataId, {\n    backend: KernelBackend,\n    bytes: number,\n    dtype: DataType,\n    shape: number[]\n  }>();\n\n  profiling = false;\n  activeProfile: ProfileInfo = {\n    newBytes: 0,\n    newTensors: 0,\n    peakBytes: 0,\n    kernels: [],\n    result: null,\n    get kernelNames():\n        string[] {\n          return Array.from(new Set(this.kernels.map(k => k.name)));\n        }\n  };\n\n  dispose() {\n    for (const variableName in this.registeredVariables) {\n      this.registeredVariables[variableName].dispose();\n    }\n  }\n}\n\nexport class Engine implements TensorTracker, DataMover {\n  state: EngineState;\n  backendName: string;\n  registry: {[id: string]: KernelBackend} = {};\n  registryFactory: {\n    [id: string]: {\n      factory: () => KernelBackend | Promise<KernelBackend>,\n      priority: number\n    }\n  } = {};\n\n  private profiler: Profiler;\n  private backendInstance: KernelBackend;\n  private pendingBackendInit: Promise<boolean>;\n  private pendingBackendInitId = 0;\n\n  constructor(public ENV: Environment) {\n    this.state = new EngineState();\n  }\n\n  async ready(): Promise<void> {\n    if (this.pendingBackendInit != null) {\n      return this.pendingBackendInit.then(() => {});\n    }\n    if (this.backendInstance != null) {\n      return;\n    }\n    const sortedBackends = this.getSortedBackends();\n\n    for (let i = 0; i < sortedBackends.length; i++) {\n      const backendName = sortedBackends[i];\n      const success = await this.initializeBackend(backendName).success;\n      if (success) {\n        await this.setBackend(backendName);\n        return;\n      }\n    }\n\n    throw new Error(\n        `Could not initialize any backends, all backend initializations ` +\n        `failed.`);\n  }\n\n  get backend(): KernelBackend {\n    if (this.pendingBackendInit != null) {\n      throw new Error(\n          `Backend '${this.backendName}' has not yet been initialized. Make ` +\n          `sure to await tf.ready() or await tf.setBackend() before calling ` +\n          `other methods`);\n    }\n    if (this.backendInstance == null) {\n      const {name, asyncInit} = this.initializeBackendsAndReturnBest();\n      if (asyncInit) {\n        throw new Error(\n            `The highest priority backend '${name}' has not yet been ` +\n            `initialized. Make sure to await tf.ready() or ` +\n            `await tf.setBackend() before calling other methods`);\n      }\n      this.setBackend(name);\n    }\n    return this.backendInstance;\n  }\n\n  backendNames(): string[] {\n    return Object.keys(this.registryFactory);\n  }\n\n  findBackend(backendName: string): KernelBackend {\n    if (!(backendName in this.registry)) {\n      // If the backend hasn't been initialized but we have a registry entry for\n      // it, initialize it and return it.\n      if (backendName in this.registryFactory) {\n        const {asyncInit} = this.initializeBackend(backendName);\n        if (asyncInit) {\n          // Backend is not ready yet.\n          return null;\n        }\n      } else {\n        return null;\n      }\n    }\n    return this.registry[backendName];\n  }\n\n  findBackendFactory(backendName: string):\n      () => KernelBackend | Promise<KernelBackend> {\n    if (!(backendName in this.registryFactory)) {\n      return null;\n    }\n    return this.registryFactory[backendName].factory;\n  }\n\n  registerBackend(\n      backendName: string,\n      factory: () => KernelBackend | Promise<KernelBackend>,\n      priority = 1): boolean {\n    if (backendName in this.registryFactory) {\n      console.warn(\n          `${backendName} backend was already registered. ` +\n          `Reusing existing backend factory.`);\n      return false;\n    }\n    this.registryFactory[backendName] = {factory, priority};\n    return true;\n  }\n\n  async setBackend(backendName: string): Promise<boolean> {\n    if (this.registryFactory[backendName] == null) {\n      throw new Error(`Backend name '${backendName}' not found in registry`);\n    }\n    this.backendName = backendName;\n    if (this.registry[backendName] == null) {\n      this.backendInstance = null;\n      const {success, asyncInit} = this.initializeBackend(backendName);\n      const result = asyncInit ? await success : success;\n      if (!result) {\n        return false;\n      }\n    }\n    this.backendInstance = this.registry[backendName];\n    this.setupRegisteredKernels();\n    // Reset the profiler.\n    this.profiler = new Profiler(this.backendInstance);\n\n    return true;\n  }\n\n  private setupRegisteredKernels(): void {\n    const kernels = getKernelsForBackend(this.backendName);\n    kernels.forEach(kernel => {\n      if (kernel.setupFunc != null) {\n        kernel.setupFunc(this.backendInstance);\n      }\n    });\n  }\n\n  private disposeRegisteredKernels(backendName: string): void {\n    const kernels = getKernelsForBackend(backendName);\n    kernels.forEach(kernel => {\n      if (kernel.disposeFunc != null) {\n        kernel.disposeFunc(this.registry[backendName]);\n      }\n    });\n  }\n\n  /**\n   * Initializes a backend by looking up the backend name in the factory\n   * registry and calling the factory method. Returns a boolean representing\n   * whether the initialization of the backend suceeded. Throws an error if\n   * there is no backend in the factory registry.\n   */\n  private initializeBackend(backendName: string):\n      {success: boolean|Promise<boolean>, asyncInit: boolean} {\n    const registryFactoryEntry = this.registryFactory[backendName];\n    if (registryFactoryEntry == null) {\n      throw new Error(\n          `Cannot initialize backend ${backendName}, no registration found.`);\n    }\n\n    try {\n      const backend = registryFactoryEntry.factory();\n      /* Test if the factory returns a promise.\n      Done in a more liberal way than\n      previous 'Promise.resolve(backend)===backend'\n      as we needed to account for custom Promise\n      implementations (e.g. Angular) */\n      if (backend && !(backend instanceof KernelBackend) &&\n          typeof backend.then === 'function') {\n        const promiseId = ++this.pendingBackendInitId;\n        const success =\n            backend\n                .then(backendInstance => {\n                  // Outdated promise. Another backend was set in the meantime.\n                  if (promiseId < this.pendingBackendInitId) {\n                    return false;\n                  }\n                  this.registry[backendName] = backendInstance;\n                  this.pendingBackendInit = null;\n                  return true;\n                })\n                .catch(err => {\n                  // Outdated promise. Another backend was set in the meantime.\n                  if (promiseId < this.pendingBackendInitId) {\n                    return false;\n                  }\n                  this.pendingBackendInit = null;\n                  console.warn(\n                      `Initialization of backend ${backendName} failed`);\n                  console.warn(err.stack || err.message);\n                  return false;\n                });\n        this.pendingBackendInit = success;\n        return {success, asyncInit: true};\n      } else {\n        this.registry[backendName] = backend as KernelBackend;\n        return {success: true, asyncInit: false};\n      }\n    } catch (err) {\n      console.warn(`Initialization of backend ${backendName} failed`);\n      console.warn(err.stack || err.message);\n      return {success: false, asyncInit: false};\n    }\n  }\n\n  removeBackend(backendName: string): void {\n    if (!(backendName in this.registryFactory)) {\n      throw new Error(`${backendName} backend not found in registry`);\n    }\n    if (this.backendName === backendName && this.pendingBackendInit != null) {\n      // There is a pending promise of the backend we want to remove. Make it\n      // obsolete.\n      this.pendingBackendInitId++;\n    }\n\n    if (backendName in this.registry) {\n      this.disposeRegisteredKernels(backendName);\n      this.registry[backendName].dispose();\n      delete this.registry[backendName];\n    }\n\n    delete this.registryFactory[backendName];\n\n    // Unset the backend if it is active.\n    if (this.backendName === backendName) {\n      this.pendingBackendInit = null;\n      this.backendName = null;\n      this.backendInstance = null;\n    }\n  }\n\n  private getSortedBackends(): string[] {\n    if (Object.keys(this.registryFactory).length === 0) {\n      throw new Error('No backend found in registry.');\n    }\n    return Object.keys(this.registryFactory).sort((a: string, b: string) => {\n      // Highest priority comes first.\n      return this.registryFactory[b].priority -\n          this.registryFactory[a].priority;\n    });\n  }\n\n  private initializeBackendsAndReturnBest():\n      {name: string, asyncInit: boolean} {\n    const sortedBackends = this.getSortedBackends();\n\n    for (let i = 0; i < sortedBackends.length; i++) {\n      const backendName = sortedBackends[i];\n      const {success, asyncInit} = this.initializeBackend(backendName);\n      if (asyncInit || success) {\n        return {name: backendName, asyncInit};\n      }\n    }\n    throw new Error(\n        `Could not initialize any backends, all backend initializations ` +\n        `failed.`);\n  }\n\n  moveData(backend: KernelBackend, dataId: DataId) {\n    const info = this.state.tensorInfo.get(dataId);\n    const srcBackend = info.backend;\n    const values = this.readSync(dataId);\n    const refCount = srcBackend.refCount(dataId);\n    // Delete the tensor from the old backend and move it to the new\n    // backend.\n    srcBackend.disposeData(dataId, true);\n    info.backend = backend;\n    backend.move(dataId, values, info.shape, info.dtype, refCount);\n    if (this.shouldCheckForMemLeaks()) {\n      // Track the number of moves during a kernel execution to correctly\n      // detect memory leaks.\n      this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n    }\n  }\n\n  tidy<T extends TensorContainer>(nameOrFn: string|ScopeFn<T>, fn?: ScopeFn<T>):\n      T {\n    let name: string = null;\n    if (fn == null) {\n      // Called with only 1 argument.\n      if (typeof nameOrFn !== 'function') {\n        throw new Error('Please provide a function to tidy()');\n      }\n      fn = nameOrFn;\n    } else {\n      // Called with 2 arguments.\n      if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n        throw new Error(\n            'When calling with two arguments, the first argument ' +\n            'to tidy() must be a string');\n      }\n      if (typeof fn !== 'function') {\n        throw new Error(\n            'When calling with two arguments, the 2nd argument ' +\n            'to tidy() must be a function');\n      }\n      name = nameOrFn as string;\n      // TODO(nsthorat,smilkov): Do operation logging and performance\n      // profiling.\n    }\n    let result: T;\n    return this.scopedRun(\n        () => this.startScope(name), () => this.endScope(result), () => {\n          result = fn();\n          if (result instanceof Promise) {\n            console.error('Cannot return a Promise inside of tidy.');\n          }\n          return result;\n        });\n  }\n\n  private scopedRun<T>(start: () => void, end: () => void, f: () => T): T {\n    start();\n    try {\n      const res = f();\n      end();\n      return res;\n    } catch (ex) {\n      end();\n      throw ex;\n    }\n  }\n\n  private static nextTensorId = 0;\n  private nextTensorId(): number {\n    return Engine.nextTensorId++;\n  }\n\n  private static nextVariableId = 0;\n  private nextVariableId(): number {\n    return Engine.nextVariableId++;\n  }\n\n  /**\n   * This method is called instead of the public-facing tensor.clone() when\n   * saving a tensor for backwards pass. It makes sure to add the clone\n   * operation to the tape regardless of being called inside a kernel\n   * execution.\n   */\n  private clone(x: Tensor): Tensor {\n    const y: Tensor = ENGINE.runKernel(Identity, {x} as {} as NamedTensorMap);\n    const inputs = {x};\n    const grad = (dy: Tensor) => ({\n      x: () => {\n        const dtype = 'float32';\n        const gradInputs = {x: dy};\n        const attrs = {dtype};\n\n        return ENGINE.runKernel(\n                   Cast, gradInputs as {} as NamedTensorMap,\n                   // tslint:disable-next-line: no-unnecessary-type-assertion\n                   attrs as {} as NamedAttrMap) as Tensor;\n      }\n    });\n    const saved: Tensor[] = [];\n    this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n    return y;\n  }\n\n  /**\n   * Execute a kernel with the given name and return the output tensor.\n   *\n   * @param kernelName The name of the kernel to execute.\n   * @param inputs A map of input names to tensors.\n   * @param attrs A map of attribute names to their values. An attribute is a\n   *     primitive (non-tensor) input to the kernel.\n   * @param inputsToSave A list of tensors, inputs to save for the backprop\n   *     computation.\n   * @param outputsToSave A list of booleans, specifying which output to save\n   *     for the backprop computation. These are booleans since the output\n   * tensors are not visible to the user.\n   */\n  runKernel<T extends Tensor|Tensor[]>(\n      kernelName: string, inputs: NamedTensorMap, attrs?: NamedAttrMap): T {\n    const hasKernel = getKernel(kernelName, this.backendName) != null;\n    if (!hasKernel) {\n      throw new Error(`Kernel '${kernelName}' not registered for backend '${\n          this.backendName}'`);\n    }\n    return this.runKernelFunc({kernelName, inputs, attrs});\n  }\n\n  private shouldCheckForMemLeaks(): boolean {\n    return this.ENV.getBool('IS_TEST');\n  }\n\n  private checkKernelForMemLeak(\n      kernelName: string, numDataIdsBefore: number,\n      outInfos: TensorInfo[]): void {\n    const numDataIdsAfter = this.backend.numDataIds();\n\n    // Count the number of data ids associated with the result of the kernel.\n    let numOutputDataIds = 0;\n    outInfos.forEach(info => {\n      // Complex numbers allocate 3 data ids, one for 'real', one for\n      // 'imaginary', and one for the container that holds the former two.\n      numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n    });\n\n    // Account for the number of moves during kernel execution. A \"data move\"\n    // can happen in the middle of a kernel execution, placing a new (key,value)\n    // pair in the data storage. Since data moves have net zero effect (we\n    // always remove the data from the old backend), we have to cancel them out\n    // when detecting memory leaks.\n    const numMoves =\n        this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n    const dataIdsLeaked =\n        numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n    if (dataIdsLeaked > 0) {\n      throw new Error(\n          `Backend '${this.backendName}' has an internal memory leak ` +\n          `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n    }\n  }\n\n  /**\n   * Internal helper method to execute a kernel Func\n   *\n   * Use `runKernel` to execute kernels from outside of engine.\n   */\n  private runKernelFunc<T extends Tensor|Tensor[], I extends NamedTensorMap>(\n      kernelParams: RegisteredKernelInvocation<I>|\n      CustomGradKernelInvocation<T, I>): T {\n    let outputs: Tensor[];\n    let saved: Tensor[] = [];\n    const isTapeOn = this.isTapeOn();\n\n    const startingBytecount = this.state.numBytes;\n    const startingNumTensors = this.state.numTensors;\n\n    if (this.shouldCheckForMemLeaks()) {\n      this.state.numDataMovesStack.push(0);\n    }\n\n    let kernelFunc: () => Tensor[];\n    if (this.backendName == null) {\n      // backend has not been initialized yet (backend initialization is lazy\n      // can be deferred until an op/ kernel is run).\n      // The below getter has side effects that will try to initialize the\n      // backend and set properties like this.backendName\n      // tslint:disable-next-line: no-unused-expression\n      this.backend;\n    }\n\n    let out: TensorInfo|TensorInfo[];\n\n    const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ?\n        kernelParams.kernelName :\n        this.state.activeScope != null ? this.state.activeScope.name : '';\n\n    // Create the kernelFunc from either a registered kernel OR passed in\n    // forward/backward functions (used by custom grad). In this context a\n    // kernelFunc wraps a kernel implementation with some bookkeeping.\n\n    if (isRegisteredKernelInvocation(kernelParams)) {\n      const {kernelName, inputs, attrs} = kernelParams;\n      if (this.backendName == null) {\n        // backend has not been initialized yet (backend initialization is lazy\n        // can be deferred until an op/ kernel is run).\n        // The below getter has side effects that will try to initialize the\n        // backend and set properties like this.backendName\n        // tslint:disable-next-line: no-unused-expression\n        this.backend;\n      }\n      const kernel = getKernel(kernelName, this.backendName);\n      util.assert(\n          kernel != null,\n          () => `Cannot find registered kernel '${kernelName}' for backend '${\n              this.backendName}'`);\n\n      kernelFunc = () => {\n        const numDataIdsBefore = this.backend.numDataIds();\n        out = kernel.kernelFunc({inputs, attrs, backend: this.backend});\n        const outInfos = Array.isArray(out) ? out : [out];\n        if (this.shouldCheckForMemLeaks()) {\n          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n        }\n\n        const outTensors = outInfos.map((outInfo: TensorInfo|Tensor) => {\n          // todo (yassogba) remove this option (Tensor) when node backend\n          // methods have been modularized and they all return tensorInfo.\n          // TensorInfos do not have a rank attribute.\n          if ((outInfo as Tensor).rank != null) {\n            return outInfo as Tensor;\n          }\n          const {dataId, shape, dtype} = outInfo as TensorInfo;\n          return this.makeTensorFromDataId(dataId, shape, dtype);\n        });\n\n        // Save any required inputs and outputs.\n\n        // Do not save unless we are recording to the tape. Otherwise it would\n        // cause a mem leak since there would be no backprop for these tensors\n        // (which would otherwise dispose them).\n        if (isTapeOn) {\n          const tensorsToSave =\n              this.getTensorsForGradient(kernelName, inputs, outTensors);\n          saved = this.saveTensorsForBackwardMode(tensorsToSave);\n        }\n        return outTensors;\n      };\n    } else {\n      const {forwardFunc} = kernelParams;\n      // Running a customGrad op.\n      const saveFunc: GradSaveFunc = (tensors) => {\n        // Do not save unless we are recording to the tape. Otherwise it would\n        // cause a mem leak since we would never run backprop, which disposes\n        // the kept tensors.\n        if (!isTapeOn) {\n          return;\n        }\n        saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n      };\n\n      kernelFunc = () => {\n        const numDataIdsBefore = this.backend.numDataIds();\n        out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n        const outs = (Array.isArray(out) ? out : [out]) as Tensor[];\n        if (this.shouldCheckForMemLeaks()) {\n          // Scope name is used to print a more helpful error message if needed.\n          this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);\n        }\n        return outs;\n      };\n    }\n\n    //\n    // Run the kernelFunc. Optionally profiling it.\n    //\n    const {inputs, attrs} = kernelParams;\n    const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ?\n        null :\n        kernelParams.backwardsFunc;\n\n    let kernelProfile: KernelProfile;\n    this.scopedRun(\n        // Stop recording to a tape when running a kernel.\n        () => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n          if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n            outputs = kernelFunc();\n          } else {\n            kernelProfile = this.profiler.profileKernel(\n                kernelOrScopeName, inputs, () => kernelFunc());\n            if (this.ENV.getBool('DEBUG')) {\n              this.profiler.logKernelProfile(kernelProfile);\n            }\n            outputs = kernelProfile.outputs;\n          }\n        });\n\n    if (isTapeOn) {\n      this.addTapeNode(\n          kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);\n    }\n\n    if (this.state.profiling) {\n      this.state.activeProfile.kernels.push({\n        name: kernelOrScopeName,\n        bytesAdded: this.state.numBytes - startingBytecount,\n        totalBytesSnapshot: this.state.numBytes,\n        tensorsAdded: this.state.numTensors - startingNumTensors,\n        totalTensorsSnapshot: this.state.numTensors,\n        inputShapes: Object.keys(inputs).map(\n            key => inputs[key] != null ? inputs[key].shape : null),\n        outputShapes: outputs.map(item => item.shape),\n        kernelTimeMs: kernelProfile.timeMs,\n        extraInfo: kernelProfile.extraInfo\n      });\n    }\n    return (Array.isArray(out) ? outputs : outputs[0]) as T;\n  }\n\n  /**\n   * Saves tensors used in forward mode for use in backward mode.\n   *\n   * @param tensors the list of tensors to save.\n   */\n  private saveTensorsForBackwardMode(tensors: Tensor[]): Tensor[] {\n    const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n    return saved;\n  }\n\n  /**\n   * Returns a list of tensors to save for a given gradient calculation.\n   *\n   * @param kernelName name of kernel to look up gradient for.\n   * @param inputs a map of input tensors.\n   * @param outputs an array of output tensors from forward mode of kernel.\n   */\n  private getTensorsForGradient(\n      kernelName: string, inputs: NamedTensorMap,\n      outputs: Tensor[]): Tensor[]|null {\n    const gradConfig = getGradient(kernelName);\n    if (gradConfig != null) {\n      const inputsToSave: string[] = gradConfig.inputsToSave || [];\n      const outputsToSave: boolean[] = gradConfig.outputsToSave || [];\n\n      // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n      // specified in inputsToSave will be saved.\n      let inputTensorsToSave: Tensor[];\n      if (gradConfig.saveAllInputs) {\n        util.assert(\n            Array.isArray(inputs),\n            () => 'saveAllInputs is true, expected inputs to be an array.');\n\n        inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n      } else {\n        inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n      }\n\n      const outputTensorsToSave: Tensor[] =\n          outputs.filter((_, i) => outputsToSave[i]);\n\n      return inputTensorsToSave.concat(outputTensorsToSave);\n    }\n    // We return an empty list rather than throw an error because the kernel we\n    // are looking up may not actually be relevant to backproping through the\n    // overall function\n    //\n    // See 'does not error if irrelevant (pruned) ops are missing grads' test\n    // in gradients_test.ts for an example.\n    return [];\n  }\n\n  /**\n   * Internal method used by public APIs for tensor creation. Makes a new\n   * tensor with the provided shape, dtype and values. It always\n   * creates a new data id and writes the values to the underlying backend.\n   */\n  makeTensor(\n      values: DataValues, shape: number[], dtype: DataType,\n      backend?: KernelBackend): Tensor {\n    if (values == null) {\n      throw new Error('Values passed to engine.makeTensor() are null');\n    }\n    dtype = dtype || 'float32';\n    backend = backend || this.backend;\n    let backendVals = values as BackendValues;\n    if (dtype === 'string' && util.isString(values[0])) {\n      backendVals = (values as string[]).map(d => util.encodeString(d));\n    }\n    const dataId = backend.write(backendVals, shape, dtype);\n    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n    this.trackTensor(t, backend);\n\n    // Count bytes for string tensors.\n    if (dtype === 'string') {\n      const info = this.state.tensorInfo.get(dataId);\n      const newBytes = bytesFromStringArray(backendVals as Uint8Array[]);\n      this.state.numBytes += newBytes - info.bytes;\n      info.bytes = newBytes;\n    }\n    return t;\n  }\n\n  /**\n   * Internal method used by backends. Makes a new tensor\n   * that is a wrapper around an existing data id. It doesn't create\n   * a new data id, only increments the ref count used in memory tracking.\n   */\n  makeTensorFromDataId(\n      dataId: DataId, shape: number[], dtype: DataType,\n      backend?: KernelBackend): Tensor {\n    dtype = dtype || 'float32';\n    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n    this.trackTensor(t, backend);\n    return t;\n  }\n\n  makeVariable(\n      initialValue: Tensor, trainable = true, name?: string,\n      dtype?: DataType): Variable {\n    name = name || this.nextVariableId().toString();\n    if (dtype != null && dtype !== initialValue.dtype) {\n      initialValue = initialValue.cast(dtype);\n    }\n    const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n    if (this.state.registeredVariables[v.name] != null) {\n      throw new Error(`Variable with name ${v.name} was already registered`);\n    }\n    this.state.registeredVariables[v.name] = v;\n    this.incRef(v, this.backend);\n    return v;\n  }\n\n  trackTensor(a: Tensor, backend: KernelBackend): void {\n    this.state.numTensors++;\n    if (a.dtype === 'string') {\n      this.state.numStringTensors++;\n    }\n    // Bytes for complex numbers are counted by their components. Bytes for\n    // string tensors are counted when writing values.\n    let bytes = 0;\n    if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n      bytes = a.size * util.bytesPerElement(a.dtype);\n    }\n    this.state.numBytes += bytes;\n\n    if (!this.state.tensorInfo.has(a.dataId)) {\n      this.state.numDataBuffers++;\n      this.state.tensorInfo.set(a.dataId, {\n        backend: backend || this.backend,\n        dtype: a.dtype,\n        shape: a.shape,\n        bytes\n      });\n    }\n\n    if (!(a instanceof Variable)) {\n      this.track(a);\n    }\n  }\n\n  // Track the tensor by dataId and increase the refCount for the dataId in the\n  // backend.\n  // TODO(pyu10055): This is currently used by makeVariable method, to increase\n  // refCount on the backend for the dataId. It can potentially be replaced with\n  // Identity op indead of calling backend directly.\n  incRef(a: Tensor, backend: KernelBackend): void {\n    this.trackTensor(a, backend);\n    this.backend.incRef(a.dataId);\n  }\n\n  removeDataId(dataId: DataId, backend: KernelBackend) {\n    if (this.state.tensorInfo.has(dataId) &&\n        this.state.tensorInfo.get(dataId).backend === backend) {\n      this.state.tensorInfo.delete(dataId);\n      this.state.numDataBuffers--;\n    }\n  }\n  disposeTensor(a: Tensor): void {\n    if (!this.state.tensorInfo.has(a.dataId)) {\n      return;\n    }\n    const info = this.state.tensorInfo.get(a.dataId);\n\n    this.state.numTensors--;\n    if (a.dtype === 'string') {\n      this.state.numStringTensors--;\n      this.state.numBytes -= info.bytes;\n    }\n    // Don't count bytes for complex numbers as they are counted by their\n    // components.\n    if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n      const bytes = a.size * util.bytesPerElement(a.dtype);\n      this.state.numBytes -= bytes;\n    }\n\n    // Remove the reference to dataId if backend dispose the data successfully\n    if (info.backend.disposeData(a.dataId)) {\n      this.removeDataId(a.dataId, info.backend);\n    }\n\n    // TODO(nsthorat): Construct an error and save the stack trace for\n    // debugging when in debug mode. Creating a stack trace is too expensive\n    // to do unconditionally.\n  }\n\n  disposeVariables(): void {\n    for (const varName in this.state.registeredVariables) {\n      const v = this.state.registeredVariables[varName];\n      this.disposeVariable(v);\n    }\n  }\n\n  disposeVariable(v: Variable): void {\n    this.disposeTensor(v);\n    if (this.state.registeredVariables[v.name] != null) {\n      delete this.state.registeredVariables[v.name];\n    }\n  }\n\n  memory(): MemoryInfo {\n    const info = this.backend.memory() as MemoryInfo;\n    info.numTensors = this.state.numTensors;\n    info.numDataBuffers = this.state.numDataBuffers;\n    info.numBytes = this.state.numBytes;\n    if (this.state.numStringTensors > 0) {\n      info.unreliable = true;\n      if (info.reasons == null) {\n        info.reasons = [];\n      }\n      info.reasons.push(\n          'Memory usage by string tensors is approximate ' +\n          '(2 bytes per character)');\n    }\n    return info;\n  }\n\n  async profile(query: () => (TensorContainer | Promise<TensorContainer>)):\n      Promise<ProfileInfo> {\n    this.state.profiling = true;\n\n    const startBytes = this.state.numBytes;\n    const startNumTensors = this.state.numTensors;\n\n    this.state.activeProfile.kernels = [];\n    this.state.activeProfile.result = await query();\n\n    this.state.profiling = false;\n\n    this.state.activeProfile.peakBytes = Math.max(\n        ...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n    this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n    this.state.activeProfile.newTensors =\n        this.state.numTensors - startNumTensors;\n    for (const kernel of this.state.activeProfile.kernels) {\n      kernel.kernelTimeMs = await kernel.kernelTimeMs;\n      kernel.extraInfo = await kernel.extraInfo;\n    }\n    return this.state.activeProfile;\n  }\n\n  isTapeOn(): boolean {\n    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n  }\n\n  private addTapeNode(\n      kernelName: string, inputs: NamedTensorMap, outputs: Tensor[],\n      gradientsFunc: GradFunc, saved: Tensor[], attrs: NamedAttrMap): void {\n    const tapeNode: TapeNode =\n        {id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved};\n\n    const gradConfig = getGradient(kernelName);\n    if (gradConfig != null) {\n      gradientsFunc = gradConfig.gradFunc;\n    }\n    if (gradientsFunc != null) {\n      tapeNode.gradient = (dys: Tensor[]) => {\n        // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n        // the backprop graph to the user as null instead of zeros\n        dys = dys.map((dy, i) => {\n          if (dy == null) {\n            const output = outputs[i];\n            const vals = util.makeZerosTypedArray(output.size, output.dtype);\n            return this.makeTensor(vals, output.shape, output.dtype);\n          }\n          return dy;\n        });\n        // Grad functions of ops with single outputs expect a dy, while ops\n        // with multiple outputs expect dys (array of dy).\n        return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n      };\n    }\n    this.state.activeTape.push(tapeNode);\n  }\n\n  keep<T extends Tensor>(result: T): T {\n    result.kept = true;\n    return result;\n  }\n\n  private startTape() {\n    if (this.state.gradientDepth === 0) {\n      this.state.activeTape = [];\n    }\n    this.state.gradientDepth++;\n  }\n\n  private endTape() {\n    this.state.gradientDepth--;\n  }\n\n  /**\n   * Start a scope. Use this with endScope() to achieve the same functionality\n   * as scope() without the need for a function closure.\n   */\n  startScope(name?: string) {\n    const scopeInfo: ScopeState = {\n      track: [],\n      name: 'unnamed scope',\n      id: this.state.nextScopeId++\n    };\n    if (name) {\n      scopeInfo.name = name;\n    }\n    this.state.scopeStack.push(scopeInfo);\n    this.state.activeScope = scopeInfo;\n  }\n\n  /**\n   * End a scope. Use this with startScope() to achieve the same functionality\n   * as scope() without the need for a function closure.\n   */\n  endScope(result?: TensorContainer) {\n    const tensorsToTrackInParent = getTensorsInContainer(result);\n    const tensorsToTrackInParentSet =\n        new Set(tensorsToTrackInParent.map(t => t.id));\n\n    // Dispose the arrays tracked in this scope.\n    for (let i = 0; i < this.state.activeScope.track.length; i++) {\n      const tensor = this.state.activeScope.track[i];\n      if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n        tensor.dispose();\n      }\n    }\n\n    const oldScope = this.state.scopeStack.pop();\n    this.state.activeScope = this.state.scopeStack.length === 0 ?\n        null :\n        this.state.scopeStack[this.state.scopeStack.length - 1];\n\n    // Track the current result in the parent scope.\n    tensorsToTrackInParent.forEach(tensor => {\n      // Only track the tensor if was allocated in the inner scope and is not\n      // globally kept.\n      if (!tensor.kept && tensor.scopeId === oldScope.id) {\n        this.track(tensor);\n      }\n    });\n  }\n\n  /**\n   * Returns gradients of `f` with respect to each of the `xs`. The gradients\n   * returned are of the same length as `xs`, but some might be null if `f`\n   * was not a function of that `x`. It also takes optional dy to multiply the\n   * gradient, which defaults to `1`.\n   */\n  gradients<T extends Tensor>(\n      f: () => T, xs: Tensor[], dy?: T,\n      allowNoGradients = false): {value: T, grads: Tensor[]} {\n    util.assert(\n        xs.length > 0, () => 'gradients() received an empty list of xs.');\n    if (dy != null && dy.dtype !== 'float32') {\n      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n    }\n\n    const y = this.scopedRun(\n        () => this.startTape(), () => this.endTape(),\n        () => this.tidy('forward', f));\n\n    util.assert(\n        y instanceof Tensor,\n        () => 'The result y returned by f() must be a tensor.');\n    // Filter out the nodes that don't connect x => y.\n    const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n    if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n      throw new Error(\n          'Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n          'that the f you passed encloses all operations that lead from x ' +\n          'to y.');\n    }\n\n    return this.tidy('backward', () => {\n      const accumulatedGradientMap: {[tensorId: number]: Tensor} = {};\n      accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n\n      // Backprop gradients through the filtered nodes.\n      backpropagateGradients(\n          accumulatedGradientMap, filteredTape,\n          // Pass the tidy function to avoid circular dep with `tape.ts`.\n          f => this.tidy(f as ScopeFn<Tensor>),\n          // Pass an add function to avoide a circular dep with `tape.ts`.\n          add);\n      const grads = xs.map(x => accumulatedGradientMap[x.id]);\n\n      if (this.state.gradientDepth === 0) {\n        // This means that we are not computing higher-order gradients\n        // and can clean up the tape.\n        this.state.activeTape.forEach(node => {\n          for (const tensor of node.saved) {\n            tensor.dispose();\n          }\n        });\n        this.state.activeTape = null;\n      }\n      return {value: y, grads};\n    });\n  }\n\n  customGrad<T extends Tensor>(f: CustomGradientFunc<T>):\n      (...args: Array<Tensor|GradSaveFunc>) => T {\n    util.assert(\n        util.isFunction(f),\n        () => 'The f passed in customGrad(f) must be a function.');\n    return (...inputs: Tensor[]): T => {\n      util.assert(\n          inputs.every(t => t instanceof Tensor),\n          () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n              'tensors');\n\n      let res: {\n        value: T,\n        gradFunc: (dy: T, saved: Tensor[]) => Tensor | Tensor[],\n      };\n      const inputMap: NamedTensorMap = {};\n      inputs.forEach((input, i) => {\n        inputMap[i] = input;\n      });\n\n      const forwardFunc: ForwardFunc<T> = (_, save) => {\n        res = f(...[...inputs, save]);\n        util.assert(\n            res.value instanceof Tensor,\n            () => 'The function f passed in customGrad(f) must return an ' +\n                'object where `obj.value` is a tensor');\n        util.assert(\n            util.isFunction(res.gradFunc),\n            () => 'The function f passed in customGrad(f) must return an ' +\n                'object where `obj.gradFunc` is a function.');\n        return res.value;\n      };\n\n      const backwardsFunc = (dy: T, saved: Tensor[]) => {\n        const gradRes = res.gradFunc(dy, saved);\n        const grads: Tensor[] = Array.isArray(gradRes) ? gradRes : [gradRes];\n        util.assert(\n            grads.length === inputs.length,\n            () => 'The function f passed in customGrad(f) must return an ' +\n                'object where `obj.gradFunc` is a function that returns ' +\n                'the same number of tensors as inputs passed to f(...).');\n        util.assert(\n            grads.every(t => t instanceof Tensor),\n            () => 'The function f passed in customGrad(f) must return an ' +\n                'object where `obj.gradFunc` is a function that returns ' +\n                'a list of only tensors.');\n        const gradMap: {[key: string]: () => Tensor} = {};\n        grads.forEach((grad, i) => {\n          gradMap[i] = () => grad;\n        });\n        return gradMap;\n      };\n\n      return this.runKernelFunc({\n        forwardFunc,\n        backwardsFunc,\n        inputs: inputMap,\n      });\n    };\n  }\n\n  readSync(dataId: DataId): BackendValues {\n    // Route the read to the correct backend.\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.readSync(dataId);\n  }\n  read(dataId: DataId): Promise<BackendValues> {\n    // Route the read to the correct backend.\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.read(dataId);\n  }\n\n  async time(query: () => void): Promise<TimingInfo> {\n    const start = now();\n    const timingInfo = await this.backend.time(query) as TimingInfo;\n    timingInfo.wallMs = now() - start;\n    return timingInfo;\n  }\n\n  /**\n   * Tracks a Tensor in the current scope to be automatically cleaned up\n   * when the current scope ends, and returns the value.\n   *\n   * @param result The Tensor to track in the current scope.\n   */\n  private track<T extends Tensor>(result: T): T {\n    if (this.state.activeScope != null) {\n      result.scopeId = this.state.activeScope.id;\n      this.state.activeScope.track.push(result);\n    }\n\n    return result;\n  }\n\n  get registeredVariables(): NamedVariableMap {\n    return this.state.registeredVariables;\n  }\n\n  /**\n   * Resets the engine state. Removes all backends but does not remove\n   * registered backend factories.\n   */\n  reset(): void {\n    // Make any pending promise obsolete.\n    this.pendingBackendInitId++;\n\n    this.state.dispose();\n    this.ENV.reset();\n    this.state = new EngineState();\n\n    for (const backendName in this.registry) {\n      this.disposeRegisteredKernels(backendName);\n      this.registry[backendName].dispose();\n      delete this.registry[backendName];\n    }\n    this.backendName = null;\n    this.backendInstance = null;\n    this.pendingBackendInit = null;\n  }\n}\n\nfunction ones(shape: number[]): Tensor {\n  const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n  return ENGINE.makeTensor(values, shape, 'float32');\n}\n\nexport function getOrMakeEngine(): Engine {\n  const ns = getGlobalNamespace() as {} as {_tfengine: Engine};\n  if (ns._tfengine == null) {\n    const environment = new Environment(ns);\n    ns._tfengine = new Engine(environment);\n  }\n  setEnvironmentGlobal(ns._tfengine.ENV);\n\n  // Tell the current tensor interface that the global engine is responsible\n  // for tracking.\n  setTensorTracker(() => ns._tfengine);\n  return ns._tfengine;\n}\n\nexport const ENGINE = getOrMakeEngine();\n\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a: Tensor, b: Tensor): Tensor {\n  // We duplicate Add here to avoid a circular dependency with add.ts.\n  const inputs = {a, b};\n  return ENGINE.runKernel(Add, inputs as {} as NamedTensorMap);\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined(): boolean {\n  return typeof navigator !== 'undefined' && navigator != null;\n}\n\nexport function isMobile(nav?: Navigator): boolean {\n  if (nav || _isNavigatorDefined()) {\n    if (!nav) {\n      nav = navigator;\n    }\n    if (nav.product === 'ReactNative') {\n      return true;\n    }\n\n    // tslint:disable-next-line:no-any\n    const a = nav.userAgent || nav.vendor || (window as any).opera;\n    // tslint:disable-next-line:max-line-length\n    return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n               .test(a) ||\n        // tslint:disable-next-line:max-line-length\n        /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n            .test(a.substr(0, 4));\n  }\n  return false;\n}\n\nexport function isBrowser(): boolean {\n  return (typeof window !== 'undefined' && window.document != null) ||\n      //@ts-ignore\n      (typeof WorkerGlobalScope !== 'undefined');\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\n\nimport * as device_util from './device_util';\nimport {env} from './environment';\n\nconst ENV = env();\n\n/**\n * This file contains environment-related flag registrations.\n */\n\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n  if (debugValue) {\n    console.warn(\n        'Debugging mode is ON. The output of every math call will ' +\n        'be downloaded to CPU and checked for NaNs. ' +\n        'This significantly impacts performance.');\n  }\n});\n\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag(\n    'IS_NODE',\n    () => (typeof process !== 'undefined') &&\n        (typeof process.versions !== 'undefined') &&\n        (typeof process.versions.node !== 'undefined'));\n\n/** Whether this browser is Chrome. */\nENV.registerFlag(\n    'IS_CHROME',\n    () => typeof navigator !== 'undefined' && navigator != null &&\n        navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n        /Google Inc/.test(navigator.vendor));\n\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag(\n    'TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n\n/** Whether to check computation result for errors. */\nENV.registerFlag('CHECK_COMPUTATION_FOR_ERRORS', () => true);\n\n/** Whether the backend needs to wrap input to imageBitmap. */\nENV.registerFlag('WRAP_TO_IMAGEBITMAP', () => false);\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from './engine';\nimport {env} from './environment';\nimport {Tensor} from './tensor';\nimport {DataType, TensorLike} from './types';\nimport {assert, flatten, inferDtype, isTypedArray, toTypedArray} from './util';\n\nexport function inferShape(val: TensorLike, dtype?: DataType): number[] {\n  let firstElem: typeof val = val;\n\n  if (isTypedArray(val)) {\n    return dtype === 'string' ? [] : [val.length];\n  }\n  if (!Array.isArray(val)) {\n    return [];  // Scalar.\n  }\n  const shape: number[] = [];\n\n  while (Array.isArray(firstElem) ||\n         isTypedArray(firstElem) && dtype !== 'string') {\n    shape.push(firstElem.length);\n    firstElem = firstElem[0];\n  }\n  if (Array.isArray(val) &&\n      env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n    deepAssertShapeConsistency(val, shape, []);\n  }\n\n  return shape;\n}\n\nfunction deepAssertShapeConsistency(\n    val: TensorLike, shape: number[], indices: number[]) {\n  indices = indices || [];\n  if (!(Array.isArray(val)) && !isTypedArray(val)) {\n    assert(\n        shape.length === 0,\n        () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n    return;\n  }\n  assert(\n      shape.length > 0,\n      () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n          `but is an array of ${val.length} elements`);\n  assert(\n      val.length === shape[0],\n      () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n          `elements, but has ${val.length} elements`);\n  const subShape = shape.slice(1);\n  for (let i = 0; i < val.length; ++i) {\n    deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n  }\n}\n\nfunction assertDtype(\n    expectedDtype: DataType|'numeric'|'string_or_numeric',\n    actualDType: DataType, argName: string, functionName: string) {\n  if (expectedDtype === 'string_or_numeric') {\n    return;\n  }\n  if (expectedDtype == null) {\n    throw new Error(`Expected dtype cannot be null.`);\n  }\n  if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n      expectedDtype === 'numeric' && actualDType === 'string') {\n    throw new Error(\n        `Argument '${argName}' passed to '${functionName}' must ` +\n        `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n  }\n}\n\nexport function convertToTensor<T extends Tensor>(\n    x: T|TensorLike, argName: string, functionName: string,\n    parseAsDtype: DataType|'numeric'|'string_or_numeric' = 'numeric'): T {\n  if (x instanceof Tensor) {\n    assertDtype(parseAsDtype, x.dtype, argName, functionName);\n    return x;\n  }\n  let inferredDtype = inferDtype(x);\n  // If the user expects a bool/int/float, use that info to update the\n  // inferredDtype when it is not a string.\n  if (inferredDtype !== 'string' &&\n      ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n    inferredDtype = parseAsDtype as DataType;\n  }\n  assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n\n  if ((x == null) ||\n      (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n       typeof x !== 'boolean' && typeof x !== 'string')) {\n    const type = x == null ? 'null' : (x as {}).constructor.name;\n    throw new Error(\n        `Argument '${argName}' passed to '${functionName}' must be a ` +\n        `Tensor or TensorLike, but got '${type}'`);\n  }\n  const inferredShape = inferShape(x, inferredDtype);\n  if (!isTypedArray(x) && !Array.isArray(x)) {\n    x = [x] as number[];\n  }\n  const skipTypedArray = true;\n  const values = inferredDtype !== 'string' ?\n      toTypedArray(x, inferredDtype as DataType) :\n      flatten(x as string[], [], skipTypedArray) as string[];\n  return ENGINE.makeTensor(values, inferredShape, inferredDtype) as T;\n}\n\nexport function convertToTensorArray<T extends Tensor>(\n    arg: Array<T|TensorLike>, argName: string, functionName: string,\n    parseAsDtype: DataType|'numeric'|'string_or_numeric' = 'numeric'): T[] {\n  if (!Array.isArray(arg)) {\n    throw new Error(\n        `Argument ${argName} passed to ${functionName} must be a ` +\n        '`Tensor[]` or `TensorLike[]`');\n  }\n  const tensors = arg as T[];\n  return tensors.map(\n      (t, i) =>\n          convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype));\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {isPromise} from '../util';\n\nexport const OP_SCOPE_SUFFIX = '__op';\n\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op<T extends Function>(f: {[name: string]: T}): T {\n  const keys = Object.keys(f);\n  if (keys.length !== 1) {\n    throw new Error(\n        `Please provide an object with a single key ` +\n        `(operation name) mapping to a function. Got an object with ` +\n        `${keys.length} keys.`);\n  }\n\n  let opName = keys[0];\n  const fn = f[opName];\n\n  // Strip the underscore from the end of the function name.\n  if (opName.endsWith('_')) {\n    opName = opName.substring(0, opName.length - 1);\n  }\n\n  // add an __op suffix to distinguish ops from kernels in tf.profile\n  opName = opName + OP_SCOPE_SUFFIX;\n\n  // tslint:disable-next-line:no-any\n  const f2 = (...args: any[]) => {\n    ENGINE.startScope(opName);\n    try {\n      const result = fn(...args);\n      if (isPromise(result)) {\n        console.error('Cannot return a Promise inside of tidy.');\n      }\n      ENGINE.endScope(result);\n      return result;\n    } catch (ex) {\n      ENGINE.endScope(null);\n      throw ex;\n    }\n  };\n  Object.defineProperty(f2, 'name', {value: opName, configurable: true});\n\n  // tslint:disable-next-line:no-any\n  return f2 as any as T;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Complex, ComplexInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_<T extends Tensor>(real: T|TensorLike, imag: T|TensorLike): T {\n  const $real = convertToTensor(real, 'real', 'complex');\n  const $imag = convertToTensor(imag, 'imag', 'complex');\n  util.assertShapesMatch(\n      $real.shape, $imag.shape,\n      `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n          `must match in call to tf.complex().`);\n\n  const inputs: ComplexInputs = {real: $real, imag: $imag};\n  return ENGINE.runKernel(Complex, inputs as {} as NamedTensorMap);\n}\n\nexport const complex = op({complex_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor} from '../tensor';\nimport {TensorLike, TypedArray} from '../types';\nimport {DataType} from '../types';\nimport {assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray} from '../util';\n\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(\n    values: TensorLike, shape: number[], inferredShape: number[],\n    dtype?: DataType): Tensor {\n  if (dtype == null) {\n    dtype = inferDtype(values);\n  }\n  if (dtype === 'complex64') {\n    throw new Error(\n        `Cannot construct a complex64 tensor directly. ` +\n        `Please use tf.complex(real, imag).`);\n  }\n  if (!isTypedArray(values) && !Array.isArray(values) &&\n      typeof values !== 'number' && typeof values !== 'boolean' &&\n      typeof values !== 'string') {\n    throw new Error(\n        'values passed to tensor(values) must be a number/boolean/string or ' +\n        'an array of numbers/booleans/strings, or a TypedArray');\n  }\n  if (shape != null) {\n    assertNonNegativeIntegerDimensions(shape);\n\n    const providedSize = sizeFromShape(shape);\n    const inferredSize = sizeFromShape(inferredShape);\n    assert(\n        providedSize === inferredSize,\n        () =>\n            `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n\n    for (let i = 0; i < inferredShape.length; ++i) {\n      const inferred = inferredShape[i];\n      const flatDimsDontMatch = i === inferredShape.length - 1 ?\n          inferred !== sizeFromShape(shape.slice(i)) :\n          true;\n      assert(\n          inferredShape[i] === shape[i] || !flatDimsDontMatch,\n          () => `Error creating a new Tensor. Inferred shape ` +\n              `(${inferredShape}) does not match the provided ` +\n              `shape (${shape}). `);\n    }\n  }\n\n  if (!isTypedArray(values) && !Array.isArray(values)) {\n    values = [values] as number[];\n  }\n\n  shape = shape || inferredShape;\n  values = dtype !== 'string' ?\n      toTypedArray(values, dtype) :\n      flatten(values as string[], [], true) as string[];\n  return ENGINE.makeTensor(values as TypedArray, shape, dtype);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {DataType, Rank, ShapeMap} from '../types';\n\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`. If the values are strings,\n *     they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor<R extends Rank>(\n    values: TensorLike, shape?: ShapeMap[R], dtype?: DataType): Tensor<R> {\n  const inferredShape = inferShape(values, dtype);\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor<R>;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/* Type definitions for exporting and importing of models. */\n\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP: {[dtype: string]: number} = {\n  'float32': 4,\n  'float16': 2,\n  'int32': 4,\n  'uint16': 2,\n  'uint8': 1,\n  'bool': 1,\n  'complex64': 8\n};\n\n/**\n * A weight manifest.\n *\n * The weight manifest consists of an ordered list of weight-manifest groups.\n * Each weight-manifest group (\"group\" for short hereafter) consists of a\n * number of weight values stored in a number of paths.\n * See the documentation of `WeightManifestGroupConfig` below for more details.\n */\nexport declare type WeightsManifestConfig = WeightsManifestGroupConfig[];\n\n/**\n * A weight-manifest group.\n *\n * Consists of an ordered list of weight values encoded in binary format,\n * stored in an ordered list of paths.\n */\nexport declare interface WeightsManifestGroupConfig {\n  /**\n   * An ordered list of paths.\n   *\n   * Paths are intentionally abstract in order to be general. For example, they\n   * can be relative URL paths or relative paths on the file system.\n   */\n  paths: string[];\n\n  /**\n   * Specifications of the weights stored in the paths.\n   */\n  weights: WeightsManifestEntry[];\n}\n\n/**\n * Group to which the weight belongs.\n *\n * - 'optimizer': Weight from a stateful optimizer.\n */\nexport type WeightGroup = 'model'|'optimizer';\n\n/**\n * An entry in the weight manifest.\n *\n * The entry contains specification of a weight.\n */\nexport declare interface WeightsManifestEntry {\n  /**\n   * Name of the weight, e.g., 'Dense_1/bias'\n   */\n  name: string;\n\n  /**\n   * Shape of the weight.\n   */\n  shape: number[];\n\n  /**\n   * Data type of the weight.\n   */\n  dtype: 'float32'|'int32'|'bool'|'string'|'complex64';\n\n  /**\n   * Type of the weight.\n   *\n   * Optional.\n   *\n   * The value 'optimizer' indicates the weight belongs to an optimizer\n   * (i.e., used only during model training and not during inference).\n   */\n  group?: WeightGroup;\n\n  /**\n   * Information for dequantization of the weight.\n   */\n  quantization?: {\n    scale?: number,  // The scaling constant to multiply by.\n    min?: number,    // The (possibly nudged) minimum weight to add.\n       dtype: 'uint16'|'uint8'|'float16'  // The dtype of the quantized weights.\n  };\n}\n\n/**\n * Options for saving a model.\n * @innamespace io\n */\nexport interface SaveConfig {\n  /**\n   * Whether to save only the trainable weights of the model, ignoring the\n   * non-trainable ones.\n   */\n  trainableOnly?: boolean;\n\n  /**\n   * Whether the optimizer will be saved (if exists).\n   *\n   * Default: `false`.\n   */\n  includeOptimizer?: boolean;\n}\n\n/**\n * Result of a saving operation.\n */\nexport interface SaveResult {\n  /**\n   * Information about the model artifacts saved.\n   */\n  modelArtifactsInfo: ModelArtifactsInfo;\n\n  /**\n   * HTTP responses from the server that handled the model-saving request (if\n   * any). This is applicable only to server-based saving routes.\n   */\n  responses?: Response[];\n\n  /**\n   * Error messages and related data (if any).\n   */\n  errors?: Array<{}|string>;\n}\n\nexport declare interface ModelArtifactsInfo {\n  /**\n   * Timestamp for when the model is saved.\n   */\n  dateSaved: Date;\n\n  /**\n   * TODO (cais,yassogba) consider removing GraphDef as GraphDefs now\n   * come in a JSON format and none of our IOHandlers support a non json\n   * format. We could conder replacing this with 'Binary' if we want to\n   * allow future handlers to save to non json formats (though they will\n   * probably want more information than 'Binary').\n   * Type of the model topology\n   *\n   * Type of the model topology\n   *\n   * Possible values:\n   *   - JSON: JSON config (human-readable, e.g., Keras JSON).\n   *   - GraphDef: TensorFlow\n   *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)\n   *     protocol buffer (binary).\n   */\n  modelTopologyType: 'JSON'|'GraphDef';\n\n  /**\n   * Size of model topology (Keras JSON or GraphDef), in bytes.\n   */\n  modelTopologyBytes?: number;\n\n  /**\n   * Size of weight specification or manifest, in bytes.\n   */\n  weightSpecsBytes?: number;\n\n  /**\n   * Size of weight value data, in bytes.\n   */\n  weightDataBytes?: number;\n}\n\n/** Model training configuration. */\nexport declare interface TrainingConfig {\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  // See\n  // tslint:disable-next-line:max-line-length\n  // https://github.com/tensorflow/tfjs-layers/blob/master/src/keras_format/training_config.ts\n  /** Optimizer used for the model training. */\n  optimizer_config: {};\n\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  /** Loss function(s) for the model's output(s). */\n  loss: string|string[]|{[key: string]: string};\n\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  /** Metric function(s) for the model's output(s). */\n  metrics?: string[]|{[key: string]: string};\n\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  weighted_metrics?: string[];\n\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  sample_weight_mode?: string;\n\n  loss_weights?: number[]|{[key: string]: number};\n}\n\n/**\n * The serialized artifacts of a model, including topology and weights.\n *\n * The `modelTopology`, `trainingConfig`, `weightSpecs` and `weightData` fields\n * of this interface are optional, in order to support topology- or weights-only\n * saving and loading.\n *\n * Note this interface is used internally in IOHandlers.  For the file format\n * written to disk as `model.json`, see `ModelJSON`.\n */\nexport declare interface ModelArtifacts {\n  /**\n   * Model topology.\n   *\n   * For Keras-style `tf.Model`s, this is a JSON object.\n   * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON\n   * encoding of the `GraphDef` protocol buffer.\n   */\n  modelTopology?: {}|ArrayBuffer;\n\n  /**\n   * Serialized configuration for the model's training.\n   */\n  trainingConfig?: TrainingConfig;\n\n  /**\n   * Weight specifications.\n   *\n   * This corresponds to the weightsData below.\n   */\n  weightSpecs?: WeightsManifestEntry[];\n\n  /**\n   * Binary buffer for all weight values concatenated in the order specified\n   * by `weightSpecs`.\n   */\n  weightData?: ArrayBuffer;\n\n  /**\n   * Hard-coded format name for models saved from TensorFlow.js or converted\n   * by TensorFlow.js Converter.\n   */\n  format?: string;\n\n  /**\n   * What library is responsible for originally generating this artifact.\n   *\n   * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.\n   */\n  generatedBy?: string;\n\n  /**\n   * What library or tool is responsible for converting the original model\n   * to this format, applicable only if the model is output by a converter.\n   *\n   * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.\n   *\n   * A value of `null` means the model artifacts are generated without any\n   * conversion process (e.g., saved directly from a TensorFlow.js\n   * `tf.LayersModel` instance.)\n   */\n  convertedBy?: string|null;\n\n  /**\n   * Inputs and outputs signature for saved model.\n   */\n  signature?: {};\n\n  /**\n   * User-defined metadata about the model.\n   */\n  userDefinedMetadata?: {[key: string]: {}};\n\n  /**\n   * Initializer for the model.\n   */\n  modelInitializer?: {};\n}\n\n/**\n * The on-disk format of the `model.json` file.\n *\n * TF.js 1.0 always populates the optional fields when writing model.json.\n * Prior versions did not provide those fields.\n */\nexport declare interface ModelJSON {\n  /**\n   * Model topology.\n   *\n   * For Keras-style `tf.Model`s, this is a JSON object.\n   * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON\n   * encoding of the `GraphDef` protocol buffer.\n   */\n  modelTopology: {};\n\n  /** Model training configuration. */\n  trainingConfig?: TrainingConfig;\n\n  /**\n   * Weights manifest.\n   *\n   * The weights manifest consists of an ordered list of weight-manifest\n   * groups. Each weight-manifest group consists of a number of weight values\n   * stored in a number of paths. See the documentation of\n   * `WeightsManifestConfig` for more details.\n   */\n  weightsManifest: WeightsManifestConfig;\n\n  /**\n   * Hard-coded format name for models saved from TensorFlow.js or converted\n   * by TensorFlow.js Converter.\n   */\n  format?: string;\n\n  /**\n   * What library is responsible for originally generating this artifact.\n   *\n   * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.\n   */\n  generatedBy?: string;\n\n  /**\n   * What library or tool is responsible for converting the original model\n   * to this format, applicable only if the model is output by a converter.\n   *\n   * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.\n   *\n   * A value of `null` means the model artifacts are generated without any\n   * conversion process (e.g., saved directly from a TensorFlow.js\n   * `tf.LayersModel` instance.)\n   */\n  convertedBy?: string|null;\n\n  /**\n   * Inputs and outputs signature for saved model.\n   */\n  signature?: {};\n\n  /**\n   * User-defined metadata about the model.\n   */\n  userDefinedMetadata?: {[key: string]: {}};\n\n  /**\n   * Initializer for the model.\n   */\n  modelInitializer?: {};\n}\n\n/**\n * Type definition for handlers of loading operations.\n */\nexport type LoadHandler = () => Promise<ModelArtifacts>;\n\n/**\n * Type definition for handlers of saving operations.\n */\nexport type SaveHandler = (modelArtifact: ModelArtifacts) =>\n    Promise<SaveResult>;\n\n/**\n * Interface for a model import/export handler.\n *\n * The `save` and `load` handlers are both optional, in order to allow handlers\n * that support only saving or loading.\n */\n// tslint:disable-next-line:interface-name\nexport interface IOHandler {\n  save?: SaveHandler;\n  load?: LoadHandler;\n}\n\n/**\n * An interface for the manager of a model store.\n *\n * A model store is defined as a storage medium on which multiple models can\n * be stored. Each stored model has a unique `path` as its identifier.\n * A `ModelStoreManager` for the store allows actions including\n *\n * - Listing the models stored in the store.\n * - Deleting a model from the store.\n */\nexport interface ModelStoreManager {\n  /**\n   * List all models in the model store.\n   *\n   * @returns A dictionary mapping paths of existing models to their\n   *   model artifacts info. Model artifacts info include type of the model's\n   *   topology, byte sizes of the topology, weights, etc.\n   */\n  listModels(): Promise<{[path: string]: ModelArtifactsInfo}>;\n\n  /**\n   * Remove a model specified by `path`.\n   *\n   * @param path\n   * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n   *   is successful).\n   * @throws Error if deletion fails, e.g., if no model exists at `path`.\n   */\n  removeModel(path: string): Promise<ModelArtifactsInfo>;\n}\n\n/**\n * Callback for the progress of a long-running action such as an HTTP\n * request for a large binary object.\n *\n * `fraction` should be a number in the [0, 1] interval, indicating how\n * much of the action has completed.\n */\nexport type OnProgressCallback = (fraction: number) => void;\n\n/** @innamespace io */\nexport interface LoadOptions {\n  /**\n   * RequestInit (options) for HTTP requests.\n   *\n   * For detailed information on the supported fields, see\n   * [https://developer.mozilla.org/en-US/docs/Web/API/Request/Request](\n   *     https://developer.mozilla.org/en-US/docs/Web/API/Request/Request)\n   */\n  requestInit?: RequestInit;\n\n  /**\n   * Progress callback.\n   */\n  onProgress?: OnProgressCallback;\n\n  /**\n   * A function used to override the `window.fetch` function.\n   */\n  fetchFunc?: Function;\n\n  /**\n   * Strict loading model: whether extraneous weights or missing\n   * weights should trigger an `Error`.\n   *\n   * If `true`, require that the provided weights exactly match those\n   * required by the layers. `false` means that both extra weights\n   * and missing weights will be silently ignored.\n   *\n   * Default: `true`.\n   */\n  strict?: boolean;\n\n  /**\n   * Path prefix for weight files, by default this is calculated from the\n   * path of the model JSON file.\n   *\n   * For instance, if the path to the model JSON file is\n   * `http://localhost/foo/model.json`, then the default path prefix will be\n   * `http://localhost/foo/`. If a weight file has the path value\n   * `group1-shard1of2` in the weight manifest, then the weight file will be\n   * loaded from `http://localhost/foo/group1-shard1of2` by default. However,\n   * if you provide a `weightPathPrefix` value of\n   * `http://localhost/foo/alt-weights`, then the weight file will be loaded\n   * from the path `http://localhost/foo/alt-weights/group1-shard1of2` instead.\n   */\n  weightPathPrefix?: string;\n\n  /**\n   * Whether the module or model is to be loaded from TF Hub.\n   *\n   * Setting this to `true` allows passing a TF-Hub module URL, omitting the\n   * standard model file name and the query parameters.\n   *\n   * Default: `false`.\n   */\n  fromTFHub?: boolean;\n\n  /**\n   * An async function to convert weight file name to URL. The weight file\n   * names are stored in model.json's weightsManifest.paths field. By default we\n   * consider weight files are colocated with the model.json file. For example:\n   *     model.json URL: https://www.google.com/models/1/model.json\n   *     group1-shard1of1.bin url:\n   *        https://www.google.com/models/1/group1-shard1of1.bin\n   *\n   * With this func you can convert the weight file name to any URL.\n   */\n  weightUrlConverter?: (weightFileName: string) => Promise<string>;\n}\n\n/**\n * Additional options for Platform.fetch\n */\nexport interface RequestDetails {\n  /**\n   * Is this request for a binary file (as opposed to a json file)\n   */\n  isBinary?: boolean;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {complex} from '../ops/complex';\n\nimport {tensor} from '../ops/tensor';\nimport {NamedTensor, NamedTensorMap} from '../tensor_types';\nimport {TypedArray} from '../types';\nimport {sizeFromShape} from '../util';\n\nimport {DTYPE_VALUE_SIZE_MAP, ModelArtifacts, ModelArtifactsInfo, WeightGroup, WeightsManifestEntry} from './types';\n\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(\n    tensors: NamedTensorMap|NamedTensor[], group?: WeightGroup):\n    Promise<{data: ArrayBuffer, specs: WeightsManifestEntry[]}> {\n  // TODO(adarob, cais): Support quantization.\n  const specs: WeightsManifestEntry[] = [];\n  const dataPromises: Array<Promise<TypedArray>> = [];\n\n  const names: string[] = Array.isArray(tensors) ?\n      tensors.map(tensor => tensor.name) :\n      Object.keys(tensors);\n\n  for (let i = 0; i < names.length; ++i) {\n    const name = names[i];\n    const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n    if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n        t.dtype !== 'string' && t.dtype !== 'complex64') {\n      throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n    }\n    const spec: WeightsManifestEntry = {name, shape: t.shape, dtype: t.dtype};\n    if (t.dtype === 'string') {\n      const utf8bytes = new Promise<TypedArray>(async resolve => {\n        const vals = await t.bytes() as Uint8Array[];\n        const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n            NUM_BYTES_STRING_LENGTH * vals.length;\n        const bytes = new Uint8Array(totalNumBytes);\n        let offset = 0;\n        for (let i = 0; i < vals.length; i++) {\n          const val = vals[i];\n          const bytesOfLength =\n              new Uint8Array(new Uint32Array([val.length]).buffer);\n          bytes.set(bytesOfLength, offset);\n          offset += NUM_BYTES_STRING_LENGTH;\n          bytes.set(val, offset);\n          offset += val.length;\n        }\n        resolve(bytes);\n      });\n      dataPromises.push(utf8bytes);\n    } else {\n      dataPromises.push(t.data());\n    }\n    if (group != null) {\n      spec.group = group;\n    }\n    specs.push(spec);\n  }\n\n  const tensorValues = await Promise.all(dataPromises);\n  return {data: concatenateTypedArrays(tensorValues), specs};\n}\n\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(\n    buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap {\n  // TODO(adarob, cais): Support quantization.\n  const out: NamedTensorMap = {};\n  let float16Decode: (buffer: Uint16Array) => Float32Array | undefined;\n  let offset = 0;\n  for (const spec of specs) {\n    const name = spec.name;\n    const dtype = spec.dtype;\n    const shape = spec.shape;\n    const size = sizeFromShape(shape);\n    let values: TypedArray|string[]|Uint8Array[];\n\n    if ('quantization' in spec) {\n      const quantization = spec.quantization;\n      if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n        if (!('min' in quantization && 'scale' in quantization)) {\n          throw new Error(\n              `Weight ${spec.name} with quantization ${quantization.dtype} ` +\n              `doesn't have corresponding metadata min and scale.`);\n        }\n      } else if (quantization.dtype === 'float16') {\n        if (dtype !== 'float32') {\n          throw new Error(\n              `Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n              `which only supports weights of type float32 not ${dtype}.`);\n        }\n      } else {\n        throw new Error(\n            `Weight ${spec.name} has unknown ` +\n            `quantization dtype ${quantization.dtype}. ` +\n            `Supported quantization dtypes are: ` +\n            `'uint8', 'uint16', and 'float16'.`);\n      }\n      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n      const byteBuffer =\n          buffer.slice(offset, offset + size * quantizationSizeFactor);\n      const quantizedArray = (quantization.dtype === 'uint8') ?\n          new Uint8Array(byteBuffer) :\n          new Uint16Array(byteBuffer);\n      if (dtype === 'float32') {\n        if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n          values = new Float32Array(quantizedArray.length);\n          for (let i = 0; i < quantizedArray.length; i++) {\n            const v = quantizedArray[i];\n            values[i] = v * quantization.scale + quantization.min;\n          }\n        } else if (quantization.dtype === 'float16') {\n          if (float16Decode === undefined) {\n            float16Decode = getFloat16Decoder();\n          }\n          values = float16Decode(quantizedArray as Uint16Array);\n        } else {\n          throw new Error(\n              `Unsupported quantization type ${quantization.dtype} ` +\n              `for weight type float32.`);\n        }\n      } else if (dtype === 'int32') {\n        if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n          throw new Error(\n              `Unsupported quantization type ${quantization.dtype} ` +\n              `for weight type int32.`);\n        }\n        values = new Int32Array(quantizedArray.length);\n        for (let i = 0; i < quantizedArray.length; i++) {\n          const v = quantizedArray[i];\n          values[i] = Math.round(v * quantization.scale + quantization.min);\n        }\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * quantizationSizeFactor;\n    } else if (dtype === 'string') {\n      const size = sizeFromShape(spec.shape);\n      values = [];\n      for (let i = 0; i < size; i++) {\n        const byteLength = new Uint32Array(\n            buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n        offset += NUM_BYTES_STRING_LENGTH;\n        const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n        (values as Uint8Array[]).push(bytes);\n        offset += byteLength;\n      }\n    } else {\n      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n      const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n\n      if (dtype === 'float32') {\n        values = new Float32Array(byteBuffer);\n      } else if (dtype === 'int32') {\n        values = new Int32Array(byteBuffer);\n      } else if (dtype === 'bool') {\n        values = new Uint8Array(byteBuffer);\n      } else if (dtype === 'complex64') {\n        values = new Float32Array(byteBuffer);\n        const real = new Float32Array(values.length / 2);\n        const image = new Float32Array(values.length / 2);\n        for (let i = 0; i < real.length; i++) {\n          real[i] = values[i * 2];\n          image[i] = values[i * 2 + 1];\n        }\n        const realTensor = tensor(real, shape, 'float32');\n        const imageTensor = tensor(image, shape, 'float32');\n        out[name] = complex(realTensor, imageTensor);\n        realTensor.dispose();\n        imageTensor.dispose();\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * dtypeFactor;\n    }\n    if (dtype !== 'complex64') {\n      out[name] = tensor(values, shape, dtype);\n    }\n  }\n  return out;\n}\n\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs: TypedArray[]): ArrayBuffer {\n  // TODO(adarob, cais): Support quantization.\n  if (xs === null) {\n    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n  }\n\n  let totalByteLength = 0;\n\n  // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n  // can have a different byte length from that of the `TypedArray` itself,\n  // for example, when the `TypedArray` is created from an offset in an\n  // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n  // the `TypedArray` in byte length. If an element of `xs` does not show\n  // this property, a new `TypedArray` that satisfy this property will be\n  // constructed and pushed into `normalizedXs`.\n  const normalizedXs: TypedArray[] = [];\n  xs.forEach((x: TypedArray) => {\n    totalByteLength += x.byteLength;\n    // tslint:disable:no-any\n    normalizedXs.push(\n        x.byteLength === x.buffer.byteLength ? x :\n                                               new (x.constructor as any)(x));\n    if (!(x as any instanceof Float32Array || x as any instanceof Int32Array ||\n          x as any instanceof Uint8Array)) {\n      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n    }\n    // tslint:enable:no-any\n  });\n\n  const y = new Uint8Array(totalByteLength);\n  let offset = 0;\n  normalizedXs.forEach((x: TypedArray) => {\n    y.set(new Uint8Array(x.buffer), offset);\n    offset += x.byteLength;\n  });\n\n  return y.buffer;\n}\n\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n     typeof btoa === 'undefined');\n\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str: string): number {\n  if (useNodeBuffer) {\n    return Buffer.byteLength(str);\n  }\n  return new Blob([str]).size;\n}\n\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer: ArrayBuffer): string {\n  if (useNodeBuffer) {\n    return Buffer.from(buffer).toString('base64');\n  }\n  const buf = new Uint8Array(buffer);\n  let s = '';\n  for (let i = 0, l = buf.length; i < l; i++) {\n    s += String.fromCharCode(buf[i]);\n  }\n  return btoa(s);\n}\n\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str: string): ArrayBuffer {\n  if (useNodeBuffer) {\n    const buf = Buffer.from(str, 'base64');\n    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n  }\n  const s = atob(str);\n  const buffer = new Uint8Array(s.length);\n  for (let i = 0; i < s.length; ++i) {\n    buffer.set([s.charCodeAt(i)], i);\n  }\n  return buffer.buffer;\n}\n\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer {\n  if (buffers.length === 1) {\n    return buffers[0];\n  }\n\n  let totalByteLength = 0;\n  buffers.forEach((buffer: ArrayBuffer) => {\n    totalByteLength += buffer.byteLength;\n  });\n\n  const temp = new Uint8Array(totalByteLength);\n  let offset = 0;\n  buffers.forEach((buffer: ArrayBuffer) => {\n    temp.set(new Uint8Array(buffer), offset);\n    offset += buffer.byteLength;\n  });\n  return temp.buffer;\n}\n\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path: string): string {\n  const SEPARATOR = '/';\n  path = path.trim();\n  while (path.endsWith(SEPARATOR)) {\n    path = path.slice(0, path.length - 1);\n  }\n  const items = path.split(SEPARATOR);\n  return items[items.length - 1];\n}\n\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts):\n    ModelArtifactsInfo {\n  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n    throw new Error('Expected JSON model topology, received ArrayBuffer.');\n  }\n\n  return {\n    dateSaved: new Date(),\n    modelTopologyType: 'JSON',\n    modelTopologyBytes: modelArtifacts.modelTopology == null ?\n        0 :\n        stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n    weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n        0 :\n        stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n    weightDataBytes: modelArtifacts.weightData == null ?\n        0 :\n        modelArtifacts.weightData.byteLength,\n  };\n}\n\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable(): Uint32Array {\n  const convertMantissa = (i: number): number => {\n    let m = i << 13;\n    let e = 0;\n\n    while ((m & 0x00800000) === 0) {\n      e -= 0x00800000;\n      m <<= 1;\n    }\n    m &= ~0x00800000;\n    e += 0x38800000;\n\n    return m | e;\n  };\n\n  const mantisaTable = new Uint32Array(2048);\n\n  mantisaTable[0] = 0;\n  for (let i = 1; i < 1024; i++) {\n    mantisaTable[i] = convertMantissa(i);\n  }\n  for (let i = 1024; i < 2048; i++) {\n    mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n  }\n\n  return mantisaTable;\n}\n\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable(): Uint32Array {\n  const exponentTable = new Uint32Array(64);\n\n  exponentTable[0] = 0;\n  exponentTable[31] = 0x47800000;\n  exponentTable[32] = 0x80000000;\n  exponentTable[63] = 0xc7800000;\n  for (let i = 1; i < 31; i++) {\n    exponentTable[i] = i << 23;\n  }\n  for (let i = 33; i < 63; i++) {\n    exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n  }\n\n  return exponentTable;\n}\n\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable(): Uint32Array {\n  const offsetTable = new Uint32Array(64);\n\n  for (let i = 0; i < 64; i++) {\n    offsetTable[i] = 1024;\n  }\n  offsetTable[0] = offsetTable[32] = 0;\n\n  return offsetTable;\n}\n\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n *          the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder(): (buffer: Uint16Array) => Float32Array {\n  // Algorithm is based off of\n  // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n\n  // Cache lookup tables\n  const mantisaTable = computeFloat16MantisaTable();\n  const exponentTable = computeFloat16ExponentTable();\n  const offsetTable = computeFloat16OffsetTable();\n\n  return (quantizedArray: Uint16Array) => {\n    const buffer = new ArrayBuffer(4 * quantizedArray.length);\n    const bufferUint32View = new Uint32Array(buffer);\n    for (let index = 0; index < quantizedArray.length; index++) {\n      const float16Bits = quantizedArray[index];\n      const float32Bits =\n          mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n          exponentTable[float16Bits >> 10];\n      bufferUint32View[index] = float32Bits;\n    }\n    return new Float32Array(buffer);\n  };\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IOHandler, LoadOptions} from './types';\n\nexport type IORouter = (url: string|string[], loadOptions?: LoadOptions) =>\n    IOHandler;\n\nexport class IORouterRegistry {\n  // Singleton instance.\n  private static instance: IORouterRegistry;\n\n  private saveRouters: IORouter[];\n  private loadRouters: IORouter[];\n\n  private constructor() {\n    this.saveRouters = [];\n    this.loadRouters = [];\n  }\n\n  private static getInstance(): IORouterRegistry {\n    if (IORouterRegistry.instance == null) {\n      IORouterRegistry.instance = new IORouterRegistry();\n    }\n    return IORouterRegistry.instance;\n  }\n\n  /**\n   * Register a save-handler router.\n   *\n   * @param saveRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `save` method defined or `null`.\n   */\n  static registerSaveRouter(saveRouter: IORouter) {\n    IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n  }\n\n  /**\n   * Register a load-handler router.\n   *\n   * @param loadRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `load` method defined or `null`.\n   */\n  static registerLoadRouter(loadRouter: IORouter) {\n    IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n  }\n\n  /**\n   * Look up IOHandler for saving, given a URL-like string.\n   *\n   * @param url\n   * @returns If only one match is found, an instance of IOHandler with the\n   * `save` method defined. If no match is found, `null`.\n   * @throws Error, if more than one match is found.\n   */\n  static getSaveHandlers(url: string|string[]): IOHandler[] {\n    return IORouterRegistry.getHandlers(url, 'save');\n  }\n\n  /**\n   * Look up IOHandler for loading, given a URL-like string.\n   *\n   * @param url\n   * @param loadOptions Optional, custom load options.\n   * @returns All valid handlers for `url`, given the currently registered\n   *   handler routers.\n   */\n  static getLoadHandlers(url: string|string[], loadOptions?: LoadOptions):\n      IOHandler[] {\n    return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n  }\n\n  private static getHandlers(\n      url: string|string[], handlerType: 'save'|'load',\n      loadOptions?: LoadOptions): IOHandler[] {\n    const validHandlers: IOHandler[] = [];\n    const routers = handlerType === 'load' ?\n        IORouterRegistry.getInstance().loadRouters :\n        IORouterRegistry.getInstance().saveRouters;\n    routers.forEach(router => {\n      const handler = router(url, loadOptions);\n      if (handler !== null) {\n        validHandlers.push(handler);\n      }\n    });\n    return validHandlers;\n  }\n}\n\nexport const registerSaveRouter = (loudRouter: IORouter) =>\n    IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter: IORouter) =>\n    IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url: string|string[]) =>\n    IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers =\n    (url: string|string[], loadOptions?: LoadOptions) =>\n        IORouterRegistry.getLoadHandlers(url, loadOptions);\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport '../flags';\n\nimport {env} from '../environment';\n\nimport {getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase(): Promise<void> {\n  const idbFactory = getIndexedDBFactory();\n\n  return new Promise<void>((resolve, reject) => {\n    const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n    deleteRequest.onsuccess = () => resolve();\n    deleteRequest.onerror = error => reject(error);\n  });\n}\n\nfunction getIndexedDBFactory(): IDBFactory {\n  if (!env().getBool('IS_BROWSER')) {\n    // TODO(cais): Add more info about what IOHandler subtypes are available.\n    //   Maybe point to a doc page on the web and/or automatically determine\n    //   the available IOHandlers and print them in the error message.\n    throw new Error(\n        'Failed to obtain IndexedDB factory because the current environment' +\n        'is not a web browser.');\n  }\n  // tslint:disable-next-line:no-any\n  const theWindow: any = typeof window === 'undefined' ? self : window;\n  const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n      theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n      theWindow.shimIndexedDB;\n  if (factory == null) {\n    throw new Error(\n        'The current browser does not appear to support IndexedDB.');\n  }\n  return factory;\n}\n\nfunction setUpDatabase(openRequest: IDBRequest) {\n  const db = openRequest.result as IDBDatabase;\n  db.createObjectStore(MODEL_STORE_NAME, {keyPath: 'modelPath'});\n  db.createObjectStore(INFO_STORE_NAME, {keyPath: 'modelPath'});\n}\n\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB implements IOHandler {\n  protected readonly indexedDB: IDBFactory;\n  protected readonly modelPath: string;\n\n  static readonly URL_SCHEME = 'indexeddb://';\n\n  constructor(modelPath: string) {\n    this.indexedDB = getIndexedDBFactory();\n\n    if (modelPath == null || !modelPath) {\n      throw new Error(\n          'For IndexedDB, modelPath must not be null, undefined or empty.');\n    }\n    this.modelPath = modelPath;\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    // TODO(cais): Support saving GraphDef models.\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserLocalStorage.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    }\n\n    return this.databaseAction(this.modelPath, modelArtifacts) as\n        Promise<SaveResult>;\n  }\n\n  async load(): Promise<ModelArtifacts> {\n    return this.databaseAction(this.modelPath) as Promise<ModelArtifacts>;\n  }\n\n  /**\n   * Perform database action to put model artifacts into or read model artifacts\n   * from IndexedDB object store.\n   *\n   * Whether the action is put or get depends on whether `modelArtifacts` is\n   * specified. If it is specified, the action will be put; otherwise the action\n   * will be get.\n   *\n   * @param modelPath A unique string path for the model.\n   * @param modelArtifacts If specified, it will be the model artifacts to be\n   *   stored in IndexedDB.\n   * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n   *   of `ModelArtifacts`, if the action is get.\n   */\n  private databaseAction(modelPath: string, modelArtifacts?: ModelArtifacts):\n      Promise<ModelArtifacts|SaveResult> {\n    return new Promise<ModelArtifacts|SaveResult>((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n\n        if (modelArtifacts == null) {\n          // Read model out from object store.\n          const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n          const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n          const getRequest = modelStore.get(this.modelPath);\n          getRequest.onsuccess = () => {\n            if (getRequest.result == null) {\n              db.close();\n              return reject(new Error(\n                  `Cannot find model with path '${this.modelPath}' ` +\n                  `in IndexedDB.`));\n            } else {\n              resolve(getRequest.result.modelArtifacts);\n            }\n          };\n          getRequest.onerror = error => {\n            db.close();\n            return reject(getRequest.error);\n          };\n          modelTx.oncomplete = () => db.close();\n        } else {\n          // Put model into object store.\n          const modelArtifactsInfo: ModelArtifactsInfo =\n              getModelArtifactsInfoForJSON(modelArtifacts);\n          // First, put ModelArtifactsInfo into info store.\n          const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n          let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n          const putInfoRequest =\n              infoStore.put({modelPath: this.modelPath, modelArtifactsInfo});\n          let modelTx: IDBTransaction;\n          putInfoRequest.onsuccess = () => {\n            // Second, put model data into model store.\n            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n            const putModelRequest = modelStore.put({\n              modelPath: this.modelPath,\n              modelArtifacts,\n              modelArtifactsInfo\n            });\n            putModelRequest.onsuccess = () => resolve({modelArtifactsInfo});\n            putModelRequest.onerror = error => {\n              // If the put-model request fails, roll back the info entry as\n              // well.\n              infoStore = infoTx.objectStore(INFO_STORE_NAME);\n              const deleteInfoRequest = infoStore.delete(this.modelPath);\n              deleteInfoRequest.onsuccess = () => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n              deleteInfoRequest.onerror = error => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n            };\n          };\n          putInfoRequest.onerror = error => {\n            db.close();\n            return reject(putInfoRequest.error);\n          };\n          infoTx.oncomplete = () => {\n            if (modelTx == null) {\n              db.close();\n            } else {\n              modelTx.oncomplete = () => db.close();\n            }\n          };\n        }\n      };\n      openRequest.onerror = error => reject(openRequest.error);\n    });\n  }\n}\n\nexport const indexedDBRouter: IORouter = (url: string|string[]) => {\n  if (!env().getBool('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n      return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath: string): IOHandler {\n  return new BrowserIndexedDB(modelPath);\n}\n\nfunction maybeStripScheme(key: string) {\n  return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n      key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n      key;\n}\n\nexport class BrowserIndexedDBManager implements ModelStoreManager {\n  private indexedDB: IDBFactory;\n\n  constructor() {\n    this.indexedDB = getIndexedDBFactory();\n  }\n\n  async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n    return new Promise<{[path: string]: ModelArtifactsInfo}>(\n        (resolve, reject) => {\n          const openRequest =\n              this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n          openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n          openRequest.onsuccess = () => {\n            const db = openRequest.result;\n            const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n            const store = tx.objectStore(INFO_STORE_NAME);\n            // tslint:disable:max-line-length\n            // Need to cast `store` as `any` here because TypeScript's DOM\n            // library does not have the `getAll()` method even though the\n            // method is supported in the latest version of most mainstream\n            // browsers:\n            // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n            // tslint:enable:max-line-length\n            // tslint:disable-next-line:no-any\n            const getAllInfoRequest = (store as any).getAll() as IDBRequest;\n            getAllInfoRequest.onsuccess = () => {\n              const out: {[path: string]: ModelArtifactsInfo} = {};\n              for (const item of getAllInfoRequest.result) {\n                out[item.modelPath] = item.modelArtifactsInfo;\n              }\n              resolve(out);\n            };\n            getAllInfoRequest.onerror = error => {\n              db.close();\n              return reject(getAllInfoRequest.error);\n            };\n            tx.oncomplete = () => db.close();\n          };\n          openRequest.onerror = error => reject(openRequest.error);\n        });\n  }\n\n  async removeModel(path: string): Promise<ModelArtifactsInfo> {\n    path = maybeStripScheme(path);\n    return new Promise<ModelArtifactsInfo>((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n        const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n        const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n\n        const getInfoRequest = infoStore.get(path);\n        let modelTx: IDBTransaction;\n        getInfoRequest.onsuccess = () => {\n          if (getInfoRequest.result == null) {\n            db.close();\n            return reject(new Error(\n                `Cannot find model with path '${path}' ` +\n                `in IndexedDB.`));\n          } else {\n            // First, delete the entry in the info store.\n            const deleteInfoRequest = infoStore.delete(path);\n            const deleteModelData = () => {\n              // Second, delete the entry in the model store.\n              modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n              const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n              const deleteModelRequest = modelStore.delete(path);\n              deleteModelRequest.onsuccess = () =>\n                  resolve(getInfoRequest.result.modelArtifactsInfo);\n              deleteModelRequest.onerror = error =>\n                  reject(getInfoRequest.error);\n            };\n            // Proceed with deleting model data regardless of whether deletion\n            // of info data succeeds or not.\n            deleteInfoRequest.onsuccess = deleteModelData;\n            deleteInfoRequest.onerror = error => {\n              deleteModelData();\n              db.close();\n              return reject(getInfoRequest.error);\n            };\n          }\n        };\n        getInfoRequest.onerror = error => {\n          db.close();\n          return reject(getInfoRequest.error);\n        };\n\n        infoTx.oncomplete = () => {\n          if (modelTx == null) {\n            db.close();\n          } else {\n            modelTx.oncomplete = () => db.close();\n          }\n        };\n      };\n      openRequest.onerror = error => reject(openRequest.error);\n    });\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport '../flags';\nimport {env} from '../environment';\n\nimport {assert} from '../util';\nimport {arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts(): string[] {\n  if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n      typeof window.localStorage === 'undefined') {\n    throw new Error(\n        'purgeLocalStorageModels() cannot proceed because local storage is ' +\n        'unavailable in the current environment.');\n  }\n  const LS = window.localStorage;\n  const purgedModelPaths: string[] = [];\n  for (let i = 0; i < LS.length; ++i) {\n    const key = LS.key(i);\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    if (key.startsWith(prefix) && key.length > prefix.length) {\n      LS.removeItem(key);\n      const modelName = getModelPathFromKey(key);\n      if (purgedModelPaths.indexOf(modelName) === -1) {\n        purgedModelPaths.push(modelName);\n      }\n    }\n  }\n  return purgedModelPaths;\n}\n\nfunction getModelKeys(path: string): {\n  info: string,\n  topology: string,\n  weightSpecs: string,\n  weightData: string,\n  modelMetadata: string\n} {\n  return {\n    info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n    topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n    weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n    weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n    modelMetadata:\n        [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n  };\n}\n\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key: string) {\n  const items = key.split(PATH_SEPARATOR);\n  if (items.length < 3) {\n    throw new Error(`Invalid key format: ${key}`);\n  }\n  return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\n\nfunction maybeStripScheme(key: string) {\n  return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n      key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n      key;\n}\n\ndeclare type LocalStorageKeys = {\n  info: string,\n  topology: string,\n  weightSpecs: string,\n  weightData: string,\n  modelMetadata: string\n};\n\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage implements IOHandler {\n  protected readonly LS: Storage;\n  protected readonly modelPath: string;\n  protected readonly keys: LocalStorageKeys;\n\n  static readonly URL_SCHEME = 'localstorage://';\n\n  constructor(modelPath: string) {\n    if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n        typeof window.localStorage === 'undefined') {\n      // TODO(cais): Add more info about what IOHandler subtypes are\n      // available.\n      //   Maybe point to a doc page on the web and/or automatically determine\n      //   the available IOHandlers and print them in the error message.\n      throw new Error(\n          'The current environment does not support local storage.');\n    }\n    this.LS = window.localStorage;\n\n    if (modelPath == null || !modelPath) {\n      throw new Error(\n          'For local storage, modelPath must not be null, undefined or empty.');\n    }\n    this.modelPath = modelPath;\n    this.keys = getModelKeys(this.modelPath);\n  }\n\n  /**\n   * Save model artifacts to browser local storage.\n   *\n   * See the documentation to `browserLocalStorage` for details on the saved\n   * artifacts.\n   *\n   * @param modelArtifacts The model artifacts to be stored.\n   * @returns An instance of SaveResult.\n   */\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserLocalStorage.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    } else {\n      const topology = JSON.stringify(modelArtifacts.modelTopology);\n      const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n\n      const modelArtifactsInfo: ModelArtifactsInfo =\n          getModelArtifactsInfoForJSON(modelArtifacts);\n\n      try {\n        this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n        this.LS.setItem(this.keys.topology, topology);\n        this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n        this.LS.setItem(\n            this.keys.weightData,\n            arrayBufferToBase64String(modelArtifacts.weightData));\n        const result: ModelArtifacts = {\n          format: modelArtifacts.format,\n          generatedBy: modelArtifacts.generatedBy,\n          convertedBy: modelArtifacts.convertedBy\n        };\n        if (modelArtifacts.signature != null) {\n          result.signature = modelArtifacts.signature;\n        }\n        if (modelArtifacts.userDefinedMetadata != null) {\n          result.userDefinedMetadata = modelArtifacts.userDefinedMetadata;\n        }\n        if (modelArtifacts.modelInitializer != null) {\n          result.modelInitializer = modelArtifacts.modelInitializer;\n        }\n        this.LS.setItem(this.keys.modelMetadata, JSON.stringify(result));\n\n        return {modelArtifactsInfo};\n      } catch (err) {\n        // If saving failed, clean up all items saved so far.\n        this.LS.removeItem(this.keys.info);\n        this.LS.removeItem(this.keys.topology);\n        this.LS.removeItem(this.keys.weightSpecs);\n        this.LS.removeItem(this.keys.weightData);\n        this.LS.removeItem(this.keys.modelMetadata);\n\n        throw new Error(\n            `Failed to save model '${this.modelPath}' to local storage: ` +\n            `size quota being exceeded is a possible cause of this failure: ` +\n            `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n            `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n            `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n      }\n    }\n  }\n\n  /**\n   * Load a model from local storage.\n   *\n   * See the documentation to `browserLocalStorage` for details on the saved\n   * artifacts.\n   *\n   * @returns The loaded model (if loading succeeds).\n   */\n  async load(): Promise<ModelArtifacts> {\n    const info =\n        JSON.parse(this.LS.getItem(this.keys.info)) as ModelArtifactsInfo;\n    if (info == null) {\n      throw new Error(\n          `In local storage, there is no model with name '${this.modelPath}'`);\n    }\n\n    if (info.modelTopologyType !== 'JSON') {\n      throw new Error(\n          'BrowserLocalStorage does not support loading non-JSON model ' +\n          'topology yet.');\n    }\n\n    const out: ModelArtifacts = {};\n\n    // Load topology.\n    const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n    if (topology == null) {\n      throw new Error(\n          `In local storage, the topology of model '${this.modelPath}' ` +\n          `is missing.`);\n    }\n    out.modelTopology = topology;\n\n    // Load weight specs.\n    const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n    if (weightSpecs == null) {\n      throw new Error(\n          `In local storage, the weight specs of model '${this.modelPath}' ` +\n          `are missing.`);\n    }\n    out.weightSpecs = weightSpecs;\n\n    // Load meta-data fields.\n    const metadataString = this.LS.getItem(this.keys.modelMetadata);\n    if (metadataString != null) {\n      const metadata = JSON.parse(metadataString) as ModelArtifacts;\n      out.format = metadata['format'];\n      out.generatedBy = metadata['generatedBy'];\n      out.convertedBy = metadata['convertedBy'];\n      if (metadata['signature'] != null) {\n        out.signature = metadata['signature'];\n      }\n      if (metadata['userDefinedMetadata'] != null) {\n        out.userDefinedMetadata = metadata['userDefinedMetadata'];\n      }\n      if (metadata['modelInitializer'] != null) {\n        out.modelInitializer = metadata['modelInitializer'];\n      }\n    }\n\n    // Load weight data.\n    const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n    if (weightDataBase64 == null) {\n      throw new Error(\n          `In local storage, the binary weight values of model ` +\n          `'${this.modelPath}' are missing.`);\n    }\n    out.weightData = base64StringToArrayBuffer(weightDataBase64);\n\n    return out;\n  }\n}\n\nexport const localStorageRouter: IORouter = (url: string|string[]) => {\n  if (!env().getBool('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n      return browserLocalStorage(\n          url.slice(BrowserLocalStorage.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath: string): IOHandler {\n  return new BrowserLocalStorage(modelPath);\n}\n\nexport class BrowserLocalStorageManager implements ModelStoreManager {\n  private readonly LS: Storage;\n\n  constructor() {\n    assert(\n        env().getBool('IS_BROWSER'),\n        () => 'Current environment is not a web browser');\n    assert(\n        typeof window === 'undefined' ||\n            typeof window.localStorage !== 'undefined',\n        () => 'Current browser does not appear to support localStorage');\n    this.LS = window.localStorage;\n  }\n\n  async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n    const out: {[path: string]: ModelArtifactsInfo} = {};\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n    for (let i = 0; i < this.LS.length; ++i) {\n      const key = this.LS.key(i);\n      if (key.startsWith(prefix) && key.endsWith(suffix)) {\n        const modelPath = getModelPathFromKey(key);\n        out[modelPath] = JSON.parse(this.LS.getItem(key)) as ModelArtifactsInfo;\n      }\n    }\n    return out;\n  }\n\n  async removeModel(path: string): Promise<ModelArtifactsInfo> {\n    path = maybeStripScheme(path);\n    const keys = getModelKeys(path);\n    if (this.LS.getItem(keys.info) == null) {\n      throw new Error(`Cannot find model at path '${path}'`);\n    }\n    const info = JSON.parse(this.LS.getItem(keys.info)) as ModelArtifactsInfo;\n\n    this.LS.removeItem(keys.info);\n    this.LS.removeItem(keys.topology);\n    this.LS.removeItem(keys.weightSpecs);\n    this.LS.removeItem(keys.weightData);\n    return info;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\n\nimport {assert} from '../util';\n\nimport {IORouterRegistry} from './router_registry';\nimport {ModelArtifactsInfo, ModelStoreManager} from './types';\n\nconst URL_SCHEME_SUFFIX = '://';\n\nexport class ModelStoreManagerRegistry {\n  // Singleton instance.\n  private static instance: ModelStoreManagerRegistry;\n\n  private managers: {[scheme: string]: ModelStoreManager};\n\n  private constructor() {\n    this.managers = {};\n  }\n\n  private static getInstance(): ModelStoreManagerRegistry {\n    if (ModelStoreManagerRegistry.instance == null) {\n      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n    }\n    return ModelStoreManagerRegistry.instance;\n  }\n\n  /**\n   * Register a save-handler router.\n   *\n   * @param saveRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `save` method defined or `null`.\n   */\n  static registerManager(scheme: string, manager: ModelStoreManager) {\n    assert(scheme != null, () => 'scheme must not be undefined or null.');\n    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n    }\n    assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n    const registry = ModelStoreManagerRegistry.getInstance();\n    assert(\n        registry.managers[scheme] == null,\n        () => `A model store manager is already registered for scheme '${\n            scheme}'.`);\n    registry.managers[scheme] = manager;\n  }\n\n  static getManager(scheme: string): ModelStoreManager {\n    const manager = this.getInstance().managers[scheme];\n    if (manager == null) {\n      throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n    }\n    return manager;\n  }\n\n  static getSchemes(): string[] {\n    return Object.keys(this.getInstance().managers);\n  }\n}\n\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url: string): {scheme: string, path: string} {\n  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n    throw new Error(\n        `The url string provided does not contain a scheme. ` +\n        `Supported schemes are: ` +\n        `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n  }\n  return {\n    scheme: url.split(URL_SCHEME_SUFFIX)[0],\n    path: url.split(URL_SCHEME_SUFFIX)[1],\n  };\n}\n\nasync function cloneModelInternal(\n    sourceURL: string, destURL: string,\n    deleteSource = false): Promise<ModelArtifactsInfo> {\n  assert(\n      sourceURL !== destURL,\n      () => `Old path and new path are the same: '${sourceURL}'`);\n\n  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n  assert(\n      loadHandlers.length > 0,\n      () => `Copying failed because no load handler is found for source URL ${\n          sourceURL}.`);\n  assert(\n      loadHandlers.length < 2,\n      () => `Copying failed because more than one (${loadHandlers.length}) ` +\n          `load handlers for source URL ${sourceURL}.`);\n  const loadHandler = loadHandlers[0];\n\n  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n  assert(\n      saveHandlers.length > 0,\n      () => `Copying failed because no save handler is found for destination ` +\n          `URL ${destURL}.`);\n  assert(\n      saveHandlers.length < 2,\n      () => `Copying failed because more than one (${loadHandlers.length}) ` +\n          `save handlers for destination URL ${destURL}.`);\n  const saveHandler = saveHandlers[0];\n\n  const sourceScheme = parseURL(sourceURL).scheme;\n  const sourcePath = parseURL(sourceURL).path;\n  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n\n  const modelArtifacts = await loadHandler.load();\n\n  // If moving within the same storage medium, remove the old model as soon as\n  // the loading is done. Without doing this, it is possible that the combined\n  // size of the two models will cause the cloning to fail.\n  if (deleteSource && sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme)\n        .removeModel(sourcePath);\n  }\n\n  const saveResult = await saveHandler.save(modelArtifacts);\n\n  // If moving between mediums, the deletion is done after the save succeeds.\n  // This guards against the case in which saving to the destination medium\n  // fails.\n  if (deleteSource && !sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme)\n        .removeModel(sourcePath);\n  }\n\n  return saveResult.modelArtifactsInfo;\n}\n\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function listModels(): Promise<{[url: string]: ModelArtifactsInfo}> {\n  const schemes = ModelStoreManagerRegistry.getSchemes();\n  const out: {[url: string]: ModelArtifactsInfo} = {};\n  for (const scheme of schemes) {\n    const schemeOut =\n        await ModelStoreManagerRegistry.getManager(scheme).listModels();\n    for (const path in schemeOut) {\n      const url = scheme + URL_SCHEME_SUFFIX + path;\n      out[url] = schemeOut[path];\n    }\n  }\n  return out;\n}\n\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function removeModel(url: string): Promise<ModelArtifactsInfo> {\n  const schemeAndPath = parseURL(url);\n  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n  return manager.removeModel(schemeAndPath.path);\n}\n\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function copyModel(\n    sourceURL: string, destURL: string): Promise<ModelArtifactsInfo> {\n  const deleteSource = false;\n  return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function moveModel(\n    sourceURL: string, destURL: string): Promise<ModelArtifactsInfo> {\n  const deleteSource = true;\n  return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\nexport {moveModel, copyModel, removeModel, listModels};\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport '../flags';\n\nimport {env} from '../environment';\nimport {BrowserIndexedDB, BrowserIndexedDBManager} from '../io/indexed_db';\nimport {BrowserLocalStorage, BrowserLocalStorageManager} from '../io/local_storage';\nimport {ModelStoreManagerRegistry} from '../io/model_management';\n\nimport {Platform} from './platform';\n\nexport class PlatformBrowser implements Platform {\n  // According to the spec, the built-in encoder can do only UTF-8 encoding.\n  // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n  private textEncoder: TextEncoder;\n\n  fetch(path: string, init?: RequestInit): Promise<Response> {\n    return fetch(path, init);\n  }\n\n  now(): number {\n    return performance.now();\n  }\n\n  encode(text: string, encoding: string): Uint8Array {\n    if (encoding !== 'utf-8' && encoding !== 'utf8') {\n      throw new Error(\n          `Browser's encoder only supports utf-8, but got ${encoding}`);\n    }\n    if (this.textEncoder == null) {\n      this.textEncoder = new TextEncoder();\n    }\n    return this.textEncoder.encode(text);\n  }\n  decode(bytes: Uint8Array, encoding: string): string {\n    return new TextDecoder(encoding).decode(bytes);\n  }\n}\n\nif (env().get('IS_BROWSER')) {\n  env().setPlatform('browser', new PlatformBrowser());\n\n  // Register LocalStorage IOHandler\n  try {\n    ModelStoreManagerRegistry.registerManager(\n        BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n  } catch (err) {\n  }\n\n  // Register IndexedDB IOHandler\n  try {\n    ModelStoreManagerRegistry.registerManager(\n        BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n  } catch (err) {\n  }\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {env} from '../environment';\n\nimport {Platform} from './platform';\n\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n  // tslint:disable-next-line:no-require-imports\n  importFetch: () => require('node-fetch')\n};\n\ntype FetchFn = (url: string, init?: RequestInit) => Promise<Response>;\nlet systemFetch: FetchFn;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n  systemFetch = null;\n}\nexport function setSystemFetch(fetchFn: FetchFn) {\n  systemFetch = fetchFn;\n}\nexport function getSystemFetch(): FetchFn {\n  return systemFetch;\n}\n\nexport class PlatformNode implements Platform {\n  private textEncoder: TextEncoder;\n  // tslint:disable-next-line:no-any\n  util: any;\n\n  constructor() {\n    // tslint:disable-next-line:no-require-imports\n    this.util = require('util');\n    // According to the spec, the built-in encoder can do only UTF-8 encoding.\n    // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n    this.textEncoder = new this.util.TextEncoder();\n  }\n\n  fetch(path: string, requestInits?: RequestInit): Promise<Response> {\n    if (env().global.fetch != null) {\n      return env().global.fetch(path, requestInits);\n    }\n\n    if (systemFetch == null) {\n      systemFetch = getNodeFetch.importFetch();\n    }\n    return systemFetch(path, requestInits);\n  }\n\n  now(): number {\n    const time = process.hrtime();\n    return time[0] * 1000 + time[1] / 1000000;\n  }\n\n  encode(text: string, encoding: string): Uint8Array {\n    if (encoding !== 'utf-8' && encoding !== 'utf8') {\n      throw new Error(\n          `Node built-in encoder only supports utf-8, but got ${encoding}`);\n    }\n    return this.textEncoder.encode(text);\n  }\n  decode(bytes: Uint8Array, encoding: string): string {\n    if (bytes.length === 0) {\n      return '';\n    }\n    return new this.util.TextDecoder(encoding).decode(bytes);\n  }\n}\n\nif (env().get('IS_NODE')) {\n  env().setPlatform('node', new PlatformNode());\n}\n","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorBuffer} from '../tensor';\nimport {DataType, DataTypeMap, Rank, ShapeMap} from '../types';\nimport * as util from '../util';\n\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer<R extends Rank, D extends DataType = 'float32'>(\n    shape: ShapeMap[R], dtype: D = 'float32' as D,\n    values?: DataTypeMap[D]): TensorBuffer<R, D> {\n  dtype = dtype || 'float32' as D;\n  util.assertNonNegativeIntegerDimensions(shape);\n  return new TensorBuffer<R, D>(shape, dtype, values);\n}\n","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Cast, CastAttrs, CastInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {DataType, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_<T extends Tensor>(x: T|TensorLike, dtype: DataType): T {\n  const $x = convertToTensor(x, 'x', 'cast');\n\n  // Sanity checks.\n  if (!util.isValidDtype(dtype)) {\n    throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n  }\n  if (dtype === 'string' && $x.dtype !== 'string' ||\n      dtype !== 'string' && $x.dtype === 'string') {\n    throw new Error('Only strings can be casted to strings');\n  }\n\n  const inputs: CastInputs = {x: $x};\n  const attrs: CastAttrs = {dtype};\n\n  return ENGINE.runKernel(\n      Cast, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const cast = op({cast_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Identity, IdentityInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'clone', 'string_or_numeric');\n  const inputs: IdentityInputs = {x: $x};\n\n  // Note this op is called tf.identity in python. Hence the kernel name used\n  // here.\n  return ENGINE.runKernel(Identity, inputs as {} as NamedTensorMap);\n}\n\nexport const clone = op({clone_});\n","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\n\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print<T extends Tensor>(x: T, verbose = false): void {\n  console.log(x.toString(verbose));\n}\n","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Required side effectful code for tfjs-core\n\n// Set up Engine and ENV\nimport {getOrMakeEngine} from './engine';\ngetOrMakeEngine();\n\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n\n// Set up OpHandler\nimport {buffer} from './ops/buffer';\nimport {cast} from './ops/cast';\nimport {clone} from './ops/clone';\nimport {print} from './ops/print';\nimport {OpHandler, setOpHandler} from './tensor';\nconst opHandler: OpHandler = {\n  buffer,\n  cast,\n  clone,\n  print\n};\nsetOpHandler(opHandler);\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\n\nimport '../flags';\nimport {env} from '../environment';\n\nimport {basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelJSON, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\n\nfunction defer<T>(f: () => T): Promise<T> {\n  return new Promise(resolve => setTimeout(resolve)).then(f);\n}\n\nexport class BrowserDownloads implements IOHandler {\n  private readonly modelTopologyFileName: string;\n  private readonly weightDataFileName: string;\n  private readonly jsonAnchor: HTMLAnchorElement;\n  private readonly weightDataAnchor: HTMLAnchorElement;\n\n  static readonly URL_SCHEME = 'downloads://';\n\n  constructor(fileNamePrefix?: string) {\n    if (!env().getBool('IS_BROWSER')) {\n      // TODO(cais): Provide info on what IOHandlers are available under the\n      //   current environment.\n      throw new Error(\n          'browserDownloads() cannot proceed because the current environment ' +\n          'is not a browser.');\n    }\n\n    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n    }\n    if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n    }\n\n    this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n    this.weightDataFileName =\n        fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    if (typeof (document) === 'undefined') {\n      throw new Error(\n          'Browser downloads are not supported in ' +\n          'this environment since `document` is not present');\n    }\n    const weightsURL = window.URL.createObjectURL(new Blob(\n        [modelArtifacts.weightData], {type: 'application/octet-stream'}));\n\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserDownloads.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    } else {\n      const weightsManifest: WeightsManifestConfig = [{\n        paths: ['./' + this.weightDataFileName],\n        weights: modelArtifacts.weightSpecs\n      }];\n      const modelTopologyAndWeightManifest: ModelJSON = {\n        modelTopology: modelArtifacts.modelTopology,\n        format: modelArtifacts.format,\n        generatedBy: modelArtifacts.generatedBy,\n        convertedBy: modelArtifacts.convertedBy,\n        weightsManifest\n      };\n      if (modelArtifacts.signature != null) {\n        modelTopologyAndWeightManifest.signature = modelArtifacts.signature;\n      }\n      if (modelArtifacts.userDefinedMetadata != null) {\n        modelTopologyAndWeightManifest.userDefinedMetadata =\n            modelArtifacts.userDefinedMetadata;\n      }\n      if (modelArtifacts.modelInitializer != null) {\n        modelTopologyAndWeightManifest.modelInitializer =\n            modelArtifacts.modelInitializer;\n      }\n      const modelTopologyAndWeightManifestURL =\n          window.URL.createObjectURL(new Blob(\n              [JSON.stringify(modelTopologyAndWeightManifest)],\n              {type: 'application/json'}));\n\n      // If anchor elements are not provided, create them without attaching them\n      // to parents, so that the downloaded file names can be controlled.\n      const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                                                   this.jsonAnchor;\n      jsonAnchor.download = this.modelTopologyFileName;\n      jsonAnchor.href = modelTopologyAndWeightManifestURL;\n      // Trigger downloads by evoking a click event on the download anchors.\n      // When multiple downloads are started synchronously, Firefox will only\n      // save the last one.\n      await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n\n      if (modelArtifacts.weightData != null) {\n        const weightDataAnchor = this.weightDataAnchor == null ?\n            document.createElement('a') :\n            this.weightDataAnchor;\n        weightDataAnchor.download = this.weightDataFileName;\n        weightDataAnchor.href = weightsURL;\n        await defer(\n            () => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n      }\n\n      return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};\n    }\n  }\n}\n\nclass BrowserFiles implements IOHandler {\n  private readonly files: File[];\n\n  constructor(files: File[]) {\n    if (files == null || files.length < 1) {\n      throw new Error(\n          `When calling browserFiles, at least 1 file is required, ` +\n          `but received ${files}`);\n    }\n    this.files = files;\n  }\n\n  async load(): Promise<ModelArtifacts> {\n    const jsonFile = this.files[0];\n    const weightFiles = this.files.slice(1);\n\n    return new Promise<ModelArtifacts>((resolve, reject) => {\n      const jsonReader = new FileReader();\n      jsonReader.onload = (event: Event) => {\n        // tslint:disable-next-line:no-any\n        const modelJSON = JSON.parse((event.target as any).result) as ModelJSON;\n        const modelTopology = modelJSON.modelTopology;\n        if (modelTopology == null) {\n          reject(new Error(\n              `modelTopology field is missing from file ${jsonFile.name}`));\n          return;\n        }\n\n        if (weightFiles.length === 0) {\n          resolve({modelTopology});\n        }\n\n        const weightsManifest = modelJSON.weightsManifest;\n        if (weightsManifest == null) {\n          reject(new Error(\n              `weightManifest field is missing from file ${jsonFile.name}`));\n          return;\n        }\n\n        let pathToFile: {[path: string]: File};\n        try {\n          pathToFile =\n              this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n        } catch (err) {\n          reject(err);\n          return;\n        }\n\n        const weightSpecs: WeightsManifestEntry[] = [];\n        const paths: string[] = [];\n        const perFileBuffers: ArrayBuffer[] = [];\n        weightsManifest.forEach(weightsGroup => {\n          weightsGroup.paths.forEach(path => {\n            paths.push(path);\n            perFileBuffers.push(null);\n          });\n          weightSpecs.push(...weightsGroup.weights);\n        });\n\n        weightsManifest.forEach(weightsGroup => {\n          weightsGroup.paths.forEach(path => {\n            const weightFileReader = new FileReader();\n            weightFileReader.onload = (event: Event) => {\n              // tslint:disable-next-line:no-any\n              const weightData = (event.target as any).result as ArrayBuffer;\n              const index = paths.indexOf(path);\n              perFileBuffers[index] = weightData;\n              if (perFileBuffers.indexOf(null) === -1) {\n                const result: ModelArtifacts = {\n                  modelTopology,\n                  weightSpecs,\n                  weightData: concatenateArrayBuffers(perFileBuffers),\n                  format: modelJSON.format,\n                  generatedBy: modelJSON.generatedBy,\n                  convertedBy: modelJSON.convertedBy\n                };\n                if (modelJSON.signature != null) {\n                  result.signature = modelJSON.signature;\n                }\n                if (modelJSON.userDefinedMetadata != null) {\n                  result.userDefinedMetadata = modelJSON.userDefinedMetadata;\n                }\n                if (modelJSON.modelInitializer != null) {\n                  result.modelInitializer = modelJSON.modelInitializer;\n                }\n                resolve(result);\n              }\n            };\n            weightFileReader.onerror = error =>\n                reject(`Failed to weights data from file of path '${path}'.`);\n            weightFileReader.readAsArrayBuffer(pathToFile[path]);\n          });\n        });\n      };\n      jsonReader.onerror = error => reject(\n          `Failed to read model topology and weights manifest JSON ` +\n          `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n          `Keras-style tf.Model artifacts only.`);\n      jsonReader.readAsText(jsonFile);\n    });\n  }\n\n  /**\n   * Check the compatibility between weights manifest and weight files.\n   */\n  private checkManifestAndWeightFiles(\n      manifest: WeightsManifestConfig, files: File[]): {[path: string]: File} {\n    const basenames: string[] = [];\n    const fileNames = files.map(file => basename(file.name));\n    const pathToFile: {[path: string]: File} = {};\n    for (const group of manifest) {\n      group.paths.forEach(path => {\n        const pathBasename = basename(path);\n        if (basenames.indexOf(pathBasename) !== -1) {\n          throw new Error(\n              `Duplicate file basename found in weights manifest: ` +\n              `'${pathBasename}'`);\n        }\n        basenames.push(pathBasename);\n        if (fileNames.indexOf(pathBasename) === -1) {\n          throw new Error(\n              `Weight file with basename '${pathBasename}' is not provided.`);\n        } else {\n          pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n        }\n      });\n    }\n\n    if (basenames.length !== files.length) {\n      throw new Error(\n          `Mismatch in the number of files in weights manifest ` +\n          `(${basenames.length}) and the number of weight files provided ` +\n          `(${files.length}).`);\n    }\n    return pathToFile;\n  }\n}\n\nexport const browserDownloadsRouter: IORouter = (url: string|string[]) => {\n  if (!env().getBool('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model'): IOHandler {\n  return new BrowserDownloads(fileNamePrefix);\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserFiles(files: File[]): IOHandler {\n  return new BrowserFiles(files);\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {assert} from '../util';\n\nimport {OnProgressCallback} from './types';\n\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(\n    promises: Array<Promise<{}|void>>, onProgress: OnProgressCallback,\n    startFraction?: number, endFraction?: number) {\n  checkPromises(promises);\n  startFraction = startFraction == null ? 0 : startFraction;\n  endFraction = endFraction == null ? 1 : endFraction;\n  checkFraction(startFraction, endFraction);\n  let resolvedPromise = 0;\n\n  const registerMonitor = (promise: Promise<{}>) => {\n    promise.then(value => {\n      const fraction = startFraction +\n          ++resolvedPromise / promises.length * (endFraction - startFraction);\n      // pass fraction as parameter to callback function.\n      onProgress(fraction);\n      return value;\n    });\n    return promise;\n  };\n\n  function checkPromises(promises: Array<Promise<{}|void>>): void {\n    assert(\n        promises != null && Array.isArray(promises) && promises.length > 0,\n        () => 'promises must be a none empty array');\n  }\n\n  function checkFraction(startFraction: number, endFraction: number): void {\n    assert(\n        startFraction >= 0 && startFraction <= 1,\n        () => `Progress fraction must be in range [0, 1], but ` +\n            `got startFraction ${startFraction}`);\n    assert(\n        endFraction >= 0 && endFraction <= 1,\n        () => `Progress fraction must be in range [0, 1], but ` +\n            `got endFraction ${endFraction}`);\n    assert(\n        endFraction >= startFraction,\n        () => `startFraction must be no more than endFraction, but ` +\n            `got startFraction ${startFraction} and endFraction ` +\n            `${endFraction}`);\n  }\n\n  return Promise.all(promises.map(registerMonitor));\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from '../environment';\n\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\nimport {decodeWeights} from './io_utils';\nimport {monitorPromisesProgress} from './progress';\nimport {DTYPE_VALUE_SIZE_MAP, LoadOptions, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n *   before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(\n    fetchURLs: string[], loadOptions?: LoadOptions): Promise<ArrayBuffer[]> {\n  if (loadOptions == null) {\n    loadOptions = {};\n  }\n\n  const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n                                                    loadOptions.fetchFunc;\n\n  // Create the requests for all of the weights in parallel.\n  const requests = fetchURLs.map(\n      fetchURL =>\n          fetchFunc(fetchURL, loadOptions.requestInit, {isBinary: true}));\n\n  const fetchStartFraction = 0;\n  const fetchEndFraction = 0.5;\n\n  const responses = loadOptions.onProgress == null ?\n      await Promise.all(requests) :\n      await monitorPromisesProgress(\n          requests, loadOptions.onProgress, fetchStartFraction,\n          fetchEndFraction);\n\n  const bufferPromises = responses.map(response => response.arrayBuffer());\n\n  const bufferStartFraction = 0.5;\n  const bufferEndFraction = 1;\n\n  const buffers = loadOptions.onProgress == null ?\n      await Promise.all(bufferPromises) :\n      await monitorPromisesProgress(\n          bufferPromises, loadOptions.onProgress, bufferStartFraction,\n          bufferEndFraction);\n  return buffers;\n}\n\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(\n    manifest: WeightsManifestConfig, filePathPrefix = '',\n    weightNames?: string[],\n    requestInit?: RequestInit): Promise<NamedTensorMap> {\n  // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n  // single weight from a group, the whole group will be fetched. At a future\n  // date, we should support fetching only the individual shards within a\n  // group that are needed to reconstruct the requested weight.\n  // TODO(cais): Use `decodeWeights` for implementation.\n\n  const fetchWeights = (fetchUrls: string[]) =>\n      loadWeightsAsArrayBuffer(fetchUrls, {requestInit});\n  const loadWeights = weightsLoaderFactory(fetchWeights);\n\n  return loadWeights(manifest, filePathPrefix, weightNames);\n}\n\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(\n    fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>):\n    (manifest: WeightsManifestConfig, filePathPrefix?: string,\n     weightNames?: string[]) => Promise<NamedTensorMap> {\n  return async(\n             manifest: WeightsManifestConfig, filePathPrefix = '',\n             weightNames?: string[]): Promise<NamedTensorMap> => {\n    // Collect all the groups, weights, and their relative offsets to be\n    // fetched.\n    const groupIndicesToFetchMap = manifest.map(() => false);\n    const groupWeightsToFetch: {\n      [group: number]: Array<{\n        manifestEntry: WeightsManifestEntry; groupOffset: number;\n        sizeBytes: number;\n      }>\n    } = {};\n    const weightsFound =\n        weightNames != null ? weightNames.map(() => false) : [];\n    const allManifestWeightNames: string[] = [];\n    manifest.forEach((manifestGroupConfig, groupIndex) => {\n      let groupOffset = 0;\n      manifestGroupConfig.weights.forEach(weightsEntry => {\n        const rawDtype = ('quantization' in weightsEntry) ?\n            weightsEntry.quantization.dtype :\n            weightsEntry.dtype;\n\n        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n            util.sizeFromShape(weightsEntry.shape);\n\n        const enqueueWeightsForFetchingFn = () => {\n          groupIndicesToFetchMap[groupIndex] = true;\n          if (groupWeightsToFetch[groupIndex] == null) {\n            groupWeightsToFetch[groupIndex] = [];\n          }\n\n          groupWeightsToFetch[groupIndex].push({\n            manifestEntry: weightsEntry,\n            groupOffset,\n            sizeBytes: weightsBytes\n          });\n        };\n\n        if (weightNames != null) {\n          weightNames.forEach((weightName, weightIndex) => {\n            if (weightName === weightsEntry.name) {\n              enqueueWeightsForFetchingFn();\n              weightsFound[weightIndex] = true;\n            }\n          });\n        } else {\n          enqueueWeightsForFetchingFn();\n        }\n\n        allManifestWeightNames.push(weightsEntry.name);\n        groupOffset += weightsBytes;\n      });\n    });\n\n    if (!weightsFound.every(found => found)) {\n      const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n      throw new Error(\n          `Could not find weights in manifest with names: ` +\n          `${weightsNotFound.join(', ')}. \\n` +\n          `Manifest JSON has weights with names: ` +\n          `${allManifestWeightNames.join(', ')}.`);\n    }\n\n    // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n    // IDs.\n    const groupIndicesToFetch =\n        groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n          if (shouldFetch) {\n            accumulator.push(i);\n          }\n          return accumulator;\n        }, []);\n\n    const fetchUrls: string[] = [];\n    groupIndicesToFetch.forEach(i => {\n      manifest[i].paths.forEach(filepath => {\n        const fetchUrl = filePathPrefix +\n            (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n        fetchUrls.push(fetchUrl);\n      });\n    });\n    const buffers = await fetchWeightsFunction(fetchUrls);\n\n    const weightsTensorMap: NamedTensorMap = {};\n    let bufferIndexOffset = 0;\n    groupIndicesToFetch.forEach(i => {\n      const numBuffers = manifest[i].paths.length;\n\n      let groupBytes = 0;\n      for (let i = 0; i < numBuffers; i++) {\n        groupBytes += buffers[bufferIndexOffset + i].byteLength;\n      }\n\n      // Create a buffer for the whole group.\n      const groupBuffer = new ArrayBuffer(groupBytes);\n      const groupByteBuffer = new Uint8Array(groupBuffer);\n      let groupBufferOffset = 0;\n      for (let i = 0; i < numBuffers; i++) {\n        const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n        groupByteBuffer.set(buffer, groupBufferOffset);\n        groupBufferOffset += buffer.byteLength;\n      }\n\n      const weightsEntries = groupWeightsToFetch[i];\n      weightsEntries.forEach(weightsEntry => {\n        const byteBuffer = groupBuffer.slice(\n            weightsEntry.groupOffset,\n            weightsEntry.groupOffset + weightsEntry.sizeBytes);\n        const nameToTensorMap =\n            decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n        for (const name in nameToTensorMap) {\n          weightsTensorMap[name] = nameToTensorMap[name];\n        }\n      });\n\n      bufferIndexOffset += numBuffers;\n    });\n\n    return weightsTensorMap;\n  };\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\n\nimport {env} from '../environment';\n\nimport {assert} from '../util';\nimport {concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, LoadOptions, ModelArtifacts, ModelJSON, OnProgressCallback, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeightsAsArrayBuffer} from './weights_loader';\n\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest implements IOHandler {\n  protected readonly path: string;\n  protected readonly requestInit: RequestInit;\n\n  private readonly fetch: Function;\n  private readonly weightUrlConverter: (weightName: string) => Promise<string>;\n\n  readonly DEFAULT_METHOD = 'POST';\n\n  static readonly URL_SCHEME_REGEX = /^https?:\\/\\//;\n\n  private readonly weightPathPrefix: string;\n  private readonly onProgress: OnProgressCallback;\n\n  constructor(path: string, loadOptions?: LoadOptions) {\n    if (loadOptions == null) {\n      loadOptions = {};\n    }\n    this.weightPathPrefix = loadOptions.weightPathPrefix;\n    this.onProgress = loadOptions.onProgress;\n    this.weightUrlConverter = loadOptions.weightUrlConverter;\n\n    if (loadOptions.fetchFunc != null) {\n      assert(\n          typeof loadOptions.fetchFunc === 'function',\n          () => 'Must pass a function that matches the signature of ' +\n              '`fetch` (see ' +\n              'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n      this.fetch = loadOptions.fetchFunc;\n    } else {\n      this.fetch = env().platform.fetch;\n    }\n\n    assert(\n        path != null && path.length > 0,\n        () => 'URL path for http must not be null, undefined or ' +\n            'empty.');\n\n    if (Array.isArray(path)) {\n      assert(\n          path.length === 2,\n          () => 'URL paths for http must have a length of 2, ' +\n              `(actual length is ${path.length}).`);\n    }\n    this.path = path;\n\n    if (loadOptions.requestInit != null &&\n        loadOptions.requestInit.body != null) {\n      throw new Error(\n          'requestInit is expected to have no pre-existing body, but has one.');\n    }\n    this.requestInit = loadOptions.requestInit || {};\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserHTTPRequest.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    }\n\n    const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);\n    init.body = new FormData();\n\n    const weightsManifest: WeightsManifestConfig = [{\n      paths: ['./model.weights.bin'],\n      weights: modelArtifacts.weightSpecs,\n    }];\n    const modelTopologyAndWeightManifest: ModelJSON = {\n      modelTopology: modelArtifacts.modelTopology,\n      format: modelArtifacts.format,\n      generatedBy: modelArtifacts.generatedBy,\n      convertedBy: modelArtifacts.convertedBy,\n      weightsManifest\n    };\n    if (modelArtifacts.signature != null) {\n      modelTopologyAndWeightManifest.signature = modelArtifacts.signature;\n    }\n    if (modelArtifacts.userDefinedMetadata != null) {\n      modelTopologyAndWeightManifest.userDefinedMetadata =\n          modelArtifacts.userDefinedMetadata;\n    }\n    if (modelArtifacts.modelInitializer != null) {\n      modelTopologyAndWeightManifest.modelInitializer =\n          modelArtifacts.modelInitializer;\n    }\n\n    init.body.append(\n        'model.json',\n        new Blob(\n            [JSON.stringify(modelTopologyAndWeightManifest)],\n            {type: JSON_TYPE}),\n        'model.json');\n\n    if (modelArtifacts.weightData != null) {\n      init.body.append(\n          'model.weights.bin',\n          new Blob([modelArtifacts.weightData], {type: OCTET_STREAM_MIME_TYPE}),\n          'model.weights.bin');\n    }\n\n    const response = await this.fetch(this.path, init);\n\n    if (response.ok) {\n      return {\n        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n        responses: [response],\n      };\n    } else {\n      throw new Error(\n          `BrowserHTTPRequest.save() failed due to HTTP response status ` +\n          `${response.status}.`);\n    }\n  }\n\n  /**\n   * Load model artifacts via HTTP request(s).\n   *\n   * See the documentation to `tf.io.http` for details on the saved\n   * artifacts.\n   *\n   * @returns The loaded model artifacts (if loading succeeds).\n   */\n  async load(): Promise<ModelArtifacts> {\n    const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n\n    if (!modelConfigRequest.ok) {\n      throw new Error(\n          `Request to ${this.path} failed with status code ` +\n          `${modelConfigRequest.status}. Please verify this URL points to ` +\n          `the model JSON of the model to load.`);\n    }\n    let modelConfig: ModelJSON;\n    try {\n      modelConfig = await modelConfigRequest.json();\n    } catch (e) {\n      let message = `Failed to parse model JSON of response from ${this.path}.`;\n      // TODO(nsthorat): Remove this after some time when we're comfortable that\n      // .pb files are mostly gone.\n      if (this.path.endsWith('.pb')) {\n        message += ' Your path contains a .pb file extension. ' +\n            'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n            'in favor of .json models. You can re-convert your Python ' +\n            'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n            'or you can convert your.pb models with the \\'pb2json\\'' +\n            'NPM script in the tensorflow/tfjs-converter repository.';\n      } else {\n        message += ' Please make sure the server is serving valid ' +\n            'JSON for this request.';\n      }\n      throw new Error(message);\n    }\n    const modelTopology = modelConfig.modelTopology;\n    const weightsManifest = modelConfig.weightsManifest;\n    const generatedBy = modelConfig.generatedBy;\n    const convertedBy = modelConfig.convertedBy;\n    const format = modelConfig.format;\n    const signature = modelConfig.signature;\n    const userDefinedMetadata = modelConfig.userDefinedMetadata;\n\n    // We do not allow both modelTopology and weightsManifest to be missing.\n    if (modelTopology == null && weightsManifest == null) {\n      throw new Error(\n          `The JSON from HTTP path ${this.path} contains neither model ` +\n          `topology or manifest for weights.`);\n    }\n\n    let weightSpecs: WeightsManifestEntry[];\n    let weightData: ArrayBuffer;\n    if (weightsManifest != null) {\n      const results = await this.loadWeights(weightsManifest);\n      [weightSpecs, weightData] = results;\n    }\n\n    const artifacts: ModelArtifacts = {\n      modelTopology,\n      weightSpecs,\n      weightData,\n      generatedBy,\n      convertedBy,\n      format\n    };\n\n    if (signature != null) {\n      artifacts.signature = signature;\n    }\n    if (userDefinedMetadata != null) {\n      artifacts.userDefinedMetadata = userDefinedMetadata;\n    }\n\n    const initializer = modelConfig.modelInitializer;\n    if (initializer) {\n      artifacts.modelInitializer = initializer;\n    }\n\n    return artifacts;\n  }\n\n  private async loadWeights(weightsManifest: WeightsManifestConfig):\n      Promise<[WeightsManifestEntry[], ArrayBuffer]> {\n    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n    const [prefix, suffix] = parseUrl(weightPath);\n    const pathPrefix = this.weightPathPrefix || prefix;\n\n    const weightSpecs = [];\n    for (const entry of weightsManifest) {\n      weightSpecs.push(...entry.weights);\n    }\n\n    const fetchURLs: string[] = [];\n    const urlPromises: Array<Promise<string>> = [];\n    for (const weightsGroup of weightsManifest) {\n      for (const path of weightsGroup.paths) {\n        if (this.weightUrlConverter != null) {\n          urlPromises.push(this.weightUrlConverter(path));\n        } else {\n          fetchURLs.push(pathPrefix + path + suffix);\n        }\n      }\n    }\n\n    if (this.weightUrlConverter) {\n      fetchURLs.push(...await Promise.all(urlPromises));\n    }\n\n    const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n      requestInit: this.requestInit,\n      fetchFunc: this.fetch,\n      onProgress: this.onProgress\n    });\n    return [weightSpecs, concatenateArrayBuffers(buffers)];\n  }\n}\n\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url: string): [string, string] {\n  const lastSlash = url.lastIndexOf('/');\n  const lastSearchParam = url.lastIndexOf('?');\n  const prefix = url.substring(0, lastSlash);\n  const suffix =\n      lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n  return [prefix + '/', suffix];\n}\n\nexport function isHTTPScheme(url: string): boolean {\n  return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\n\nexport const httpRouter: IORouter =\n    (url: string, loadOptions?: LoadOptions) => {\n      if (typeof fetch === 'undefined' &&\n          (loadOptions == null || loadOptions.fetchFunc == null)) {\n        // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n        // an environment that is not the browser or node they have to setup a\n        // global fetch polyfill.\n        return null;\n      } else {\n        let isHTTP = true;\n        if (Array.isArray(url)) {\n          isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n        } else {\n          isHTTP = isHTTPScheme(url);\n        }\n        if (isHTTP) {\n          return http(url, loadOptions);\n        }\n      }\n      return null;\n    };\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n *   following fields:\n *   - weightPathPrefix Optional, this specifies the path prefix for weight\n *     files, by default this is calculated from the path param.\n *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *     the `fetch` from node-fetch can be used here.\n *   - onProgress Optional, progress callback function, fired periodically\n *     before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function http(path: string, loadOptions?: LoadOptions): IOHandler {\n  return new HTTPRequest(path, loadOptions);\n}\n\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(\n    path: string, loadOptions?: LoadOptions): IOHandler {\n  return http(path, loadOptions);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers that pass through the in-memory ModelArtifacts format.\n */\n\nimport {IOHandler, ModelArtifacts, SaveResult, TrainingConfig, WeightsManifestEntry} from './types';\n\nclass PassthroughLoader implements IOHandler {\n  constructor(private readonly modelArtifacts?: ModelArtifacts) {}\n\n  async load(): Promise<ModelArtifacts> {\n    return this.modelArtifacts;\n  }\n}\n\nclass PassthroughSaver implements IOHandler {\n  constructor(\n      private readonly saveHandler:\n          (artifacts: ModelArtifacts) => Promise<SaveResult>) {}\n\n  async save(modelArtifacts: ModelArtifacts) {\n    return this.saveHandler(modelArtifacts);\n  }\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(\n    modelArtifacts: {}|ModelArtifacts, weightSpecs?: WeightsManifestEntry[],\n    weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandler {\n  if (arguments.length === 1) {\n    const isModelArtifacts =\n        (modelArtifacts as ModelArtifacts).modelTopology != null ||\n        (modelArtifacts as ModelArtifacts).weightSpecs != null;\n    if (isModelArtifacts) {\n      return new PassthroughLoader(modelArtifacts as ModelArtifacts);\n    } else {\n      // Legacy support: with only modelTopology.\n      // TODO(cais): Remove this deprecated API.\n      console.warn(\n          'Please call tf.io.fromMemory() with only one argument. ' +\n          'The argument should be of type ModelArtifacts. ' +\n          'The multi-argument signature of tf.io.fromMemory() has been ' +\n          'deprecated and will be removed in a future release.');\n      return new PassthroughLoader({modelTopology: modelArtifacts as {}});\n    }\n  } else {\n    // Legacy support.\n    // TODO(cais): Remove this deprecated API.\n    console.warn(\n        'Please call tf.io.fromMemory() with only one argument. ' +\n        'The argument should be of type ModelArtifacts. ' +\n        'The multi-argument signature of tf.io.fromMemory() has been ' +\n        'deprecated and will be removed in a future release.');\n    return new PassthroughLoader({\n      modelTopology: modelArtifacts as {},\n      weightSpecs,\n      weightData,\n      trainingConfig\n    });\n  }\n}\n\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(\n    saveHandler: (artifacts: ModelArtifacts) =>\n        Promise<SaveResult>): IOHandler {\n  return new PassthroughSaver(saveHandler);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\n\nimport {browserFiles} from './browser_files';\nimport {browserHTTPRequest, http, isHTTPScheme} from './http';\nimport {concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON} from './io_utils';\nimport {fromMemory, withSaveHandler} from './passthrough';\nimport {getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter} from './router_registry';\nimport {IOHandler, LoadHandler, LoadOptions, ModelArtifacts, ModelArtifactsInfo, ModelJSON, ModelStoreManager, OnProgressCallback, RequestDetails, SaveConfig, SaveHandler, SaveResult, WeightGroup, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeights, weightsLoaderFactory} from './weights_loader';\n\nexport {copyModel, listModels, moveModel, removeModel} from './model_management';\nexport {\n  browserFiles,\n  browserHTTPRequest,\n  concatenateArrayBuffers,\n  decodeWeights,\n  encodeWeights,\n  fromMemory,\n  getLoadHandlers,\n  getModelArtifactsInfoForJSON,\n  getSaveHandlers,\n  http,\n  IOHandler,\n  isHTTPScheme,\n  LoadHandler,\n  LoadOptions,\n  loadWeights,\n  ModelArtifacts,\n  ModelArtifactsInfo,\n  ModelJSON,\n  ModelStoreManager,\n  OnProgressCallback,\n  registerLoadRouter,\n  registerSaveRouter,\n  RequestDetails,\n  SaveConfig,\n  SaveHandler,\n  SaveResult,\n  WeightGroup,\n  weightsLoaderFactory,\n  WeightsManifestConfig,\n  WeightsManifestEntry,\n  withSaveHandler\n};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike, transposeA = false,\n    transposeB = false): T {\n  let $a = convertToTensor(a, 'a', 'matMul');\n  let $b = convertToTensor(b, 'b', 'matMul');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const inputs: BatchMatMulInputs = {a: $a, b: $b};\n  const attrs: BatchMatMulAttrs = {transposeA, transposeB};\n\n  return ENGINE.runKernel(\n      BatchMatMul, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const matMul = op({matMul_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {OneHot, OneHotAttrs, OneHotInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(\n    indices: Tensor|TensorLike, depth: number, onValue = 1,\n    offValue = 0): Tensor {\n  if (depth < 2) {\n    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n  }\n  const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n\n  const inputs: OneHotInputs = {indices: $indices};\n  const attrs: OneHotAttrs = {depth, onValue, offValue};\n\n  return ENGINE.runKernel(\n      OneHot, inputs as unknown as NamedTensorMap,\n      attrs as unknown as NamedAttrMap);\n}\n\nexport const oneHot = op({oneHot_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Transpose, TransposeAttrs, TransposeInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_<T extends Tensor>(x: T|TensorLike, perm?: number[]): T {\n  const $x = convertToTensor(x, 'x', 'transpose');\n\n  if (perm == null) {\n    perm = $x.shape.map((s, i) => i).reverse();\n  }\n  util.assert(\n      $x.rank === perm.length,\n      () => `Error in transpose: rank of input ${$x.rank} ` +\n          `must match length of perm ${perm}.`);\n  perm.forEach(axis => {\n    util.assert(\n        axis >= 0 && axis < $x.rank,\n        () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n  });\n\n  if ($x.rank <= 1) {\n    return $x.clone();\n  }\n\n  const inputs: TransposeInputs = {x: $x};\n  const attrs: TransposeAttrs = {perm};\n\n  return ENGINE.runKernel(\n      Transpose, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const transpose = op({transpose_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {cast} from './cast';\nimport {matMul} from './mat_mul';\nimport {oneHot} from './one_hot';\nimport {op} from './operation';\nimport {transpose} from './transpose';\n\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(\n    labels: Tensor1D|TensorLike, predictions: Tensor1D|TensorLike,\n    numClasses: number): Tensor2D {\n  const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'confusionMatrix');\n\n  util.assert(\n      numClasses == null || numClasses > 0 && Number.isInteger(numClasses),\n      () => `If provided, numClasses must be a positive integer, ` +\n          `but got ${numClasses}`);\n  util.assert(\n      $labels.rank === 1,\n      () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n  util.assert(\n      $predictions.rank === 1,\n      () => `Expected the rank of predictions to be 1, ` +\n          `but got ${$predictions.rank}`);\n  util.assert(\n      $labels.shape[0] === $predictions.shape[0],\n      () => `Mismatch in the number of examples: ` +\n          `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n          `Labels and predictions should have the same number of elements.`);\n  util.assert(\n      numClasses > 0 && Number.isInteger(numClasses),\n      () => `numClasses is required to be a positive integer, but got ` +\n          `${numClasses}`);\n  // TODO(cais): In the future, if oneHot supports tensors inputs for\n  //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n\n  const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses) as Tensor2D;\n  const oneHotPredictions =\n      oneHot(cast($predictions, 'int32'), numClasses) as Tensor2D;\n  const oneHotLabelsT: Tensor2D = transpose(oneHotLabels);\n  const product: Tensor2D = matMul(oneHotLabelsT, oneHotPredictions);\n  return cast(product, 'int32');\n}\n\nexport const confusionMatrix = op({confusionMatrix_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Exports under the tf.math.* namespace.\n */\n\nimport {confusionMatrix} from './ops/confusion_matrix';\n\nexport {confusionMatrix};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor3D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike3D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(\n    values: TensorLike3D, shape?: [number, number, number],\n    dtype?: DataType): Tensor3D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 3) {\n    throw new Error('tensor3d() requires shape to have three numbers');\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor3d() requires values to be number[][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor3d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor3D;\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {env} from '../environment';\nimport {FromPixels, FromPixelsAttrs, FromPixelsInputs} from '../kernel_names';\nimport {getKernel, NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor2D, Tensor3D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {PixelData, TensorLike} from '../types';\n\nimport {cast} from './cast';\nimport {op} from './operation';\nimport {tensor3d} from './tensor3d';\n\nlet fromPixels2DContext: CanvasRenderingContext2D;\n\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @returns A Tensor3D with the shape `[height, width, numChannels]`.\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(\n    pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|\n    HTMLVideoElement|ImageBitmap,\n    numChannels = 3): Tensor3D {\n  // Sanity checks.\n  if (numChannels > 4) {\n    throw new Error(\n        'Cannot construct Tensor with more than 4 channels from pixels.');\n  }\n  if (pixels == null) {\n    throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n  }\n  let isPixelData = false;\n  let isImageData = false;\n  let isVideo = false;\n  let isImage = false;\n  let isCanvasLike = false;\n  let isImageBitmap = false;\n  if ((pixels as PixelData).data instanceof Uint8Array) {\n    isPixelData = true;\n  } else if (\n      typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n    isImageData = true;\n  } else if (\n      typeof (HTMLVideoElement) !== 'undefined' &&\n      pixels instanceof HTMLVideoElement) {\n    isVideo = true;\n  } else if (\n      typeof (HTMLImageElement) !== 'undefined' &&\n      pixels instanceof HTMLImageElement) {\n    isImage = true;\n    // tslint:disable-next-line: no-any\n  } else if ((pixels as any).getContext != null) {\n    isCanvasLike = true;\n  } else if (\n      typeof (ImageBitmap) !== 'undefined' && pixels instanceof ImageBitmap) {\n    isImageBitmap = true;\n  } else {\n    throw new Error(\n        'pixels passed to tf.browser.fromPixels() must be either an ' +\n        `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n        `in browser, or OffscreenCanvas, ImageData in webworker` +\n        ` or {data: Uint32Array, width: number, height: number}, ` +\n        `but was ${(pixels as {}).constructor.name}`);\n  }\n  if (isVideo) {\n    const HAVE_CURRENT_DATA_READY_STATE = 2;\n    if (isVideo &&\n        (pixels as HTMLVideoElement).readyState <\n            HAVE_CURRENT_DATA_READY_STATE) {\n      throw new Error(\n          'The video element has not loaded data yet. Please wait for ' +\n          '`loadeddata` event on the <video> element.');\n    }\n  }\n  // If the current backend has 'FromPixels' registered, it has a more\n  // efficient way of handling pixel uploads, so we call that.\n  const kernel = getKernel(FromPixels, ENGINE.backendName);\n  if (kernel != null) {\n    const inputs: FromPixelsInputs = {pixels};\n    const attrs: FromPixelsAttrs = {numChannels};\n    return ENGINE.runKernel(\n        FromPixels, inputs as {} as NamedTensorMap,\n        attrs as {} as NamedAttrMap);\n  }\n\n  const [width, height] = isVideo ?\n      [\n        (pixels as HTMLVideoElement).videoWidth,\n        (pixels as HTMLVideoElement).videoHeight\n      ] :\n      [pixels.width, pixels.height];\n  let vals: Uint8ClampedArray|Uint8Array;\n\n  if (isCanvasLike) {\n    vals =\n        // tslint:disable-next-line:no-any\n        (pixels as any).getContext('2d').getImageData(0, 0, width, height).data;\n  } else if (isImageData || isPixelData) {\n    vals = (pixels as PixelData | ImageData).data;\n  } else if (isImage || isVideo || isImageBitmap) {\n    if (fromPixels2DContext == null) {\n      fromPixels2DContext = document.createElement('canvas').getContext('2d');\n    }\n    fromPixels2DContext.canvas.width = width;\n    fromPixels2DContext.canvas.height = height;\n    fromPixels2DContext.drawImage(\n        pixels as HTMLVideoElement, 0, 0, width, height);\n    vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n  }\n  let values: Int32Array;\n  if (numChannels === 4) {\n    values = new Int32Array(vals);\n  } else {\n    const numPixels = width * height;\n    values = new Int32Array(numPixels * numChannels);\n    for (let i = 0; i < numPixels; i++) {\n      for (let channel = 0; channel < numChannels; ++channel) {\n        values[i * numChannels + channel] = vals[i * 4 + channel];\n      }\n    }\n  }\n  const outShape: [number, number, number] = [height, width, numChannels];\n  return tensor3d(values, outShape, 'int32');\n}\n\n// Helper functions for |fromPixelsAsync| to check whether the input can\n// be wrapped into imageBitmap.\nfunction isPixelData(pixels: PixelData|ImageData|HTMLImageElement|\n                     HTMLCanvasElement|HTMLVideoElement|\n                     ImageBitmap): pixels is PixelData {\n  return (pixels != null) && ((pixels as PixelData).data instanceof Uint8Array);\n}\n\nfunction isImageBitmapFullySupported() {\n  return typeof window !== 'undefined' &&\n      typeof (ImageBitmap) !== 'undefined' &&\n      window.hasOwnProperty('createImageBitmap');\n}\n\nfunction isNonEmptyPixels(pixels: PixelData|ImageData|HTMLImageElement|\n                          HTMLCanvasElement|HTMLVideoElement|ImageBitmap) {\n  return pixels != null && pixels.width !== 0 && pixels.height !== 0;\n}\n\nfunction canWrapPixelsToImageBitmap(pixels: PixelData|ImageData|\n                                    HTMLImageElement|HTMLCanvasElement|\n                                    HTMLVideoElement|ImageBitmap) {\n  return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) &&\n      isNonEmptyPixels(pixels) && !isPixelData(pixels);\n}\n\n/**\n * Creates a `tf.Tensor` from an image in async way.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * (await tf.browser.fromPixelsAsync(image)).print();\n * ```\n * This API is the async version of fromPixels. The API will first\n * check |WRAP_TO_IMAGEBITMAP| flag, and try to wrap the input to\n * imageBitmap if the flag is set to true.\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nexport async function fromPixelsAsync(\n    pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|\n    HTMLVideoElement|ImageBitmap,\n    numChannels = 3) {\n  let inputs: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|\n      HTMLVideoElement|ImageBitmap = null;\n\n  // Check whether the backend needs to wrap |pixels| to imageBitmap and\n  // whether |pixels| can be wrapped to imageBitmap.\n  if (env().getBool('WRAP_TO_IMAGEBITMAP') &&\n      canWrapPixelsToImageBitmap(pixels)) {\n    // Force the imageBitmap creation to not do any premultiply alpha\n    // ops.\n    let imageBitmap;\n\n    try {\n      // wrap in try-catch block, because createImageBitmap may not work\n      // properly in some browsers, e.g.\n      // https://bugzilla.mozilla.org/show_bug.cgi?id=1335594\n      // tslint:disable-next-line: no-any\n      imageBitmap = await (createImageBitmap as any)(\n          pixels as ImageBitmapSource, {premultiplyAlpha: 'none'});\n    } catch (e) {\n      imageBitmap = null;\n    }\n\n    // createImageBitmap will clip the source size.\n    // In some cases, the input will have larger size than its content.\n    // E.g. new Image(10, 10) but with 1 x 1 content. Using\n    // createImageBitmap will clip the size from 10 x 10 to 1 x 1, which\n    // is not correct. We should avoid wrapping such resouce to\n    // imageBitmap.\n    if (imageBitmap != null && imageBitmap.width === pixels.width &&\n        imageBitmap.height === pixels.height) {\n      inputs = imageBitmap;\n    } else {\n      inputs = pixels;\n    }\n  } else {\n    inputs = pixels;\n  }\n\n  return fromPixels_(inputs, numChannels);\n}\n\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 tensor with shape `[height, width]`, or a rank-3 tensor\n * of shape `[height, width, numChannels]`. If rank-2, draws grayscale. If\n * rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n *\n * @doc {heading: 'Browser', namespace: 'browser'}\n */\nexport async function toPixels(\n    img: Tensor2D|Tensor3D|TensorLike,\n    canvas?: HTMLCanvasElement): Promise<Uint8ClampedArray> {\n  let $img = convertToTensor(img, 'img', 'toPixels');\n  if (!(img instanceof Tensor)) {\n    // Assume int32 if user passed a native array.\n    const originalImgTensor = $img;\n    $img = cast(originalImgTensor, 'int32');\n    originalImgTensor.dispose();\n  }\n  if ($img.rank !== 2 && $img.rank !== 3) {\n    throw new Error(\n        `toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n  }\n  const [height, width] = $img.shape.slice(0, 2);\n  const depth = $img.rank === 2 ? 1 : $img.shape[2];\n\n  if (depth > 4 || depth === 2) {\n    throw new Error(\n        `toPixels only supports depth of size ` +\n        `1, 3 or 4 but got ${depth}`);\n  }\n\n  if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {\n    throw new Error(\n        `Unsupported type for toPixels: ${$img.dtype}.` +\n        ` Please use float32 or int32 tensors.`);\n  }\n\n  const data = await $img.data();\n  const multiplier = $img.dtype === 'float32' ? 255 : 1;\n  const bytes = new Uint8ClampedArray(width * height * 4);\n\n  for (let i = 0; i < height * width; ++i) {\n    const rgba = [0, 0, 0, 255];\n\n    for (let d = 0; d < depth; d++) {\n      const value = data[i * depth + d];\n\n      if ($img.dtype === 'float32') {\n        if (value < 0 || value > 1) {\n          throw new Error(\n              `Tensor values for a float32 Tensor must be in the ` +\n              `range [0 - 1] but encountered ${value}.`);\n        }\n      } else if ($img.dtype === 'int32') {\n        if (value < 0 || value > 255) {\n          throw new Error(\n              `Tensor values for a int32 Tensor must be in the ` +\n              `range [0 - 255] but encountered ${value}.`);\n        }\n      }\n\n      if (depth === 1) {\n        rgba[0] = value * multiplier;\n        rgba[1] = value * multiplier;\n        rgba[2] = value * multiplier;\n      } else {\n        rgba[d] = value * multiplier;\n      }\n    }\n\n    const j = i * 4;\n    bytes[j + 0] = Math.round(rgba[0]);\n    bytes[j + 1] = Math.round(rgba[1]);\n    bytes[j + 2] = Math.round(rgba[2]);\n    bytes[j + 3] = Math.round(rgba[3]);\n  }\n\n  if (canvas != null) {\n    canvas.width = width;\n    canvas.height = height;\n    const ctx = canvas.getContext('2d');\n    const imageData = new ImageData(bytes, width, height);\n    ctx.putImageData(imageData, 0, 0);\n  }\n  if ($img !== img) {\n    $img.dispose();\n  }\n  return bytes;\n}\n\nexport const fromPixels = op({fromPixels_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {TensorInfo} from '../kernel_registry';\nimport {computeStrides, sizeFromShape} from '../util';\n\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(tensor: TensorInfo, indices: TensorInfo):\n    [number[], number, number, number[]] {\n  const tensorRank = tensor.shape.length;\n  const indicesRank = indices.shape.length;\n  if (tensorRank < 1) {\n    throw new Error(\n        'tf.gatherND() expects the input to be rank 1 or higher,' +\n        ` but the rank was ${tensorRank}.`);\n  }\n  if (indicesRank < 1) {\n    throw new Error(\n        'tf.gatherND() expects the indices to be rank 1 or higher,' +\n        ` but the rank was ${indicesRank}.`);\n  }\n  if (indices.dtype !== 'int32') {\n    throw new Error(\n        'tf.gatherND() expects the indices to be int32 type,' +\n        ` but the dtype was ${indices.dtype}.`);\n  }\n  if (indices.shape[indicesRank - 1] > tensorRank) {\n    throw new Error(\n        'index innermost dimension length must be <= tensor rank; saw: ' +\n        `${indices.shape[indicesRank - 1]} vs. ${tensorRank}`);\n  }\n\n  if (sizeFromShape(tensor.shape) === 0) {\n    throw new Error(\n        'Requested more than 0 entries, but input is empty.' +\n        ` Input shape: ${tensor.shape}.`);\n  }\n\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n\n  // The result shape is\n  //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n  let nResult = 1;\n  for (let i = 0; i < indicesShape.length - 1; ++i) {\n    nResult *= indicesShape[i];\n  }\n\n  const inputShape = tensor.shape;\n\n  const resultShape = indicesShape.slice();\n  resultShape.pop();\n\n  let sliceSize = 1;\n  for (let i = sliceRank; i < tensorRank; ++i) {\n    sliceSize *= inputShape[i];\n    resultShape.push(inputShape[i]);\n  }\n\n  const strides =\n      [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n       1].slice(0, sliceRank);\n\n  return [resultShape, nResult, sliceSize, strides];\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {TensorInfo} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {computeStrides, sizeFromShape} from '../util';\n\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(\n    shape: number[], indices: Tensor, updates: Tensor) {\n  const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n  const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n\n  const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n      `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n      `, indices.shape: ${indices.shape}, shape: ${shape}` +\n      `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n\n  if (updates.rank < batchDim) {\n    throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n  }\n  if (shape.length < sliceDim + (updates.rank - batchDim)) {\n    throw new Error(\n        shapeError +\n        ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n  }\n  if (updates.rank !== batchDim + shape.length - sliceDim) {\n    throw new Error(\n        shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n  }\n  for (let d = 0; d < batchDim; ++d) {\n    if (updates.shape[d] !== indices.shape[d]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${\n              indices.shape[d]}).`);\n    }\n  }\n  for (let d = 0; d < updates.rank - batchDim; ++d) {\n    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d + batchDim}] (${\n              updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${\n              shape[d + batchDim]})`);\n    }\n  }\n}\n\nexport interface ScatterShapeInfo {\n  sliceRank: number;\n  numUpdates: number;\n  sliceSize: number;\n  strides: number[];\n  outputSize: number;\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(\n    updates: Tensor, indices: Tensor, shape: number[]) {\n  if (indices.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the indices to be rank 1 or higher,' +\n        ` but the rank was ${indices.rank}.`);\n  }\n  if (updates.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the updates to be rank 1 or higher,' +\n        ` but the rank was ${updates.rank}.`);\n  }\n  if (indices.dtype !== 'int32') {\n    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${\n        indices.dtype}`);\n  }\n  if (shape.length < 1) {\n    throw new Error(\n        `Output rank must be greater or equal to 1, but got shape: ${shape}`);\n  }\n\n  if (shape.length === 0) {\n    if (indices.size === 0) {\n      throw new Error(`Indices specified for empty output. indices shape: ${\n          indices.shape}`);\n    }\n    if (updates.size === 0) {\n      throw new Error(`Updates specified for empty output. updates shape: ${\n          updates.shape}`);\n    }\n  }\n\n  validateUpdateShape(shape, indices, updates);\n}\n\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(\n    updates: TensorInfo, indices: TensorInfo,\n    shape: number[]): ScatterShapeInfo {\n  // Calculate the number of dimensions in indices\n  const indicesRank = indices.shape.length;\n  const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n\n  // Calculate the number of elements that make up each slice of our updated\n  // tensor. This allows us to work with flattened tensors and copy over whole\n  // slices at a time.\n  const totalNd = shape.length;\n\n  let sliceSize = 1;\n  for (let i = sliceRank; i < totalNd; ++i) {\n    sliceSize *= shape[i];\n  }\n\n  const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n\n  const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n  const outputSize = sizeFromShape(shape);\n  return {sliceRank, numUpdates, sliceSize, strides, outputSize};\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorInfo} from '../kernel_registry';\nimport * as util from '../util';\n\nexport type SliceInfo = {\n  nonStrided: boolean,\n  $begin: number[],\n  $end: number[],\n  $strides: number[],\n  size: number[],\n  newShape: number[],\n  outShape: number[]\n};\n\nexport function assertParamsValid(\n    input: TensorInfo, begin: number[], size: number[]): void {\n  const inputRank = input.shape.length;\n  util.assert(\n      inputRank === begin.length,\n      () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n          `match the rank of the array (${inputRank}).`);\n  util.assert(\n      inputRank === size.length,\n      () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n          `match the rank of the array (${inputRank}).`);\n\n  for (let i = 0; i < inputRank; ++i) {\n    util.assert(\n        begin[i] + size[i] <= input.shape[i],\n        () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${\n                  input.shape[i]})`);\n  }\n}\n\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask: number): number[] {\n  const axes = [];\n  let axis = 0;\n  while (mask > 0) {\n    if (mask & 1) {\n      axes.push(axis);\n    }\n    mask /= 2;\n    axis++;\n  }\n  return axes;\n}\n\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(\n    begin: number[], end: number[], strides: number[]): number[] {\n  const size = [];\n  for (let axis = 0; axis < begin.length; axis++) {\n    size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n  }\n  return size;\n}\n\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(\n    strides: number[], ellipsisInsertionIndex: number, numElidedAxes: number,\n    inputShape: number[]): number[] {\n  const newStrides = [...strides];\n  for (let i = newStrides.length; i < inputShape.length; i++) {\n    newStrides.push(1);\n  }\n  for (let i = 0; i < numElidedAxes; i++) {\n    if (i === 0) {\n      newStrides[ellipsisInsertionIndex] = 1;\n    } else {\n      newStrides.splice(\n          ellipsisInsertionIndex, 0 /* num elements to delete */,\n          1 /* element to add */);\n      newStrides.pop();\n    }\n  }\n  return newStrides;\n}\n\nfunction unnormalizeAxis(\n    ellipsisInsertionIndex: number, numElidedAxes: number,\n    normalizedAxis: number): number {\n  if (normalizedAxis <= ellipsisInsertionIndex) {\n    return normalizedAxis;\n  }\n\n  return normalizedAxis - (numElidedAxes - 1);\n}\n\nfunction getElidedAxes(numElidedAxes: number, ellipsisInsertionIndex: number) {\n  const elidedAxes = [];\n  for (let i = 0; i < numElidedAxes; i++) {\n    elidedAxes.push(ellipsisInsertionIndex + i);\n  }\n  return elidedAxes;\n}\n\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(\n    inputShape: number[], ellipsisAxes: number[], numInterpolatedAxes: number,\n    begin: number[], end: number[], strides: number[], beginMask: number,\n    endMask: number,\n    ellipsisMask: number): {begin: number[], end: number[], strides: number[]} {\n  const inputRank = inputShape.length;\n  let normalizedBegin = new Array(inputRank),\n      normalizedEnd = new Array(inputRank),\n      normalizedStrides = new Array(inputRank);\n  if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n    const fullIndex = ellipsisAxes[0];\n\n    // The ellipsis applies to the masked index as well as any dimensions\n    // that are interpolated.\n    const numElidedAxes = numInterpolatedAxes + 1;\n    normalizedBegin = startIndicesWithElidedDims(\n        beginMask, fullIndex, numElidedAxes, begin, inputShape);\n    normalizedEnd = stopIndicesWithElidedDims(\n        endMask, fullIndex, numElidedAxes, end, inputShape);\n    normalizedStrides =\n        stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n  } else {\n    for (let axis = 0; axis < inputRank; axis++) {\n      normalizedBegin[axis] = startForAxis(\n          beginMask, begin, strides, inputShape, axis, ellipsisMask);\n      normalizedEnd[axis] =\n          stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n      normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n    }\n  }\n\n  return {\n    begin: normalizedBegin,\n    end: normalizedEnd,\n    strides: normalizedStrides\n  };\n}\n\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(\n    beginMask: number, ellipsisInsertionIndex: number, numElidedAxes: number,\n    originalBegin: number[], inputShape: number[]): number[] {\n  const newIndices = [...inputShape];\n  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n\n  for (let axis = 0; axis < newIndices.length; axis++) {\n    if (elidedAxes.indexOf(axis) > -1) {\n      newIndices[axis] = 0;\n    } else {\n      const originalAxis =\n          unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n      let originalValue = originalBegin[originalAxis];\n      if (beginMask & 1 << originalAxis) {\n        originalValue = 0;\n      }\n\n      newIndices[axis] = originalValue;\n    }\n  }\n  return newIndices;\n}\n\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(\n    endMask: number, ellipsisInsertionIndex: number, numElidedAxes: number,\n    originalEnd: number[], inputShape: number[]): number[] {\n  const newIndices = [...inputShape];\n  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n\n  for (let axis = 0; axis < newIndices.length; axis++) {\n    if (elidedAxes.indexOf(axis) > -1) {\n      newIndices[axis] = Number.MAX_SAFE_INTEGER;\n    } else {\n      const originalAxis =\n          unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n      let originalValue = originalEnd[originalAxis];\n      if (endMask & 1 << originalAxis) {\n        originalValue = Number.MAX_SAFE_INTEGER;\n      }\n      newIndices[axis] = originalValue;\n    }\n  }\n\n  for (let i = 0; i < newIndices.length; i++) {\n    // Handle negative indices\n    const axisSize = inputShape[i];\n    if (newIndices[i] < 0) {\n      newIndices[i] += axisSize;\n    }\n    newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n  }\n  return newIndices;\n}\n\nexport function stridesForAxis(\n    strides: number[], axis: number, ellipsisMask: number): number {\n  let stride = strides[axis];\n  if (ellipsisMask & (1 << axis) || stride == null) {\n    stride = 1;\n  }\n\n  return stride;\n}\n\nexport function startForAxis(\n    beginMask: number, startIndices: number[], strides: number[],\n    inputShape: number[], axis: number, ellipsisMask: number): number {\n  // Begin with the specified index\n  let start = startIndices[axis];\n  const stride = strides[axis] || 1;\n\n  // Check the axis bit from right of masked axes, or the begin index is not set\n  // for the axis.\n  if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n    if (stride > 0) {\n      // Forward iteration - use the first element. These values will get\n      // clamped below (Note: We could have set them to 0 and axis_size-1, but\n      // use lowest() and max() to maintain symmetry with StopForAxis())\n      start = Number.MIN_SAFE_INTEGER;\n    } else {\n      // Backward iteration - use the last element.\n      start = Number.MAX_SAFE_INTEGER;\n    }\n  }\n\n  // Handle negative indices\n  const axisSize = inputShape[axis];\n  if (start < 0) {\n    start += axisSize;\n  }\n\n  // Clamping\n  start = util.clamp(0, start, axisSize - 1);\n\n  return start;\n}\n\nexport function stopForAxis(\n    endMask: number, stopIndices: number[], strides: number[],\n    inputShape: number[], axis: number, ellipsisMask: number): number {\n  // Begin with the specified index\n  let stop = stopIndices[axis];\n  const stride = strides[axis] || 1;\n\n  // Check the axis bit from right of masked axes, or if the stop index is not\n  // set for this axis.\n  if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n    if (stride > 0) {\n      // Forward iteration - use the last element. These values will get\n      // clamped below\n      stop = Number.MAX_SAFE_INTEGER;\n    } else {\n      // Backward iteration - use the first element.\n      stop = Number.MIN_SAFE_INTEGER;\n    }\n  }\n\n  // Handle negative indices\n  const axisSize = inputShape[axis];\n  if (stop < 0) {\n    stop += axisSize;\n  }\n\n  // Clamping\n  // Because the end index points one past the last element, we need slightly\n  // different clamping ranges depending on the direction.\n  if (stride > 0) {\n    // Forward iteration\n    stop = util.clamp(0, stop, axisSize);\n  } else {\n    // Backward iteration\n    stop = util.clamp(-1, stop, axisSize - 1);\n  }\n\n  return stop;\n}\n\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(\n    shape: number[], begin: number[], size: number[]) {\n  // Index of the first axis that has size > 1.\n  let firstNonOneAxis = size.length;\n  for (let i = 0; i < size.length; i++) {\n    if (size[i] > 1) {\n      firstNonOneAxis = i;\n      break;\n    }\n  }\n\n  for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n    if (begin[i] > 0 || size[i] !== shape[i]) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function computeFlatOffset(begin: number[], strides: number[]): number {\n  let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n  for (let i = 0; i < begin.length - 1; i++) {\n    flatOffset += begin[i] * strides[i];\n  }\n  return flatOffset;\n}\n\nexport function parseSliceParams(\n    x: TensorInfo, begin: number|number[], size?: number|number[]) {\n  // The following logic allows for more ergonomic calls.\n  let begin_: number[];\n  const xRank = x.shape.length;\n  if (typeof begin === 'number') {\n    begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n  } else if (begin.length < xRank) {\n    begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n  } else {\n    begin_ = begin.slice();\n  }\n  begin_.forEach(d => {\n    util.assert(\n        d !== -1, () => 'slice() does not support negative begin indexing.');\n  });\n  let size_: number[];\n  if (size == null) {\n    size_ = new Array(xRank).fill(-1);\n  } else if (typeof size === 'number') {\n    size_ = [size, ...new Array(xRank - 1).fill(-1)];\n  } else if (size.length < xRank) {\n    size_ = size.concat(new Array(xRank - size.length).fill(-1));\n  } else {\n    size_ = size;\n  }\n  size_ = size_.map((d, i) => {\n    if (d >= 0) {\n      return d;\n    } else {\n      util.assert(\n          d === -1,\n          () => `Negative size values should be exactly -1 but got ` +\n              `${d} for the slice() size at index ${i}.`);\n      return x.shape[i] - begin_[i];\n    }\n  });\n  return [begin_, size_];\n}\n\nexport function sliceInfo(\n    xShape: number[], begin: number[], end: number[], strides: number[],\n    beginMask: number, endMask: number, ellipsisMask: number,\n    newAxisMask: number, shrinkAxisMask: number): SliceInfo {\n  // make a copy because it may be modified further down.\n  let $begin = begin.slice();\n  let $end = end.slice();\n  let $strides = strides;\n\n  if (strides == null) {\n    $strides = new Array($begin.length);\n  }\n\n  const ellipsisAxes = maskToAxes(ellipsisMask);\n  if (ellipsisAxes.length > 1) {\n    throw new Error('Multiple ellipses in slice is not allowed.');\n  }\n\n  if (ellipsisMask !== 0 && newAxisMask !== 0) {\n    throw new Error(\n        'Using both ellipsisMask and newAxisMask is not yet supported.');\n  }\n\n  if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {\n    throw new Error(\n        'Using both ellipsisMask and shrinkAxisMask is not yet supported.');\n  }\n\n  const numInterpolatedAxes = xShape.length - $begin.length;\n\n  // Expand the dims of x based on the newAxisMask.\n  const expandAxes = maskToAxes(newAxisMask);\n  const newShape = xShape.slice();\n  expandAxes.forEach(axis => {\n    $begin[axis] = 0;\n    $end[axis] = 1;\n    newShape.splice(axis, 0, 1);\n  });\n\n  const {\n    begin: normalizedBegin,\n    end: normalizedEnd,\n    strides: normalizedStrides\n  } =\n      getNormalizedAxes(\n          newShape, ellipsisAxes, numInterpolatedAxes, $begin, $end, $strides,\n          beginMask, endMask, ellipsisMask);\n  $begin = normalizedBegin;\n  $end = normalizedEnd;\n  $strides = normalizedStrides;\n\n  const shrinkAxes = maskToAxes(shrinkAxisMask);\n  // Adjust the ends based on the shrink mask.\n  shrinkAxes.forEach(axis => {\n    $end[axis] = $begin[axis] + 1;\n    $strides[axis] = 1;\n  });\n\n  // Figure out the output shape.\n  const size = computeOutShape($begin, $end, $strides);\n  // Remove the axes based on shrinkMask.\n  const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);\n\n  const nonStrided = $strides.every(v => v === 1);\n\n  return {nonStrided, $begin, $end, $strides, size, newShape, outShape};\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {assert} from './util';\n\n/**\n * Types to support JSON-esque data structures internally.\n *\n * Internally ConfigDict's use camelCase keys and values where the\n * values are class names to be instantiated.  On the python side, these\n * will be snake_case.  Internally we allow Enums into the values for better\n * type safety, but these need to be converted to raw primitives (usually\n * strings) for round-tripping with python.\n *\n * toConfig returns the TS-friendly representation. model.toJSON() returns\n * the pythonic version as that's the portable format.  If you need to\n * python-ify a non-model level toConfig output, you'll need to use a\n * convertTsToPythonic from serialization_utils in -Layers.\n *\n */\nexport declare type ConfigDictValue =\n    boolean | number | string | null | ConfigDictArray | ConfigDict;\nexport declare interface ConfigDict {\n  [key: string]: ConfigDictValue;\n}\nexport declare interface ConfigDictArray extends Array<ConfigDictValue> {}\n\n/**\n * Type to represent the class-type of Serializable objects.\n *\n * Ie the class prototype with access to the constructor and any\n * static members/methods. Instance methods are not listed here.\n *\n * Source for this idea: https://stackoverflow.com/a/43607255\n */\nexport declare type SerializableConstructor<T extends Serializable> = {\n  // tslint:disable-next-line:no-any\n  new (...args: any[]): T; className: string; fromConfig: FromConfigMethod<T>;\n};\nexport declare type FromConfigMethod<T extends Serializable> =\n    (cls: SerializableConstructor<T>, config: ConfigDict) => T;\n\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport abstract class Serializable {\n  /**\n   * Return the class name for this class to use in serialization contexts.\n   *\n   * Generally speaking this will be the same thing that constructor.name\n   * would have returned.  However, the class name needs to be robust\n   * against minification for serialization/deserialization to work properly.\n   *\n   * There's also places such as initializers.VarianceScaling, where\n   * implementation details between different languages led to different\n   * class hierarchies and a non-leaf node is used for serialization purposes.\n   */\n  getClassName(): string {\n    return (this.constructor as SerializableConstructor<Serializable>)\n        .className;\n  }\n\n  /**\n   * Return all the non-weight state needed to serialize this object.\n   */\n  abstract getConfig(): ConfigDict;\n\n  /**\n   * Creates an instance of T from a ConfigDict.\n   *\n   * This works for most descendants of serializable.  A few need to\n   * provide special handling.\n   * @param cls A Constructor for the class to instantiate.\n   * @param config The Configuration for the object.\n   */\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config);\n  }\n}\n\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n  private static instance: SerializationMap;\n  classNameMap: {\n    [className: string]:\n        [SerializableConstructor<Serializable>, FromConfigMethod<Serializable>]\n  };\n\n  private constructor() {\n    this.classNameMap = {};\n  }\n\n  /**\n   * Returns the singleton instance of the map.\n   */\n  static getMap(): SerializationMap {\n    if (SerializationMap.instance == null) {\n      SerializationMap.instance = new SerializationMap();\n    }\n    return SerializationMap.instance;\n  }\n\n  /**\n   * Registers the class as serializable.\n   */\n  static register<T extends Serializable>(cls: SerializableConstructor<T>) {\n    SerializationMap.getMap().classNameMap[cls.className] =\n        [cls, cls.fromConfig];\n  }\n}\n\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n *\n * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}\n */\nexport function registerClass<T extends Serializable>(\n    cls: SerializableConstructor<T>) {\n  assert(\n      cls.className != null,\n      () => `Class being registered does not have the static className ` +\n          `property defined.`);\n  assert(\n      typeof cls.className === 'string',\n      () => `className is required to be a string, but got type ` +\n          typeof cls.className);\n  assert(\n      cls.className.length > 0,\n      () => `Class being registered has an empty-string as its className, ` +\n          `which is disallowed.`);\n\n  SerializationMap.register(cls);\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from './engine';\nimport {inferShape} from './tensor_util_env';\nimport {RecursiveArray, TensorLike, TypedArray} from './types';\nimport {arraysEqual, encodeString, flatten, isString, isTypedArray} from './util';\n\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\n\nexport function expectArraysClose(\n    actual: TypedArray|number|RecursiveArray<number>,\n    expected: TypedArray|number|RecursiveArray<number>, epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = testEpsilon();\n  }\n  return expectArraysPredicate(\n      actual, expected, (a, b) => areClose(a as number, b as number, epsilon));\n}\n\nexport function testEpsilon() {\n  return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n                                                  TEST_EPSILON_FLOAT16;\n}\n\nfunction expectArraysPredicate(\n    actual: TensorLike, expected: TensorLike,\n    predicate: (a: {}, b: {}) => boolean) {\n  let checkClassType = true;\n  if (isTypedArray(actual) || isTypedArray(expected)) {\n    checkClassType = false;\n  }\n  if (isTypedArray(actual) && isTypedArray(expected)) {\n    checkClassType = true;\n  }\n  if (checkClassType) {\n    const aType = actual.constructor.name;\n    const bType = expected.constructor.name;\n\n    if (aType !== bType) {\n      throw new Error(\n          `Arrays are of different type. Actual: ${aType}. ` +\n          `Expected: ${bType}`);\n    }\n  }\n\n  if (Array.isArray(actual) && Array.isArray(expected)) {\n    const actualShape = inferShape(actual);\n    const expectedShape = inferShape(expected);\n    if (!arraysEqual(actualShape, expectedShape)) {\n      throw new Error(\n          `Arrays have different shapes. ` +\n          `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n    }\n  }\n\n  const actualFlat =\n      isTypedArray(actual) ? actual : flatten(actual as RecursiveArray<number>);\n  const expectedFlat = isTypedArray(expected) ?\n      expected :\n      flatten(expected as RecursiveArray<number>);\n\n  if (actualFlat.length !== expectedFlat.length) {\n    throw new Error(\n        `Arrays have different lengths actual: ${actualFlat.length} vs ` +\n        `expected: ${expectedFlat.length}.\\n` +\n        `Actual:   ${actualFlat}.\\n` +\n        `Expected: ${expectedFlat}.`);\n  }\n  for (let i = 0; i < expectedFlat.length; ++i) {\n    const a = actualFlat[i];\n    const e = expectedFlat[i];\n\n    if (!predicate(a, e)) {\n      throw new Error(\n          `Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n          `Actual:   ${actualFlat}.\\n` +\n          `Expected: ${expectedFlat}.`);\n    }\n  }\n}\n\nexport interface DoneFn {\n  (): void;\n  fail: (message?: Error|string) => void;\n}\n\nexport function expectPromiseToFail(fn: () => Promise<{}>, done: DoneFn): void {\n  fn().then(() => done.fail(), () => done());\n}\n\nexport function expectArraysEqual(actual: TensorLike, expected: TensorLike) {\n  const exp = typeof expected === 'string' || typeof expected === 'number' ||\n          typeof expected === 'boolean' ?\n      [expected] as number[] :\n      expected as number[];\n  if (isString(actual) || isString((actual as string[])[0]) ||\n      isString(expected) || isString((expected as string[])[0])) {\n    // tslint:disable-next-line: triple-equals\n    return expectArraysPredicate(actual, exp, (a, b) => a == b);\n  }\n  return expectArraysPredicate(\n      actual, expected, (a, b) => areClose(a as number, b as number, 0));\n}\n\nexport function expectNumbersClose(a: number, e: number, epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = testEpsilon();\n  }\n  if (!areClose(a, e, epsilon)) {\n    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n  }\n}\n\nfunction areClose(a: number, e: number, epsilon: number): boolean {\n  if (!isFinite(a) && !isFinite(e)) {\n    return true;\n  }\n  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n    return false;\n  }\n  return true;\n}\n\nexport function expectValuesInRange(\n    actual: TypedArray|number[], low: number, high: number) {\n  for (let i = 0; i < actual.length; i++) {\n    if (actual[i] < low || actual[i] > high) {\n      throw new Error(\n          `Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n    }\n  }\n}\n\nexport function expectArrayBuffersEqual(\n    actual: ArrayBuffer, expected: ArrayBuffer) {\n  // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n  // a Float32Array solves this issue.\n  expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n\n/** Encodes strings into utf-8 bytes. */\nexport function encodeStrings(a: RecursiveArray<{}>):\n    RecursiveArray<Uint8Array> {\n  for (let i = 0; i < (a as Array<{}>).length; i++) {\n    const val = a[i];\n    if (Array.isArray(val)) {\n      encodeStrings(val);\n    } else {\n      a[i] = encodeString(val as string);\n    }\n  }\n  return a as RecursiveArray<Uint8Array>;\n}\n","/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '3.7.0';\nexport {version};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelBackend} from './backends/backend';\nimport {ENGINE, Engine, MemoryInfo, ProfileInfo, ScopeFn, TimingInfo} from './engine';\nimport {env} from './environment';\n\nimport {Platform} from './platforms/platform';\nimport {setDeprecationWarningFn, Tensor} from './tensor';\nimport {TensorContainer} from './tensor_types';\nimport {getTensorsInContainer} from './tensor_util';\n\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableProdMode(): void {\n  env().set('PROD', true);\n}\n\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableDebugMode(): void {\n  env().set('DEBUG', true);\n}\n\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings(): void {\n  env().set('DEPRECATION_WARNINGS_ENABLED', false);\n  console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n\n/** Warn users about deprecated functionality. */\nexport function deprecationWarn(msg: string) {\n  if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {\n    console.warn(\n        msg + ' You can disable deprecation warnings with ' +\n        'tf.disableDeprecationWarnings().');\n  }\n}\nsetDeprecationWarningFn(deprecationWarn);\n\n/**\n * Dispose all variables kept in backend engine.\n *\n * @doc {heading: 'Environment'}\n */\nexport function disposeVariables(): void {\n  ENGINE.disposeVariables();\n}\n\n/**\n * It returns the global engine that keeps track of all tensors and backends.\n *\n * @doc {heading: 'Environment'}\n */\nexport function engine(): Engine {\n  return ENGINE;\n}\n\n/**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n *   (undisposed) at this time, which is ≤ the number of tensors\n *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n *   data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n *    `unreliable` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n *    `unreliable` is true.\n *\n * WebGL Properties:\n * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at\n *     this time.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function memory(): MemoryInfo {\n  return ENGINE.memory();\n}\n\n/**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: the number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n * - `kernelNames`: an array of unique strings with just the names of the\n * kernels in the `kernels` array.\n *\n * ```js\n * const profile = await tf.profile(() => {\n *   const x = tf.tensor1d([1, 2, 3]);\n *   let x2 = x.square();\n *   x2.dispose();\n *   x2 = x.square();\n *   x2.dispose();\n *   return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n *\n * @doc {heading: 'Performance', subheading: 'Profile'}\n */\nexport function profile(f: () => (TensorContainer | Promise<TensorContainer>)):\n    Promise<ProfileInfo> {\n  return ENGINE.profile(f);\n}\n\n/**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to\n * dispose variables, please use `tf.disposeVariables` or call dispose()\n * directly on variables.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n *   // a, b, and one will be cleaned up when the tidy ends.\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *   const b = a.square();\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If debug mode is on, the timing and the memory usage of the function\n *     will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function tidy<T extends TensorContainer>(\n    nameOrFn: string|ScopeFn<T>, fn?: ScopeFn<T>): T {\n  return ENGINE.tidy(nameOrFn, fn);\n}\n\n/**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n *     happens. In general it is safe to pass any object here, except that\n *     `Promise`s are not supported.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function dispose(container: TensorContainer) {\n  const tensors = getTensorsInContainer(container);\n  tensors.forEach(tensor => tensor.dispose());\n}\n\n/**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *\n *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n *   // when the tidy ends.\n *   b = tf.keep(a.square());\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function keep<T extends Tensor>(result: T): T {\n  return ENGINE.keep(result);\n}\n\n/**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the\n * WebGL backend and the query timer extension is not available, this will\n * return an error object.\n * - On `WebGL` The following additional properties exist:\n *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nexport function time(f: () => void): Promise<TimingInfo> {\n  return ENGINE.time(f);\n}\n\n/**\n * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and\n * executing operations on those tensors. Returns a promise that resolves\n * to a boolean if the backend initialization was successful.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js\n *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).\n *\n * @doc {heading: 'Backends'}\n */\nexport function setBackend(backendName: string): Promise<boolean> {\n  return ENGINE.setBackend(backendName);\n}\n\n/**\n * Returns a promise that resolves when the currently selected backend (or the\n * highest priority one) has initialized. Await this promise when you are using\n * a backend that has async initialization.\n *\n * @doc {heading: 'Backends'}\n */\nexport function ready(): Promise<void> {\n  return ENGINE.ready();\n}\n\n/**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n *\n * @doc {heading: 'Backends'}\n */\nexport function getBackend(): string {\n  return ENGINE.backendName;\n}\n\n/**\n * Removes a backend and the registered factory.\n *\n * @doc {heading: 'Backends'}\n */\nexport function removeBackend(name: string): void {\n  ENGINE.removeBackend(name);\n}\n\n/**\n * Finds the backend registered under the provided name. Returns null if the\n * name is not in the registry, or the registration hasn't finished yet.\n */\nexport function findBackend(name: string): KernelBackend {\n  return ENGINE.findBackend(name);\n}\n\n/**\n * Finds the backend factory registered under the provided name. Returns a\n * function that produces a new backend when called. Returns null if the name\n * is not in the registry.\n */\nexport function findBackendFactory(name: string): () =>\n    KernelBackend | Promise<KernelBackend> {\n  return ENGINE.findBackendFactory(name);\n}\n\n/**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return a backend instance, or a promise of an instance.\n * @param priority The priority of the backend (higher = more important).\n *     In case multiple backends are registered, the priority is used to find\n *     the best backend. Defaults to 1.\n * @return False if there is already a registered backend under this name, true\n *     if not.\n *\n * @doc {heading: 'Backends'}\n */\nexport function registerBackend(\n    name: string, factory: () => KernelBackend | Promise<KernelBackend>,\n    priority = 1): boolean {\n  return ENGINE.registerBackend(name, factory, priority);\n}\n\n/**\n * Gets the current backend. If no backends have been initialized, this will\n * attempt to initialize the best backend. Will throw an error if the highest\n * priority backend has async initialization, in which case, you should call\n * 'await tf.ready()' before running other code.\n *\n * @doc {heading: 'Backends'}\n */\nexport function backend(): KernelBackend {\n  return ENGINE.backend;\n}\n\n/**\n * Sets the global platform.\n *\n * @param platformName The name of this platform.\n * @param platform A platform implementation.\n */\nexport function setPlatform(platformName: string, platform: Platform) {\n  env().setPlatform(platformName, platform);\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Add, AddInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction add_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'add');\n  let $b = convertToTensor(b, 'b', 'add');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const inputs: AddInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(Add, inputs as {} as NamedTensorMap);\n}\n\nexport const add = op({add_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {FloorDiv, FloorDivInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'floorDiv');\n  let $b = convertToTensor(b, 'b', 'floorDiv');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const inputs: FloorDivInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(FloorDiv, inputs as {} as NamedTensorMap);\n}\n\nexport const floorDiv = op({floorDiv_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {RealDiv, RealDivInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {floorDiv} from './floorDiv';\nimport {op} from './operation';\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction div_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'div');\n  let $b = convertToTensor(b, 'b', 'div');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n    return floorDiv($a, $b);\n  }\n\n  const inputs: RealDivInputs = {a: $a, b: $b};\n  const attrs = {};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  return ENGINE.runKernel(RealDiv, inputs as {} as NamedTensorMap, attrs) as T;\n}\n\nexport const div = op({div_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Multiply, MultiplyInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mul_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'mul');\n  let $b = convertToTensor(b, 'b', 'mul');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const inputs: MultiplyInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(Multiply, inputs as {} as NamedTensorMap);\n}\nexport const mul = op({mul_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Abs, AbsInputs, ComplexAbs, ComplexAbsInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction abs_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'abs');\n\n  if ($x.dtype === 'complex64') {\n    const inputs: ComplexAbsInputs = {x: $x};\n    return ENGINE.runKernel(ComplexAbs, inputs as {} as NamedTensorMap);\n  } else {\n    const inputs: AbsInputs = {x: $x};\n    return ENGINE.runKernel(Abs, inputs as {} as NamedTensorMap);\n  }\n}\n\nexport const abs = op({abs_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Acos, AcosInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'acos');\n  const inputs: AcosInputs = {x: $x};\n\n  return ENGINE.runKernel(Acos, inputs as {} as NamedTensorMap);\n}\nexport const acos = op({acos_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Acosh, AcoshInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'acosh');\n  const inputs: AcoshInputs = {x: $x};\n\n  return ENGINE.runKernel(Acosh, inputs as {} as NamedTensorMap);\n}\nexport const acosh = op({acosh_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {AddN, AddNInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_<T extends Tensor>(tensors: Array<T|TensorLike>): T {\n  util.assert(\n      Array.isArray(tensors),\n      () => 'The argument passed to tf.addN() must be a list of tensors');\n  util.assert(\n      tensors.length >= 1,\n      () => `Must pass at least one tensor to tf.addN(), but got ` +\n          `${tensors.length}`);\n\n  const $tensors =\n      tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n\n  const firstTensor = $tensors[0];\n  $tensors.forEach(t => {\n    if (t.dtype !== firstTensor.dtype) {\n      throw new Error(\n          'All tensors passed to tf.addN() must have the same dtype');\n    }\n  });\n\n  $tensors.forEach(t => {\n    if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n      throw new Error(\n          'All tensors passed to tf.addN() must have the same shape');\n    }\n  });\n\n  const inputs: AddNInputs = $tensors;\n\n  return ENGINE.runKernel(AddN, inputs as {} as NamedTensorMap);\n}\n\nexport const addN = op({addN_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {All, AllAttrs, AllInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'all', 'bool');\n\n  const inputs: AllInputs = {x: $x};\n  const attrs: AllAttrs = {axis, keepDims};\n\n  return ENGINE.runKernel(\n      All, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const all = op({all_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Any, AnyAttrs, AnyInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'any', 'bool');\n\n  const inputs: AnyInputs = {x: $x};\n  const attrs: AnyAttrs = {axis, keepDims};\n\n  return ENGINE.runKernel(\n      Any, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\n// tslint:disable-next-line:variable-name\nexport const any = op({any_});\n","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {ArgMax, ArgMaxAttrs, ArgMaxInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {\n  const $x = convertToTensor(x, 'x', 'argMax');\n\n  const inputs: ArgMaxInputs = {x: $x};\n  const attrs: ArgMaxAttrs = {axis};\n\n  return ENGINE.runKernel(\n      ArgMax, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const argMax = op({argMax_});\n","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {ArgMin, ArgMinAttrs, ArgMinInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {\n  const $x = convertToTensor(x, 'x', 'argMin');\n\n  const inputs: ArgMinInputs = {x: $x};\n  const attrs: ArgMinAttrs = {axis};\n\n  return ENGINE.runKernel(\n      ArgMin, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const argMin = op({argMin_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Asin, AsinInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'asin');\n  const inputs: AsinInputs = {x: $x};\n\n  return ENGINE.runKernel(Asin, inputs as {} as NamedTensorMap);\n}\nexport const asin = op({asin_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Asinh, AsinhInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'asinh');\n\n  const inputs: AsinhInputs = {x: $x};\n\n  return ENGINE.runKernel(Asinh, inputs as {} as NamedTensorMap);\n}\nexport const asinh = op({asinh_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Atan, AtanInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'atan');\n\n  const inputs: AtanInputs = {x: $x};\n\n  return ENGINE.runKernel(Atan, inputs as {} as NamedTensorMap);\n}\nexport const atan = op({atan_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Atan2, Atan2Inputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'atan2');\n  let $b = convertToTensor(b, 'b', 'atan2');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const inputs: Atan2Inputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(Atan2, inputs as {} as NamedTensorMap);\n}\n\nexport const atan2 = op({atan2_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Atanh, AtanhInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'atanh');\n\n  const inputs: AtanhInputs = {x: $x};\n\n  return ENGINE.runKernel(Atanh, inputs as {} as NamedTensorMap);\n}\nexport const atanh = op({atanh_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\ntype PadType = 'SAME'|'VALID'|'NUMBER'|'EXPLICIT';\n\n// For NHWC should be in the following form:\n//  [[0, 0], [pad_top,pad_bottom], [pad_left, pad_right], [0, 0]]\n// For NCHW should be in the following form:\n//  [[0, 0], [0, 0], [pad_top,pad_bottom], [pad_left, pad_right]]\n// Reference: https://www.tensorflow.org/api_docs/python/tf/nn/conv2d\nexport type ExplicitPadding =\n    [[number, number], [number, number], [number, number], [number, number]];\n\nexport type PadInfo = {\n  top: number,\n  left: number,\n  right: number,\n  bottom: number,\n  type: PadType\n};\n\nexport type PadInfo3D = {\n  top: number,\n  left: number,\n  right: number,\n  bottom: number,\n  front: number,\n  back: number,\n  type: PadType\n};\n\n/**\n * Information about the forward pass of a convolution/pooling operation.\n * It includes input and output shape, strides, filter size and padding\n * information.\n */\nexport type Conv2DInfo = {\n  batchSize: number,\n  inHeight: number,\n  inWidth: number,\n  inChannels: number,\n  outHeight: number,\n  outWidth: number,\n  outChannels: number,\n  dataFormat: 'channelsFirst'|'channelsLast',\n  strideHeight: number,\n  strideWidth: number,\n  dilationHeight: number,\n  dilationWidth: number,\n  filterHeight: number,\n  filterWidth: number,\n  effectiveFilterHeight: number,\n  effectiveFilterWidth: number,\n  padInfo: PadInfo,\n  inShape: [number, number, number, number],\n  outShape: [number, number, number, number],\n  filterShape: [number, number, number, number]\n};\n\n/**\n *\n * @param inputShape Input tensor shape is of the following dimensions:\n *     `[batch, height, width, inChannels]`.\n * @param filterShape The filter shape is of the following dimensions:\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat The data format of the input and output data.\n *     Defaults to 'NHWC'.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.\n *     Defaults to `[1, 1]`. If `dilations` is a single number, then\n *     `dilationHeight == dilationWidth`.\n */\nexport function computeDilation2DInfo(\n    inputShape: [number, number, number, number],\n    filterShape: [number, number, number], strides: number|[number, number],\n    pad: 'same'|'valid'|number, dataFormat: 'NHWC' = 'NHWC',\n    dilations: number|[number, number]) {\n  // `computerConv2DInfo` require filterShape to be in the dimension of:\n  // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have\n  // outDepth, it should have the same depth as the input.\n  // Input shape: [batch, height, width, inChannels]\n  const inputChannels = inputShape[3];\n  const $filterShape =\n      [...filterShape, inputChannels] as [number, number, number, number];\n  const $dataFormat = convertConv2DDataFormat(dataFormat);\n\n  return computeConv2DInfo(\n      inputShape, $filterShape, strides, dilations, pad,\n      null /* roundingMode */, null /* depthWise */, $dataFormat);\n}\n\nexport function computePool2DInfo(\n    inShape: [number, number, number, number],\n    filterSize: [number, number]|number, strides: number|[number, number],\n    dilations: number|[number, number], pad: 'same'|'valid'|number,\n    roundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {\n  const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n\n  let filterShape: [number, number, number, number];\n  if (dataFormat === 'channelsLast') {\n    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n  } else if (dataFormat === 'channelsFirst') {\n    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  return computeConv2DInfo(\n      inShape, filterShape, strides, dilations, pad, roundingMode, false,\n      dataFormat);\n}\n\n/**\n * Computes the information for a forward pass of a pooling3D operation.\n */\nexport function computePool3DInfo(\n    inShape: [number, number, number, number, number],\n    filterSize: number|[number, number, number],\n    strides: number|[number, number, number],\n    dilations: number|[number, number, number], pad: 'same'|'valid'|number,\n    roundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): Conv3DInfo {\n  const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n\n  let filterShape: [number, number, number, number, number];\n  let $dataFormat: 'channelsFirst'|'channelsLast';\n  if (dataFormat === 'NDHWC') {\n    $dataFormat = 'channelsLast';\n    filterShape =\n        [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n  } else if (dataFormat === 'NCDHW') {\n    $dataFormat = 'channelsFirst';\n    filterShape =\n        [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  return computeConv3DInfo(\n      inShape, filterShape, strides, dilations, pad, false, $dataFormat,\n      roundingMode);\n}\n\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(\n    inShape: [number, number, number, number],\n    filterShape: [number, number, number, number],\n    strides: number|[number, number], dilations: number|[number, number],\n    pad: 'same'|'valid'|number|ExplicitPadding,\n    roundingMode?: 'floor'|'round'|'ceil', depthwise = false,\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {\n  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n  if (dataFormat === 'channelsLast') {\n    [batchSize, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === 'channelsFirst') {\n    [batchSize, inChannels, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  const [filterHeight, filterWidth, , filterChannels] = filterShape;\n  const [strideHeight, strideWidth] = parseTupleParam(strides);\n  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n\n  const effectiveFilterHeight =\n      getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth =\n      getEffectiveFilterSize(filterWidth, dilationWidth);\n  const {padInfo, outHeight, outWidth} = getPadAndOutInfo(\n      pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight,\n      effectiveFilterWidth, roundingMode, dataFormat);\n\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n\n  let outShape: [number, number, number, number];\n  if (dataFormat === 'channelsFirst') {\n    outShape = [batchSize, outChannels, outHeight, outWidth];\n  } else if (dataFormat === 'channelsLast') {\n    outShape = [batchSize, outHeight, outWidth, outChannels];\n  }\n\n  return {\n    batchSize,\n    dataFormat,\n    inHeight,\n    inWidth,\n    inChannels,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideHeight,\n    strideWidth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\n\n/**\n * Information about the forward pass of a 3D convolution/pooling operation.\n * It includes input and output shape, strides, filter size and padding\n * information.\n */\nexport type Conv3DInfo = {\n  batchSize: number,\n  inDepth: number,\n  inHeight: number,\n  inWidth: number,\n  inChannels: number,\n  outDepth: number,\n  outHeight: number,\n  outWidth: number,\n  outChannels: number,\n  dataFormat: 'channelsFirst'|'channelsLast',\n  strideDepth: number,\n  strideHeight: number,\n  strideWidth: number,\n  dilationDepth: number,\n  dilationHeight: number,\n  dilationWidth: number,\n  filterDepth: number,\n  filterHeight: number,\n  filterWidth: number,\n  effectiveFilterDepth: number,\n  effectiveFilterHeight: number,\n  effectiveFilterWidth: number,\n  padInfo: PadInfo3D,\n  inShape: [number, number, number, number, number],\n  outShape: [number, number, number, number, number],\n  filterShape: [number, number, number, number, number]\n};\n\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(\n    inShape: [number, number, number, number, number],\n    filterShape: [number, number, number, number, number],\n    strides: number|[number, number, number],\n    dilations: number|[number, number, number], pad: 'same'|'valid'|number,\n    depthwise = false,\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast',\n    roundingMode?: 'floor'|'round'|'ceil'): Conv3DInfo {\n  let [batchSize, inDepth, inHeight, inWidth, inChannels] =\n      [-1, -1, -1, -1, -1];\n  if (dataFormat === 'channelsLast') {\n    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === 'channelsFirst') {\n    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  const [filterDepth, filterHeight, filterWidth, , filterChannels] =\n      filterShape;\n  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n  const [dilationDepth, dilationHeight, dilationWidth] =\n      parse3TupleParam(dilations);\n\n  const effectiveFilterDepth =\n      getEffectiveFilterSize(filterDepth, dilationDepth);\n  const effectiveFilterHeight =\n      getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth =\n      getEffectiveFilterSize(filterWidth, dilationWidth);\n  const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(\n      pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth,\n      effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth,\n      roundingMode);\n\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n\n  let outShape: [number, number, number, number, number];\n  if (dataFormat === 'channelsFirst') {\n    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n  } else if (dataFormat === 'channelsLast') {\n    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n  }\n\n  return {\n    batchSize,\n    dataFormat,\n    inDepth,\n    inHeight,\n    inWidth,\n    inChannels,\n    outDepth,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideDepth,\n    strideHeight,\n    strideWidth,\n    filterDepth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterDepth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationDepth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\n\nfunction computeOutputShape2D(\n    inShape: [number, number], fieldSize: number, stride: number,\n    zeroPad?: number, roundingMode?: 'floor'|'round'|'ceil'): [number, number] {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputRows = inShape[0];\n  const inputCols = inShape[1];\n\n  const outputRows =\n      round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  const outputCols =\n      round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n\n  return [outputRows, outputCols];\n}\n\nfunction computeOutputShape4D(\n    inShape: [number, number, number, number], fieldSize: number,\n    outChannels: number, stride: number, zeroPad?: number,\n    roundingMode?: 'floor'|'round'|'ceil'): [number, number, number, number] {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputDepth = inShape[0];\n  const inputRows = inShape[1];\n  const inputCols = inShape[2];\n\n  const outputDepths =\n      round((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  const outputRows =\n      round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  const outputCols =\n      round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n\n  return [outputDepths, outputRows, outputCols, outChannels];\n}\n\nexport function computeDefaultPad(\n    inputShape: [number, number]|[number, number, number, number],\n    fieldSize: number, stride: number, dilation = 1): number {\n  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n  return Math.floor(\n      (inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\n\nfunction parseTupleParam(param: number|number[]): [number, number, number] {\n  if (typeof param === 'number') {\n    return [param, param, param];\n  }\n  if (param.length === 2) {\n    return [param[0], param[1], 1];\n  }\n  return param as [number, number, number];\n}\n\nfunction parse3TupleParam(param: number|[number, number, number]):\n    [number, number, number] {\n  return typeof param === 'number' ? [param, param, param] : param;\n}\n\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize: number, dilation: number) {\n  if (dilation <= 1) {\n    return filterSize;\n  }\n\n  return filterSize + (filterSize - 1) * (dilation - 1);\n}\n\nfunction getPadAndOutInfo(\n    pad: 'same'|'valid'|number|ExplicitPadding, inHeight: number,\n    inWidth: number, strideHeight: number, strideWidth: number,\n    filterHeight: number, filterWidth: number,\n    roundingMode: 'floor'|'round'|'ceil',\n    dataFormat: 'channelsFirst'|\n    'channelsLast'): {padInfo: PadInfo, outHeight: number, outWidth: number} {\n  let padInfo: PadInfo;\n  let outHeight: number;\n  let outWidth: number;\n\n  if (typeof pad === 'number') {\n    const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n    padInfo = {top: pad, bottom: pad, left: pad, right: pad, type: padType};\n    const outShape = computeOutputShape2D(\n        [inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);\n    outHeight = outShape[0];\n    outWidth = outShape[1];\n  } else if (pad === 'same') {\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongHeight =\n        Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n    const padAlongWidth =\n        Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n    padInfo = {top, bottom, left, right, type: 'SAME'};\n  } else if (pad === 'valid') {\n    padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: 'VALID'};\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else if (typeof pad === 'object') {\n    const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];\n    const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];\n    const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];\n    const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];\n    const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?\n        'VALID' :\n        'EXPLICIT';\n    padInfo = {top, bottom, left, right, type: padType};\n    outHeight = round(\n        (inHeight - filterHeight + top + bottom) / strideHeight + 1,\n        roundingMode);\n    outWidth = round(\n        (inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad}`);\n  }\n  return {padInfo, outHeight, outWidth};\n}\n\nfunction get3DPadAndOutInfo(\n    pad: 'same'|'valid'|number, inDepth: number, inHeight: number,\n    inWidth: number, strideDepth: number, strideHeight: number,\n    strideWidth: number, filterDepth: number, filterHeight: number,\n    filterWidth: number, roundingMode?: 'floor'|'round'|'ceil'): {\n  padInfo: PadInfo3D,\n  outDepth: number,\n  outHeight: number,\n  outWidth: number\n} {\n  let padInfo: PadInfo3D;\n  let outDepth: number;\n  let outHeight: number;\n  let outWidth: number;\n\n  if (typeof pad === 'number') {\n    const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n    padInfo = {\n      top: pad,\n      bottom: pad,\n      left: pad,\n      right: pad,\n      front: pad,\n      back: pad,\n      type: padType\n    };\n    const outShape = computeOutputShape4D(\n        [inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad,\n        roundingMode);\n    outDepth = outShape[0];\n    outHeight = outShape[1];\n    outWidth = outShape[2];\n  } else if (pad === 'same') {\n    outDepth = Math.ceil(inDepth / strideDepth);\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n    const padAlongHeight =\n        (outHeight - 1) * strideHeight + filterHeight - inHeight;\n    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n    const front = Math.floor(padAlongDepth / 2);\n    const back = padAlongDepth - front;\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n\n    padInfo = {top, bottom, left, right, front, back, type: 'SAME'};\n  } else if (pad === 'valid') {\n    padInfo = {\n      top: 0,\n      bottom: 0,\n      left: 0,\n      right: 0,\n      front: 0,\n      back: 0,\n      type: 'VALID'\n    };\n    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad}`);\n  }\n  return {padInfo, outDepth, outHeight, outWidth};\n}\n\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n */\nfunction round(value: number, roundingMode?: 'floor'|'round'|'ceil') {\n  if (!roundingMode) {\n    return Math.trunc(value);\n  }\n  switch (roundingMode) {\n    case 'round':\n      // used for Caffe Conv\n      return Math.round(value);\n    case 'ceil':\n      // used for Caffe Pool\n      return Math.ceil(value);\n    case 'floor':\n      return Math.floor(value);\n    default:\n      throw new Error(`Unknown roundingMode ${roundingMode}`);\n  }\n}\n\nexport function tupleValuesAreOne(param: number|number[]): boolean {\n  const [dimA, dimB, dimC] = parseTupleParam(param);\n  return dimA === 1 && dimB === 1 && dimC === 1;\n}\n\nexport function eitherStridesOrDilationsAreOne(\n    strides: number|number[], dilations: number|number[]): boolean {\n  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n\n/**\n * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to\n *    'channelsLast'|'channelsFirst'\n * @param dataFormat in 'NHWC'|'NCHW' mode\n * @return dataFormat in 'channelsLast'|'channelsFirst' mode\n * @throws unknown dataFormat\n */\nexport function convertConv2DDataFormat(dataFormat: 'NHWC'|'NCHW'):\n    'channelsLast'|'channelsFirst' {\n  if (dataFormat === 'NHWC') {\n    return 'channelsLast';\n  } else if (dataFormat === 'NCHW') {\n    return 'channelsFirst';\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Reshape, ReshapeAttrs, ReshapeInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_<R extends Rank>(\n    x: Tensor|TensorLike, shape: ShapeMap[R]): Tensor<R> {\n  const $x = convertToTensor(x, 'x', 'reshape', 'string_or_numeric');\n\n  const inputs: ReshapeInputs = {x: $x};\n  const attrs: ReshapeAttrs = {shape};\n  return ENGINE.runKernel(\n      Reshape, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\nexport const reshape = op({reshape_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {AvgPool, AvgPoolAttrs, AvgPoolInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {cast} from './cast';\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n */\nfunction avgPool_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n  const dilations = 1;\n\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const inputs: AvgPoolInputs = {x: x4D};\n\n  const attrs: AvgPoolAttrs = {filterSize, strides, pad, dimRoundingMode};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  let res = ENGINE.runKernel(\n                AvgPool, inputs as {} as NamedTensorMap,\n                attrs as {} as NamedAttrMap) as T;\n\n  res = cast(res, $x.dtype);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n\n  return res;\n}\n\nexport const avgPool = op({avgPool_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {AvgPool3D, AvgPool3DAttrs, AvgPool3DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {cast} from './cast';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filterSize: [number, number, number]|number,\n    strides: [number, number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): T {\n  const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n\n  util.assert(\n      x5D.rank === 5,\n      () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n  util.assert(\n      dataFormat === 'NDHWC',\n      () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n          `but got dataFormat of ${dataFormat}`);\n\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in avgPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const inputs: AvgPool3DInputs = {x: x5D};\n\n  const attrs:\n      AvgPool3DAttrs = {filterSize, strides, pad, dimRoundingMode, dataFormat};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  let res = ENGINE.runKernel(\n                AvgPool3D, inputs as {} as NamedTensorMap,\n                attrs as {} as NamedAttrMap) as T;\n\n  res = cast(res, x5D.dtype);\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n\n  return res;\n}\n\nexport const avgPool3d = op({avgPool3d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Concat, ConcatAttrs, ConcatInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert} from '../util';\n\nimport {clone} from './clone';\nimport {op} from './operation';\n\n/**\n * Concatenates a list of `tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction concat_<T extends Tensor>(tensors: Array<T|TensorLike>, axis = 0): T {\n  assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');\n\n  const $tensors =\n      convertToTensorArray(tensors, 'tensors', 'concat', 'string_or_numeric');\n\n  if ($tensors[0].dtype === 'complex64') {\n    $tensors.forEach(tensor => {\n      if (tensor.dtype !== 'complex64') {\n        throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor.dtype}. `);\n      }\n    });\n  }\n\n  if ($tensors.length === 1) {\n    return clone($tensors[0]);\n  }\n\n  const inputs: ConcatInputs = $tensors;\n  const attr: ConcatAttrs = {axis};\n\n  return ENGINE.runKernel(\n      Concat, inputs as {} as NamedTensorMap, attr as {} as NamedAttrMap);\n}\n\nexport const concat = op({concat_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Sigmoid, SigmoidInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sigmoid');\n\n  const inputs: SigmoidInputs = {x: $x};\n\n  return ENGINE.runKernel(Sigmoid, inputs as {} as NamedTensorMap);\n}\nexport const sigmoid = op({sigmoid_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Slice, SliceAttrs, SliceInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction slice_<R extends Rank, T extends Tensor<R>>(\n    x: T|TensorLike, begin: number|number[], size?: number|number[]): T {\n  const $x = convertToTensor(x, 'x', 'slice', 'string_or_numeric');\n\n  if ($x.rank === 0) {\n    throw new Error('Slicing scalar is not possible');\n  }\n\n  const inputs: SliceInputs = {x: $x};\n  const attrs: SliceAttrs = {begin, size};\n\n  return ENGINE.runKernel(\n      Slice, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const slice = op({slice_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tanh, TanhInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'tanh');\n\n  const inputs: TanhInputs = {x: $x};\n\n  return ENGINE.runKernel(Tanh, inputs as {} as NamedTensorMap);\n}\nexport const tanh = op({tanh_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {add} from './add';\nimport {concat} from './concat';\nimport {matMul} from './mat_mul';\nimport {mul} from './mul';\nimport {op} from './operation';\nimport {sigmoid} from './sigmoid';\nimport {slice} from './slice';\nimport {tanh} from './tanh';\n\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(\n    forgetBias: Scalar|TensorLike, lstmKernel: Tensor2D|TensorLike,\n    lstmBias: Tensor1D|TensorLike, data: Tensor2D|TensorLike,\n    c: Tensor2D|TensorLike, h: Tensor2D|TensorLike): [Tensor2D, Tensor2D] {\n  const $forgetBias =\n      convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n  const $lstmKernel =\n      convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n  const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n  const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n  const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n  const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n\n  const combined = concat([$data, $h], 1);\n  const weighted = matMul(combined, $lstmKernel);\n  const res: Tensor2D = add(weighted, $lstmBias);\n\n  // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n  const batchSize = res.shape[0];\n  const sliceCols = res.shape[1] / 4;\n  const sliceSize: [number, number] = [batchSize, sliceCols];\n  const i = slice(res, [0, 0], sliceSize);\n  const j = slice(res, [0, sliceCols], sliceSize);\n  const f = slice(res, [0, sliceCols * 2], sliceSize);\n  const o = slice(res, [0, sliceCols * 3], sliceSize);\n\n  const newC: Tensor2D =\n      add(mul(sigmoid(i), tanh(j)),\n          mul($c, sigmoid(add($forgetBias, f)) as Tensor2D));\n  const newH: Tensor2D = mul(tanh(newC), sigmoid(o));\n  return [newC, newH];\n}\n\nexport const basicLSTMCell = op({basicLSTMCell_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {BatchToSpaceND, BatchToSpaceNDAttrs, BatchToSpaceNDInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_<T extends Tensor>(\n    x: T|TensorLike, blockShape: number[], crops: number[][]): T {\n  const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n  const prod = blockShape.reduce((a, b) => a * b);\n\n  util.assert(\n      $x.rank >= 1 + blockShape.length,\n      () => `input rank is ${$x.rank} but should be > than blockShape.length ${\n          blockShape.length}`);\n\n  util.assert(\n      crops.length === blockShape.length,\n      () => `crops.length is ${\n          crops.length} but should be equal to blockShape.length  ${\n          blockShape.length}`);\n\n  util.assert(\n      $x.shape[0] % prod === 0,\n      () => `input tensor batch is ${\n                $x.shape[0]} but is not divisible by the product of ` +\n          `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n\n  const inputs: BatchToSpaceNDInputs = {x: $x};\n  const attrs: BatchToSpaceNDAttrs = {blockShape, crops};\n\n  return ENGINE.runKernel(\n      BatchToSpaceND, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const batchToSpaceND = op({batchToSpaceND_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor, Tensor4D} from '../tensor';\nimport {Rank} from '../types';\nimport {reshape} from './reshape';\n\nexport function xAs4D<R extends Rank>(x: Tensor<R>) {\n  let x4D: Tensor4D;\n  if (x.rank === 0 || x.rank === 1) {\n    x4D = reshape(x, [1, 1, 1, x.size]);\n  } else if (x.rank === 2) {\n    x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n  } else if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  } else {\n    x4D = x as Tensor4D;\n  }\n\n  return x4D;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {FusedBatchNorm, FusedBatchNormAttrs, FusedBatchNormInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor1D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {xAs4D} from './batchnorm_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNorm2d`\n *   - `tf.batchNorm3d`\n *   - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_<R extends Rank>(\n    x: Tensor<R>|TensorLike, mean: Tensor<R>|Tensor1D|TensorLike,\n    variance: Tensor<R>|Tensor1D|TensorLike,\n    offset?: Tensor<R>|Tensor1D|TensorLike,\n    scale?: Tensor<R>|Tensor1D|TensorLike,\n    varianceEpsilon?: number): Tensor<R> {\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 0.001;\n  }\n  const $x = convertToTensor(x, 'x', 'batchNorm');\n  const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n  const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n  let $scale: Tensor<R>|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNorm');\n  }\n  let $offset: Tensor<R>|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNorm');\n  }\n\n  util.assert(\n      $mean.rank === $variance.rank,\n      () => 'Batch normalization gradient requires mean and variance to have ' +\n          'equal ranks.');\n  util.assert(\n      $offset == null || $mean.rank === $offset.rank,\n      () => 'Batch normalization gradient requires mean and offset to have ' +\n          'equal ranks.');\n  util.assert(\n      $scale == null || $mean.rank === $scale.rank,\n      () => 'Batch normalization gradient requires mean and scale to have ' +\n          'equal ranks.');\n\n  const x4D: Tensor4D = xAs4D($x);\n\n  const inputs: FusedBatchNormInputs = {\n    x: x4D,\n    scale: $scale,\n    offset: $offset,\n    mean: $mean,\n    variance: $variance\n  };\n\n  const attrs: FusedBatchNormAttrs = {varianceEpsilon};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  FusedBatchNorm, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as Tensor<R>;\n\n  return reshape(res, $x.shape);\n}\n\nexport const batchNorm = op({batchNorm_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {batchNorm} from './batchnorm';\nimport {op} from './operation';\n\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(\n    x: Tensor2D|TensorLike, mean: Tensor2D|Tensor1D|TensorLike,\n    variance: Tensor2D|Tensor1D|TensorLike,\n    offset?: Tensor2D|Tensor1D|TensorLike, scale?: Tensor2D|Tensor1D|TensorLike,\n    varianceEpsilon?: number): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'batchNorm');\n  const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n  const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n  let $scale: Tensor2D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNorm');\n  }\n  let $offset: Tensor2D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNorm');\n  }\n  util.assert(\n      $x.rank === 2,\n      () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 2 || $mean.rank === 1,\n      () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 2 || $variance.rank === 1,\n      () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 2 || $scale.rank === 1,\n        () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 2 || $offset.rank === 1,\n        () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\n\nexport const batchNorm2d = op({batchNorm2d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor1D, Tensor3D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {batchNorm} from './batchnorm';\nimport {op} from './operation';\n\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(\n    x: Tensor3D|TensorLike, mean: Tensor3D|Tensor1D|TensorLike,\n    variance: Tensor3D|Tensor1D|TensorLike,\n    offset?: Tensor3D|Tensor1D|TensorLike, scale?: Tensor3D|Tensor1D|TensorLike,\n    varianceEpsilon?: number): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'batchNorm');\n  const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n  const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n  let $scale: Tensor3D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNorm');\n  }\n  let $offset: Tensor3D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNorm');\n  }\n  util.assert(\n      $x.rank === 3,\n      () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 3 || $mean.rank === 1,\n      () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 3 || $variance.rank === 1,\n      () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 3 || $scale.rank === 1,\n        () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 3 || $offset.rank === 1,\n        () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\n\nexport const batchNorm3d = op({batchNorm3d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor1D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {batchNorm} from './batchnorm';\nimport {op} from './operation';\n\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(\n    x: Tensor4D|TensorLike, mean: Tensor4D|Tensor1D|TensorLike,\n    variance: Tensor4D|Tensor1D|TensorLike,\n    offset?: Tensor4D|Tensor1D|TensorLike, scale?: Tensor4D|Tensor1D|TensorLike,\n    varianceEpsilon?: number): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'batchNorm');\n  const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n  const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n  let $scale: Tensor4D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNorm');\n  }\n  let $offset: Tensor4D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNorm');\n  }\n  util.assert(\n      $x.rank === 4,\n      () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 4 || $mean.rank === 1,\n      () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 4 || $variance.rank === 1,\n      () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 4 || $scale.rank === 1,\n        () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 4 || $offset.rank === 1,\n        () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\n\nexport const batchNorm4d = op({batchNorm4d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Bincount, BincountAttrs, BincountInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor1D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Outputs a vector with length `size` and the same dtype as `weights`.\n *\n * If `weights` are empty, then index `i` stores the number of times the value\n * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the\n * sum of the value in `weights` at each index where the corresponding value in\n * `x` is `i`.\n *\n * Values in `x` outside of the range [0, size) are ignored.\n *\n * @param x The input int tensor, rank 1.\n * @param weights The weights tensor, must have the same shape as x, or a\n *     length-0 Tensor, in which case it acts as all weights equal to 1.\n * @param size Non-negative integer.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction bincount_<T extends Tensor1D>(\n    x: T|TensorLike, weights: T|TensorLike, size: number): T {\n  const $x = convertToTensor(x, 'x', 'bincount');\n  const $weights = convertToTensor(weights, 'weights', 'bincount');\n\n  util.assert(\n      $x.dtype === 'int32',\n      () => `Error in bincount: input ` +\n          `dtype must be int32, but got ${$x.dtype}`);\n  util.assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n  util.assert(\n      $weights.size === $x.size || $weights.size === 0,\n      () => `Error in bincount: weights must have the same size as input or` +\n          `0-length, but got input shape: ${$x.shape}, weights shape: ` +\n          `${$weights.shape}.`);\n\n  const inputs: BincountInputs = {x: $x, weights: $weights};\n  const attrs: BincountAttrs = {size};\n\n  return ENGINE.runKernel(\n      Bincount, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const bincount = op({bincount_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tile, TileAttrs, TileInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {clone} from './clone';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_<R extends Rank>(\n    x: Tensor|TensorLike, shape: ShapeMap[R]): Tensor<R> {\n  let input = convertToTensor(x, 'broadcastTo', 'x');\n  const xShape = input.shape;\n\n  if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n    throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n  }\n\n  if (shape.length < input.rank) {\n    throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${\n        input.rank}.`);\n  }\n\n  if (shape.length > input.rank) {\n    const newShape = input.shape.slice();\n    while (newShape.length < shape.length) {\n      newShape.unshift(1);\n    }\n    input = reshape(input, newShape);\n  }\n\n  const inputShape = input.shape;\n  const reps: number[] = Array.from(shape);\n  for (let i = shape.length - 1; i >= 0; i--) {\n    if (inputShape[i] === shape[i]) {\n      reps[i] = 1;\n    } else if (input.shape[i] !== 1) {\n      throw new Error(\n          `broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n    }\n  }\n  const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n\n  if (axes.length === 0) {\n    return clone(input) as Tensor<R>;\n  }\n\n  // TODO call broadcastTo kernel directly once backends implement broadcstTo\n  const inputs: TileInputs = {x: input};\n  const attrs: TileAttrs = {reps};\n  return ENGINE.runKernel(\n      Tile, inputs as {} as NamedTensorMap, attrs as unknown as NamedAttrMap);\n}\n\nexport const broadcastTo = op({broadcastTo_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Ceil, CeilInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'ceil');\n\n  const inputs: CeilInputs = {x: $x};\n  return ENGINE.runKernel(Ceil, inputs as {} as NamedTensorMap);\n}\nexport const ceil = op({ceil_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {ClipByValue, ClipByValueAttrs, ClipByValueInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_<T extends Tensor>(\n    x: T|TensorLike, clipValueMin: number, clipValueMax: number): T {\n  const $x = convertToTensor(x, 'x', 'clipByValue');\n  util.assert(\n      (clipValueMin <= clipValueMax),\n      () => `Error in clip: min (${clipValueMin}) must be ` +\n          `less than or equal to max (${clipValueMax}).`);\n\n  const inputs: ClipByValueInputs = {x: $x};\n  const attrs: ClipByValueAttrs = {clipValueMin, clipValueMax};\n\n  return ENGINE.runKernel(\n      ClipByValue, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const clipByValue = op({clipByValue_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor1D} from '../tensor';\nimport {TensorLike} from '../types';\n\nimport {concat} from './concat';\nimport {op} from './operation';\n\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors: Array<Tensor1D|TensorLike>): Tensor1D {\n  return concat(tensors, 0 /* axis */);\n}\n\nexport const concat1d = op({concat1d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor2D} from '../tensor';\nimport {TensorLike} from '../types';\n\nimport {concat} from './concat';\nimport {op} from './operation';\n\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(\n    tensors: Array<Tensor2D|TensorLike>, axis: number): Tensor2D {\n  return concat(tensors, axis);\n}\n\nexport const concat2d = op({concat2d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor3D} from '../tensor';\nimport {TensorLike} from '../types';\n\nimport {concat} from './concat';\nimport {op} from './operation';\n\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(\n    tensors: Array<Tensor3D|TensorLike>, axis: number): Tensor3D {\n  return concat(tensors, axis);\n}\n\nexport const concat3d = op({concat3d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor4D} from '../tensor';\nimport {TensorLike} from '../types';\n\nimport {concat} from './concat';\nimport {op} from './operation';\n\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(\n    tensors: Array<Tensor4D|TensorLike>, axis: number): Tensor4D {\n  return concat(tensors, axis);\n}\n\nexport const concat4d = op({concat4d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Conv2D, Conv2DAttrs, Conv2DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    strides: [number, number]|number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv2d');\n  const $filter = convertToTensor(filter, 'filter', 'conv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in conv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n  util.assert(\n      inDepth === $filter.shape[2],\n      () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n          `input depth for filter ${$filter.shape[2]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  const inputs: Conv2DInputs = {x: x4D, filter: $filter};\n  const attrs:\n      Conv2DAttrs = {strides, pad, dataFormat, dilations, dimRoundingMode};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  Conv2D, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res;\n}\n\nexport const conv2d = op({conv2d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {conv2d} from './conv2d';\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_<T extends Tensor2D|Tensor3D>(\n    x: T|TensorLike, filter: Tensor3D|TensorLike, stride: number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv1d');\n  const $filter = convertToTensor(filter, 'filter', 'conv1d');\n\n  let x3D = $x as Tensor3D;\n  let reshapedTo3D = false;\n  if ($x.rank === 2) {\n    reshapedTo3D = true;\n    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n  }\n\n  util.assert(\n      x3D.rank === 3,\n      () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n  util.assert(\n      $filter.rank === 3,\n      () => `Error in conv1d: filter must be rank 3, but got rank ` +\n          `${$filter.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  util.assert(\n      x3D.shape[2] === $filter.shape[1],\n      () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n          `input depth for filter ${$filter.shape[1]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(stride, dilation),\n      () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n          `Got stride ${stride} and dilation '${dilation}'`);\n  util.assert(\n      dataFormat === 'NWC',\n      () => `Error in conv1d: got dataFormat of ${\n          dataFormat} but only NWC is currently supported.`);\n\n  const filter4D = reshape(\n      $filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n  const strides: [number, number] = [1, stride];\n  const dilations: [number, number] = [1, dilation];\n\n  const conv2dDataFormat = 'NHWC';\n\n  const res = conv2d(\n      (input4D as Tensor4D), (filter4D as Tensor4D), strides, pad,\n      conv2dDataFormat, dilations, dimRoundingMode);\n\n  if (reshapedTo3D) {\n    return reshape(res, [res.shape[2], res.shape[3]]) as T;\n  }\n\n  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]) as T;\n}\n\nexport const conv1d = op({conv1d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Conv2DBackpropInput, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n */\nfunction conv2DBackpropInput_<T extends Tensor3D|Tensor4D>(\n    xShape: [number, number, number, number]|[number, number, number], dy: T,\n    filter: Tensor4D, strides: [number, number]|number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  util.assert(\n      xShape.length === dy.rank,\n      () => `Length of inShape ` +\n          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n  let xShape4D = xShape as [number, number, number, number];\n  let dy4D = dy as Tensor4D;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n  }\n\n  util.assert(\n      xShape4D.length === 4,\n      () =>\n          `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n          `${xShape4D.length}.`);\n  util.assert(\n      dy4D.rank === 4,\n      () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n          `rank ${dy4D.rank}`);\n  util.assert(\n      filter.rank === 4,\n      () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n          `rank ${filter.rank}`);\n  const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n  const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n  util.assert(\n      inDepth === filter.shape[2],\n      () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n          `match input depth for filter ${filter.shape[2]}.`);\n  util.assert(\n      outDepth === filter.shape[3],\n      () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n          `match output depth for filter ${filter.shape[3]}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const inputs: Conv2DBackpropInputInputs = {dy: dy4D, filter};\n\n  const attrs: Conv2DBackpropInputAttrs =\n      {strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  Conv2DBackpropInput, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res;\n}\n\nexport const conv2DBackpropInput = op({conv2DBackpropInput_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {conv2DBackpropInput} from './conv2d_backprop_input';\nimport {op} from './operation';\n\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    outputShape: [number, number, number, number]|[number, number, number],\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n  const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n\n  return conv2DBackpropInput(\n      outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\n\nexport const conv2dTranspose = op({conv2dTranspose_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Conv3D, Conv3DAttrs, Conv3DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {eitherStridesOrDilationsAreOne} from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filter: Tensor5D|TensorLike,\n    strides: [number, number, number]|number, pad: 'valid'|'same',\n    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC',\n    dilations: [number, number, number]|number = [1, 1, 1]): T {\n  const $x = convertToTensor(x, 'x', 'conv3d');\n  const $filter = convertToTensor(filter, 'filter', 'conv3d');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  util.assert(\n      x5D.rank === 5,\n      () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n  util.assert(\n      $filter.rank === 5,\n      () => `Error in conv3d: filter must be rank 5, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x5D.shape[4] === $filter.shape[3],\n      () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n          `input depth for filter ${$filter.shape[3]}.`);\n  util.assert(\n      eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NDHWC',\n      () => `Error in conv3d: got dataFormat of ${\n          dataFormat} but only NDHWC is currently supported.`);\n\n  const inputs: Conv3DInputs = {x: x5D, filter: $filter};\n\n  const attrs: Conv3DAttrs = {strides, pad, dataFormat, dilations};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  Conv3D, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n  return res;\n}\n\nexport const conv3d = op({conv3d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Conv3DBackpropInputV2, Conv3DBackpropInputV2Attrs, Conv3DBackpropInputV2Inputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3DBackpropInput_<T extends Tensor4D|Tensor5D>(\n    xShape:\n        [number, number, number, number,\n         number]|[number, number, number, number],\n    dy: T, filter: Tensor5D, strides: [number, number, number]|number,\n    pad: 'valid'|'same'): T {\n  util.assert(\n      xShape.length === dy.rank,\n      () => `Length of inShape ` +\n          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n  let xShape5D = xShape as [number, number, number, number, number];\n  let dy5D = dy as Tensor5D;\n  let reshapedTo5D = false;\n  if (dy.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n  }\n\n  const inDepth = xShape5D[4];\n  const outDepth = dy5D.shape[4];\n  util.assert(\n      xShape5D.length === 5,\n      () =>\n          `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n          `${xShape5D.length}.`);\n  util.assert(\n      dy5D.rank === 5,\n      () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n          `rank ${dy5D.rank}`);\n  util.assert(\n      filter.rank === 5,\n      () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n          `rank ${filter.rank}`);\n  util.assert(\n      inDepth === filter.shape[3],\n      () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n          `match input depth for filter ${filter.shape[3]}.`);\n  util.assert(\n      outDepth === filter.shape[4],\n      () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n          `match output depth for filter ${filter.shape[4]}.`);\n\n  const inputs: Conv3DBackpropInputV2Inputs = {dy: dy5D, filter};\n\n  const attrs:\n      Conv3DBackpropInputV2Attrs = {pad, strides, inputShape: xShape5D};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  Conv3DBackpropInputV2, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n  return res;\n}\n\nexport const conv3DBackpropInput = op({conv3DBackpropInput_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor4D, Tensor5D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {conv3DBackpropInput} from './conv3d_backprop_input';\nimport {op} from './operation';\n\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n *    assumed.\n * @param strides The strides of the original convolution:\n *     `[strideDepth, strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filter: Tensor5D|TensorLike,\n    outputShape:\n        [number, number, number, number,\n         number]|[number, number, number, number],\n    strides: [number, number, number]|number, pad: 'valid'|'same'): T {\n  const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n  const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n\n  return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\n\nexport const conv3dTranspose = op({conv3dTranspose_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Cos, CosInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'cos');\n\n  const inputs: CosInputs = {x: $x};\n\n  return ENGINE.runKernel(Cos, inputs as {} as NamedTensorMap);\n}\nexport const cos = op({cos_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Cosh, CoshInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'cosh');\n  const inputs: CoshInputs = {x: $x};\n\n  return ENGINE.runKernel(Cosh, inputs as {} as NamedTensorMap);\n}\nexport const cosh = op({cosh_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Cumsum, CumsumAttrs, CumsumInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_<T extends Tensor>(\n    x: Tensor|TensorLike, axis = 0, exclusive = false, reverse = false): T {\n  const $x = convertToTensor(x, 'x', 'cumsum');\n\n  const inputs: CumsumInputs = {x: $x};\n  const attrs: CumsumAttrs = {axis, exclusive, reverse};\n\n  return ENGINE.runKernel(\n      Cumsum, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const cumsum = op({cumsum_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {DenseBincount, DenseBincountAttrs, DenseBincountInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Outputs a vector with length `size` and the same dtype as `weights`.\n *\n * If `weights` are empty, then index `i` stores the number of times the value\n * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the\n * sum of the value in `weights` at each index where the corresponding value in\n * `x` is `i`.\n *\n * Values in `x` outside of the range [0, size) are ignored.\n *\n * @param x The input int tensor, rank 1 or rank 2.\n * @param weights The weights tensor, must have the same shape as x, or a\n *     length-0 Tensor, in which case it acts as all weights equal to 1.\n * @param size Non-negative integer.\n * @param binaryOutput Optional. Whether the kernel should count the appearance\n *     or number of occurrences. Defaults to False.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction denseBincount_<T extends Tensor1D|Tensor2D>(\n    x: T|TensorLike, weights: T|TensorLike, size: number,\n    binaryOutput = false): T {\n  const $x = convertToTensor(x, 'x', 'denseBincount');\n  const $weights = convertToTensor(weights, 'weights', 'denseBincount');\n\n  util.assert(\n      $x.dtype === 'int32',\n      () => `Error in denseBincount: input ` +\n          `dtype must be int32, but got ${$x.dtype}`);\n  util.assert(\n      $x.rank <= 2,\n      () => `Error in denseBincount: input must be at most rank 2, but got ` +\n          `rank ${$x.rank}.`);\n  util.assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n  util.assert(\n      $weights.size === $x.size || $weights.size === 0,\n      () =>\n          `Error in denseBincount: weights must have the same shape as x or ` +\n          `0-length, but got x shape: ${$x.shape}, weights shape: ` +\n          `${$weights.shape}.`);\n\n  const inputs: DenseBincountInputs = {x: $x, weights: $weights};\n  const attrs: DenseBincountAttrs = {size, binaryOutput};\n\n  return ENGINE.runKernel(\n      DenseBincount, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const denseBincount = op({denseBincount_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {DepthToSpace, DepthToSpaceAttrs, DepthToSpaceInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike4D} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(\n    x: Tensor4D|TensorLike4D, blockSize: number,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'depthToSpace') as Tensor4D;\n\n  const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n  const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n  const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n\n  util.assert(\n      inputHeight * blockSize >= 0,\n      () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n\n  util.assert(\n      inputWidth * blockSize >= 0,\n      () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n\n  util.assert(\n      (inputDepth % (blockSize * blockSize) === 0),\n      () => `Dimension size must be evenly divisible by ${\n          blockSize * blockSize} but is ${\n          inputDepth} for depthToSpace with input shape ${$x.shape}`);\n\n  const inputs: DepthToSpaceInputs = {x: $x};\n  const attrs: DepthToSpaceAttrs = {blockSize, dataFormat};\n\n  return ENGINE.runKernel(\n      DepthToSpace, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const depthToSpace = op({depthToSpace_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {ExplicitPadding} from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    strides: [number, number]|number,\n    pad: 'valid'|'same'|number|ExplicitPadding,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n  const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      () => `Error in depthwiseConv2d: number of input channels ` +\n          `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n          `filter ${$filter.shape[2]}.`);\n\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const inputs: DepthwiseConv2dNativeInputs = {x: x4D, filter: $filter};\n  const attrs: DepthwiseConv2dNativeAttrs =\n      {strides, pad, dataFormat, dilations, dimRoundingMode};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  DepthwiseConv2dNative, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res;\n}\n\nexport const depthwiseConv2d = op({depthwiseConv2d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Diag, DiagInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\n\nimport {op} from './operation';\n\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction diag_(x: Tensor): Tensor {\n  const $x = convertToTensor(x, 'x', 'diag');\n\n  const inputs: DiagInputs = {x: $x};\n\n  return ENGINE.runKernel(Diag, inputs as {} as NamedTensorMap);\n}\n\nexport const diag = op({diag_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Dilation2D, Dilation2DAttrs, Dilation2DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat Specify the data format of the input and output data.\n *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n *      default format \"NHWC\", the data is stored in the order of: [batch,\n *      height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n *     is a single number, then `dilationHeight == dilationWidth`. If it is\n *     greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction dilation2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor3D|TensorLike,\n    strides: [number, number]|number, pad: 'valid'|'same',\n    dilations: [number, number]|number = [1, 1],\n    dataFormat: 'NHWC' = 'NHWC'): T {\n  const $x = convertToTensor(x, 'x', 'dilation2d');\n  const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n\n  util.assert(\n      $x.rank === 3 || $x.rank === 4,\n      () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $filter.rank === 3,\n      () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      dataFormat === 'NHWC',\n      () => `Error in dilation2d: Only NHWC is currently supported, ` +\n          `but got dataFormat of ${dataFormat}`);\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    reshapedTo4D = true;\n  }\n\n  const inputs: Dilation2DInputs = {x: x4D, filter: $filter};\n  const attrs: Dilation2DAttrs = {strides, pad, dilations};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  Dilation2D, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n\n  return res;\n}\n\nexport const dilation2d = op({dilation2d_});\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(\n    inShape: number[], outShape: number[]): number[] {\n  const inRank = inShape.length;\n  const dims: number[] = [];\n  for (let i = 0; i < inRank; i++) {\n    const dim = inRank - 1 - i;\n    const a = inShape[dim] || 1;\n    const b = outShape[outShape.length - 1 - i] || 1;\n    if (b > 1 && a === 1) {\n      dims.unshift(dim);\n    }\n  }\n  return dims;\n}\n\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(\n    inShape: number[], outShape: number[]): number[] {\n  const result: number[] = [];\n  for (let i = 0; i < outShape.length; i++) {\n    const inDim = inShape[inShape.length - i - 1];\n    const outAxis = outShape.length - i - 1;\n    const outDim = outShape[outAxis];\n    if (inDim == null || (inDim === 1 && outDim > 1)) {\n      result.unshift(outAxis);\n    }\n  }\n  return result;\n}\n\nexport function assertAndGetBroadcastShape(\n    shapeA: number[], shapeB: number[]): number[] {\n  const result: number[] = [];\n  const l = Math.max(shapeA.length, shapeB.length);\n\n  for (let i = 0; i < l; i++) {\n    let a = shapeA[shapeA.length - i - 1];\n    if (a == null) {\n      a = 1;\n    }\n    let b = shapeB[shapeB.length - i - 1];\n    if (b == null) {\n      b = 1;\n    }\n    if (a === 1) {\n      result.unshift(b);\n    } else if (b === 1) {\n      result.unshift(a);\n    } else if (a !== b) {\n      const errMsg = `Operands could not be broadcast together with shapes ` +\n          `${shapeA} and ${shapeB}.`;\n      throw Error(errMsg);\n    } else {\n      result.unshift(a);\n    }\n  }\n  return result;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Equal, EqualInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'equal', 'string_or_numeric');\n  let $b = convertToTensor(b, 'b', 'equal', 'string_or_numeric');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: EqualInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(Equal, inputs as {} as NamedTensorMap);\n}\n\nexport const equal = op({equal_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Select, SelectInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {broadcastTo} from './broadcast_to';\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same dtype as `a` and with shape that is\n *     compatible with `a`.\n * @return A tensor with same dtype as `a` and `b`, and shape that is\n *     broadcastable from `a` and `b`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction where_<T extends Tensor>(\n    condition: Tensor|TensorLike, a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'where');\n  const $b = convertToTensor(b, 'b', 'where');\n  const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n  // TODO: move this logic to forward function when the broadcastTo op is\n  // implemented in WASM.\n  // Find the broadcastable shape for $condition, $a, and $b.\n  const broadcastShape = assertAndGetBroadcastShape(\n      assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape);\n  const $broadcastedCondition = broadcastTo($condition, broadcastShape);\n  const $broadcastedA = broadcastTo($a, broadcastShape);\n  const $broadcastedB = broadcastTo($b, broadcastShape);\n\n  const inputs: SelectInputs = {\n    condition: $broadcastedCondition,\n    t: $broadcastedA,\n    e: $broadcastedB\n  };\n  return ENGINE.runKernel(Select, inputs as {} as NamedTensorMap);\n}\n\nexport const where = op({where_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {ZerosLike, ZerosLikeInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zerosLike_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'zerosLike');\n  const inputs: ZerosLikeInputs = {x: $x};\n  return ENGINE.runKernel(ZerosLike, inputs as {} as NamedTensorMap);\n}\nexport const zerosLike = op({zerosLike_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {div} from './div';\nimport {equal} from './equal';\nimport {op} from './operation';\nimport {where} from './where';\nimport {zerosLike} from './zeros_like';\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  // TODO: Make this into its own kernel.\n  let $a = convertToTensor(a, 'a', 'div');\n  let $b = convertToTensor(b, 'b', 'div');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const divResult = div($a, $b);\n  const zeros = zerosLike(divResult);\n  const bEqualsZero = equal($b, zeros);\n  return where(bEqualsZero, zeros, divResult) as T;\n}\n\nexport const divNoNan = op({divNoNan_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor,} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {matMul} from './mat_mul';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1: Tensor|TensorLike, t2: Tensor|TensorLike): Tensor {\n  const $t1 = convertToTensor(t1, 't1', 'dot');\n  const $t2 = convertToTensor(t2, 't2', 'dot');\n\n  util.assert(\n      ($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2),\n      () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n          `${$t1.rank} and ${$t2.rank}.`);\n\n  const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n  const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n\n  util.assert(\n      t1Inner === t2Inner,\n      () => `Error in dot: inner dimensions of inputs must match, but got ` +\n          `${t1Inner} and ${t2Inner}.`);\n\n  if ($t1.rank === 1 && $t2.rank === 1) {\n    const t12D = reshape($t1, [1, -1]);\n    const t22D = reshape($t2, [-1, 1]);\n    const t1t2 = matMul(t12D, t22D);\n    return reshape(t1t2, []);\n  } else if ($t1.rank === 1 && $t2.rank === 2) {\n    const t12D = reshape($t1, [1, -1]);\n    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n    const t1t2 = matMul(t12D, t22D);\n    return reshape(t1t2, [t1t2.size]);\n  } else if ($t1.rank === 2 && $t2.rank === 1) {\n    const t22D = reshape($t2, [-1, 1]);\n    const t1t2 = matMul($t1, t22D);\n    return reshape(t1t2, [t1t2.size]);\n  } else {\n    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n    const t1t2 = matMul($t1, t22D);\n    return t1t2;\n  }\n}\n\nexport const dot = op({dot_});\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Einsum, EinsumAttrs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\n\nimport {op} from './operation';\n\n/**\n * Tensor contraction over specified indices and outer product.\n *\n * `einsum` allows defining Tensors by defining their element-wise computation.\n * This computation is based on\n * [Einstein summation](https://en.wikipedia.org/wiki/Einstein_notation).\n *\n * Some special cases include:\n *\n * Matrix multiplication:\n * ```js\n * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n * const y = tf.tensor2d([[0, 1], [2, 3], [4, 5]]);\n * x.print();\n * y.print();\n * tf.einsum('ij,jk->ik', x, y).print();\n * ```\n *\n * Dot product:\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n * const y = tf.tensor1d([0, 1, 2]);\n * x.print();\n * y.print();\n * tf.einsum('i,i->', x, y).print();\n * ```\n *\n * Batch dot product:\n * ```js\n * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n * const y = tf.tensor2d([[0, 1, 2], [3, 4, 5]]);\n * x.print();\n * y.print();\n * tf.einsum('bi,bi->b', x, y).print();\n * ```\n *\n * Outer prouduct:\n * ```js\n * const x = tf.tensor1d([1, 3, 5]);\n * const y = tf.tensor1d([2, 4, 6]);\n * x.print();\n * y.print();\n * tf.einsum('i,j->ij', x, y).print();\n * ```\n *\n * Matrix transpose:\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * x.print();\n * tf.einsum('ij->ji', x).print();\n * ```\n *\n * Batch matrix transpose:\n * ```js\n * const x = tf.tensor3d([[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]);\n * x.print();\n * tf.einsum('bij->bji', x).print();\n * ```\n *\n * Limitations:\n *\n * This implementation of einsum has the following limitations:\n *\n * - Does not support >2 input tensors.\n * - Does not support duplicate axes for any given input tensor. E.g., equation\n *   'ii->' is not suppoted.\n * - The `...` notation is not supported.\n *\n * @param equation a string describing the contraction, in the same format as\n * [numpy.einsum](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html).\n * @param tensors the input(s) to contract (each one a Tensor), whose shapes\n *     should be consistent with equation.\n * @returns The output tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Matrices'}\n */\nexport function einsum_(equation: string, ...tensors: Tensor[]): Tensor {\n  const $tensors =\n      tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'einsum'));\n  const attrs: EinsumAttrs = {equation};\n  return ENGINE.runKernel(\n      Einsum, $tensors as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const einsum = op({einsum_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Elu, EluInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'elu');\n\n  const inputs: EluInputs = {x: $x};\n\n  return ENGINE.runKernel(Elu, inputs as {} as NamedTensorMap);\n}\n\nexport const elu = op({elu_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Erf, ErfInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {cast} from './cast';\nimport {op} from './operation';\n\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_<T extends Tensor>(x: T|TensorLike): T {\n  let $x = convertToTensor(x, 'x', 'erf');\n  util.assert(\n      $x.dtype === 'int32' || $x.dtype === 'float32',\n      () => 'Input dtype must be `int32` or `float32`.');\n\n  if ($x.dtype === 'int32') {\n    $x = cast($x, 'float32');\n  }\n\n  const inputs: ErfInputs = {x: $x};\n  return ENGINE.runKernel(Erf, inputs as {} as NamedTensorMap);\n}\nexport const erf = op({erf_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Exp, ExpInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction exp_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'exp');\n\n  const inputs: ExpInputs = {x: $x};\n  return ENGINE.runKernel(Exp, inputs as {} as NamedTensorMap);\n}\nexport const exp = op({exp_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {ExpandDims, ExpandDimsAttrs, ExpandDimsInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {\n  const $x = convertToTensor(x, 'x', 'expandDims', 'string_or_numeric');\n\n  util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n\n  const inputs: ExpandDimsInputs = {input: $x};\n  const attrs: ExpandDimsAttrs = {dim: axis};\n\n  return ENGINE.runKernel(\n      ExpandDims, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const expandDims = op({expandDims_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Expm1, Expm1Inputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'expm1');\n\n  const inputs: Expm1Inputs = {x: $x};\n  return ENGINE.runKernel(Expm1, inputs as {} as NamedTensorMap);\n}\nexport const expm1 = op({expm1_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tile, TileAttrs, TileInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_<T extends Tensor>(x: T|TensorLike, reps: number[]): T {\n  const $x = convertToTensor(x, 'x', 'tile', 'string_or_numeric');\n  util.assert(\n      $x.rank === reps.length,\n      () => `Error in transpose: rank of input ${$x.rank} ` +\n          `must match length of reps ${reps}.`);\n\n  const inputs: TileInputs = {x: $x};\n  const attrs: TileAttrs = {reps};\n\n  return ENGINE.runKernel(\n      Tile, inputs as unknown as NamedTensorMap,\n      attrs as unknown as NamedAttrMap);\n}\n\nexport const tile = op({tile_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor2D} from '../tensor';\nimport {DataType} from '../types';\n\nimport {buffer} from './buffer';\nimport {expandDims} from './expand_dims';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {tile} from './tile';\n\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(\n    numRows: number, numColumns?: number,\n    batchShape?:\n        [\n          number\n        ]|[number,\n           number]|[number, number, number]|[number, number, number, number],\n    dtype: DataType = 'float32'): Tensor2D {\n  if (numColumns == null) {\n    numColumns = numRows;\n  }\n  const buff = buffer([numRows, numColumns], dtype);\n  const n = numRows <= numColumns ? numRows : numColumns;\n  for (let i = 0; i < n; ++i) {\n    buff.set(1, i, i);\n  }\n  const out: Tensor2D = reshape(buff.toTensor(), [numRows, numColumns]);\n  if (batchShape == null) {\n    return out;\n  } else {\n    if (batchShape.length === 1) {\n      return tile(expandDims(out, 0), [batchShape[0], 1, 1]) as Tensor2D;\n    } else if (batchShape.length === 2) {\n      // tslint:disable-next-line:no-unnecessary-type-assertion\n      return tile(\n                 expandDims(expandDims(out, 0), 0),\n                 [batchShape[0], batchShape[1], 1, 1]) as Tensor2D;\n    } else if (batchShape.length === 3) {\n      // tslint:disable-next-line:no-unnecessary-type-assertion\n      return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n               batchShape[0], batchShape[1], batchShape[2], 1, 1\n             ]) as Tensor2D;\n    } else {\n      throw new Error(\n          `eye() currently supports only 1D and 2D ` +\n          // tslint:disable-next-line:no-any\n          `batchShapes, but received ${(batchShape as any).length}D.`);\n    }\n  }\n}\n\nexport const eye = op({eye_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Fill, FillAttrs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\n\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill<R extends Rank>(\n    shape: ShapeMap[R], value: number|string, dtype?: DataType): Tensor<R> {\n  const attrs: FillAttrs = {shape, value, dtype};\n\n  return ENGINE.runKernel(Fill, {}, attrs as {} as NamedAttrMap);\n}\n\nexport {fill};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Floor, FloorInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'floor');\n\n  const inputs: FloorInputs = {x: $x};\n  return ENGINE.runKernel(Floor, inputs as {} as NamedTensorMap);\n}\nexport const floor = op({floor_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {GatherV2, GatherV2Attrs, GatherV2Inputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n * @param batchDims Optional. The number of batch dimensions. It must be less\n *     than or equal to rank(indices). Defaults to 0.\n *     The output tensor will have shape of\n *     `x.shape[:axis] + indices.shape[batchDims:] + x.shape[axis + 1:]`\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_<T extends Tensor>(\n    x: T|TensorLike, indices: Tensor|TensorLike, axis = 0, batchDims = 0): T {\n  const $x = convertToTensor(x, 'x', 'gather');\n  const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n\n  const inputs: GatherV2Inputs = {x: $x, indices: $indices};\n  const attrs: GatherV2Attrs = {axis, batchDims};\n\n  return ENGINE.runKernel(\n      GatherV2, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const gather = op({gather_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Greater, GreaterInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'greater', 'string_or_numeric');\n  let $b = convertToTensor(b, 'b', 'greater', 'string_or_numeric');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: GreaterInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(Greater, inputs as {} as NamedTensorMap);\n}\n\nexport const greater = op({greater_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {GreaterEqual, GreaterEqualInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'greaterEqual', 'string_or_numeric');\n  let $b = convertToTensor(b, 'b', 'greaterEqual', 'string_or_numeric');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: GreaterEqualInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(GreaterEqual, inputs as {} as NamedTensorMap);\n}\n\nexport const greaterEqual = op({greaterEqual_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Imag, ImagInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_<T extends Tensor>(input: T|TensorLike): T {\n  const $input = convertToTensor(input, 'input', 'imag');\n\n  const inputs: ImagInputs = {input: $input};\n  return ENGINE.runKernel(Imag, inputs as {} as NamedTensorMap);\n}\n\nexport const imag = op({imag_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {IsFinite, IsFiniteInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'isFinite');\n\n  const inputs: IsFiniteInputs = {x: $x};\n\n  return ENGINE.runKernel(IsFinite, inputs as {} as NamedTensorMap);\n}\nexport const isFinite = op({isFinite_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {IsInf, IsInfInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'isInf');\n\n  const inputs: IsInfInputs = {x: $x};\n\n  return ENGINE.runKernel(IsInf, inputs as {} as NamedTensorMap);\n}\nexport const isInf = op({isInf_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {IsNan, IsNanInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'isNaN');\n  const inputs: IsNanInputs = {x: $x};\n\n  return ENGINE.runKernel(IsNan, inputs as {} as NamedTensorMap);\n}\nexport const isNaN = op({isNaN_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {LeakyRelu, LeakyReluAttrs, LeakyReluInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_<T extends Tensor>(x: T|TensorLike, alpha = 0.2): T {\n  const $x = convertToTensor(x, 'x', 'leakyRelu');\n\n  const inputs: LeakyReluInputs = {x: $x};\n  const attrs: LeakyReluAttrs = {alpha};\n\n  return ENGINE.runKernel(\n      LeakyRelu, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const leakyRelu = op({leakyRelu_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Less, LessInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'less', 'string_or_numeric');\n  let $b = convertToTensor(b, 'b', 'less', 'string_or_numeric');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: LessInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(Less, inputs as {} as NamedTensorMap);\n}\n\nexport const less = op({less_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {LessEqual, LessEqualInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'lessEqual', 'string_or_numeric');\n  let $b = convertToTensor(b, 'b', 'lessEqual', 'string_or_numeric');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: LessEqualInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(LessEqual, inputs as {} as NamedTensorMap);\n}\n\nexport const lessEqual = op({lessEqual_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {LinSpace, LinSpaceAttrs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor1D} from '../tensor';\n\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function linspace(start: number, stop: number, num: number): Tensor1D {\n  if (num <= 0) {\n    throw new Error('The number of values should be positive.');\n  }\n\n  const attrs: LinSpaceAttrs = {start, stop, num};\n  return ENGINE.runKernel(LinSpace, {}, attrs as {} as NamedAttrMap);\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {LRN, LRNAttrs, LRNInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5): T {\n  const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n  util.assert(\n      $x.rank === 4 || $x.rank === 3,\n      () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n  util.assert(\n      util.isInt(depthRadius),\n      () => `Error in localResponseNormalization: depthRadius must be an ` +\n          `integer but got depthRadius ${depthRadius}.`);\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  const inputs: LRNInputs = {x: x4D};\n\n  const attrs: LRNAttrs = {depthRadius, bias, alpha, beta};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  LRN, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  } else {\n    return res;\n  }\n}\n\nexport const localResponseNormalization = op({localResponseNormalization_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Log, LogInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'log');\n\n  const inputs: LogInputs = {x: $x};\n  return ENGINE.runKernel(Log, inputs as {} as NamedTensorMap);\n}\nexport const log = op({log_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Log1p, Log1pInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'log1p');\n\n  const inputs: Log1pInputs = {x: $x};\n  return ENGINE.runKernel(Log1p, inputs as {} as NamedTensorMap);\n}\nexport const log1p = op({log1p_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {CustomGradientFunc, ENGINE} from './engine';\nimport {Scalar, Tensor, Variable} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport {convertToTensor, convertToTensorArray} from './tensor_util_env';\nimport {TensorLike} from './types';\nimport * as util from './util';\n\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grad(f: (x: Tensor) => Tensor): (\n    x: TensorLike|Tensor, dy?: TensorLike|Tensor) => Tensor {\n  util.assert(\n      util.isFunction(f), () => 'The f passed in grad(f) must be a function');\n  return (x: TensorLike|Tensor, dy?: TensorLike|Tensor): Tensor => {\n    // x can be of any dtype, thus null as the last argument.\n    const $x = convertToTensor(x, 'x', 'tf.grad', 'string_or_numeric');\n    const $dy: Tensor =\n        (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;\n    return ENGINE.tidy(() => {\n      const {value, grads} = ENGINE.gradients(() => f($x), [$x], $dy);\n      if ($dy != null) {\n        util.assertShapesMatch(\n            value.shape, $dy.shape,\n            'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                'returned by f(x)');\n      }\n      checkGrads(grads);\n      return grads[0];\n    });\n  };\n}\n\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grads(f: (...args: Tensor[]) => Tensor): (\n    args: Array<Tensor|TensorLike>, dy?: Tensor|TensorLike) => Tensor[] {\n  util.assert(\n      util.isFunction(f), () => 'The f passed in grads(f) must be a function');\n  return (args: Array<Tensor|TensorLike>, dy?: Tensor|TensorLike): Tensor[] => {\n    util.assert(\n        Array.isArray(args),\n        () => 'The args passed in grads(f)(args) must be an array ' +\n            'of `Tensor`s or `TensorLike`s');\n    // args can be of any dtype, thus null as the last argument.\n    const $args =\n        convertToTensorArray(args, 'args', 'tf.grads', 'string_or_numeric');\n    const $dy: Tensor =\n        (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;\n    return ENGINE.tidy(() => {\n      const {value, grads} = ENGINE.gradients(() => f(...$args), $args, $dy);\n      if ($dy != null) {\n        util.assertShapesMatch(\n            value.shape, $dy.shape,\n            'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n      }\n      checkGrads(grads);\n      return grads;\n    });\n  };\n}\n\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (\n    x: I, dy?: O) => {\n  value: O;\n  grad: I;\n} {\n  util.assert(\n      util.isFunction(f),\n      () => 'The f passed in valueAndGrad(f) must be a function');\n  return (x: I, dy?: O) => {\n    util.assert(\n        x instanceof Tensor,\n        () => 'The x passed in valueAndGrad(f)(x) must be a tensor');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n    const {grads, value} = ENGINE.gradients(() => f(x), [x], dy);\n    checkGrads(grads);\n    return {grad: grads[0] as I, value};\n  };\n}\n\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrads<O extends Tensor>(f: (...args: Tensor[]) => O): (\n    args: Tensor[], dy?: O) => {\n  grads: Tensor[];\n  value: O;\n} {\n  util.assert(\n      util.isFunction(f),\n      () => 'The f passed in valueAndGrads(f) must be a function');\n  return (args: Tensor[], dy?: O) => {\n    util.assert(\n        Array.isArray(args) && args.every(arg => arg instanceof Tensor),\n        () => 'The args passed in valueAndGrads(f)(args) must be array of ' +\n            'tensors');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n    const res = ENGINE.gradients(() => f(...args), args, dy);\n    if (dy != null) {\n      util.assertShapesMatch(\n          res.value.shape, dy.shape,\n          'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n              'match the shape returned by f([x1,...])');\n    }\n    checkGrads(res.grads);\n    return res;\n  };\n}\n\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of variables to compute the gradients with respect\n *     to. Defaults to all trainable variables.\n * @returns An object with the following keys and values:\n *   - `value`: The value of the function `f`.\n *   - `grads`: A map from the names of the variables to the gradients.\n *     If the `varList` argument is provided explicitly and contains a subset of\n *     non-trainable variables, this map in the return value will contain keys\n *     that map the names of the non-trainable variables to `null`.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction variableGrads(f: () => Scalar, varList?: Variable[]):\n    {value: Scalar, grads: NamedTensorMap} {\n  util.assert(\n      util.isFunction(f),\n      () => 'The f passed in variableGrads(f) must be a function');\n  util.assert(\n      varList == null ||\n          Array.isArray(varList) && varList.every(v => v instanceof Variable),\n      () =>\n          'The varList passed in variableGrads(f, varList) must be an array ' +\n          'of variables');\n\n  const specifiedVarList = varList != null;\n  if (!specifiedVarList) {\n    // Get all of the trainable variables.\n    varList = [];\n    for (const varName in ENGINE.registeredVariables) {\n      varList.push(ENGINE.registeredVariables[varName]);\n    }\n  }\n\n  const specifiedNonTrainable: Variable[] =\n      specifiedVarList ? varList.filter(variable => !variable.trainable) : null;\n\n  // Prune non-trainable variables.\n  const originalVarCount = varList.length;\n  varList = varList.filter(variable => variable.trainable);\n  util.assert(\n      varList.length > 0,\n      () => `variableGrads() expects at least one of the input variables to ` +\n          `be trainable, but none of the ${originalVarCount} variables is ` +\n          `trainable.`);\n\n  const allowNoGradients = true;\n  const {value, grads} = ENGINE.gradients(f, varList, null, allowNoGradients);\n\n  util.assert(\n      grads.some(g => g != null),\n      () => 'Cannot find a connection between any variable and the result of ' +\n          'the loss function y=f(x). Please make sure the operations that ' +\n          'use variables are inside the function f passed to minimize().');\n  util.assert(\n      value.rank === 0,\n      () => `The f passed in variableGrads(f) must return a scalar, but it ` +\n          `returned a rank-${value.rank} tensor`);\n\n  const namedGrads: NamedTensorMap = {};\n  varList.forEach((v, i) => {\n    if (grads[i] != null) {\n      namedGrads[v.name] = grads[i];\n    }\n  });\n  if (specifiedNonTrainable != null) {\n    // If varList is explicitly provided and contains non-trainable values,\n    // add them to the returned gradients with `null` values.\n    specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);\n  }\n  return {value, grads: namedGrads};\n}\n\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`\n * and returns another function `g(...inputs)` which takes the same inputs as\n * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients\n * with respect to each input of `f` are computed using `f().gradFunc`.\n *\n * The `save` function passsed to `f` should be used for saving tensors needed\n * in the gradient. And the `saved` passed to the `gradFunc` is a\n * `NamedTensorMap`, which contains those saved tensor.\n *\n * ```js\n * const customOp = tf.customGrad((x, save) => {\n *   // Save x to make sure it's available later for the gradient.\n *   save([x]);\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {\n *     value: x.square(),\n *     // Note `saved.x` which points to the `x` we saved earlier.\n *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]\n *   };\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`\n *     returns the custom gradients of `f` with respect to its inputs.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction customGrad<T extends Tensor>(f: CustomGradientFunc<T>):\n    (...args: Tensor[]) => T {\n  return ENGINE.customGrad(f);\n}\n\nfunction checkGrads(grads: Tensor[]) {\n  const numNullGradients = grads.filter(g => g == null).length;\n  if (numNullGradients > 0) {\n    throw new Error(\n        `Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n  }\n}\n\nexport {\n  customGrad,\n  variableGrads,\n  valueAndGrad,\n  valueAndGrads,\n  grad,\n  grads,\n};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Neg, NegInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction neg_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'neg');\n\n  const inputs: NegInputs = {x: $x};\n  return ENGINE.runKernel(Neg, inputs as {} as NamedTensorMap);\n}\nexport const neg = op({neg_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Softplus, SoftplusInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'softplus');\n\n  const inputs: SoftplusInputs = {x: $x};\n  return ENGINE.runKernel(Softplus, inputs as {} as NamedTensorMap);\n}\nexport const softplus = op({softplus_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {customGrad} from '../gradients';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {mul} from './mul';\nimport {neg} from './neg';\nimport {op} from './operation';\nimport {sigmoid} from './sigmoid';\nimport {softplus} from './softplus';\n\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'logSigmoid');\n\n  // Use a custom gradient to maintain previous implementation.\n  // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n  // directly\n  const customOp = customGrad((x: Tensor) => {\n    // TODO(yassogba) we can remove the chained softplus call here only\n    // after backends have modualrized softplus at which point we can call\n    // engine runKernel(..., Sotfplus, ...) directly.\n    const value = neg(softplus(neg(x)));\n\n    const gradFunc = (dy: T) => {\n      const derX = mul(dy, sigmoid(neg(x)));\n      return derX;\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp($x) as T;\n}\nexport const logSigmoid = op({logSigmoid_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Max, MaxAttrs, MaxInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'max');\n\n  const inputs: MaxInputs = {x: $x};\n  const attrs: MaxAttrs = {reductionIndices: axis, keepDims};\n\n  return ENGINE.runKernel(\n      Max, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const max = op({max_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Sub, SubInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction sub_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'sub');\n  let $b = convertToTensor(b, 'b', 'sub');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const inputs: SubInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(Sub, inputs as {} as NamedTensorMap);\n}\n\nexport const sub = op({sub_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Sum, SumAttrs, SumInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {cast} from './cast';\nimport {op} from './operation';\n\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction sum_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'sum');\n  if ($x.dtype === 'bool') {\n    $x = cast($x, 'int32');\n  }\n\n  const inputs: SumInputs = {x: $x};\n  const attrs: SumAttrs = {axis, keepDims};\n\n  return ENGINE.runKernel(\n      Sum, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const sum = op({sum_});\n","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {customGrad} from '../gradients';\n\nimport {Tensor} from '../tensor';\nimport {GradSaveFunc} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {cast} from './cast';\nimport {exp} from './exp';\nimport {log} from './log';\nimport {max} from './max';\nimport {mul} from './mul';\nimport {op} from './operation';\nimport {sub} from './sub';\nimport {sum} from './sum';\n\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_<T extends Tensor>(logits: T|TensorLike, axis = -1): T {\n  const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n\n  if (axis === -1) {\n    axis = $logits.rank - 1;\n  }\n  if (axis !== $logits.rank - 1) {\n    throw Error(\n        'Log Softmax along a non-last dimension is not yet supported. ' +\n        `Logits was rank ${$logits.rank} and axis was ${axis}`);\n  }\n\n  // const forward: ForwardFunc<Tensor> = (backend, save) => {\n  //   const keepDims = true;\n  //   const xMax = max(logits, axis, true);\n  //   const shifted = sub(logits, xMax);\n  //   const value =\n  //       sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis,\n  //       keepDims)));\n  //   save([value]);\n  //   return value;\n  // };\n\n  // Use a custom gradient for numerical stability.\n  const customOp = customGrad((logits: Tensor, save: GradSaveFunc) => {\n    const keepDims = true;\n    const xMax = max(logits, axis, true);\n    const shifted = sub(logits, xMax);\n    const value =\n        sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));\n    save([value]);\n\n    const gradFunc = (dy: Tensor, saved: Tensor[]) => {\n      const [value] = saved;\n      const keepDims = true;\n      const softmax = exp(value);\n      return sub(dy, mul(sum(dy, axis, keepDims), softmax));\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp($logits) as T;\n\n  // TODO Use Engine.runKernel when CPU/WebGL/WASM backends implement this.\n  // const inputs: LogSoftmaxInputs = {logits: $logits};\n  // const attrs: LogSoftmaxAttrs = {axis};\n  // return ENGINE.runKernel(\n  //            LogSoftmax, inputs as {} as NamedTensorMap,\n  //            attrs as {} as NamedAttrMap);\n}\n\nexport const logSoftmax = op({logSoftmax_});\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes: number[], rank: number): boolean {\n  for (let i = 0; i < axes.length; ++i) {\n    if (axes[axes.length - i - 1] !== rank - 1 - i) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function combineLocations(\n    outputLoc: number[], reduceLoc: number[], axes: number[]): number[] {\n  const rank = outputLoc.length + reduceLoc.length;\n  const loc = [];\n  let outIdx = 0;\n  let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      loc.push(outputLoc[outIdx++]);\n    } else {\n      loc.push(reduceLoc[reduceIdx++]);\n    }\n  }\n  return loc;\n}\n\nexport function computeOutAndReduceShapes(\n    aShape: number[], axes: number[]): [number[], number[]] {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      outShape.push(aShape[dim]);\n    }\n  }\n  const reduceShape = axes.map(dim => aShape[dim]);\n  return [outShape, reduceShape];\n}\n\nexport function expandShapeToKeepDim(\n    shape: number[], axes: number[]): number[] {\n  const reduceSubShape = axes.map(x => 1);\n  return combineLocations(shape, reduceSubShape, axes);\n}\n\nexport function assertAxesAreInnerMostDims(\n    msg: string, axes: number[], rank: number): void {\n  util.assert(\n      axesAreInnerMostDims(axes, rank),\n      () => `${msg} supports only inner-most axes for now. ` +\n          `Got axes ${axes} and rank-${rank} input.`);\n}\n\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes: number[], rank: number): number[]|\n    null {\n  if (axesAreInnerMostDims(axes, rank)) {\n    return null;\n  }\n  const result: number[] = [];\n  for (let i = 0; i < rank; ++i) {\n    if (axes.indexOf(i) === -1) {\n      result.push(i);\n    }\n  }\n  axes.forEach(axis => result.push(axis));\n  return result;\n}\n\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes: number[]): number[] {\n  return axes.map((axis, i) => [i, axis])\n      .sort((a, b) => a[1] - b[1])\n      .map(x => x[0]);\n}\n\nexport function getInnerMostAxes(numAxes: number, rank: number): number[] {\n  const res: number[] = [];\n  for (let i = rank - numAxes; i < rank; ++i) {\n    res.push(i);\n  }\n  return res;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {add} from './add';\nimport {expandShapeToKeepDim} from './axis_util';\nimport {exp} from './exp';\nimport {log} from './log';\nimport {max} from './max';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {sub} from './sub';\nimport {sum} from './sum';\n\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'logSumExp');\n\n  const axes = parseAxisParam(axis, $x.shape);\n  const xMax = max($x, axes, true /* keepDims */);\n  const a = sub($x, xMax);\n  const b = exp(a);\n  const c = sum(b, axes);\n  const d = log(c);\n  const res = add(reshape(xMax, d.shape), d);\n\n  if (keepDims) {\n    const newShape = expandShapeToKeepDim(res.shape, axes);\n    return reshape(res, newShape) as T;\n  }\n  return res as T;\n}\n\nexport const logSumExp = op({logSumExp_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {LogicalAnd, LogicalAndInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: LogicalAndInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(LogicalAnd, inputs as {} as NamedTensorMap);\n}\n\nexport const logicalAnd = op({logicalAnd_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {LogicalNot, LogicalNotInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n  const inputs: LogicalNotInputs = {x: $x};\n  return ENGINE.runKernel(LogicalNot, inputs as {} as NamedTensorMap);\n}\n\nexport const logicalNot = op({logicalNot_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {LogicalOr, LogicalOrInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: LogicalOrInputs = {a: $a, b: $b};\n  return ENGINE.runKernel(LogicalOr, inputs as {} as NamedTensorMap);\n}\nexport const logicalOr = op({logicalOr_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {logicalAnd} from './logical_and';\nimport {logicalNot} from './logical_not';\nimport {logicalOr} from './logical_or';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  // x ^ y = (x | y) & ~(x & y)\n  return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\n\nexport const logicalXor = op({logicalXor_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {MaxPool, MaxPoolAttrs, MaxPoolInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n */\nfunction maxPool_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'maxPool');\n  const dilations = 1;\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const inputs: MaxPoolInputs = {x: x4D};\n\n  const attrs: MaxPoolAttrs = {filterSize, strides, pad, dimRoundingMode};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  MaxPool, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res;\n}\n\nexport const maxPool = op({maxPool_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {MaxPool3D, MaxPool3DAttrs, MaxPool3DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filterSize: [number, number, number]|number = [1, 1, 1],\n    strides: [number, number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): T {\n  const $x = convertToTensor(x, 'x', 'maxPool3d');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n\n  util.assert(\n      x5D.rank === 5,\n      () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n  util.assert(\n      dataFormat === 'NDHWC',\n      () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n          `but got dataFormat of ${dataFormat}`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in maxPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const inputs: MaxPool3DInputs = {x: x5D};\n\n  const attrs:\n      MaxPool3DAttrs = {filterSize, strides, pad, dimRoundingMode, dataFormat};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  MaxPool3D, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n\n  return res;\n}\n\nexport const maxPool3d = op({maxPool3d_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {MaxPoolWithArgmax, MaxPoolWithArgmaxAttrs, MaxPoolWithArgmaxInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n *    dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_<T extends Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    includeBatchInIndex = false): NamedTensorMap {\n  const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n\n  const inputs: MaxPoolWithArgmaxInputs = {x: $x};\n  const attrs:\n      MaxPoolWithArgmaxAttrs = {filterSize, strides, pad, includeBatchInIndex};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const result = ENGINE.runKernel(\n                     MaxPoolWithArgmax, inputs as {} as NamedTensorMap,\n                     attrs as {} as NamedAttrMap) as Tensor[];\n\n  return {result: result[0], indexes: result[1]};\n}\n\nexport const maxPoolWithArgmax = op({maxPoolWithArgmax_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Maximum, MaximumInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {cast} from './cast';\nimport {op} from './operation';\n\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'maximum');\n  let $b = convertToTensor(b, 'b', 'maximum');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  if ($a.dtype === 'bool') {\n    $a = cast($a, 'int32');\n    $b = cast($b, 'int32');\n  }\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: MaximumInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(Maximum, inputs as {} as NamedTensorMap);\n}\n\nexport const maximum = op({maximum_});\n","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Mean, MeanAttrs, MeanInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'mean');\n\n  const inputs: MeanInputs = {x: $x};\n  const attrs: MeanAttrs = {axis, keepDims};\n\n  return ENGINE.runKernel(\n      Mean, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const mean = op({mean_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\nimport {makeZerosTypedArray, sizeFromShape} from '../util';\n\nimport {complex} from './complex';\n\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function zeros<R extends Rank>(\n    shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor<R> {\n  if (dtype === 'complex64') {\n    const real = zeros(shape, 'float32');\n    const imag = zeros(shape, 'float32');\n    return complex(real, imag);\n  }\n  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n  return ENGINE.makeTensor(values, shape, dtype) as Tensor<R>;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\nimport {makeOnesTypedArray, sizeFromShape} from '../util';\n\nimport {complex} from './complex';\nimport {zeros} from './zeros';\n\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function ones<R extends Rank>(\n    shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor<R> {\n  if (dtype === 'complex64') {\n    const real = ones(shape, 'float32');\n    const imag = zeros(shape, 'float32');\n    return complex(real, imag);\n  }\n  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n  return ENGINE.makeTensor(values, shape, dtype) as Tensor<R>;\n}\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {matMul} from './mat_mul';\nimport {ones} from './ones';\nimport {reshape} from './reshape';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {sizeFromShape} from '../util_base';\n\n/**\n * Broadcasts parameters for evaluation on an N-D grid.\n *\n * Given N one-dimensional coordinate arrays `*args`, returns a list `outputs`\n * of N-D coordinate arrays for evaluating expressions on an N-D grid.\n *\n * Notes:\n * `meshgrid` supports cartesian ('xy') and matrix ('ij') indexing conventions.\n * When the `indexing` argument is set to 'xy' (the default), the broadcasting\n * instructions for the first two dimensions are swapped.\n * Examples:\n * Calling `const [X, Y] = meshgrid(x, y)` with the tensors\n *\n * ```javascript\n * const x = [1, 2, 3];\n * const y = [4, 5, 6];\n * const [X, Y] = tf.meshgrid(x, y);\n * // X = [[1, 2, 3],\n * //      [1, 2, 3],\n * //      [1, 2, 3]]\n * // Y = [[4, 4, 4],\n * //      [5, 5, 5],\n * //      [6, 6, 6]]\n * ```\n *\n * @param x Tensor with rank geq 1.\n * @param y Tensor with rank geq 1.\n * @param indexing\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nexport function meshgrid<T extends Tensor>(\n    x?: T|TensorLike, y?: T|TensorLike, {indexing = 'xy'} = {}): T[] {\n  if (indexing !== 'xy' && indexing !== 'ij') {\n    throw new TypeError(\n        `${indexing} is not a valid third argument to meshgrid`);\n  }\n  if (x === undefined) {\n    return [];\n  }\n  let $x = convertToTensor(\n      x, 'x', 'meshgrid', x instanceof Tensor ? x.dtype : 'float32');\n\n  if (y === undefined) {\n    return [$x];\n  }\n  let $y = convertToTensor(\n      y, 'y', 'meshgrid', y instanceof Tensor ? y.dtype : 'float32');\n\n  const w = sizeFromShape($x.shape);\n  const h = sizeFromShape($y.shape);\n\n  if (indexing === 'xy') {\n    $x = reshape($x, [1, -1]) as T;\n    $y = reshape($y, [-1, 1]) as T;\n    return [\n      matMul(ones([h, 1], $x.dtype), $x),\n      matMul($y, ones([1, w], $y.dtype)),\n    ];\n  }\n\n  $x = reshape($x, [-1, 1]) as T;\n  $y = reshape($y, [1, -1]) as T;\n  return [\n    matMul($x, ones([1, h], $x.dtype)),\n    matMul(ones([w, 1], $y.dtype), $y),\n  ];\n}\n","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Min, MinAttrs, MinInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'min');\n\n  const inputs: MinInputs = {x: $x};\n  const attrs: MinAttrs = {axis, keepDims};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  return ENGINE.runKernel(\n             Min, inputs as {} as NamedTensorMap,\n             attrs as {} as NamedAttrMap) as T;\n}\n\nexport const min = op({min_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Minimum, MinimumInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {cast} from './cast';\nimport {op} from './operation';\n\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'minimum');\n  let $b = convertToTensor(b, 'b', 'minimum');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  if ($a.dtype === 'bool') {\n    $a = cast($a, 'int32');\n    $b = cast($b, 'int32');\n  }\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: MinimumInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(Minimum, inputs as {} as NamedTensorMap);\n}\n\nexport const minimum = op({minimum_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {MirrorPad, MirrorPadAttrs, MirrorPadInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Pads a `tf.Tensor` using mirror padding.\n *\n * This operation implements the `REFLECT` and `SYMMETRIC` modes of pad.\n *\n * ```js\n * const x = tf.range(0, 9).reshape([1, 1, 3, 3]);\n * x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * In \"reflect\" mode, the padded regions do not include the borders,\n * while in \"symmetric\" mode the padded regions do include the borders.\n * For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`,\n * then the output is `[1, 2, 3, 2, 1]` in \"reflect\" mode, and\n * `[1, 2, 3, 3, 2]` in \"symmetric\" mode.\n * If `mode` is \"reflect\" then both `paddings[D, 0]` and `paddings[D, 1]`\n * must be no greater than `x.shape[D] - 1`. If mode is \"symmetric\"\n * then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than\n * `x.shape[D]`\n * @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction mirrorPad_<T extends Tensor>(\n    x: T|TensorLike, paddings: Array<[number, number]>,\n    mode: 'reflect'|'symmetric'): T {\n  util.assert(\n      mode === 'reflect' || mode === 'symmetric',\n      () => `Invalid mode. Mode must be either reflect or symmetric. ` +\n          `Got ${mode}.`);\n\n  const $x = convertToTensor(x, 'x', 'mirrorPad');\n  if ($x.rank === 0) {\n    throw new Error(\n        'mirrorPad(scalar) is not defined. ' +\n        'Pass non-scalar to mirrorPad');\n  }\n  util.assert(\n      paddings.length === $x.rank,\n      () => `Padding doesn't match input. Must be ${$x.rank}. ` +\n          `Got ${paddings.length}.`);\n  const shapeOffset = mode === 'reflect' ? 1 : 0;\n  for (let i = 0; i < $x.rank; i++) {\n    util.assert(\n        paddings[i].length === 2,\n        () => `Invalid number of paddings. Must be length of 2 each.`);\n    util.assert(\n        paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset &&\n            paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset,\n        () => `Padding in dimension ${i} cannot be greater than or equal ` +\n            `to ${$x.shape[i] - shapeOffset} or less than 0 for input of ` +\n            `shape ${$x.shape}`);\n  }\n\n  const attrs: MirrorPadAttrs = {paddings, mode};\n  const inputs: MirrorPadInputs = {x: $x};\n  return ENGINE.runKernel(\n      MirrorPad, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const mirrorPad = op({mirrorPad_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Mod, ModInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'mod');\n  let $b = convertToTensor(b, 'b', 'mod');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const inputs: ModInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(Mod, inputs as {} as NamedTensorMap);\n}\n\nexport const mod = op({mod_});\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction square_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'square');\n  const attrs = {};\n  return ENGINE.runKernel('Square', {x: $x}, attrs);\n}\n\nexport const square = op({square_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {expandShapeToKeepDim} from './axis_util';\nimport {cast} from './cast';\nimport {mean} from './mean';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {square} from './square';\nimport {sub} from './sub';\n\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(\n    x: Tensor|TensorLike, axis: number|number[] = null,\n    keepDims = false): {mean: Tensor, variance: Tensor} {\n  x = convertToTensor(x, 'x', 'moments');\n  const axes = parseAxisParam(axis, x.shape);\n  const xMean = mean(x, axes, keepDims);\n  let keepDimsShape = xMean.shape;\n  if (!keepDims) {\n    keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n  }\n  const devSquared =\n      square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n  const variance = mean(devSquared, axes, keepDims);\n  return {mean: xMean, variance};\n}\n\nexport const moments = op({moments_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor2D} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * @docalias (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D]\n */\nexport type LSTMCellFunc = {\n  (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D];\n};\n\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(\n    lstmCells: LSTMCellFunc[], data: Tensor2D|TensorLike,\n    c: Array<Tensor2D|TensorLike>,\n    h: Array<Tensor2D|TensorLike>): [Tensor2D[], Tensor2D[]] {\n  const $data = convertToTensor(data, 'data', 'multiRNNCell');\n  const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n  const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n\n  let input = $data;\n  const newStates = [];\n  for (let i = 0; i < lstmCells.length; i++) {\n    const output = lstmCells[i](input, $c[i], $h[i]);\n    newStates.push(output[0]);\n    newStates.push(output[1]);\n    input = output[1];\n  }\n  const newC: Tensor2D[] = [];\n  const newH: Tensor2D[] = [];\n  for (let i = 0; i < newStates.length; i += 2) {\n    newC.push(newStates[i]);\n    newH.push(newStates[i + 1]);\n  }\n  return [newC, newH];\n}\nexport const multiRNNCell = op({multiRNNCell_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Multinomial, MultinomialAttrs, MultinomialInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(\n    logits: Tensor1D|Tensor2D|TensorLike, numSamples: number, seed?: number,\n    normalized = false): Tensor1D|Tensor2D {\n  const $logits = convertToTensor(logits, 'logits', 'multinomial');\n  const numOutcomes = $logits.size;\n  const origRank = $logits.rank;\n  if (numOutcomes < 2) {\n    throw new Error(\n        `Error in multinomial: you need at least 2 outcomes, but got ` +\n        `${numOutcomes}.`);\n  }\n  if (origRank > 2) {\n    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n  }\n  // TODO(lina128): Investigate correct seed behavior. The code seems not allow\n  // setting see to 0.\n  seed = seed || Math.random();\n\n  // The kernel only accepts (and returns) rank 2 tensors.\n  const logits2D: Tensor2D =\n      origRank === 1 ? reshape($logits, [1, -1]) : $logits as Tensor2D;\n\n  const inputs: MultinomialInputs = {logits: logits2D};\n  const attrs: MultinomialAttrs = {numSamples, seed, normalized};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  Multinomial, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as Tensor2D;\n\n  // tslint:disable-next-line:no-unnecessary-type-assertion\n  return origRank === 1 ? reshape(res, [res.size]) as Tensor1D : res;\n}\n\nexport const multinomial = op({multinomial_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {NotEqual, NotEqualInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'notEqual', 'string_or_numeric');\n  let $b = convertToTensor(b, 'b', 'notEqual', 'string_or_numeric');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: NotEqualInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernel(NotEqual, inputs as {} as NamedTensorMap);\n}\n\nexport const notEqual = op({notEqual_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {OnesLike, OnesLikeInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'onesLike');\n\n  const inputs: OnesLikeInputs = {x: $x};\n  return ENGINE.runKernel(OnesLike, inputs as {} as NamedTensorMap);\n}\n\nexport const onesLike = op({onesLike_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {matMul} from './mat_mul';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(\n    v1: Tensor1D|TensorLike, v2: Tensor1D|TensorLike): Tensor2D {\n  const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n  const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n\n  util.assert(\n      $v1.rank === 1 && $v2.rank === 1,\n      () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n          `${$v1.rank} and ${$v2.rank}.`);\n\n  const v12D = reshape($v1, [-1, 1]);\n  const v22D = reshape($v2, [1, -1]);\n  return matMul(v12D, v22D);\n}\n\nexport const outerProduct = op({outerProduct_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {PadV2, PadV2Attrs, PadV2Inputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation implements `CONSTANT` mode. For `REFLECT` and `SYMMETRIC`,\n * refer to `tf.mirrorPad`\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_<T extends Tensor>(\n    x: T|TensorLike, paddings: Array<[number, number]>, constantValue = 0): T {\n  const $x = convertToTensor(x, 'x', 'pad');\n  if ($x.rank === 0) {\n    throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n  }\n\n  const attrs: PadV2Attrs = {paddings, constantValue};\n  const inputs: PadV2Inputs = {x: $x};\n  return ENGINE.runKernel(\n      PadV2, inputs as unknown as NamedTensorMap,\n      attrs as unknown as NamedAttrMap);\n}\n\nexport const pad = op({pad_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor1D} from '../tensor';\nimport {TensorLike} from '../types';\nimport {assert} from '../util';\nimport {op} from './operation';\nimport {pad} from './pad';\n\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(\n    x: Tensor1D|TensorLike, paddings: [number, number],\n    constantValue = 0): Tensor1D {\n  assert(\n      paddings.length === 2,\n      () => 'Invalid number of paddings. Must be length of 2.');\n  return pad(x, [paddings], constantValue);\n}\n\nexport const pad1d = op({pad1d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor2D} from '../tensor';\nimport {TensorLike} from '../types';\nimport {assert} from '../util';\nimport {op} from './operation';\nimport {pad} from './pad';\n\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(\n    x: Tensor2D|TensorLike, paddings: [[number, number], [number, number]],\n    constantValue = 0): Tensor2D {\n  assert(\n      paddings.length === 2 && paddings[0].length === 2 &&\n          paddings[1].length === 2,\n      () => 'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\nexport const pad2d = op({pad2d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor3D} from '../tensor';\nimport {TensorLike} from '../types';\nimport {assert} from '../util';\nimport {op} from './operation';\nimport {pad} from './pad';\n\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(\n    x: Tensor3D|TensorLike,\n    paddings: [[number, number], [number, number], [number, number]],\n    constantValue = 0): Tensor3D {\n  assert(\n      paddings.length === 3 && paddings[0].length === 2 &&\n          paddings[1].length === 2 && paddings[2].length === 2,\n      () => 'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\nexport const pad3d = op({pad3d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor4D} from '../tensor';\nimport {TensorLike} from '../types';\nimport {assert} from '../util';\nimport {op} from './operation';\nimport {pad} from './pad';\n\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(\n    x: Tensor4D|TensorLike,\n    paddings:\n        [\n          [number, number], [number, number], [number, number], [number, number]\n        ],\n    constantValue = 0): Tensor4D {\n  assert(\n      paddings.length === 4 && paddings[0].length === 2 &&\n          paddings[1].length === 2 && paddings[2].length === 2 &&\n          paddings[3].length === 2,\n      () => 'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\nexport const pad4d = op({pad4d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {SpaceToBatchND, SpaceToBatchNDAttrs, SpaceToBatchNDInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_<T extends Tensor>(\n    x: T|TensorLike, blockShape: number[], paddings: number[][]): T {\n  const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n\n  util.assert(\n      $x.rank >= 1 + blockShape.length,\n      () => `input rank ${$x.rank} should be > than [blockShape] ${\n          blockShape.length}`);\n\n  util.assert(\n      paddings.length === blockShape.length,\n      () => `paddings.shape[0] ${\n          paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n\n  util.assert(\n      $x.shape.reduce(\n          (a, b, i) => {\n            if (i > 0 && i <= blockShape.length) {\n              return a &&\n                  ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                       blockShape[i - 1] ===\n                   0);\n            }\n            return a;\n          },\n          true),\n      () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${\n          paddings.toString()} must be divisible by blockShapes ${\n          blockShape.toString()}`);\n\n  const inputs: SpaceToBatchNDInputs = {x: $x};\n  const attrs: SpaceToBatchNDAttrs = {blockShape, paddings};\n\n  return ENGINE.runKernel(\n      SpaceToBatchND, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const spaceToBatchND = op({spaceToBatchND_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {avgPool} from './avg_pool';\nimport {batchToSpaceND} from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport {maxPool} from './max_pool';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {spaceToBatchND} from './space_to_batch_nd';\n\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_<T extends Tensor3D|Tensor4D>(\n    input: T|TensorLike, windowShape: [number, number]|number,\n    poolingType: 'avg'|'max', pad: 'valid'|'same'|number,\n    dilations?: [number, number]|number, strides?: [number, number]|number) {\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  if (strides == null) {\n    strides = 1;\n  }\n  if (pad === 0) {\n    pad = 'valid';\n  }\n\n  const $x = convertToTensor(input, 'x', 'maxPool');\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in pool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  const convInfo = conv_util.computePool2DInfo(\n      x4D.shape, windowShape, strides, dilations, pad);\n  const dilation: [number, number] =\n      [convInfo.dilationHeight, convInfo.dilationWidth];\n\n  // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n  // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n  // tslint:disable-next-line:max-line-length\n  // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n\n  let basePadding: number[][];\n  if (pad === 'same') {\n    basePadding = withSpaceToBatchBasePaddings(\n        [convInfo.filterHeight, convInfo.filterWidth], dilation);\n  } else {\n    basePadding = [[0, 0], [0, 0]];\n  }\n\n  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings(\n      [convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n  const convertedPad = isDilationOne ? pad : 'valid';\n  const convertedX =\n      isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n\n  const forwardOp = poolingType === 'avg' ?\n      () => avgPool(convertedX, windowShape, strides, convertedPad) :\n      () => maxPool(convertedX, windowShape, strides, convertedPad);\n  const y = forwardOp();\n\n  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n\n  return res as T;\n}\n\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(\n    inputShape: [number, number], blockShape: [number, number],\n    basePadding: number[][]) {\n  const padStart = basePadding.map(b => b[0]);\n  const origPadEnd = basePadding.map(b => b[1]);\n  const fullInputShape = inputShape.concat(padStart, origPadEnd);\n  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n  return [paddings, crops];\n}\n\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(\n    filterShape: [number, number], dilation: [number, number]) {\n  // Spatial dimensions of the filters and the upsampled filters in which we\n  // introduce (rate - 1) zeros between consecutive filter values.\n  const dilatedFilterShape = filterShape.map((s, i) => {\n    return s + (s - 1) * (dilation[i] - 1);\n  });\n  const padExtraShape = dilatedFilterShape.map(s => s - 1);\n\n  // When padding is odd, we pad more at end, following the same\n  // convention as conv2d.\n  const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n  return padExtraShape.map((_, i) => {\n    return [padExtraStart[i], padExtraEnd[i]];\n  });\n}\n\nexport const pool = op({pool_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Pow, PowInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_<T extends Tensor>(\n    base: Tensor|TensorLike, exp: Tensor|TensorLike): T {\n  let $base = convertToTensor(base, 'base', 'pow');\n  let $exp = convertToTensor(exp, 'exp', 'pow');\n  [$base, $exp] = makeTypesMatch($base, $exp);\n\n  const inputs: PowInputs = {a: $base, b: $exp};\n\n  return ENGINE.runKernel(Pow, inputs as {} as NamedTensorMap);\n}\n\nexport const pow = op({pow_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Prelu, PreluInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_<T extends Tensor>(x: T|TensorLike, alpha: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'prelu');\n  const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n\n  const inputs: PreluInputs = {x: $x, alpha: $alpha};\n  return ENGINE.runKernel(Prelu, inputs as {} as NamedTensorMap);\n}\n\nexport const prelu = op({prelu_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Prod, ProdAttrs, ProdInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {cast} from './cast';\nimport {op} from './operation';\n\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'prod');\n\n  if ($x.dtype === 'bool') {\n    // bool is not an allowed type for the underlying kernel.\n    $x = cast($x, 'int32');\n  }\n\n  const inputs: ProdInputs = {x: $x};\n  const attrs: ProdAttrs = {axis, keepDims};\n\n  return ENGINE.runKernel(\n      Prod, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const prod = op({prod_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\nimport {sizeFromShape} from '../util';\n\nimport {op} from './operation';\n\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction rand_<R extends Rank>(\n    shape: ShapeMap[R], randFunction: () => number,\n    dtype?: DataType): Tensor<R> {\n  const size = sizeFromShape(shape);\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  for (let i = 0; i < size; i++) {\n    values[i] = randFunction();\n  }\n  return ENGINE.makeTensor(values, shape, dtype) as Tensor<R>;\n}\n\nexport const rand = op({rand_});\n","// A port of an algorithm by Johannes Baagøe <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baagøe <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xorshift7\" algorithm by\n// François Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n","// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n","// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n","// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by François Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as seedrandom from 'seedrandom';\n\nimport {expectNumbersClose, testEpsilon} from '../test_util';\nimport {TypedArray} from '../types';\n\nexport interface RandomBase {\n  nextValue(): number;\n}\n\nexport interface RandomGamma {\n  nextValue(): number;\n}\n\nexport interface RandNormalDataTypes {\n  float32: Float32Array;\n  int32: Int32Array;\n}\n\nexport interface RandGammaDataTypes {\n  float32: Float32Array;\n  int32: Int32Array;\n}\n\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss implements RandomBase {\n  private mean: number;\n  private stdDev: number;\n  private nextVal: number;\n  private dtype?: keyof RandNormalDataTypes;\n  private truncated?: boolean;\n  private upper?: number;\n  private lower?: number;\n  private random: seedrandom.prng;\n\n  constructor(\n      mean: number, stdDeviation: number, dtype?: keyof RandNormalDataTypes,\n      truncated?: boolean, seed?: number) {\n    this.mean = mean;\n    this.stdDev = stdDeviation;\n    this.dtype = dtype;\n    this.nextVal = NaN;\n    this.truncated = truncated;\n    if (this.truncated) {\n      this.upper = this.mean + this.stdDev * 2;\n      this.lower = this.mean - this.stdDev * 2;\n    }\n    const seedValue = seed ? seed : Math.random();\n    this.random = seedrandom.alea(seedValue.toString());\n  }\n\n  /** Returns next sample from a Gaussian distribution. */\n  public nextValue(): number {\n    if (!isNaN(this.nextVal)) {\n      const value = this.nextVal;\n      this.nextVal = NaN;\n      return value;\n    }\n\n    let resultX: number, resultY: number;\n    let isValid = false;\n    while (!isValid) {\n      let v1: number, v2: number, s: number;\n      do {\n        v1 = 2 * this.random() - 1;\n        v2 = 2 * this.random() - 1;\n        s = v1 * v1 + v2 * v2;\n      } while (s >= 1 || s === 0);\n\n      const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n      resultX = this.mean + this.stdDev * v1 * mul;\n      resultY = this.mean + this.stdDev * v2 * mul;\n\n      if (!this.truncated || this.isValidTruncated(resultX)) {\n        isValid = true;\n      }\n    }\n\n    if (!this.truncated || this.isValidTruncated(resultY)) {\n      this.nextVal = this.convertValue(resultY);\n    }\n    return this.convertValue(resultX);\n  }\n\n  /** Handles proper rounding for non-floating-point numbers. */\n  private convertValue(value: number): number {\n    if (this.dtype == null || this.dtype === 'float32') {\n      return value;\n    }\n    return Math.round(value);\n  }\n\n  /** Returns true if less than 2-standard-deviations from the mean. */\n  private isValidTruncated(value: number): boolean {\n    return value <= this.upper && value >= this.lower;\n  }\n}\n\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma implements RandomGamma {\n  private alpha: number;\n  private beta: number;\n  private d: number;\n  private c: number;\n  private dtype?: keyof RandGammaDataTypes;\n  private randu: seedrandom.prng;\n  private randn: MPRandGauss;\n\n  constructor(\n      alpha: number, beta: number, dtype: keyof RandGammaDataTypes,\n      seed?: number) {\n    this.alpha = alpha;\n    this.beta = 1 / beta;  // convert rate to scale parameter\n    this.dtype = dtype;\n\n    const seedValue = seed ? seed : Math.random();\n    this.randu = seedrandom.alea(seedValue.toString());\n    this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n\n    if (alpha < 1) {\n      this.d = alpha + (2 / 3);\n    } else {\n      this.d = alpha - (1 / 3);\n    }\n    this.c = 1 / Math.sqrt(9 * this.d);\n  }\n\n  /** Returns next sample from a gamma distribution. */\n  public nextValue(): number {\n    let x2: number, v0: number, v1: number, x: number, u: number, v: number;\n    while (true) {\n      do {\n        x = this.randn.nextValue();\n        v = 1 + (this.c * x);\n      } while (v <= 0);\n      v *= v * v;\n      x2 = x * x;\n      v0 = 1 - (0.331 * x2 * x2);\n      v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n      u = this.randu();\n      if (u < v0 || Math.log(u) < v1) {\n        break;\n      }\n    }\n    v = (1 / this.beta) * this.d * v;\n    if (this.alpha < 1) {\n      v *= Math.pow(this.randu(), 1 / this.alpha);\n    }\n    return this.convertValue(v);\n  }\n  /** Handles proper rounding for non-floating-point numbers. */\n  private convertValue(value: number): number {\n    if (this.dtype === 'float32') {\n      return value;\n    }\n    return Math.round(value);\n  }\n}\n\nexport class UniformRandom implements RandomBase {\n  private min: number;\n  private range: number;\n  private random: seedrandom.prng;\n  private dtype?: keyof RandNormalDataTypes;\n\n  constructor(\n      min = 0, max = 1, dtype?: keyof RandNormalDataTypes,\n      seed?: string|number) {\n    this.min = min;\n    this.range = max - min;\n    this.dtype = dtype;\n    if (seed == null) {\n      seed = Math.random();\n    }\n    if (typeof seed === 'number') {\n      seed = seed.toString();\n    }\n\n    if (!this.canReturnFloat() && this.range <= 1) {\n      throw new Error(\n          `The difference between ${min} - ${max} <= 1 and dtype is not float`);\n    }\n    this.random = seedrandom.alea(seed);\n  }\n\n  /** Handles proper rounding for non floating point numbers. */\n  private canReturnFloat = () =>\n      (this.dtype == null || this.dtype === 'float32');\n\n  private convertValue(value: number): number {\n    if (this.canReturnFloat()) {\n      return value;\n    }\n    return Math.round(value);\n  }\n\n  nextValue() {\n    return this.convertValue(this.min + this.range * this.random());\n  }\n}\n\nexport function jarqueBeraNormalityTest(values: TypedArray|number[]) {\n  // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n  const n = values.length;\n  const s = skewness(values);\n  const k = kurtosis(values);\n  const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n  // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n  // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n  const CHI_SQUARE_2DEG = 5.991;\n  if (jb > CHI_SQUARE_2DEG) {\n    throw new Error(`Invalid p-value for JB: ${jb}`);\n  }\n}\n\nexport function expectArrayInMeanStdRange(\n    actual: TypedArray|number[], expectedMean: number, expectedStdDev: number,\n    epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = testEpsilon();\n  }\n  const actualMean = mean(actual);\n  expectNumbersClose(actualMean, expectedMean, epsilon);\n  expectNumbersClose(\n      standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\n\nfunction mean(values: TypedArray|number[]) {\n  let sum = 0;\n  for (let i = 0; i < values.length; i++) {\n    sum += values[i];\n  }\n  return sum / values.length;\n}\n\nfunction standardDeviation(values: TypedArray|number[], mean: number) {\n  let squareDiffSum = 0;\n  for (let i = 0; i < values.length; i++) {\n    const diff = values[i] - mean;\n    squareDiffSum += diff * diff;\n  }\n  return Math.sqrt(squareDiffSum / values.length);\n}\n\nfunction kurtosis(values: TypedArray|number[]) {\n  // https://en.wikipedia.org/wiki/Kurtosis\n  const valuesMean = mean(values);\n  const n = values.length;\n  let sum2 = 0;\n  let sum4 = 0;\n  for (let i = 0; i < n; i++) {\n    const v = values[i] - valuesMean;\n    sum2 += Math.pow(v, 2);\n    sum4 += Math.pow(v, 4);\n  }\n  return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\n\nfunction skewness(values: TypedArray|number[]) {\n  // https://en.wikipedia.org/wiki/Skewness\n  const valuesMean = mean(values);\n  const n = values.length;\n  let sum2 = 0;\n  let sum3 = 0;\n  for (let i = 0; i < n; i++) {\n    const v = values[i] - valuesMean;\n    sum2 += Math.pow(v, 2);\n    sum3 += Math.pow(v, 3);\n  }\n  return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {Rank, ShapeMap} from '../types';\n\nimport {buffer} from './buffer';\nimport {op} from './operation';\nimport {RandGamma} from './rand_util';\n\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n *     to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_<R extends Rank>(\n    shape: ShapeMap[R], alpha: number, beta = 1,\n    dtype: 'float32'|'int32' = 'float32', seed?: number): Tensor<R> {\n  if (beta == null) {\n    beta = 1;\n  }\n  if (dtype == null) {\n    dtype = 'float32';\n  }\n  if (dtype !== 'float32' && dtype !== 'int32') {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const rgamma = new RandGamma(alpha, beta, dtype, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = rgamma.nextValue();\n  }\n  return res.toTensor();\n}\n\nexport const randomGamma = op({randomGamma_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\n\nimport {buffer} from './buffer';\nimport {op} from './operation';\nimport {MPRandGauss} from './rand_util';\n\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_<R extends Rank>(\n    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n    seed?: number): Tensor<R> {\n  if (dtype != null && (dtype as DataType) === 'bool') {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const randGauss =\n      new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\n\nexport const randomNormal = op({randomNormal_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\n\nimport {buffer} from './buffer';\nimport {op} from './operation';\nimport {UniformRandom} from './rand_util';\n\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_<R extends Rank>(\n    shape: ShapeMap[R], minval = 0, maxval = 1, dtype: DataType = 'float32',\n    seed?: number|string): Tensor<R> {\n  const res = buffer(shape, dtype);\n  const random = new UniformRandom(minval, maxval, null, seed);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = random.nextValue();\n  }\n  return res.toTensor();\n}\n\nexport const randomUniform = op({randomUniform_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Range, RangeAttrs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor1D} from '../tensor';\n\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function range(\n    start: number, stop: number, step = 1,\n    dtype: 'float32'|'int32' = 'float32'): Tensor1D {\n  if (step === 0) {\n    throw new Error('Cannot have a step of zero');\n  }\n\n  const attrs: RangeAttrs = {start, stop, step, dtype};\n\n  return ENGINE.runKernel(Range, {} /* inputs */, attrs as {} as NamedAttrMap);\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Real, RealInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_<T extends Tensor>(input: T|TensorLike): T {\n  const $input = convertToTensor(input, 'input', 'real');\n\n  const inputs: RealInputs = {input: $input};\n  return ENGINE.runKernel(Real, inputs as {} as NamedTensorMap);\n}\n\nexport const real = op({real_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Reciprocal, ReciprocalInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'reciprocal');\n\n  const inputs: ReciprocalInputs = {x: $x};\n  return ENGINE.runKernel(Reciprocal, inputs as {} as NamedTensorMap);\n}\nexport const reciprocal = op({reciprocal_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Relu, ReluInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'relu');\n\n  const inputs: ReluInputs = {x: $x};\n\n  return ENGINE.runKernel(Relu, inputs as {} as NamedTensorMap);\n}\n\nexport const relu = op({relu_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Relu6, Relu6Inputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print();  // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'relu6');\n\n  const inputs: Relu6Inputs = {x: $x};\n\n  return ENGINE.runKernel(Relu6, inputs as {} as NamedTensorMap);\n}\n\nexport const relu6 = op({relu6_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Reverse, ReverseAttrs, ReverseInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_<T extends Tensor>(\n    x: T|TensorLike, axis?: number|number[]): T {\n  const $x = convertToTensor(x, 'x', 'reverse');\n\n  const inputs: ReverseInputs = {x: $x};\n  const attrs: ReverseAttrs = {dims: axis};\n\n  return ENGINE.runKernel(\n      Reverse, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const reverse = op({reverse_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\nimport {reverse} from './reverse';\n\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x: Tensor1D|TensorLike): Tensor1D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert(\n      $x.rank === 1,\n      () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n  return reverse($x, 0);\n}\n\nexport const reverse1d = op({reverse1d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\nimport {reverse} from './reverse';\n\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x: Tensor2D|TensorLike, axis?: number|number[]): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert(\n      $x.rank === 2,\n      () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\nexport const reverse2d = op({reverse2d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor3D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\nimport {reverse} from './reverse';\n\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x: Tensor3D|TensorLike, axis?: number|number[]): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert(\n      $x.rank === 3,\n      () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\nexport const reverse3d = op({reverse3d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\nimport {reverse} from './reverse';\n\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x: Tensor4D|TensorLike, axis?: number|number[]): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert(\n      $x.rank === 4,\n      () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\nexport const reverse4d = op({reverse4d_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Round, RoundInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'round');\n  const inputs: RoundInputs = {x: $x};\n\n  return ENGINE.runKernel(Round, inputs as {} as NamedTensorMap);\n}\n\nexport const round = op({round_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Rsqrt, RsqrtInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'rsqrt');\n\n  const inputs: RsqrtInputs = {x: $x};\n\n  return ENGINE.runKernel(Rsqrt, inputs as {} as NamedTensorMap);\n}\nexport const rsqrt = op({rsqrt_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar} from '../tensor';\nimport {DataType} from '../types';\nimport {isTypedArray} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function scalar(\n    value: number|boolean|string|Uint8Array, dtype?: DataType): Scalar {\n  if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&\n      dtype !== 'complex64') {\n    throw new Error(\n        'Error creating a new Scalar: value must be a primitive ' +\n        '(number|boolean|string)');\n  }\n  if (dtype === 'string' && isTypedArray(value) &&\n      !(value instanceof Uint8Array)) {\n    throw new Error(\n        'When making a scalar from encoded string, ' +\n        'the value must be `Uint8Array`.');\n  }\n  const shape: number[] = [];\n  const inferredShape: number[] = [];\n  return makeTensor(value, shape, inferredShape, dtype) as Scalar;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Selu, SeluInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'selu');\n\n  const inputs: SeluInputs = {x: $x};\n\n  return ENGINE.runKernel(Selu, inputs as {} as NamedTensorMap);\n}\n\nexport const selu = op({selu_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {conv2d} from './conv2d';\nimport {depthwiseConv2d} from './depthwise_conv2d';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike,\n    pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n    pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {\n  const $x = convertToTensor(x, 'x', 'separableConv2d');\n  const $depthwiseFilter =\n      convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n  const $pointwiseFilter =\n      convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  if (dataFormat === 'NCHW') {\n    throw new Error(\n        'separableConv2d currently does not support dataFormat NCHW; only ' +\n        'NHWC is supported');\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in separableConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $depthwiseFilter.rank === 4,\n      () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n          `got rank ${$depthwiseFilter.rank}.`);\n  util.assert(\n      $pointwiseFilter.rank === 4,\n      () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n          `got rank ${$depthwiseFilter.rank}.`);\n  util.assert(\n      $pointwiseFilter.shape[0] === 1,\n      () =>\n          `Error in separableConv2d: the first dimension of pointwise filter ` +\n          ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n  util.assert(\n      $pointwiseFilter.shape[1] === 1,\n      () => `Error in separableConv2d: the second dimension of pointwise ` +\n          `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n\n  const inChannels = $depthwiseFilter.shape[2];\n  const channelMultiplier = $depthwiseFilter.shape[3];\n  util.assert(\n      $pointwiseFilter.shape[2] === inChannels * channelMultiplier,\n      () =>\n          `Error in separableConv2d: the third dimension of pointwise filter ` +\n          `must be ${inChannels * channelMultiplier}, ` +\n          `but got ${$pointwiseFilter.shape[2]}.`);\n\n  const depthwise = depthwiseConv2d(\n      x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n  const pointwiseStride = 1;\n  const res =\n      conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res as T;\n}\n\nexport const separableConv2d = op({separableConv2d_});\n","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor, TensorBuffer} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(\n    x: Tensor|TensorLike, y: Tensor|TensorLike): Promise<[Tensor, Tensor]> {\n  const $x = convertToTensor(x, 'x', 'setdiff1d');\n  const $y = convertToTensor(y, 'y', 'setdiff1d');\n\n  util.assert(\n      $x.dtype === $y.dtype,\n      () => `x and y should have the same dtype, but got x (${\n          $x.dtype}) and y (${$y.dtype}).`);\n\n  util.assert(\n      $x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n\n  util.assert(\n      $y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n\n  const xVals = await $x.data();\n  const yVals = await $y.data();\n  const ySet = new Set(yVals);\n\n  let outputSize = 0;\n  for (let i = 0; i < xVals.length; i++) {\n    if (!ySet.has(xVals[i])) {\n      outputSize++;\n    }\n  }\n\n  const buffer = new TensorBuffer([outputSize], $x.dtype);\n  const indices = new TensorBuffer([outputSize], 'int32');\n  for (let i = 0, p = 0; i < xVals.length; i++) {\n    if (!ySet.has(xVals[i])) {\n      buffer.values[p] = xVals[i];\n      indices.values[p] = i;\n      p++;\n    }\n  }\n  return [buffer.toTensor(), indices.toTensor()];\n}\nexport const setdiff1dAsync = setdiff1dAsync_;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Sign, SignInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sign');\n  const inputs: SignInputs = {x: $x};\n  return ENGINE.runKernel(Sign, inputs as {} as NamedTensorMap);\n}\nexport const sign = op({sign_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Sin, SinInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sin');\n\n  const inputs: SinInputs = {x: $x};\n\n  return ENGINE.runKernel(Sin, inputs as {} as NamedTensorMap);\n}\nexport const sin = op({sin_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Sinh, SinhInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sinh');\n  const inputs: SinhInputs = {x: $x};\n\n  return ENGINE.runKernel(Sinh, inputs as {} as NamedTensorMap);\n}\nexport const sinh = op({sinh_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {slice} from './slice';\n\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(\n    x: Tensor1D|TensorLike, begin: number, size: number): Tensor1D {\n  const $x = convertToTensor(x, 'x', 'slice1d');\n  util.assert(\n      $x.rank === 1,\n      () =>\n          `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, [begin], [size]);\n}\nexport const slice1d = op({slice1d_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {slice} from './slice';\n\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(\n    x: Tensor2D|TensorLike, begin: [number, number],\n    size: [number, number]): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'slice2d');\n  util.assert(\n      $x.rank === 2,\n      () =>\n          `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nexport const slice2d = op({slice2d_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor3D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {slice} from './slice';\n\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(\n    x: Tensor3D|TensorLike, begin: [number, number, number],\n    size: [number, number, number]): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'slice3d');\n  util.assert(\n      $x.rank === 3,\n      () =>\n          `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nexport const slice3d = op({slice3d_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {slice} from './slice';\n\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(\n    x: Tensor4D|TensorLike, begin: [number, number, number, number],\n    size: [number, number, number, number]): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'slice4d');\n  util.assert(\n      $x.rank === 4,\n      () =>\n          `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nexport const slice4d = op({slice4d_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Softmax, SoftmaxAttrs, SoftmaxInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_<T extends Tensor>(logits: T|TensorLike, dim = -1): T {\n  const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n\n  if (dim === -1) {\n    dim = $logits.rank - 1;\n  }\n  if (dim !== $logits.rank - 1) {\n    throw Error(\n        'Softmax along a non-last dimension is not yet supported. ' +\n        `Logits was rank ${$logits.rank} and dim was ${dim}`);\n  }\n\n  const inputs: SoftmaxInputs = {logits: $logits};\n  const attrs: SoftmaxAttrs = {dim};\n\n  return ENGINE.runKernel(\n      Softmax, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const softmax = op({softmax_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {FFT, FFTInputs} from '../../kernel_names';\nimport {Tensor} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {assert} from '../../util';\nimport {op} from '../operation';\n\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input: Tensor): Tensor {\n  assert(\n      input.dtype === 'complex64',\n      () => `The dtype for tf.spectral.fft() must be complex64 ` +\n          `but got ${input.dtype}.`);\n\n  const inputs: FFTInputs = {input};\n\n  return ENGINE.runKernel(FFT, inputs as {} as NamedTensorMap);\n}\n\nexport const fft = op({fft_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {IFFT, IFFTInputs} from '../../kernel_names';\nimport {Tensor} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {assert} from '../../util';\nimport {op} from '../operation';\n\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input: Tensor): Tensor {\n  assert(\n      input.dtype === 'complex64',\n      () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n          `but got ${input.dtype}.`);\n\n  const inputs: IFFTInputs = {input};\n\n  return ENGINE.runKernel(IFFT, inputs as {} as NamedTensorMap);\n}\n\nexport const ifft = op({ifft_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor2D} from '../../tensor';\nimport {complex} from '../complex';\nimport {concat} from '../concat';\nimport {imag} from '../imag';\nimport {mul} from '../mul';\nimport {op} from '../operation';\nimport {real} from '../real';\nimport {reshape} from '../reshape';\nimport {reverse} from '../reverse';\nimport {scalar} from '../scalar';\nimport {slice} from '../slice';\n\nimport {ifft} from './ifft';\n\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input: Tensor): Tensor {\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n  let ret: Tensor;\n  if (innerDimensionSize <= 2) {\n    const complexInput = reshape(input, [batch, innerDimensionSize]);\n    ret = ifft(complexInput);\n  } else {\n    // The length of unique components of the DFT of a real-valued signal\n    // is 2 * (input_len - 1)\n    const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n    const realInput = reshape(real(input), [batch, innerDimensionSize]);\n    const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n\n    const realConjugate =\n        reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n    const imagConjugate: Tensor2D = mul(\n        reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1),\n        scalar(-1));\n\n    const r = concat([realInput, realConjugate], 1);\n    const i = concat([imagInput, imagConjugate], 1);\n    const complexInput =\n        reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n    ret = ifft(complexInput);\n  }\n  ret = real(ret);\n  // reshape the result if the input is 3D tensor.\n  if (input.rank === 3 && input.shape[0] !== 0) {\n    const temp = ret;\n    const batch = input.shape[0];\n    ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n    temp.dispose();\n  }\n  return ret;\n}\n\nexport const irfft = op({irfft_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {SplitV, SplitVAttrs, SplitVInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_<T extends Tensor>(\n    x: Tensor|TensorLike, numOrSizeSplits: number[]|number, axis = 0): T[] {\n  const $x = convertToTensor(x, 'x', 'split');\n\n  const inputs: SplitVInputs = {x: $x};\n  const attr: SplitVAttrs = {numOrSizeSplits, axis};\n\n  return ENGINE.runKernel(\n             SplitV, inputs as {} as NamedTensorMap,\n             attr as {} as NamedAttrMap) as {} as T[];\n}\n\nexport const split = op({split_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {assert} from '../../util';\nimport {complex} from '../complex';\nimport {concat} from '../concat';\nimport {imag} from '../imag';\nimport {op} from '../operation';\nimport {real} from '../real';\nimport {reshape} from '../reshape';\nimport {slice} from '../slice';\nimport {split} from '../split';\nimport {zeros} from '../zeros';\nimport {zerosLike} from '../zeros_like';\n\nimport {fft} from './fft';\n\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input: Tensor, fftLength?: number): Tensor {\n  assert(\n      input.dtype === 'float32',\n      () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n\n  let innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n\n  let adjustedInput: Tensor;\n  if (fftLength != null && fftLength < innerDimensionSize) {\n    // Need to crop\n    const begin = input.shape.map(v => 0);\n    const size = input.shape.map(v => v);\n    size[input.shape.length - 1] = fftLength;\n    adjustedInput = slice(input, begin, size);\n    innerDimensionSize = fftLength;\n  } else if (fftLength != null && fftLength > innerDimensionSize) {\n    // Need to pad with zeros\n    const zerosShape = input.shape.map(v => v);\n    zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n    adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n    innerDimensionSize = fftLength;\n  } else {\n    adjustedInput = input;\n  }\n\n  // Complement the input with zero imaginary numbers.\n  const zerosInput = zerosLike(adjustedInput);\n  const complexInput =\n      reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n\n  const ret = fft(complexInput);\n\n  // Exclude complex conjugations. These conjugations are put symmetrically.\n  const half = Math.floor(innerDimensionSize / 2) + 1;\n  const realValues = real(ret);\n  const imagValues = imag(ret);\n  const realComplexConjugate = split(\n      realValues, [half, innerDimensionSize - half],\n      realValues.shape.length - 1);\n  const imagComplexConjugate = split(\n      imagValues, [half, innerDimensionSize - half],\n      imagValues.shape.length - 1);\n\n  const outputShape = adjustedInput.shape.slice();\n  outputShape[adjustedInput.shape.length - 1] = half;\n\n  return reshape(\n      complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\n\nexport const rfft = op({rfft_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Sqrt, SqrtInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sqrt_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sqrt');\n\n  const inputs: SqrtInputs = {x: $x};\n\n  return ENGINE.runKernel(Sqrt, inputs as {} as NamedTensorMap);\n}\nexport const sqrt = op({sqrt_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {SquaredDifference, SquaredDifferenceInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'squaredDifference');\n  let $b = convertToTensor(b, 'b', 'squaredDifference');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: SquaredDifferenceInputs = {a: $a, b: $b};\n  const attrs = {};\n\n  return ENGINE.runKernel(\n      SquaredDifference, inputs as unknown as NamedTensorMap, attrs);\n}\n\nexport const squaredDifference = op({squaredDifference_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {squeezeShape} from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_<T extends Tensor>(x: Tensor|TensorLike, axis?: number[]): T {\n  const $x = convertToTensor(x, 'x', 'squeeze');\n  return reshape($x, squeezeShape($x.shape, axis).newShape) as T;\n}\n\nexport const squeeze = op({squeeze_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Pack, PackAttrs, PackInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_<T extends Tensor>(\n    tensors: Array<T|TensorLike>, axis = 0): Tensor {\n  const $tensors =\n      convertToTensorArray(tensors, 'tensors', 'stack', 'string_or_numeric');\n\n  util.assert(\n      $tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n\n  if ($tensors.length > 0) {\n    util.assert(\n        axis <= $tensors[0].rank, () => 'Axis must be <= rank of the tensor');\n  }\n\n  const inputs: PackInputs = $tensors;\n  const attrs: PackAttrs = {axis};\n\n  return ENGINE.runKernel(\n      Pack, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const stack = op({stack_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Step, StepAttrs, StepInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_<T extends Tensor>(x: T|TensorLike, alpha = 0.0): T {\n  const $x = convertToTensor(x, 'x', 'step');\n\n  const inputs: StepInputs = {x: $x};\n  const attrs: StepAttrs = {alpha};\n\n  return ENGINE.runKernel(\n      Step, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\nexport const step = op({step_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {StridedSlice, StridedSliceAttrs, StridedSliceInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(\n    x: Tensor|TensorLike, begin: number[], end: number[], strides?: number[],\n    beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0,\n    shrinkAxisMask = 0): Tensor {\n  const $x = convertToTensor(x, 'x', 'stridedSlice', 'string_or_numeric');\n\n  const inputs: StridedSliceInputs = {x: $x};\n  const attrs: StridedSliceAttrs = {\n    begin,\n    end,\n    strides,\n    beginMask,\n    endMask,\n    ellipsisMask,\n    newAxisMask,\n    shrinkAxisMask\n  };\n\n  return ENGINE.runKernel(\n      StridedSlice, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const stridedSlice = op({stridedSlice_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tan, TanInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'tan');\n\n  const inputs: TanInputs = {x: $x};\n\n  return ENGINE.runKernel(Tan, inputs as {} as NamedTensorMap);\n}\nexport const tan = op({tan_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike1D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor1d(values: TensorLike1D, dtype?: DataType): Tensor1D {\n  assertNonNull(values);\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 1) {\n    throw new Error('tensor1d() requires values to be a flat/TypedArray');\n  }\n  const shape: number[] = null;\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor1D;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor2D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike2D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor2d(\n    values: TensorLike2D, shape?: [number, number],\n    dtype?: DataType): Tensor2D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 2) {\n    throw new Error('tensor2d() requires shape to have two numbers');\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor2d() requires values to be number[][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor2d() requires shape to be provided when `values` ' +\n        'are a flat/TypedArray');\n  }\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor2D;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike4D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor4d(\n    values: TensorLike4D, shape?: [number, number, number, number],\n    dtype?: DataType): Tensor4D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 4) {\n    throw new Error('tensor4d() requires shape to have four numbers');\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor4d() requires values to be number[][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor4d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor4D;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor5D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike5D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor5d(\n    values: TensorLike5D, shape?: [number, number, number, number, number],\n    dtype?: DataType): Tensor5D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 5) {\n    throw new Error('tensor5d() requires shape to have five numbers');\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor5d() requires values to be ' +\n        'number[][][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor5d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor5D;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor6D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike6D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor6d(\n    values: TensorLike6D,\n    shape?: [number, number, number, number, number, number],\n    dtype?: DataType): Tensor6D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 6) {\n    throw new Error('tensor6d() requires shape to have six numbers');\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor6d() requires values to be number[][][][][][] or ' +\n        'flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor6d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape ||\n      inferredShape as [number, number, number, number, number, number];\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor6D;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {TopK, TopKAttrs, TopKInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_<T extends Tensor>(\n    x: T|TensorLike, k = 1, sorted = true): {values: T, indices: T} {\n  const $x = convertToTensor(x, 'x', 'topk');\n  if ($x.rank === 0) {\n    throw new Error('topk() expects the input to be of rank 1 or higher');\n  }\n  const lastDim = $x.shape[$x.shape.length - 1];\n  if (k > lastDim) {\n    throw new Error(\n        `'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n        `but got ${k}`);\n  }\n\n  const inputs: TopKInputs = {x: $x};\n  const attrs: TopKAttrs = {k, sorted};\n\n  const [values, indices] = ENGINE.runKernel(\n      TopK, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n\n  return {values, indices} as {values: T, indices: T};\n}\n\nexport const topk = op({topk_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\n\nimport {buffer} from './buffer';\nimport {op} from './operation';\nimport {MPRandGauss} from './rand_util';\n\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_<R extends Rank>(\n    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n    seed?: number): Tensor<R> {\n  if (dtype != null && (dtype as DataType) === 'bool') {\n    throw new Error(`Unsupported data type $ { dtype }`);\n  }\n  const randGauss =\n      new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\n\nexport const truncatedNormal = op({truncatedNormal_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Unique, UniqueAttrs, UniqueInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor1D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert} from '../util';\n\nimport {op} from './operation';\n\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print();   // [1, 2, 4, 7, 8,]\n * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print();   // [[1, 0, 0],\n *                   //  [2, 0, 0]]\n * indices.print();  // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print();   // [[1, 0],\n *                   //  [1, 0],\n *                   //  [2, 0]]\n * indices.print();  // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_<T extends Tensor>(\n    x: T|TensorLike, axis = 0): {values: T, indices: Tensor1D} {\n  const $x = convertToTensor(x, 'x', 'unique', 'string_or_numeric');\n  assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n\n  const inputs: UniqueInputs = {x: $x};\n  const attrs: UniqueAttrs = {axis};\n  const [values, indices] = ENGINE.runKernel(\n                                Unique, inputs as {} as NamedTensorMap,\n                                attrs as {} as NamedAttrMap) as [T, Tensor1D];\n  return {values, indices};\n}\n\nexport const unique = op({unique_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {UnsortedSegmentSum, UnsortedSegmentSumAttrs, UnsortedSegmentSumInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor1D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, isInt} from '../util';\n\nimport {op} from './operation';\n\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_<T extends Tensor>(\n    x: T|TensorLike, segmentIds: Tensor1D|TensorLike, numSegments: number): T {\n  const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n  const $segmentIds =\n      convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n  assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n\n  const inputs: UnsortedSegmentSumInputs = {x: $x, segmentIds: $segmentIds};\n  const attrs: UnsortedSegmentSumAttrs = {numSegments};\n\n  return ENGINE.runKernel(\n      UnsortedSegmentSum, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const unsortedSegmentSum = op({unsortedSegmentSum_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Unpack, UnpackAttrs, UnpackInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x: Tensor|TensorLike, axis = 0): Tensor[] {\n  const $x = convertToTensor(x, 'x', 'unstack', 'string_or_numeric');\n  util.assert(\n      axis >= -$x.shape.length && axis < $x.shape.length,\n      () =>\n          `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n\n  const inputs: UnpackInputs = {value: $x};\n  const attrs: UnpackAttrs = {axis};\n\n  return ENGINE.runKernel(\n      Unpack, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const unstack = op({unstack_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor, Variable} from '../tensor';\nimport {DataType, Rank} from '../types';\n\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function variable<R extends Rank>(\n    initialValue: Tensor<R>, trainable = true, name?: string,\n    dtype?: DataType): Variable<R> {\n  return ENGINE.makeVariable(initialValue, trainable, name, dtype) as\n      Variable<R>;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the Where kernel shared between cpu and webgl */\n\nimport {buffer} from '../ops/buffer';\nimport {Tensor2D} from '../tensor';\nimport {TypedArray} from '../types';\n\nexport function whereImpl(condShape: number[], condVals: TypedArray): Tensor2D {\n  const indices = [];\n  for (let i = 0; i < condVals.length; i++) {\n    if (condVals[i]) {\n      indices.push(i);\n    }\n  }\n\n  const inBuffer = buffer(condShape, 'int32');\n\n  const out = buffer([indices.length, condShape.length], 'int32');\n  for (let i = 0; i < indices.length; i++) {\n    const loc = inBuffer.indexToLoc(indices[i]);\n    const offset = i * condShape.length;\n    out.values.set(loc, offset);\n  }\n  return out.toTensor() as Tensor2D;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {whereImpl} from '../backends/where_impl';\nimport {Tensor, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition: Tensor|TensorLike): Promise<Tensor2D> {\n  const $condition =\n      convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n  const vals = await $condition.data();\n  const res = whereImpl($condition.shape, vals);\n  if (condition !== $condition) {\n    $condition.dispose();\n  }\n  return res;\n}\n\nexport const whereAsync = whereAsync_;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {gather} from './gather';\nimport {reshape} from './reshape';\nimport {squeeze} from './squeeze';\nimport {whereAsync} from './where_async';\n\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n *     By default, axis is 0 which will mask from the first dimension.\n *     Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(\n    tensor: Tensor|TensorLike, mask: Tensor|TensorLike,\n    axis?: number): Promise<Tensor> {\n  const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n  const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n\n  const axisFrom = axis == null ? 0 : axis;\n  const maskDim = $mask.rank;\n  const tensorShape = $tensor.shape;\n\n  util.assert(maskDim > 0, () => 'mask cannot be scalar');\n  util.assertShapesMatch(\n      tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape,\n      `mask's shape must match the first K dimensions of tensor's shape,`);\n\n  let leadingSize = 1;\n  for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n    leadingSize *= tensorShape[i];\n  }\n  const targetTensorShape =\n      tensorShape.slice(0, axisFrom)\n          .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n  const reshapedTensor = reshape($tensor, targetTensorShape);\n  const reshapedMask = reshape($mask, [-1]);\n  const positivePositions = await whereAsync(reshapedMask);\n  const indices = squeeze(positivePositions, [1]);\n\n  const res = gather(reshapedTensor, indices, axisFrom);\n\n  // Ensure no memory leak.\n  if (tensor !== $tensor) {\n    $tensor.dispose();\n  }\n  if (mask !== $mask) {\n    $mask.dispose();\n  }\n  indices.dispose();\n  reshapedTensor.dispose();\n  reshapedMask.dispose();\n  positivePositions.dispose();\n\n  return res;\n}\n\nexport const booleanMaskAsync = booleanMaskAsync_;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {abs} from './abs';\nimport * as axis_util from './axis_util';\nimport {max} from './max';\nimport {min} from './min';\nimport {op} from './operation';\nimport {pow} from './pow';\nimport {reshape} from './reshape';\nimport {scalar} from './scalar';\nimport {sqrt} from './sqrt';\nimport {square} from './square';\nimport {sum} from './sum';\n\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(\n    x: Tensor|TensorLike, ord: number|'euclidean'|'fro' = 'euclidean',\n    axis: number|number[] = null, keepDims = false): Tensor {\n  x = convertToTensor(x, 'x', 'norm');\n\n  const norm = normImpl(x, ord, axis);\n  let keepDimsShape = norm.shape;\n  if (keepDims) {\n    const axes = parseAxisParam(axis, x.shape);\n    keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n  }\n  return reshape(norm, keepDimsShape);\n}\n\nfunction normImpl(\n    x: Tensor, p: number|string, axis: number|number[] = null): Tensor {\n  if (x.rank === 0) {\n    return abs(x);\n  }\n\n  // consider vector when no axis is specified\n  if (x.rank !== 1 && axis === null) {\n    return normImpl(reshape(x, [-1]), p, axis);\n  }\n\n  // vector\n  if (x.rank === 1 || typeof axis === 'number' ||\n      Array.isArray(axis) && axis.length === 1) {\n    if (p === 1) {\n      return sum(abs(x), axis);\n    }\n    if (p === Infinity) {\n      return max(abs(x), axis);\n    }\n    if (p === -Infinity) {\n      return min(abs(x), axis);\n    }\n    if (p === 'euclidean' || p === 2) {\n      // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n      return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n    }\n\n    throw new Error(`Error in norm: invalid ord value: ${p}`);\n  }\n\n  // matrix (assumption axis[0] < axis[1])\n  if (Array.isArray(axis) && axis.length === 2) {\n    if (p === 1) {\n      return max(sum(abs(x), axis[0]), axis[1] - 1);\n    }\n    if (p === Infinity) {\n      return max(sum(abs(x), axis[1]), axis[0]);\n    }\n    if (p === -Infinity) {\n      return min(sum(abs(x), axis[1]), axis[0]);\n    }\n    if (p === 'fro' || p === 'euclidean') {\n      // norm(x) = sqrt(sum(pow(x, 2)))\n      return sqrt(sum(square(x), axis));\n    }\n\n    throw new Error(`Error in norm: invalid ord value: ${p}`);\n  }\n\n  throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\n\nexport const norm = op({norm_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor} from '../tensor';\nimport {assertTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {add} from './add';\nimport {div} from './div';\nimport {mul} from './mul';\nimport {op} from './operation';\nimport {pow} from './pow';\nimport {scalar} from './scalar';\nimport {sub} from './sub';\n\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_<T extends Tensor>(\n    v: T|TensorLike, x: T|TensorLike, decay: number|Scalar,\n    step?: number|Scalar, zeroDebias = true): T {\n  const $v = convertToTensor(v, 'v', 'movingAverage');\n  const $x = convertToTensor(x, 'x', 'movingAverage');\n  const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n\n  assertTypesMatch($v, $x);\n  util.assert(\n      util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n\n  const one = scalar(1);\n  const oneMinusDecay = sub(one, $decay);\n\n  let update = mul(sub($x, $v), oneMinusDecay);\n  if (zeroDebias) {\n    util.assert(\n        step != null, () => 'When using zeroDebias: true, step is required.');\n    const $step = convertToTensor(step, 'step', 'movingAverage');\n    update = div(update, sub(one, pow($decay, $step)));\n  }\n  return add($v, update);\n}\n\nexport const movingAverage = op({movingAverage_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {ScatterNd, ScatterNdAttrs, ScatterNdInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_<R extends Rank>(\n    indices: Tensor|TensorLike, updates: Tensor|TensorLike,\n    shape: ShapeMap[R]): Tensor<R> {\n  const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n  const $updates = convertToTensor(updates, 'updates', 'scatterND');\n  scatter_nd_util.validateInput($updates, $indices, shape);\n\n  const inputs: ScatterNdInputs = {indices: $indices, updates: $updates};\n  const attrs: ScatterNdAttrs = {shape};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  return ENGINE.runKernel(\n             ScatterNd, inputs as {} as NamedTensorMap,\n             attrs as {} as NamedAttrMap) as Tensor<R>;\n}\n\nexport const scatterND = op({scatterND_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\n\n/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(\n    sparseIndices: Tensor, sparseValues: Tensor, outputShape: number[],\n    defaultValues: Tensor) {\n  if (sparseIndices.dtype !== 'int32') {\n    throw new Error(\n        'tf.sparseToDense() expects the indices to be int32 type,' +\n        ` but the dtype was ${sparseIndices.dtype}.`);\n  }\n  if (sparseIndices.rank > 2) {\n    throw new Error(\n        'sparseIndices should be a scalar, vector, or matrix,' +\n        ` but got shape ${sparseIndices.shape}.`);\n  }\n\n  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n\n  if (outputShape.length !== numDims) {\n    throw new Error(\n        'outputShape has incorrect number of elements:,' +\n        ` ${outputShape.length}, should be: ${numDims}.`);\n  }\n\n  const numValues = sparseValues.size;\n  if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n    throw new Error(\n        'sparseValues has incorrect shape ' +\n        `${sparseValues.shape}, should be [] or [${numElems}]`);\n  }\n\n  if (sparseValues.dtype !== defaultValues.dtype) {\n    throw new Error('sparseValues.dtype must match defaultValues.dtype');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {SparseToDense, SparseToDenseAttrs, SparseToDenseInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport {Scalar, Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ScalarLike, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_<R extends Rank>(\n    sparseIndices: Tensor|TensorLike, sparseValues: Tensor|TensorLike,\n    outputShape: ShapeMap[R], defaultValue: Scalar|ScalarLike = 0): Tensor<R> {\n  const $sparseIndices =\n      convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n  const $sparseValues =\n      convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n  const $defaultValue = convertToTensor(\n      defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n\n  sparse_to_dense.validateInput(\n      $sparseIndices, $sparseValues, outputShape, $defaultValue);\n\n  const inputs: SparseToDenseInputs = {\n    sparseIndices: $sparseIndices,\n    sparseValues: $sparseValues,\n    defaultValue: $defaultValue\n  };\n\n  const attrs: SparseToDenseAttrs = {outputShape};\n\n  return ENGINE.runKernel(\n      SparseToDense, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const sparseToDense = op({sparseToDense_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {GatherNd, GatherNdInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x: Tensor|TensorLike, indices: Tensor|TensorLike): Tensor {\n  const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n  const $x = convertToTensor(x, 'x', 'gatherND', 'string_or_numeric');\n\n  const inputs: GatherNdInputs = {params: $x, indices: $indices};\n\n  return ENGINE.runKernel(GatherNd, inputs as {} as NamedTensorMap);\n}\n\nexport const gatherND = op({gatherND_});\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport * as util from '../util';\n\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n *   an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nexport function getNoiseShape(x: Tensor, noiseShape?: number[]): number[] {\n  if (noiseShape == null) {\n    return x.shape.slice();\n  }\n  if (util.arraysEqual(x.shape, noiseShape)) {\n    return noiseShape;\n  }\n  if (x.shape.length === noiseShape.length) {\n    const newDimension: number[] = [];\n    for (let i = 0; i < x.shape.length; i++) {\n      if (noiseShape[i] == null && x.shape[i] != null) {\n        newDimension.push(x.shape[i]);\n      } else {\n        newDimension.push(noiseShape[i]);\n      }\n    }\n    return newDimension;\n  }\n\n  return noiseShape;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {add} from './add';\nimport {div} from './div';\nimport {getNoiseShape} from './dropout_util';\nimport {floor} from './floor';\nimport {mul} from './mul';\nimport {op} from './operation';\nimport {randomUniform} from './random_uniform';\n\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n *   of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(\n    x: Tensor|TensorLike, rate: number, noiseShape?: number[],\n    seed?: number|string): Tensor {\n  const $x = convertToTensor(x, 'x', 'dropout');\n\n  util.assert(\n      $x.dtype === 'float32',\n      () => `x has to be a floating point tensor since it's going to be ` +\n          `scaled, but got a ${$x.dtype} tensor instead.`);\n  util.assert(\n      rate >= 0 && rate < 1,\n      () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n\n  if (rate === 0) {\n    return x instanceof Tensor ? $x.clone() : $x;\n  }\n\n  const $noiseShape = getNoiseShape($x, noiseShape);\n  const keepProb = 1 - rate;\n  const multiplier = div(\n      floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)),\n      keepProb);\n\n  return mul($x, multiplier);\n}\n\nexport const dropout = op({dropout_});\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D} from '../tensor';\nimport {tensor1d} from './tensor1d';\n\nexport function enclosingPowerOfTwo(value: number) {\n  // Return 2**N for integer N such that 2**N >= value.\n  return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\n\nexport function cosineWindow(\n    windowLength: number, a: number, b: number): Tensor1D {\n  const even = 1 - windowLength % 2;\n  const newValues = new Float32Array(windowLength);\n  for (let i = 0; i < windowLength; ++i) {\n    const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n    newValues[i] = a - b * Math.cos(cosArg);\n  }\n  return tensor1d(newValues, 'float32');\n}\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, assertShapesMatch, getTypedArrayFromDType} from '../util';\nimport {tensor} from './tensor';\n\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_<T extends Tensor, U extends Tensor>(\n    predictions: T|TensorLike, targets: U|TensorLike, k = 1): Promise<U> {\n  const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n  const $targets = convertToTensor(targets, 'targets', 'inTopK');\n\n  assert(\n      $predictions.rank > 1,\n      () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n          `but got ${$predictions.rank}`);\n  assert(\n      $predictions.rank - 1 === $targets.rank,\n      () => `predictions rank should be 1 larger than ` +\n          `targets rank, but got predictions rank ` +\n          `${$predictions.rank} and targets rank ${$targets.rank}`);\n  assertShapesMatch(\n      $predictions.shape.slice(0, $predictions.shape.length - 1),\n      $targets.shape,\n      `predictions's shape should be align with the targets' shape, ` +\n          'except the last dimension.');\n  const lastDim = $predictions.shape[$predictions.shape.length - 1];\n  assert(\n      k > 0 && k <= lastDim,\n      () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n          `dimension (${lastDim}), but got ${k}`);\n\n  const predictionsVals = await $predictions.data();\n  const targetsVals = await $targets.data();\n\n  // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n  // and look up topK along lastDim.\n  const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n  const precision = getTypedArrayFromDType('bool', batch);\n\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = predictionsVals.subarray(offset, offset + size);\n    const valAndInd: Array<{value: number, index: number}> = [];\n    for (let i = 0; i < vals.length; i++) {\n      valAndInd.push({value: vals[i], index: i});\n    }\n    valAndInd.sort((a, b) => b.value - a.value);\n\n    precision[b] = 0;\n    for (let i = 0; i < k; i++) {\n      if (valAndInd[i].index === targetsVals[b]) {\n        precision[b] = 1;\n        break;\n      }\n    }\n  }\n\n  if (predictions !== $predictions) {\n    $predictions.dispose();\n  }\n  if (targets !== $targets) {\n    $targets.dispose();\n  }\n\n  // Output precision has the same shape as targets.\n  return tensor(precision, $targets.shape, 'bool') as U;\n}\n\nexport const inTopKAsync = inTopKAsync_;\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Conv2DBackpropFilter, Conv2DBackpropFilterAttrs, Conv2DBackpropFilterInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n */\nfunction conv2DBackpropFilter_<T extends Tensor3D|Tensor4D>(\n    x: T, dy: T, filterShape: [number, number, number, number],\n    strides: [number, number]|number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n  let x4D = x as Tensor4D;\n  if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  }\n  let dy4D = dy as Tensor4D;\n  if (dy4D.rank === 3) {\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n          `${x4D.shape}.`);\n  util.assert(\n      dy4D.rank === 4,\n      () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n          `${dy4D.shape}.`);\n  util.assert(\n      filterShape.length === 4,\n      () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n          `${filterShape}.`);\n  const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n  const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n  util.assert(\n      inDepth === filterShape[2],\n      () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n          `match input depth in filter (${filterShape[2]}.`);\n  util.assert(\n      outDepth === filterShape[3],\n      () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n          `match output depth for filter (${filterShape[3]}).`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const inputs: Conv2DBackpropFilterInputs = {x: x4D, dy: dy4D};\n  const attrs: Conv2DBackpropFilterAttrs =\n      {strides, pad, dataFormat, dimRoundingMode, filterShape};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  return ENGINE.runKernel(\n             Conv2DBackpropFilter, inputs as {} as NamedTensorMap,\n             attrs as {} as NamedAttrMap) as Tensor4D;\n}\n\nexport const conv2DBackpropFilter = op({conv2DBackpropFilter_});\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\n\nimport * as broadcast_util from './broadcast_util';\nimport {elu} from './elu';\nimport {Activation} from './fused_types';\nimport {leakyRelu} from './leaky_relu';\nimport {mul} from './mul';\nimport {prelu} from './prelu';\nimport {relu} from './relu';\nimport {relu6} from './relu6';\nimport {reshape} from './reshape';\nimport {sigmoid} from './sigmoid';\nimport {step} from './step';\nimport {sum} from './sum';\n\n// Returns gradient for fused activation.\nexport function getFusedDyActivation(\n    dy: Tensor, y: Tensor, activation: Activation): Tensor {\n  if (activation == null || activation === 'linear') {\n    return dy;\n  }\n  if (activation === 'relu') {\n    return mul(dy, step(y));\n  }\n  throw new Error(\n      `Cannot compute gradient for fused activation ${activation}.`);\n}\n\n// Returns gradient for fused bias.\nexport function getFusedBiasGradient(\n    bias: Tensor, dyActivation: Tensor): Tensor {\n  let res = dyActivation;\n  const reduceAxes =\n      broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);\n  if (reduceAxes.length > 0) {\n    res = sum(res, reduceAxes);\n  }\n  return reshape(res, bias.shape);\n}\n\nexport function applyActivation(\n    x: Tensor, activation: Activation, preluActivationWeights?: Tensor,\n    leakyreluAlpha?: number): Tensor {\n  if (activation === 'linear') {\n    return x;\n  } else if (activation === 'relu') {\n    return relu(x);\n  } else if (activation === 'elu') {\n    return elu(x);\n  } else if (activation === 'relu6') {\n    return relu6(x);\n  } else if (activation === 'prelu') {\n    return prelu(x, preluActivationWeights);\n  } else if (activation === 'leakyrelu') {\n    return leakyRelu(x, leakyreluAlpha);\n  } else if (activation === 'sigmoid') {\n    return sigmoid(x);\n  }\n  throw new Error(`Unknown fused activation ${activation}.`);\n}\n\n// Whether we should call fused ops.\nexport const shouldFuse = (gradientDepth: number, activation: Activation) => {\n  const gradientMode = gradientDepth > 0;\n  return !gradientMode || activation === 'linear';\n};\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {customGrad} from '../../gradients';\nimport {FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../../tensor_types';\nimport {makeTypesMatch} from '../../tensor_util';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\nimport {add} from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport {conv2d as unfusedConv2d} from '../conv2d';\nimport {conv2DBackpropFilter} from '../conv2d_backprop_filter';\nimport {conv2DBackpropInput} from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport {Activation} from '../fused_types';\nimport {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`\n *     activation.\n */\nfunction fusedConv2d_<T extends Tensor3D|Tensor4D>({\n  x,\n  filter,\n  strides,\n  pad,\n  dataFormat = 'NHWC',\n  dilations = [1, 1],\n  dimRoundingMode,\n  bias,\n  activation = 'linear',\n  preluActivationWeights,\n  leakyreluAlpha\n}: {\n  x: T|TensorLike,\n  filter: Tensor4D|TensorLike,\n  strides: [number, number]|number,\n  pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n  dataFormat?: 'NHWC'|'NCHW',\n  dilations?: [number, number]|number,\n  dimRoundingMode?: 'floor'|'round'|'ceil',\n  bias?: Tensor|TensorLike,\n  activation?: Activation,\n  preluActivationWeights?: Tensor,\n  leakyreluAlpha?: number\n}): T {\n  activation = activation || 'linear';\n\n  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n    let result = unfusedConv2d(\n        x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add(result, bias);\n    }\n\n    return applyActivation(\n               result, activation, preluActivationWeights, leakyreluAlpha) as T;\n  }\n\n  const $x = convertToTensor(x, 'x', 'conv2d');\n  const $filter = convertToTensor(filter, 'filter', 'conv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n          `${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in fused conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n          `input depth for filter ${$filter.shape[2]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NHWC',\n      () => `Error in conv2d: got dataFormat of ${\n          dataFormat} but only NHWC is currently supported.`);\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n\n  let $bias: Tensor;\n  if (bias != null) {\n    $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n    [$bias] = makeTypesMatch($bias, $x);\n\n    broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n  }\n\n  let $preluActivationWeights: Tensor;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(\n        preluActivationWeights, 'prelu weights', 'fused conv2d');\n  }\n\n  const grad = (dy: Tensor4D, saved: Tensor[]) => {\n    const [$filter, x4D, y, $bias] =\n        saved as [Tensor4D, Tensor4D, Tensor4D, Tensor];\n\n    const dyActivation = getFusedDyActivation(dy, y, activation) as Tensor4D;\n\n    util.assert(\n        conv_util.tupleValuesAreOne(dilations),\n        () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n\n    const xDer =\n        conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n    const filterDer =\n        conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n    const der: Tensor[] = [xDer, filterDer];\n\n    if ($bias != null) {\n      const biasDer = getFusedBiasGradient($bias, dyActivation);\n      der.push(biasDer);\n    }\n    return der;\n  };\n\n  const inputs: FusedConv2DInputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n\n  const attrs: FusedConv2DAttrs = {\n    strides,\n    pad,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation,\n    leakyreluAlpha\n  };\n\n  // Depending on the the params passed in we will have different number of\n  // inputs and thus a a different number of elements in the gradient.\n  if (bias == null) {\n    const customOp =\n        customGrad((x4D: Tensor4D, filter: Tensor4D, save: GradSaveFunc) => {\n          let res: Tensor4D|Tensor3D =\n              // tslint:disable-next-line: no-unnecessary-type-assertion\n              ENGINE.runKernel(\n                  FusedConv2D, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap);\n\n          save([filter, x4D, res]);\n\n          if (reshapedTo4D) {\n            // tslint:disable-next-line: no-unnecessary-type-assertion\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as\n                Tensor3D;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n    return customOp(x4D, $filter) as T;\n  } else {\n    const customOpWithBias = customGrad(\n        (x4D: Tensor4D, filter: Tensor4D, bias: Tensor, save: GradSaveFunc) => {\n          let res: Tensor4D|Tensor3D = ENGINE.runKernel(\n              FusedConv2D, inputs as {} as NamedTensorMap,\n              attrs as {} as NamedAttrMap);\n\n          save([filter, x4D, res, bias]);\n\n          if (reshapedTo4D) {\n            // tslint:disable-next-line: no-unnecessary-type-assertion\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as\n                Tensor3D;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n\n    return customOpWithBias(x4D, $filter, $bias) as T;\n  }\n}\nexport const conv2d = op({fusedConv2d_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropFilterAttrs, DepthwiseConv2dNativeBackpropFilterInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\nimport {ExplicitPadding} from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\nfunction depthwiseConv2dNativeBackpropFilter_<T extends Tensor3D|Tensor4D>(\n    x: T, dy: T, filterShape: [number, number, number, number],\n    strides: [number, number]|number,\n    pad: 'valid'|'same'|number|ExplicitPadding,\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n  let x4D = x as Tensor4D;\n  if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  }\n  let dy4D = dy as Tensor4D;\n  if (dy4D.rank === 3) {\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n\n  const inputs: DepthwiseConv2dNativeBackpropFilterInputs = {x: x4D, dy: dy4D};\n  const attrs: DepthwiseConv2dNativeBackpropFilterAttrs =\n      {strides, pad, dimRoundingMode, dilations, filterShape};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  return ENGINE.runKernel(\n             DepthwiseConv2dNativeBackpropFilter,\n             inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap) as\n      Tensor4D;\n}\n\nexport const depthwiseConv2dNativeBackpropFilter =\n    op({depthwiseConv2dNativeBackpropFilter_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {DepthwiseConv2dNativeBackpropInput, DepthwiseConv2dNativeBackpropInputAttrs, DepthwiseConv2dNativeBackpropInputInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\nimport {ExplicitPadding} from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\nfunction depthwiseConv2dNativeBackpropInput_<T extends Tensor3D|Tensor4D>(\n    xShape: [number, number, number, number], dy: T, filter: Tensor4D,\n    strides: [number, number]|number,\n    pad: 'valid'|'same'|number|ExplicitPadding,\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  let dy4D = dy as Tensor4D;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n\n  const inputs: DepthwiseConv2dNativeBackpropInputInputs = {dy: dy4D, filter};\n  const attrs: DepthwiseConv2dNativeBackpropInputAttrs =\n      {strides, pad, dimRoundingMode, dilations, inputShape: xShape};\n\n  const res =\n      // tslint:disable-next-line: no-unnecessary-type-assertion\n      ENGINE.runKernel(\n          DepthwiseConv2dNativeBackpropInput, inputs as {} as NamedTensorMap,\n          attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res;\n}\n\nexport const depthwiseConv2dNativeBackpropInput =\n    op({depthwiseConv2dNativeBackpropInput_});\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {customGrad} from '../../gradients';\nimport {FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../../tensor_types';\nimport {makeTypesMatch} from '../../tensor_util';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {add} from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport {depthwiseConv2d as unfusedDepthwiseConv2d} from '../depthwise_conv2d';\nimport {depthwiseConv2dNativeBackpropFilter} from '../depthwise_conv2d_native_backprop_filter';\nimport {depthwiseConv2dNativeBackpropInput} from '../depthwise_conv2d_native_backprop_input';\nimport {Activation} from '../fused_types';\nimport {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`\n *     activation.\n */\nfunction fusedDepthwiseConv2d_<T extends Tensor3D|Tensor4D>({\n  x,\n  filter,\n  strides,\n  pad,\n  dataFormat = 'NHWC',\n  dilations = [1, 1],\n  dimRoundingMode,\n  bias,\n  activation = 'linear',\n  preluActivationWeights,\n  leakyreluAlpha\n}: {\n  x: T|TensorLike,\n  filter: Tensor4D|TensorLike,\n  strides: [number, number]|number,\n  pad: 'valid'|'same'|number,\n  dataFormat?: 'NHWC'|'NCHW',\n  dilations?: [number, number]|number,\n  dimRoundingMode?: 'floor'|'round'|'ceil',\n  bias?: Tensor|TensorLike,\n  activation?: Activation,\n  preluActivationWeights?: Tensor,\n  leakyreluAlpha?: number\n}): T {\n  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n    let result = unfusedDepthwiseConv2d(\n        x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add(result, bias);\n    }\n\n    return applyActivation(\n               result, activation, preluActivationWeights, leakyreluAlpha) as T;\n  }\n\n  const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n  const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n          `but got rank ${$filter.rank}.`);\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      () => `Error in fused depthwiseConv2d: number of input channels ` +\n          `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n          `filter ${$filter.shape[2]}.`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () =>\n          'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n          `be 1. Got strides ${strides} and dilations '${dilations}'`);\n\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in fused depthwiseConv2d: pad must be an integer when ` +\n            `using dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode,\n      true /* depthwise */);\n\n  let $bias: Tensor;\n  if (bias != null) {\n    $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n    [$bias] = makeTypesMatch($bias, $x);\n\n    broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n  }\n\n  let $preluActivationWeights: Tensor;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(\n        preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n  }\n\n  const grad = (dy: Tensor4D, saved: Tensor[]) => {\n    util.assert(\n        conv_util.tupleValuesAreOne(dilations),\n        () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n    const [$filter, x4D, y, bias] = saved;\n\n    const dyActivation = getFusedDyActivation(dy, y, activation) as Tensor4D;\n\n    const xDer = depthwiseConv2dNativeBackpropInput(\n        (x4D as Tensor4D).shape, dyActivation, $filter as Tensor4D, strides,\n        pad, dilations, dimRoundingMode);\n    const filterDer = depthwiseConv2dNativeBackpropFilter(\n        x4D as Tensor4D, dyActivation, ($filter as Tensor4D).shape, strides,\n        pad, dilations, dimRoundingMode);\n\n    if (bias != null) {\n      const biasDer = getFusedBiasGradient($bias, dyActivation);\n      return [xDer, filterDer, biasDer];\n    }\n    return [xDer, filterDer];\n  };\n\n  const inputs: FusedDepthwiseConv2DInputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs: FusedDepthwiseConv2DAttrs = {\n    strides,\n    pad,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation,\n    leakyreluAlpha\n  };\n\n  // Depending on the the params passed in we will have different number of\n  // inputs and thus a a different number of elements in the gradient.\n  if (bias == null) {\n    const customOp =\n        customGrad((x4D: Tensor4D, filter: Tensor4D, save: GradSaveFunc) => {\n          // tslint:disable-next-line: no-unnecessary-type-assertion\n          let res: Tensor4D|Tensor3D = ENGINE.runKernel(\n              FusedDepthwiseConv2D, inputs as {} as NamedTensorMap,\n              attrs as {} as NamedAttrMap);\n\n          save([filter, x4D, res]);\n\n          if (reshapedTo4D) {\n            // tslint:disable-next-line: no-unnecessary-type-assertion\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as\n                Tensor3D;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n    return customOp(x4D, $filter) as T;\n  } else {\n    const customOpWithBias = customGrad(\n        (x4D: Tensor4D, filter: Tensor4D, bias: Tensor, save: GradSaveFunc) => {\n          // tslint:disable-next-line: no-unnecessary-type-assertion\n          let res: Tensor4D|Tensor3D = ENGINE.runKernel(\n              FusedDepthwiseConv2D, inputs as {} as NamedTensorMap,\n              attrs as {} as NamedAttrMap);\n\n          save([filter, x4D, res, bias]);\n\n          if (reshapedTo4D) {\n            // tslint:disable-next-line: no-unnecessary-type-assertion\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as\n                Tensor3D;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n\n    return customOpWithBias(x4D, $filter, $bias) as T;\n  }\n}\nexport const depthwiseConv2d = op({fusedDepthwiseConv2d_});\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {customGrad} from '../../gradients';\nimport {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor3D} from '../../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../../tensor_types';\nimport {makeTypesMatch} from '../../tensor_util';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {add} from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport {Activation} from '../fused_types';\nimport {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';\nimport {matMul as unfusedMatMul} from '../mat_mul';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n * - `leakyreluAlpha` Alpha of leakyrelu.\n */\nfunction fusedMatMul_<T extends Tensor>({\n  a,\n  b,\n  transposeA = false,\n  transposeB = false,\n  bias,\n  activation = 'linear',\n  preluActivationWeights,\n  leakyreluAlpha,\n}: {\n  a: T|TensorLike,\n  b: T|TensorLike,\n  transposeA?: boolean,\n  transposeB?: boolean,\n  bias?: Tensor|TensorLike,\n  activation?: Activation,\n  preluActivationWeights?: Tensor\n  leakyreluAlpha?: number\n}): T {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n      let result = unfusedMatMul(a, b, transposeA, transposeB);\n      if (bias != null) {\n        result = add(result, bias);\n      }\n\n      return applyActivation(\n                 result, activation, preluActivationWeights, leakyreluAlpha) as\n          T;\n    }\n\n    let $a = convertToTensor(a, 'a', 'fused matMul');\n    let $b = convertToTensor(b, 'b', 'fused matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n\n    const innerShapeA =\n        transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB =\n        transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n\n    const outerShapeA =\n        transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB =\n        transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n\n    util.assert(\n        $a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank,\n        () => `Error in fused matMul: inputs must have the same rank of at ` +\n            `least 2, got ranks ${$a.rank} and ${$b.rank}.`);\n\n    util.assert(\n        util.arraysEqual(outerDimsA, outerDimsB),\n        () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (` +\n            `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n            `${$b.shape} must match.`);\n\n    util.assert(\n        innerShapeA === innerShapeB,\n        () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n            `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n            `${$b.shape} and transposeA=${transposeA}` +\n            ` and transposeB=${transposeB} must match.`);\n\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n\n    const a3D: Tensor3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D: Tensor3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n\n    let $bias: Tensor;\n    if (bias != null) {\n      $bias = convertToTensor(bias, 'bias', 'fused matMul');\n      [$bias] = makeTypesMatch($bias, $a);\n\n      broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n    }\n\n    let $preluActivationWeights: Tensor;\n    if (preluActivationWeights != null) {\n      $preluActivationWeights = convertToTensor(\n          preluActivationWeights, 'prelu weights', 'fused matMul');\n    }\n\n    const grad = (dy: Tensor3D, saved: Tensor[]) => {\n      const [a3D, b3D, y, $bias] = saved;\n      // we reshape dy because the result of the forward is not\n      // necessarily going to be a 3d tensor due to a reshape done at the end of\n      // the customOp.\n      const dyActivation =\n          getFusedDyActivation(reshape(dy, y.shape), y, activation);\n      let aDer: Tensor;\n      let bDer: Tensor;\n\n      if (!transposeA && !transposeB) {\n        aDer = unfusedMatMul(dyActivation, b3D, false, true);\n        bDer = unfusedMatMul(a3D, dyActivation, true, false);\n      } else if (!transposeA && transposeB) {\n        aDer = unfusedMatMul(dyActivation, b3D, false, false);\n        bDer = unfusedMatMul(dyActivation, a3D, true, false);\n      } else if (transposeA && !transposeB) {\n        aDer = unfusedMatMul(b3D, dyActivation, false, true);\n        bDer = unfusedMatMul(a3D, dyActivation, false, false);\n      } else {\n        aDer = unfusedMatMul(b3D, dyActivation, true, true);\n        bDer = unfusedMatMul(dyActivation, a3D, true, true);\n      }\n\n      if (bias != null) {\n        const biasDer = getFusedBiasGradient($bias, dyActivation);\n        return [aDer, bDer, biasDer];\n      } else {\n        return [aDer, bDer];\n      }\n    };\n\n    const inputs: _FusedMatMulInputs = {\n      a: a3D,\n      b: b3D,\n      bias: $bias,\n      preluActivationWeights: $preluActivationWeights\n    };\n    const attrs: _FusedMatMulAttrs =\n        {transposeA, transposeB, activation, leakyreluAlpha};\n\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n      const customOp =\n          customGrad((a3D: Tensor3D, b3D: Tensor3D, save: GradSaveFunc) => {\n            const res =\n                // tslint:disable-next-line: no-unnecessary-type-assertion\n                ENGINE.runKernel(\n                    _FusedMatMul, inputs as {} as NamedTensorMap,\n                    attrs as {} as NamedAttrMap) as T;\n\n            save([a3D, b3D, res]);\n\n            return {value: reshape(res, outShape), gradFunc: grad};\n          });\n      return customOp(a3D, b3D) as T;\n    } else {\n      const customOpWithBias = customGrad(\n          (a3D: Tensor3D, b3D: Tensor3D, $bias: Tensor, save: GradSaveFunc) => {\n            const res =\n                // tslint:disable-next-line: no-unnecessary-type-assertion\n                ENGINE.runKernel(\n                    _FusedMatMul, inputs as {} as NamedTensorMap,\n                    attrs as {} as NamedAttrMap) as T;\n\n            save([a3D, b3D, res, $bias]);\n\n            return {value: reshape(res, outShape), gradFunc: grad};\n          });\n\n      return customOpWithBias(a3D, b3D, $bias) as T;\n    }\n  }\n\n  export const matMul = op({fusedMatMul_});\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {conv2d} from './fused/conv2d';\nimport {depthwiseConv2d} from './fused/depthwise_conv2d';\nimport {matMul} from './fused/mat_mul';\nimport {Activation} from './fused_types';\n\nexport {Activation, conv2d, depthwiseConv2d, matMul};\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D} from '../../tensor';\nimport {op} from '../operation';\nimport {cosineWindow} from '../signal_ops_util';\n\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength: number): Tensor1D {\n  return cosineWindow(windowLength, 0.54, 0.46);\n}\nexport const hammingWindow = op({hammingWindow_});\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D} from '../../tensor';\nimport {op} from '../operation';\nimport {cosineWindow} from '../signal_ops_util';\n\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength: number): Tensor1D {\n  return cosineWindow(windowLength, 0.5, 0.5);\n}\n\nexport const hannWindow = op({hannWindow_});\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor1D} from '../../tensor';\nimport {concat} from '../concat';\nimport {fill} from '../fill';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\nimport {slice} from '../slice';\nimport {tensor2d} from '../tensor2d';\n\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n *     not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(\n    signal: Tensor1D, frameLength: number, frameStep: number, padEnd = false,\n    padValue = 0): Tensor {\n  let start = 0;\n  const output: Tensor[] = [];\n  while (start + frameLength <= signal.size) {\n    output.push(slice(signal, start, frameLength));\n    start += frameStep;\n  }\n\n  if (padEnd) {\n    while (start < signal.size) {\n      const padLen = (start + frameLength) - signal.size;\n      const pad = concat([\n        slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n      ]);\n      output.push(pad);\n      start += frameStep;\n    }\n  }\n\n  if (output.length === 0) {\n    return tensor2d([], [0, frameLength]);\n  }\n\n  return reshape(concat(output), [output.length, frameLength]);\n}\nexport const frame = op({frame_});\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor1D} from '../../tensor';\nimport {mul} from '../mul';\nimport {op} from '../operation';\nimport {enclosingPowerOfTwo} from '../signal_ops_util';\nimport {rfft} from '../spectral/rfft';\n\nimport {frame} from './frame';\nimport {hannWindow} from './hann_window';\n\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(\n    signal: Tensor1D, frameLength: number, frameStep: number,\n    fftLength?: number,\n    windowFn: (length: number) => Tensor1D = hannWindow): Tensor {\n  if (fftLength == null) {\n    fftLength = enclosingPowerOfTwo(frameLength);\n  }\n  const framedSignal = frame(signal, frameLength, frameStep);\n  const windowedSignal = mul(framedSignal, windowFn(frameLength));\n  return rfft(windowedSignal, fftLength);\n}\nexport const stft = op({stft_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor1D, Tensor2D, Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {op} from '../operation';\n\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by cropSize.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(\n    image: Tensor4D|TensorLike,\n    boxes: Tensor2D|TensorLike,\n    boxInd: Tensor1D|TensorLike,\n    cropSize: [number, number],\n    method: 'bilinear'|'nearest' = 'bilinear',\n    extrapolationValue = 0,\n    ): Tensor4D {\n  const $image = convertToTensor(image, 'image', 'cropAndResize');\n  const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n  const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n\n  const numBoxes = $boxes.shape[0];\n\n  util.assert(\n      $image.rank === 4,\n      () => 'Error in cropAndResize: image must be rank 4,' +\n          `but got rank ${$image.rank}.`);\n  util.assert(\n      $boxes.rank === 2 && $boxes.shape[1] === 4,\n      () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      $boxInd.rank === 1 && $boxInd.shape[0] === numBoxes,\n      () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      cropSize.length === 2,\n      () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n          `length ${cropSize.length}.`);\n  util.assert(\n      cropSize[0] >= 1 && cropSize[1] >= 1,\n      () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n  util.assert(\n      method === 'bilinear' || method === 'nearest',\n      () => `method must be bilinear or nearest, but was ${method}`);\n\n  const inputs:\n      CropAndResizeInputs = {image: $image, boxes: $boxes, boxInd: $boxInd};\n  const attrs: CropAndResizeAttrs = {method, extrapolationValue, cropSize};\n  const res = ENGINE.runKernel(\n      CropAndResize, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n  return res as Tensor4D;\n}\n\nexport const cropAndResize = op({cropAndResize_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {FlipLeftRight, FlipLeftRightInputs} from '../../kernel_names';\nimport {Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\nimport {op} from '../operation';\n\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image: Tensor4D|TensorLike): Tensor4D {\n  const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n\n  util.assert(\n      $image.rank === 4,\n      () => 'Error in flipLeftRight: image must be rank 4,' +\n          `but got rank ${$image.rank}.`);\n\n  const inputs: FlipLeftRightInputs = {image: $image};\n  const res =\n      ENGINE.runKernel(FlipLeftRight, inputs as {} as NamedTensorMap, {});\n  return res as Tensor4D;\n}\n\nexport const flipLeftRight = op({flipLeftRight_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {RotateWithOffset, RotateWithOffsetAttrs, RotateWithOffsetInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {op} from '../operation';\n\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n *     after rotation. Can be either a single grayscale value (0-255), or an\n *     array of three numbers `[red, green, blue]` specifying the red, green,\n *     and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n *     the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(\n    image: Tensor4D|TensorLike, radians: number,\n    fillValue: number|[number, number, number] = 0,\n    center: number|[number, number] = 0.5): Tensor4D {\n  const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n\n  util.assert(\n      $image.rank === 4,\n      () => 'Error in rotateWithOffset: image must be rank 4,' +\n          `but got rank ${$image.rank}.`);\n\n  const inputs: RotateWithOffsetInputs = {image: $image};\n  const attrs: RotateWithOffsetAttrs = {radians, fillValue, center};\n  const res = ENGINE.runKernel(\n      RotateWithOffset, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n  return res as Tensor4D;\n}\n\nexport const rotateWithOffset = op({rotateWithOffset_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport * as util from '../util';\n\nfunction nonMaxSuppSanityCheck(\n    boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number, softNmsSigma?: number): {\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n  softNmsSigma: number\n} {\n  if (iouThreshold == null) {\n    iouThreshold = 0.5;\n  }\n  if (scoreThreshold == null) {\n    scoreThreshold = Number.NEGATIVE_INFINITY;\n  }\n  if (softNmsSigma == null) {\n    softNmsSigma = 0.0;\n  }\n\n  const numBoxes = boxes.shape[0];\n  maxOutputSize = Math.min(maxOutputSize, numBoxes);\n\n  util.assert(\n      0 <= iouThreshold && iouThreshold <= 1,\n      () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n  util.assert(\n      boxes.rank === 2,\n      () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n  util.assert(\n      boxes.shape[1] === 4,\n      () =>\n          `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n  util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n  util.assert(\n      scores.shape[0] === numBoxes,\n      () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n          `but was ${scores.shape[0]}`);\n  util.assert(\n      0 <= softNmsSigma && softNmsSigma <= 1,\n      () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n  return {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};\n}\n\nexport {nonMaxSuppSanityCheck};\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {NonMaxSuppressionV3} from '../../kernel_names';\nimport {Tensor1D, Tensor2D} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\n\nimport {nonMaxSuppSanityCheck} from '../nonmax_util';\nimport {op} from '../operation';\n\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppression_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY): Tensor1D {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n\n  const inputs = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n\n  const attrs = {maxOutputSize, iouThreshold, scoreThreshold};\n  return ENGINE.runKernel(\n      NonMaxSuppressionV3, {boxes: $boxes, scores: $scores}, attrs);\n}\n\nexport const nonMaxSuppression = op({nonMaxSuppression_});\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert<T>(\n    arr: T[], element: T, comparator?: (a: T, b: T) => number) {\n  const index = binarySearch(arr, element, comparator);\n  const insertionPoint = index < 0 ? -(index + 1) : index;\n  arr.splice(insertionPoint, 0, element);\n}\n\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n *    zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n *    point where the target should be inserted, in the form of\n *    (-insertionPoint - 1).\n */\nexport function binarySearch<T>(\n    arr: T[], target: T, comparator?: (a: T, b: T) => number) {\n  return binarySearch_(arr, target, comparator || defaultComparator);\n}\n\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n *     argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator<T>(a: T, b: T): number {\n  return a > b ? 1 : a < b ? -1 : 0;\n}\n\nfunction binarySearch_<T>(\n    arr: T[], target: T, comparator: (a: T, b: T) => number) {\n  let left = 0;\n  let right = arr.length;\n  let middle = 0;\n  let found = false;\n  while (left < right) {\n    middle = left + ((right - left) >>> 1);\n    const compareResult = comparator(target, arr[middle]);\n    if (compareResult > 0) {\n      left = middle + 1;\n    } else {\n      right = middle;\n      // If compareResult is 0, the value is found. We record it is found,\n      // and then keep looking because there may be duplicate.\n      found = !compareResult;\n    }\n  }\n\n  return found ? left : -left - 1;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TypedArray} from '../types';\nimport {binaryInsert} from './non_max_suppression_util';\n\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\ninterface Candidate {\n  score: number;\n  boxIndex: number;\n  suppressBeginIndex: number;\n}\n\ninterface NonMaxSuppressionResult {\n  selectedIndices: number[];\n  selectedScores?: number[];\n  validOutputs?: number;\n}\n\nexport function nonMaxSuppressionV3Impl(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number): NonMaxSuppressionResult {\n  return nonMaxSuppressionImpl_(\n      boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,\n      0 /* softNmsSigma */);\n}\n\nexport function nonMaxSuppressionV4Impl(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number,\n    padToMaxOutputSize: boolean): NonMaxSuppressionResult {\n  return nonMaxSuppressionImpl_(\n      boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,\n      0 /* softNmsSigma */, false /* returnScoresTensor */,\n      padToMaxOutputSize /* padToMaxOutputSize */, true\n      /* returnValidOutputs */);\n}\n\nexport function nonMaxSuppressionV5Impl(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number,\n    softNmsSigma: number): NonMaxSuppressionResult {\n  return nonMaxSuppressionImpl_(\n      boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma,\n      true /* returnScoresTensor */);\n}\n\nfunction nonMaxSuppressionImpl_(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number, softNmsSigma: number,\n    returnScoresTensor = false, padToMaxOutputSize = false,\n    returnValidOutputs = false): NonMaxSuppressionResult {\n  // The list is sorted in ascending order, so that we can always pop the\n  // candidate with the largest score in O(1) time.\n  const candidates = [];\n\n  for (let i = 0; i < scores.length; i++) {\n    if (scores[i] > scoreThreshold) {\n      candidates.push({score: scores[i], boxIndex: i, suppressBeginIndex: 0});\n    }\n  }\n\n  candidates.sort(ascendingComparator);\n\n  // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n  // before.\n  const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n\n  const selectedIndices: number[] = [];\n  const selectedScores: number[] = [];\n\n  while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n    const candidate = candidates.pop();\n    const {score: originalScore, boxIndex, suppressBeginIndex} = candidate;\n\n    if (originalScore < scoreThreshold) {\n      break;\n    }\n\n    // Overlapping boxes are likely to have similar scores, therefore we\n    // iterate through the previously selected boxes backwards in order to\n    // see if candidate's score should be suppressed. We use\n    // suppressBeginIndex to track and ensure a candidate can be suppressed\n    // by a selected box no more than once. Also, if the overlap exceeds\n    // iouThreshold, we simply ignore the candidate.\n    let ignoreCandidate = false;\n    for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n      const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n\n      if (iou >= iouThreshold) {\n        ignoreCandidate = true;\n        break;\n      }\n\n      candidate.score =\n          candidate.score * suppressWeight(iouThreshold, scale, iou);\n\n      if (candidate.score <= scoreThreshold) {\n        break;\n      }\n    }\n\n    // At this point, if `candidate.score` has not dropped below\n    // `scoreThreshold`, then we know that we went through all of the\n    // previous selections and can safely update `suppressBeginIndex` to the\n    // end of the selected array. Then we can re-insert the candidate with\n    // the updated score and suppressBeginIndex back in the candidate list.\n    // If on the other hand, `candidate.score` has dropped below the score\n    // threshold, we will not add it back to the candidates list.\n    candidate.suppressBeginIndex = selectedIndices.length;\n\n    if (!ignoreCandidate) {\n      // Candidate has passed all the tests, and is not suppressed, so\n      // select the candidate.\n      if (candidate.score === originalScore) {\n        selectedIndices.push(boxIndex);\n        selectedScores.push(candidate.score);\n      } else if (candidate.score > scoreThreshold) {\n        // Candidate's score is suppressed but is still high enough to be\n        // considered, so add back to the candidates list.\n        binaryInsert(candidates, candidate, ascendingComparator);\n      }\n    }\n  }\n\n  // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n  const validOutputs = selectedIndices.length;\n  const elemsToPad = maxOutputSize - validOutputs;\n\n  if (padToMaxOutputSize && elemsToPad > 0) {\n    selectedIndices.push(...new Array(elemsToPad).fill(0));\n    selectedScores.push(...new Array(elemsToPad).fill(0.0));\n  }\n\n  const result: NonMaxSuppressionResult = {selectedIndices};\n\n  if (returnScoresTensor) {\n    result['selectedScores'] = selectedScores;\n  }\n\n  if (returnValidOutputs) {\n    result['validOutputs'] = validOutputs;\n  }\n\n  return result;\n}\n\nfunction intersectionOverUnion(boxes: TypedArray, i: number, j: number) {\n  const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n  const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n  const yminI = Math.min(iCoord[0], iCoord[2]);\n  const xminI = Math.min(iCoord[1], iCoord[3]);\n  const ymaxI = Math.max(iCoord[0], iCoord[2]);\n  const xmaxI = Math.max(iCoord[1], iCoord[3]);\n  const yminJ = Math.min(jCoord[0], jCoord[2]);\n  const xminJ = Math.min(jCoord[1], jCoord[3]);\n  const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n  const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold: number, scale: number, iou: number) {\n  const weight = Math.exp(scale * iou * iou);\n  return iou <= iouThreshold ? weight : 0.0;\n}\n\nfunction ascendingComparator(c1: Candidate, c2: Candidate) {\n  // For objects with same scores, we make the object with the larger index go\n  // first. In an array that pops from the end, this means that the object with\n  // the smaller index will be popped first. This ensures the same output as\n  // the TensorFlow python version.\n  return (c1.score - c2.score) ||\n      ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {nonMaxSuppressionV3Impl} from '../../backends/non_max_suppression_impl';\nimport {Tensor1D, Tensor2D} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {nonMaxSuppSanityCheck} from '../nonmax_util';\nimport {tensor1d} from '../tensor1d';\n\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY): Promise<Tensor1D> {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n\n  const inputs = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n\n  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n  const boxesVals = boxesAndScores[0];\n  const scoresVals = boxesAndScores[1];\n\n  // We call a cpu based impl directly with the typedarray data  here rather\n  // than a kernel because all kernels are synchronous (and thus cannot await\n  // .data()).\n  const {selectedIndices} = nonMaxSuppressionV3Impl(\n      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n\n  return tensor1d(selectedIndices, 'int32');\n}\n\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {NonMaxSuppressionV5, NonMaxSuppressionV5Attrs, NonMaxSuppressionV5Inputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor1D, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\n\nimport {nonMaxSuppSanityCheck} from '../nonmax_util';\nimport {op} from '../operation';\n\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY,\n    softNmsSigma = 0.0): NamedTensorMap {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n\n  const params = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,\n      softNmsSigma);\n  maxOutputSize = params.maxOutputSize;\n  iouThreshold = params.iouThreshold;\n  scoreThreshold = params.scoreThreshold;\n  softNmsSigma = params.softNmsSigma;\n\n  const inputs: NonMaxSuppressionV5Inputs = {boxes: $boxes, scores: $scores};\n  const attrs: NonMaxSuppressionV5Attrs =\n      {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const result = ENGINE.runKernel(\n                     NonMaxSuppressionV5, inputs as {} as NamedTensorMap,\n                     attrs as {} as NamedAttrMap) as Tensor[];\n\n  return {selectedIndices: result[0], selectedScores: result[1]};\n}\n\nexport const nonMaxSuppressionWithScore = op({nonMaxSuppressionWithScore_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {nonMaxSuppressionV5Impl} from '../../backends/non_max_suppression_impl';\nimport {Tensor1D, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {nonMaxSuppSanityCheck} from '../nonmax_util';\nimport {tensor1d} from '../tensor1d';\n\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY,\n    softNmsSigma = 0.0): Promise<NamedTensorMap> {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n\n  const params = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,\n      softNmsSigma);\n  maxOutputSize = params.maxOutputSize;\n  iouThreshold = params.iouThreshold;\n  scoreThreshold = params.scoreThreshold;\n  softNmsSigma = params.softNmsSigma;\n\n  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n  const boxesVals = boxesAndScores[0];\n  const scoresVals = boxesAndScores[1];\n\n  // We call a cpu based impl directly with the typedarray data  here rather\n  // than a kernel because all kernels are synchronous (and thus cannot await\n  // .data()).\n  const {selectedIndices, selectedScores} = nonMaxSuppressionV5Impl(\n      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold,\n      softNmsSigma);\n\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n\n  return {\n    selectedIndices: tensor1d(selectedIndices, 'int32'),\n    selectedScores: tensor1d(selectedScores)\n  };\n}\n\nexport const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {NonMaxSuppressionV4, NonMaxSuppressionV4Attrs, NonMaxSuppressionV4Inputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor1D, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\n\nimport {nonMaxSuppSanityCheck} from '../nonmax_util';\nimport {op} from '../operation';\n\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY,\n    padToMaxOutputSize = false): NamedTensorMap {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n\n  const params = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,\n      null /* softNmsSigma */);\n  const $maxOutputSize = params.maxOutputSize;\n  const $iouThreshold = params.iouThreshold;\n  const $scoreThreshold = params.scoreThreshold;\n\n  const inputs: NonMaxSuppressionV4Inputs = {boxes: $boxes, scores: $scores};\n  const attrs: NonMaxSuppressionV4Attrs = {\n    maxOutputSize: $maxOutputSize,\n    iouThreshold: $iouThreshold,\n    scoreThreshold: $scoreThreshold,\n    padToMaxOutputSize\n  };\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const result = ENGINE.runKernel(\n                     NonMaxSuppressionV4, inputs as {} as NamedTensorMap,\n                     attrs as {} as NamedAttrMap) as Tensor[];\n\n  return {selectedIndices: result[0], validOutputs: result[1]};\n}\n\nexport const nonMaxSuppressionPadded = op({nonMaxSuppressionPadded_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {nonMaxSuppressionV4Impl} from '../../backends/non_max_suppression_impl';\nimport {Tensor1D, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {nonMaxSuppSanityCheck} from '../nonmax_util';\nimport {scalar} from '../scalar';\nimport {tensor1d} from '../tensor1d';\n\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY,\n    padToMaxOutputSize = false): Promise<NamedTensorMap> {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n\n  const params = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,\n      null /* softNmsSigma */);\n  const $maxOutputSize = params.maxOutputSize;\n  const $iouThreshold = params.iouThreshold;\n  const $scoreThreshold = params.scoreThreshold;\n\n  const [boxesVals, scoresVals] =\n      await Promise.all([$boxes.data(), $scores.data()]);\n\n  // We call a cpu based impl directly with the typedarray data here rather\n  // than a kernel because all kernels are synchronous (and thus cannot await\n  // .data()).\n  const {selectedIndices, validOutputs} = nonMaxSuppressionV4Impl(\n      boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold,\n      padToMaxOutputSize);\n\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n\n  return {\n    selectedIndices: tensor1d(selectedIndices, 'int32'),\n    validOutputs: scalar(validOutputs, 'int32')\n  };\n}\n\nexport const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {ResizeBilinear, ResizeBilinearAttrs, ResizeBilinearInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Bilinear resize a single 3D image or a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to `false`. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n * @param halfPixelCenters Defaults to `false`. Whether to assume pixel centers\n *     are at 0.5, which would make the floating point coordinates of the top\n *     left pixel 0.5, 0.5.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_<T extends Tensor3D|Tensor4D>(\n    images: T|TensorLike, size: [number, number], alignCorners = false,\n    halfPixelCenters = false): T {\n  const $images = convertToTensor(images, 'images', 'resizeBilinear');\n\n  util.assert(\n      $images.rank === 3 || $images.rank === 4,\n      () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n          `rank ${$images.rank}.`);\n  util.assert(\n      size.length === 2,\n      () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n          `${size}.`);\n  util.assert(\n      halfPixelCenters === false || alignCorners === false,\n      () => `Error in resizeBilinear: If halfPixelCenters is true, ` +\n          `alignCorners must be false.`);\n\n  let batchImages = $images as Tensor4D;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages = reshape(\n        $images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n  }\n\n  const [] = size;\n\n  const inputs: ResizeBilinearInputs = {images: batchImages};\n  const attrs: ResizeBilinearAttrs = {alignCorners, halfPixelCenters, size};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  ResizeBilinear, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res;\n}\n\nexport const resizeBilinear = op({resizeBilinear_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {ResizeNearestNeighbor, ResizeNearestNeighborAttrs, ResizeNearestNeighborInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n * @param halfPixelCenters Defaults to `false`. Whether to assumes pixels are of\n *      half the actual dimensions, and yields more accurate resizes. This flag\n *      would also make the floating point coordinates of the top left pixel\n *      0.5, 0.5.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_<T extends Tensor3D|Tensor4D>(\n    images: T|TensorLike, size: [number, number], alignCorners = false,\n    halfPixelCenters = false): T {\n  const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n\n  util.assert(\n      $images.rank === 3 || $images.rank === 4,\n      () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n          `rank ${$images.rank}.`);\n  util.assert(\n      size.length === 2,\n      () =>\n          `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n          `${size}.`);\n  util.assert(\n      $images.dtype === 'float32' || $images.dtype === 'int32',\n      () => '`images` must have `int32` or `float32` as dtype');\n  util.assert(\n      halfPixelCenters === false || alignCorners === false,\n      () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, ` +\n          `alignCorners must be false.`);\n  let batchImages = $images as Tensor4D;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages = reshape(\n        $images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n  }\n  const [] = size;\n\n  const inputs: ResizeNearestNeighborInputs = {images: batchImages};\n  const attrs:\n      ResizeNearestNeighborAttrs = {alignCorners, halfPixelCenters, size};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  ResizeNearestNeighbor, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res;\n}\n\nexport const resizeNearestNeighbor = op({resizeNearestNeighbor_});\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport { Tensor1D, Tensor3D } from '../../tensor';\nimport { tensor1d } from '../tensor1d';\nimport { TensorLike } from '../../types';\nimport { op } from '../operation';\nimport { cast } from '../cast';\nimport { split } from '../split';\nimport { bincount } from '../bincount';\nimport { lessEqual } from '../less_equal';\nimport { greater } from '../greater';\nimport { sum } from '../sum';\nimport { add } from '../add';\nimport { mul } from '../mul';\nimport { div } from '../div';\nimport { sub } from '../sub';\nimport { round } from '../round';\nimport { where } from '../where';\nimport { fill } from '../fill';\nimport {slice} from '../slice';\nimport { range } from '../range';\nimport { tensor } from '../tensor';\nimport * as util from '../../util';\nimport { convertToTensor } from '../../tensor_util_env';\n\n/**\n * Performs image binarization with corresponding threshold\n * (depends on the method)value, which creates a binary image from a grayscale.\n * @param image 3d tensor of shape [imageHeight,imageWidth, depth],\n * where imageHeight and imageWidth must be positive.The image color\n * range should be [0, 255].\n * @param method Optional string from `'binary' | 'otsu'`\n * which specifies the method for thresholding. Defaults to 'binary'.\n * @param inverted Optional boolean whichspecifies\n * if colours should be inverted. Defaults to false.\n * @param threshValue Optional number which defines threshold value from 0 to 1.\n * Defaults to 0.5.\n * @return A 3d tensor of shape [imageHeight,imageWidth, depth], which\n * contains binarized image.\n */\n\nfunction threshold_(\n    image: Tensor3D | TensorLike,\n    method = 'binary',\n    inverted = false,\n    threshValue = 0.5\n): Tensor3D {\n    const $image = convertToTensor(image, 'image', 'threshold');\n\n    /* 0.2989, 0.5870, 0.1140 are represent luma coefficients in CCIR601.\n\tReference for converting between RGB and grayscale: https://en.wikipedia.org/wiki/Luma_%28video%29  */\n\n    const RED_INTENCITY_COEF = 0.2989;\n    const GREEN_INTENCITY_COEF = 0.5870;\n    const BLUE_INTENCITY_COEF = 0.1140;\n    const totalPixelsInImage = $image.shape[0] * $image.shape[1];\n\n    let $threshold = mul(tensor1d([threshValue]), 255);\n    let r, g, b, grayscale;\n\n    util.assert(\n        $image.rank === 3,\n        () => 'Error in threshold: image must be rank 3,' +\n            `but got rank ${$image.rank}.`);\n\n    util.assert(\n        $image.shape[2] === 3 || $image.shape[2]=== 1,\n        () => 'Error in threshold: ' +\n            'image color channel must be equal to 3 or 1' +\n            `but got ${$image.shape[2]}.`);\n\n    util.assert(\n      $image.dtype === 'int32' || $image.dtype === 'float32',\n      () => 'Error in dtype: image dtype must be int32 or float32,' +\n          `but got dtype ${$image.dtype}.`);\n\n    util.assert(\n      method === 'otsu' || method === 'binary',\n      () => `Method must be binary or otsu, but was ${method}`);\n\n    if ($image.shape[2] === 3) {\n        [r, g, b] = split($image, [1, 1, 1], -1);\n        const $r = mul(r,RED_INTENCITY_COEF);\n        const $g = mul(g,GREEN_INTENCITY_COEF);\n        const $b = mul(b,BLUE_INTENCITY_COEF);\n        grayscale = add(add($r, $g), $b);\n    } else {\n        grayscale = image;\n    }\n\n    if (method === 'otsu') {\n        const $histogram = bincount(cast(round(grayscale), 'int32') as Tensor1D,\n            tensor([]),\n            256);\n        $threshold = otsu($histogram, totalPixelsInImage);\n    }\n\n    const invCondition = inverted ?\n        lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);\n\n    const result = cast(mul(invCondition,255), 'int32');\n\n    return result as Tensor3D;\n}\n\nfunction otsu(histogram: Tensor1D, total: number):Tensor1D {\n\n    let bestThresh = tensor1d([-1]);\n    let bestInBetVar = tensor1d([0]);\n    let cInBetVar = tensor1d([0]);\n    let classFirst, classSecond, meanFirst,\n        meanSec, weightForeground, weightBack;\n\n    for (let index = 0; index < histogram.size-1; index++) {\n\n        classFirst = slice(histogram, 0, index + 1);\n\n        classSecond = slice(histogram,index + 1);\n\n        weightForeground = div(sum(classFirst),total);\n\n        weightBack = div(sum(classSecond),total);\n\n        const meanFirstDivA = sum(mul(classFirst, range(0, classFirst.size)));\n\n        meanFirst = div(meanFirstDivA, sum(classFirst) );\n\n        const meanSecFill = fill(classSecond.shape, classFirst.size);\n        const meanSecAdd = add(range(0,classSecond.size),meanSecFill);\n        const meanSecMul = mul(classSecond, (meanSecAdd));\n        meanSec = div(sum(meanSecMul), sum(classSecond));\n\n        const cInBetVarSubA = sub(meanFirst, meanSec);\n        const cInBetVarSubB = sub(meanFirst, meanSec);\n        const cInBetVarMul = mul(weightForeground, weightBack);\n        cInBetVar = mul(mul(cInBetVarMul,cInBetVarSubA), cInBetVarSubB);\n\n        const condition = greater(cInBetVar, bestInBetVar);\n\n        bestInBetVar = where(condition, cInBetVar, bestInBetVar);\n\n        bestThresh = where(condition, tensor1d([index]), bestThresh);\n\n    }\n    return bestThresh;\n}\n\nexport const threshold = op({ threshold_ });\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../../engine';\nimport {Transform, TransformAttrs, TransformInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor2D, Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {op} from '../operation';\n\n/**\n * Applies the given transform(s) to the image(s).\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param transforms Projective transform matrix/matrices. A tensor1d of length\n *     8 or tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0\n *     b1, b2, c0, c1], then it maps the output point (x, y) to a transformed\n *     input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k),\n *     where k = c0 x + c1 y + 1. The transforms are inverted compared to the\n *     transform mapping input points to output points.\n * @param interpolation Interpolation mode.\n *     Supported values: 'nearest', 'bilinear'. Default to 'nearest'.\n * @param fillMode Points outside the boundaries of the input are filled\n *     according to the given mode, one of 'constant', 'reflect', 'wrap',\n *     'nearest'. Default to 'constant'.\n *     'reflect': (d c b a | a b c d | d c b a ) The input is extended by\n *     reflecting about the edge of the last pixel.\n *     'constant': (k k k k | a b c d | k k k k) The input is extended by\n *     filling all values beyond the edge with the same constant value k.\n *     'wrap': (a b c d | a b c d | a b c d) The input is extended by\n *     wrapping around to the opposite edge.\n *     'nearest': (a a a a | a b c d | d d d d) The input is extended by\n *     the nearest pixel.\n * @param fillValue A float represents the value to be filled outside the\n *     boundaries when fillMode is 'constant'.\n * @param Output dimension after the transform, [height, width]. If undefined,\n *     output is the same size as input image.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction transform_(\n    image: Tensor4D|TensorLike, transforms: Tensor2D|TensorLike,\n    interpolation: 'nearest'|'bilinear' = 'nearest',\n    fillMode: 'constant'|'reflect'|'wrap'|'nearest' = 'constant', fillValue = 0,\n    outputShape?: [number, number]): Tensor4D {\n  const $image = convertToTensor(image, 'image', 'transform', 'float32');\n  const $transforms =\n      convertToTensor(transforms, 'transforms', 'transform', 'float32');\n\n  util.assert(\n      $image.rank === 4,\n      () => 'Error in transform: image must be rank 4,' +\n          `but got rank ${$image.rank}.`);\n\n  util.assert(\n      $transforms.rank === 2 &&\n          ($transforms.shape[0] === $image.shape[0] ||\n           $transforms.shape[0] === 1) &&\n          $transforms.shape[1] === 8,\n      () => `Error in transform: Input transform should be batch x 8 or 1 x 8`);\n\n  util.assert(\n      outputShape == null || outputShape.length === 2,\n      () =>\n          'Error in transform: outputShape must be [height, width] or null, ' +\n          `but got ${outputShape}.`);\n\n  const inputs: TransformInputs = {image: $image, transforms: $transforms};\n  const attrs:\n      TransformAttrs = {interpolation, fillMode, fillValue, outputShape};\n\n  return ENGINE.runKernel(\n      Transform, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const transform = op({transform_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assert} from '../../util';\n\nimport {greaterEqual} from '../greater_equal';\nimport {lessEqual} from '../less_equal';\nimport {logicalAnd} from '../logical_and';\nimport {op} from '../operation';\nimport {range} from '../range';\nimport {reshape} from '../reshape';\nimport {scalar} from '../scalar';\nimport {stack} from '../stack';\nimport {sub} from '../sub';\nimport {unstack} from '../unstack';\nimport {where} from '../where';\nimport {zeros} from '../zeros';\n\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_<T extends Tensor>(\n    a: T|TensorLike, numLower: number, numUpper: number): T {\n  assert(\n      numLower % 1 === 0,\n      () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n  assert(\n      numUpper % 1 === 0,\n      () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n\n  const $a = convertToTensor(a, 'a', 'bandPart');\n\n  assert(\n      $a.rank >= 2,\n      () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n\n  const shape = $a.shape;\n  const [M, N] = $a.shape.slice(-2);\n\n  if (!(numLower <= M)) {\n    throw new Error(\n        `bandPart(): numLower (${numLower})` +\n        ` must not be greater than the number of rows (${M}).`);\n  }\n  if (!(numUpper <= N)) {\n    throw new Error(\n        `bandPart(): numUpper (${numUpper})` +\n        ` must not be greater than the number of columns (${N}).`);\n  }\n\n  if (numLower < 0) {\n    numLower = M;\n  }\n  if (numUpper < 0) {\n    numUpper = N;\n  }\n\n  const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n  const j = range(0, N, 1, 'int32');\n  const ij = sub(i, j);\n\n  const inBand = logicalAnd(\n      lessEqual(ij, scalar(+numLower, 'int32')),\n      greaterEqual(ij, scalar(-numUpper, 'int32')));\n\n  const zero = zeros([M, N], $a.dtype);\n\n  return reshape(\n             stack(unstack(reshape($a, [-1, M, N]))\n                       .map(mat => where(inBand, mat, zero))),\n             shape) as T;\n}\n\nexport const bandPart = op({bandPart_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {Tensor1D, Tensor2D} from '../../tensor';\nimport {assert} from '../../util';\n\nimport {div} from '../div';\nimport {mul} from '../mul';\nimport {norm} from '../norm';\nimport {op} from '../operation';\nimport {split} from '../split';\nimport {squeeze} from '../squeeze';\nimport {stack} from '../stack';\nimport {sub} from '../sub';\nimport {sum} from '../sum';\n\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]);  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs: Tensor1D[]|Tensor2D): Tensor1D[]|Tensor2D {\n  let inputIsTensor2D: boolean;\n  if (Array.isArray(xs)) {\n    inputIsTensor2D = false;\n    assert(\n        xs != null && xs.length > 0,\n        () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n            'empty');\n    const dim = xs[0].shape[0];\n    for (let i = 1; i < xs.length; ++i) {\n      assert(\n          xs[i].shape[0] === dim,\n          () =>\n              'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n              `(${(xs as Tensor1D[])[i].shape[0]} vs. ${dim})`);\n    }\n  } else {\n    inputIsTensor2D = true;\n    xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n  }\n\n  assert(\n      xs.length <= xs[0].shape[0],\n      () => `Gram-Schmidt: Number of vectors (${\n                (xs as Tensor1D[]).length}) exceeds ` +\n          `number of dimensions (${(xs as Tensor1D[])[0].shape[0]}).`);\n\n  const ys: Tensor1D[] = [];\n  const xs1d = xs;\n  for (let i = 0; i < xs.length; ++i) {\n    ys.push(ENGINE.tidy(() => {\n      let x = xs1d[i];\n      if (i > 0) {\n        for (let j = 0; j < i; ++j) {\n          const proj = mul(sum(mul(ys[j], x)), ys[j]);\n          x = sub(x, proj);\n        }\n      }\n      return div(x, norm(x, 'euclidean'));\n    }));\n  }\n\n  if (inputIsTensor2D) {\n    return stack(ys, 0) as Tensor2D;\n  } else {\n    return ys;\n  }\n}\n\nexport const gramSchmidt = op({gramSchmidt_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../../engine';\nimport {dispose} from '../../globals';\nimport {Tensor, Tensor2D} from '../../tensor';\nimport {assert} from '../../util';\n\nimport {clone} from '../clone';\nimport {concat} from '../concat';\nimport {div} from '../div';\nimport {eye} from '../eye';\nimport {greater} from '../greater';\nimport {matMul} from '../mat_mul';\nimport {mul} from '../mul';\nimport {neg} from '../neg';\nimport {norm} from '../norm';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\nimport {slice} from '../slice';\nimport {stack} from '../stack';\nimport {sub} from '../sub';\nimport {tensor2d} from '../tensor2d';\nimport {transpose} from '../transpose';\nimport {unstack} from '../unstack';\nimport {where} from '../where';\n\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x: Tensor, fullMatrices = false): [Tensor, Tensor] {\n  assert(\n      x.rank >= 2,\n      () => `qr() requires input tensor to have a rank >= 2, but got rank ${\n          x.rank}`);\n\n  if (x.rank === 2) {\n    return qr2d(x as Tensor2D, fullMatrices);\n  } else {\n    // Rank > 2.\n    // TODO(cais): Below we split the input into individual 2D tensors,\n    //   perform QR decomposition on them and then stack the results back\n    //   together. We should explore whether this can be parallelized.\n    const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n                              .reduce((value, prev) => value * prev);\n    const x2ds = unstack(\n        reshape(\n            x,\n            [\n              outerDimsProd, x.shape[x.shape.length - 2],\n              x.shape[x.shape.length - 1]\n            ]),\n        0);\n    const q2ds: Tensor2D[] = [];\n    const r2ds: Tensor2D[] = [];\n    x2ds.forEach(x2d => {\n      const [q2d, r2d] = qr2d(x2d as Tensor2D, fullMatrices);\n      q2ds.push(q2d);\n      r2ds.push(r2d);\n    });\n    const q = reshape(stack(q2ds, 0), x.shape);\n    const r = reshape(stack(r2ds, 0), x.shape);\n    return [q, r];\n  }\n}\n\nfunction qr2d(x: Tensor2D, fullMatrices = false): [Tensor2D, Tensor2D] {\n  return ENGINE.tidy(() => {\n    assert(\n        x.shape.length === 2,\n        () => `qr2d() requires a 2D Tensor, but got a ${\n            x.shape.length}D Tensor.`);\n\n    const m = x.shape[0];\n    const n = x.shape[1];\n\n    let q = eye(m);    // Orthogonal transform so far.\n    let r = clone(x);  // Transformed matrix so far.\n\n    const one2D = tensor2d([[1]], [1, 1]);\n    let w: Tensor2D = clone(one2D);\n\n    const iters = m >= n ? n : m;\n    for (let j = 0; j < iters; ++j) {\n      // This tidy within the for-loop ensures we clean up temporary\n      // tensors as soon as they are no longer needed.\n      const rTemp = r;\n      const wTemp = w;\n      const qTemp = q;\n      [w, r, q] = ENGINE.tidy((): [Tensor2D, Tensor2D, Tensor2D] => {\n        // Find H = I - tau * w * w', to put zeros below R(j, j).\n        const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n        const normX = norm(rjEnd1);\n        const rjj = slice(r, [j, j], [1, 1]);\n\n        // The sign() function returns 0 on 0, which causes division by zero.\n        const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n\n        const u1 = sub(rjj, mul(s, normX));\n        const wPre = div(rjEnd1, u1);\n        if (wPre.shape[0] === 1) {\n          w = clone(one2D);\n        } else {\n          w = concat(\n              [\n                one2D,\n                slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]]) as\n                    Tensor2D\n              ],\n              0);\n        }\n        const tau = neg(div(matMul(s, u1), normX)) as Tensor2D;\n\n        // -- R := HR, Q := QH.\n        const rjEndAll = slice(r, [j, 0], [m - j, n]);\n        const tauTimesW: Tensor2D = mul(tau, w);\n        const wT: Tensor2D = transpose(w);\n        if (j === 0) {\n          r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n        } else {\n          const rTimesTau: Tensor2D =\n              sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n          r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n        }\n        const tawTimesWT: Tensor2D = transpose(tauTimesW);\n        const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n        if (j === 0) {\n          q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n        } else {\n          const qTimesTau: Tensor2D =\n              sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n          q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n        }\n        return [w, r, q];\n      });\n      dispose([rTemp, wTemp, qTemp]);\n    }\n\n    if (!fullMatrices && m > n) {\n      q = slice(q, [0, 0], [m, n]);\n      r = slice(r, [0, 0], [n, n]);\n    }\n\n    return [q, r];\n  }) as [Tensor2D, Tensor2D];\n}\n\nexport const qr = op({qr_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport enum Reduction {\n  NONE,\n  MEAN,\n  SUM,\n  SUM_BY_NONZERO_WEIGHTS\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\n\nimport {cast} from '../cast';\nimport {div} from '../div';\nimport {Reduction} from '../loss_ops_utils';\nimport {mean} from '../mean';\nimport {mul} from '../mul';\nimport {notEqual} from '../not_equal';\nimport {ones} from '../ones';\nimport {op} from '../operation';\nimport {scalar} from '../scalar';\nimport {sum} from '../sum';\n\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_<T extends Tensor, O extends Tensor>(\n    losses: T|TensorLike, weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n  }\n\n  const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n\n  if (reduction === Reduction.NONE) {\n    return weightedLoss as O;\n  }\n  if (reduction === Reduction.SUM) {\n    return sum(weightedLoss);\n  }\n  if (reduction === Reduction.MEAN) {\n    if ($weights == null) {\n      return mean(weightedLoss);\n    } else {\n      const broadcastFactor = $losses.size / $weights.size;\n      const result = div(sum(weightedLoss), sum($weights));\n      return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n                                   result as O;\n    }\n  }\n  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    if ($weights == null) {\n      return div(sum(weightedLoss), scalar($losses.size));\n    } else {\n      const broadcastedWeights = mul($weights, ones($losses.shape));\n\n      const numNonZeros =\n          cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');\n      return div(sum(weightedLoss), numNonZeros);\n    }\n  }\n\n  throw Error(`Unknown reduction: ${reduction}`);\n}\nexport const computeWeightedLoss = op({computeWeightedLoss_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {abs} from '../abs';\nimport {Reduction} from '../loss_ops_utils';\nimport {op} from '../operation';\nimport {sub} from '../sub';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'absoluteDifference');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n\n  const losses = abs(sub($labels, $predictions));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nexport const absoluteDifference = op({absoluteDifference_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {Reduction} from '../loss_ops_utils';\nimport {mul} from '../mul';\nimport {op} from '../operation';\nimport {scalar} from '../scalar';\nimport {sub} from '../sub';\nimport {sum} from '../sum';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike, axis: number,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'cosineDistance');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n\n  const one = scalar(1);\n  const losses = sub(one, sum(mul($labels, $predictions), axis, true));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const cosineDistance = op({cosineDistance_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {Reduction} from '../loss_ops_utils';\nimport {mul} from '../mul';\nimport {op} from '../operation';\nimport {relu} from '../relu';\nimport {scalar} from '../scalar';\nimport {sub} from '../sub';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n\n  const one = scalar(1);\n  // Convert binary labels to (-1, 1)\n  $labels = sub(mul(scalar(2), $labels), one);\n  const losses = relu(sub(one, mul($labels, $predictions)));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const hingeLoss = op({hingeLoss_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {abs} from '../abs';\nimport {add} from '../add';\nimport {Reduction} from '../loss_ops_utils';\nimport {minimum} from '../minimum';\nimport {mul} from '../mul';\nimport {op} from '../operation';\nimport {scalar} from '../scalar';\nimport {square} from '../square';\nimport {sub} from '../sub';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike, delta = 1.0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'huberLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n\n  const deltaScalar = scalar(delta);\n  const error = abs(sub($predictions, $labels));\n  const quadratic = minimum(error, deltaScalar);\n  const linear = sub(error, quadratic);\n\n  const losses =\n      add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const huberLoss = op({huberLoss_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {add} from '../add';\nimport {log} from '../log';\nimport {Reduction} from '../loss_ops_utils';\nimport {mul} from '../mul';\nimport {neg} from '../neg';\nimport {op} from '../operation';\nimport {scalar} from '../scalar';\nimport {sub} from '../sub';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike, epsilon = 1e-7,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'logLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'logLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n\n  const one = scalar(1);\n  const epsilonScalar = scalar(epsilon);\n\n  const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));\n  const l2 =\n      mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));\n  const losses = sub(l1, l2);\n  return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const logLoss = op({logLoss_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {Reduction} from '../loss_ops_utils';\nimport {op} from '../operation';\nimport {squaredDifference} from '../squared_difference';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'meanSquaredError');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n\n  const losses = squaredDifference($labels, $predictions);\n  return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const meanSquaredError = op({meanSquaredError_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {abs} from '../abs';\nimport {add} from '../add';\nimport {exp} from '../exp';\nimport {log1p} from '../log1p';\nimport {Reduction} from '../loss_ops_utils';\nimport {mul} from '../mul';\nimport {neg} from '../neg';\nimport {op} from '../operation';\nimport {relu} from '../relu';\nimport {scalar} from '../scalar';\nimport {sub} from '../sub';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\nfunction sigmoidCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, logits: T|TensorLike): O {\n  const $labels =\n      convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n  const $logits =\n      convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n  assertShapesMatch(\n      $labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n\n  /**\n   * Implementation Details:\n   *\n   * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n   *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n   *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n   *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n   *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n   *   = (1 - z) * x + log(1 + exp(-x))\n   *   = x - x * z + log(1 + exp(-x))\n   *\n   *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n   *     x - x * z + log(1 + exp(-x))\n   *   = log(exp(x)) - x * z + log(1 + exp(-x))\n   *   = - x * z + log(1 + exp(x))\n   *\n   * Hence, to ensure stability and avoid overflow, the implementation uses\n   * this equivalent formulation:\n   *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n   */\n  const maxOutput = relu($logits);\n  const outputXTarget = mul($logits, $labels);\n  const sigmoidOutput = log1p(exp(neg(abs($logits))));\n\n  return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_<T extends Tensor, O extends Tensor>(\n    multiClassLabels: T|TensorLike, logits: T|TensorLike,\n    weights?: Tensor|TensorLike, labelSmoothing = 0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $multiClassLabels = convertToTensor(\n      multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n  const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n  }\n  assertShapesMatch(\n      $multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const half = scalar(0.5);\n\n    $multiClassLabels =\n        add(mul($multiClassLabels, sub(one, labelSmoothingScalar)),\n            mul(half, labelSmoothingScalar));\n  }\n  const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nexport const sigmoidCrossEntropy = op({sigmoidCrossEntropy_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {customGrad} from '../../gradients';\nimport {Tensor} from '../../tensor';\nimport {GradSaveFunc} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {add} from '../add';\nimport {expandShapeToKeepDim} from '../axis_util';\nimport {cast} from '../cast';\nimport {div} from '../div';\nimport {exp} from '../exp';\nimport {logSumExp} from '../log_sum_exp';\nimport {Reduction} from '../loss_ops_utils';\nimport {mul} from '../mul';\nimport {neg} from '../neg';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\nimport {scalar} from '../scalar';\nimport {sub} from '../sub';\nimport {sum} from '../sum';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(\n    labels: T, logits: T, dim = -1): O {\n  if (dim === -1) {\n    dim = logits.rank - 1;\n  }\n\n  if (dim !== logits.rank - 1) {\n    throw Error(\n        `Softmax cross entropy along a non-last dimension is not yet ` +\n        `supported. Labels / logits was rank ${logits.rank} ` +\n        `and dim was ${dim}`);\n  }\n  // Use a custom gradient for numerical stability.\n  const customOp =\n      customGrad((labels: Tensor, logits: Tensor, save: GradSaveFunc) => {\n        // Reference:\n        //   1. http://cs231n.github.io/linear-classify/#softmax\n        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n        const keepDims = true;\n        const lse = logSumExp(logits, [dim], keepDims);\n        const logResult = sub(cast(logits, 'float32'), lse);\n        save([labels, logResult]);\n\n        const costVector = neg(mul(logResult, labels));\n        const value: O = sum(costVector, [dim]);\n\n        const gradFunc = (dy: O, saved: Tensor[]) => {\n          const [labels, logResult] = saved;\n          const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n          return [\n            mul(reshape(dy, dyShape),\n                sub(cast(labels, 'float32'), exp(logResult))),\n            mul(reshape(dy, dyShape),\n                sub(exp(logResult), cast(labels, 'float32'))),\n          ];\n        };\n        return {value, gradFunc};\n      });\n\n  return customOp(labels, logits);\n}\n\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_<T extends Tensor, O extends Tensor>(\n    onehotLabels: T|TensorLike, logits: T|TensorLike,\n    weights?: Tensor|TensorLike, labelSmoothing = 0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $onehotLabels =\n      convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n  const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n  let $weights: Tensor = null;\n\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n  }\n\n  assertShapesMatch(\n      $onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const numClasses = scalar($onehotLabels.shape[1]);\n\n    $onehotLabels =\n        add(mul($onehotLabels, sub(one, labelSmoothingScalar)),\n            div(labelSmoothingScalar, numClasses));\n  }\n\n  const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nexport const softmaxCrossEntropy = op({softmaxCrossEntropy_});\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {SparseFillEmptyRows, SparseFillEmptyRowsInputs} from '../../kernel_names';\nimport {Scalar, Tensor, Tensor1D, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {ScalarLike, TensorLike} from '../../types';\nimport {op} from '../operation';\n\n/**\n * The input SparseTensor is represented via the map of inputs {`indices`,\n * `values`, `denseShape`}. The output SparseTensor has the same `denseShape`\n * but with indices `outputIndices` and values `outputValues`. This op inserts a\n * single entry for every row that doesn't have any values. The index is created\n * as `[row, 0, ..., 0]` and the inserted value is `defaultValue`.\n *\n * For example, suppose `spInput` has shape [5, 6] and non-empty values:\n * [0, 1]: a\n * [0, 3]: b\n * [2, 0]: c\n * [3, 1]: d\n *\n * Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values:\n * [0, 1]: a\n * [0, 3]: b\n * [1, 0]: `defaultValue`\n * [2, 0]: c\n * [3, 1]: d\n * [4, 0]: `defaultValue`\n *\n * The output SparseTensor will be in row-major order and will have the same\n * shape as the input.\n *\n * This op also returns an indicator vector shaped [dense_shape[0]] such that\n * emptyRowIndicator[i] = True iff row i was an empty row.\n *\n * And a reverse index map vector shaped [indices.shape[0]] that is used during\n * backpropagation, reverseIndexMap[i] = outi s.t. indices[i, j] ==\n * outputIndices[outi, j] for all j\n *\n * ```js\n * const result = tf.sparse.sparseFillEmptyRows(\n *   [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]],\n *   [0, 10, 13, 14, 32, 33], [5, 6], -1);\n * console.log(result);\n * result['outputIndices'].print(); // [[0, 0], [1, 0], [1, 3], [1, 4],\n *                                  //  [2, 0], [3, 2], [3, 3], [4, 0]]\n * result['outputValues'].print(); // [0, 10, 13, 14,-1, 32, 33, -1]\n * result['emptyRowIndicator'].print(); // [false, false, true, false, true]\n * result['reverseIndexMap'].print(); // [0, 1, 2, 3, 5, 6]\n * ```\n * @param indices: 2-D. the indices of the sparse tensor.\n * @param values: 1-D. the values of the sparse tensor.\n * @param denseShape: 1-D. the shape of the sparse tensor.\n * @param defaultValue: 0-D. default value to insert into location [row, 0, ...,\n *     0] for rows missing from the input sparse tensor.\n * @return A map with the following properties:\n *     - outputIndices\n *     - outputValues: 1-D. the values of the filled sparse tensor.\n *     - emptyRowIndicator: 1-D. whether the dense row was missing in the input\n * sparse tensor.\n *     - reverseIndexMap: 1-D. a map from the input indices to the output\n * indices.\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseFillEmptyRows_(\n    indices: Tensor2D|TensorLike, values: Tensor1D|TensorLike,\n    denseShape: Tensor1D|TensorLike,\n    defaultValue: Scalar|ScalarLike): NamedTensorMap {\n  const $indices = convertToTensor(indices, 'indices', 'sparseFillEmptyRows');\n  const $values = convertToTensor(values, 'values', 'sparseFillEmptyRows');\n  const $denseShape =\n      convertToTensor(denseShape, 'denseShape', 'sparseFillEmptyRows');\n  const $defaultValue = convertToTensor(\n      defaultValue, 'defaultValue', 'sparseFillEmptyRows', $values.dtype);\n\n  if ($indices.rank !== 2) {\n    throw new Error(`Indices should be Tensor2D but received shape\n        ${$indices.shape}`);\n  }\n  if ($values.rank !== 1) {\n    throw new Error(\n        `Values should be Tensor1D but received shape ${$values.shape}`);\n  }\n  if ($denseShape.rank !== 1) {\n    throw new Error(`Dense shape should be Tensor1D but received shape ${\n        $denseShape.shape}`);\n  }\n  if ($defaultValue.rank !== 0) {\n    throw new Error(`Default value should be a scalar but received shape ${\n        $defaultValue.shape}`);\n  }\n\n  const inputs: SparseFillEmptyRowsInputs = {\n    indices: $indices,\n    values: $values,\n    denseShape: $denseShape,\n    defaultValue: $defaultValue\n  };\n\n  const result: Tensor[] = ENGINE.runKernel(SparseFillEmptyRows, inputs as {});\n  return {\n    outputIndices: result[0],\n    outputValues: result[1],\n    emptyRowIndicator: result[2],\n    reverseIndexMap: result[3]\n  };\n}\n\nexport const sparseFillEmptyRows = op({sparseFillEmptyRows_});\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {SparseReshape, SparseReshapeInputs} from '../../kernel_names';\nimport {Tensor, Tensor1D, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {op} from '../operation';\n\n/**\n * This operation has the same semantics as reshape on the represented dense\n * tensor. The `inputIndices` are recomputed based on the requested `newShape`.\n * If one component of `newShape` is the special value -1, the size of that\n * dimension is computed so that the total dense size remains constant. At most\n * one component of `newShape` can be -1. The number of dense elements implied\n * by `newShape` must be the same as the number of dense elements originally\n * implied by `inputShape`. Reshaping does not affect the order of values in the\n * SparseTensor. If the input tensor has rank R_in and N non-empty values, and\n * `newShape` has length R_out, then `inputIndices` has shape [N, R_in],\n * `inputShape` has length R_in, `outputIndices` has shape [N, R_out], and\n * `outputShape` has length R_out.\n *\n * ```js\n * const result = tf.sparse.sparseReshape(\n *   [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]],\n *   [2, 3, 6], [9, -1]);\n * console.log(result);\n * result['outputIndices'].print(); //[[0, 0], [0, 1], [1, 2], [4, 2], [8, 1]]\n * result['outputShape'].print(); // [9, 4]\n * ```\n * @param inputIndices: 2-D. N x R_in matrix with the indices of non-empty\n * values in a SparseTensor.\n * @param inputShape: 1-D. R_in Tensor1D with the input SparseTensor's dense\n * shape.\n * @param newShape: 1-D. R_out Tensor1D with the requested new dense shape.\n * @return A map with the following properties:\n *     - outputIndices: 2-D. N x R_out matrix with the updated indices of\n *       non-empty values in the output SparseTensor.\n *     - outputShape: 1-D. R_out vector with the full dense shape of the output\n *       SparseTensor. This is the same as newShape but with any -1 dimensions\n *        filled in.\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseReshape_(\n    inputIndices: Tensor2D|TensorLike, inputShape: Tensor1D|TensorLike,\n    newShape: Tensor1D|TensorLike): NamedTensorMap {\n  const $inputIndices =\n      convertToTensor(inputIndices, 'inputIndices', 'sparseReshape');\n  const $inputShape =\n      convertToTensor(inputShape, 'inputShape', 'sparseReshape');\n  const $newShape = convertToTensor(newShape, 'newShape', 'sparseReshape');\n\n  if ($inputIndices.rank !== 2) {\n    throw new Error(`Input indices should be Tensor2D but received shape\n        ${$inputIndices.shape}`);\n  }\n  if ($inputShape.rank !== 1) {\n    throw new Error(`Input shape should be Tensor1D but received shape ${\n        $inputShape.shape}`);\n  }\n  if ($newShape.rank !== 1) {\n    throw new Error(\n        `New shape should be Tensor1D but received shape ${$newShape.shape}`);\n  }\n\n  const inputs: SparseReshapeInputs = {\n    inputIndices: $inputIndices,\n    inputShape: $inputShape,\n    newShape: $newShape\n  };\n  const result: Tensor[] = ENGINE.runKernel(SparseReshape, inputs as {});\n  return {outputIndices: result[0], outputShape: result[1]};\n}\n\nexport const sparseReshape = op({sparseReshape_});\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {SparseSegmentMean, SparseSegmentMeanInputs} from '../../kernel_names';\nimport {Tensor, Tensor1D} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {op} from '../operation';\n\n/**\n * Computes the mean along sparse segments of a tensor.\n *\n * ```js\n * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [6,7,8,9]]);\n * // Select two rows, one segment.\n * const result1 = tf.sparse.sparseSegmentMean(c,\n *                                           tf.tensor1d([0, 1], 'int32'),\n *                                           tf.tensor1d([0, 0], 'int32'));\n * result1.print(); // [[0, 0, 0, 0]]\n *\n * // Select two rows, two segments.\n * const result2 = tf.sparse.sparseSegmentMean(c,\n *                                             tf.tensor1d([0, 1], 'int32'),\n *                                             tf.tensor1d([0, 1], 'int32'));\n * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]\n *\n * // Select all rows, two segments.\n * const result3 = tf.sparse.sparseSegmentMean(c,\n *                                             tf.tensor1d([0, 1, 2], 'int32'),\n *                                             tf.tensor1d([0, 1, 1], 'int32'));\n * result3.print(); // [[1.0, 2.0, 3.0, 4.0], [2.5, 2.5, 2.5, 2.5]]\n * ```\n * @param data: A Tensor of at least one dimension with data that will be\n *     assembled in the output.\n * @param indices: A 1-D Tensor with indices into data. Has same rank as\n *     segmentIds.\n * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values\n *     should be sorted and can be repeated.\n * @return Has same shape as data, except for dimension 0 which has equal to\n *         the number of segments.\n *\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseSegmentMean_(\n    data: Tensor|TensorLike, indices: Tensor1D|TensorLike,\n    segmentIds: Tensor1D|TensorLike): Tensor {\n  const $data = convertToTensor(data, 'data', 'sparseSegmentMean');\n  const $indices = convertToTensor(indices, 'indices', 'sparseSegmentMean');\n  const $segmentIds =\n      convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentMean');\n\n  if ($data.rank < 1) {\n    throw new Error(\n        `Data should be at least 1 dimensional but received scalar`);\n  }\n  if ($indices.rank !== 1) {\n    throw new Error(`Indices should be Tensor1D but received shape\n          ${$indices.shape}`);\n  }\n  if ($segmentIds.rank !== 1) {\n    throw new Error(`Segment ids should be Tensor1D but received shape\n          ${$segmentIds.shape}`);\n  }\n\n  const inputs: SparseSegmentMeanInputs = {\n    data: $data,\n    indices: $indices,\n    segmentIds: $segmentIds\n  };\n\n  return ENGINE.runKernel(SparseSegmentMean, inputs as {});\n}\n\nexport const sparseSegmentMean = op({sparseSegmentMean_});\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {SparseSegmentSum, SparseSegmentSumInputs} from '../../kernel_names';\nimport {Tensor, Tensor1D} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {op} from '../operation';\n\n/**\n * Computes the sum along sparse segments of a tensor.\n *\n * ```js\n * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]);\n * // Select two rows, one segment.\n * const result1 = tf.sparse.sparseSegmentSum(c,\n *                                           tf.tensor1d([0, 1], 'int32'),\n *                                           tf.tensor1d([0, 0], 'int32'));\n * result1.print(); // [[0, 0, 0, 0]]\n *\n * // Select two rows, two segment.\n * const result2 = tf.sparse.sparseSegmentSum(c,\n *                                           tf.tensor1d([0, 1], 'int32'),\n *                                           tf.tensor1d([0, 1], 'int32'));\n * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]\n *\n * // Select all rows, two segments.\n * const result3 = tf.sparse.sparseSegmentSum(c,\n *                                           tf.tensor1d([0, 1, 2], 'int32'),\n *                                           tf.tensor1d([0, 0, 1], 'int32'));\n * result3.print(); // [[0, 0, 0, 0], [5, 6, 7, 8]]\n * ```\n * @param data: A Tensor of at least one dimension with data that will be\n *     assembled in the output.\n * @param indices: A 1-D Tensor with indices into data. Has same rank as\n *     segmentIds.\n * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values\n *     should be sorted and can be repeated.\n * @return Has same shape as data, except for dimension 0 which has equal to\n *         the number of segments.\n *\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseSegmentSum_(\n    data: Tensor|TensorLike, indices: Tensor1D|TensorLike,\n    segmentIds: Tensor1D|TensorLike): Tensor {\n  const $data = convertToTensor(data, 'data', 'sparseSegmentSum');\n  const $indices = convertToTensor(indices, 'indices', 'sparseSegmentSum');\n  const $segmentIds =\n      convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentSum');\n\n  if ($data.rank < 1) {\n    throw new Error(\n        `Data should be at least 1 dimensional but received scalar`);\n  }\n  if ($indices.rank !== 1) {\n    throw new Error(`Indices should be Tensor1D but received shape\n         ${$indices.shape}`);\n  }\n  if ($segmentIds.rank !== 1) {\n    throw new Error(`Segment ids should be Tensor1D but received shape\n         ${$segmentIds.shape}`);\n  }\n\n  const inputs: SparseSegmentSumInputs = {\n    data: $data,\n    indices: $indices,\n    segmentIds: $segmentIds\n  };\n\n  return ENGINE.runKernel(SparseSegmentSum, inputs as {});\n}\n\nexport const sparseSegmentSum = op({sparseSegmentSum_});\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {StringNGrams, StringNGramsAttrs, StringNGramsInputs} from '../../kernel_names';\nimport {Tensor, Tensor1D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {op} from '../operation';\n\n/**\n * Creates ngrams from ragged string data.\n *\n * This op accepts a ragged tensor with 1 ragged dimension containing only\n * strings and outputs a ragged tensor with 1 ragged dimension containing ngrams\n * of that string, joined along the innermost axis.\n *\n * ```js\n * const result = tf.string.stringNGrams(\n *   ['a', 'b', 'c', 'd'], tf.tensor1d([0, 2, 4], 'int32'),\n *   '|', [1, 2], 'LP', 'RP', -1, false);\n * result['nGrams'].print(); // ['a', 'b', 'LP|a', 'a|b', 'b|RP',\n *                           //  'c', 'd', 'LP|c', 'c|d', 'd|RP']\n * result['nGramsSplits'].print(); // [0, 5, 10]\n * ```\n * @param data: The values tensor of the ragged string tensor to make ngrams out\n *     of. Must be a 1D string tensor.\n * @param dataSplits: The splits tensor of the ragged string tensor to make\n *     ngrams out of.\n * @param separator: The string to append between elements of the token. Use \"\"\n *     for no separator.\n * @param nGramWidths: The sizes of the ngrams to create.\n * @param leftPad: The string to use to pad the left side of the ngram sequence.\n *     Only used if pad_width !== 0.\n * @param rightPad: The string to use to pad the right side of the ngram\n *     sequence. Only used if pad_width !== 0.\n * @param padWidth: The number of padding elements to add to each side of each\n *     sequence. Note that padding will never be greater than `nGramWidths`-1\n *     regardless of this value. If `padWidth`=-1 , then add max(`nGramWidths)-1\n *     elements.\n * @param preserveShortSequences: If true, then ensure that at least one ngram\n *     is generated for each input sequence. In particular, if an input sequence\n *     is shorter than min(ngramWidth) + 2*padWidth, then generate a single\n *     ngram containing the entire sequence. If false, then no ngrams are\n *     generated for these short input sequences.\n * @return A map with the following properties:\n *     - nGrams: The values tensor of the output ngrams ragged tensor.\n *     - nGramsSplits: The splits tensor of the output ngrams ragged tensor.\n *\n * @doc {heading: 'Operations', subheading: 'String'}\n */\nfunction stringNGrams_(\n    data: Tensor1D|TensorLike, dataSplits: Tensor|TensorLike, separator: string,\n    nGramWidths: number[], leftPad: string, rightPad: string, padWidth: number,\n    preserveShortSequences: boolean): NamedTensorMap {\n  const $data = convertToTensor(data, 'data', 'stringNGrams', 'string');\n  if ($data.dtype !== 'string') {\n    throw new Error('Data must be of datatype string');\n  }\n  if ($data.shape.length !== 1) {\n    throw new Error(`Data must be a vector, saw: ${$data.shape}`);\n  }\n\n  const $dataSplits = convertToTensor(dataSplits, 'dataSplits', 'stringNGrams');\n  if ($dataSplits.dtype !== 'int32') {\n    throw new Error('Data splits must be of datatype int32');\n  }\n\n  const attrs: StringNGramsAttrs = {\n    separator,\n    nGramWidths,\n    leftPad,\n    rightPad,\n    padWidth,\n    preserveShortSequences\n  };\n\n  const inputs: StringNGramsInputs = {data: $data, dataSplits: $dataSplits};\n  const result: Tensor[] =\n      ENGINE.runKernel(StringNGrams, inputs as {}, attrs as {});\n  return {nGrams: result[0], nGramsSplits: result[1]};\n}\n\nexport const stringNGrams = op({stringNGrams_});\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {StringSplit, StringSplitAttrs, StringSplitInputs} from '../../kernel_names';\nimport {Scalar, Tensor, Tensor1D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {ScalarLike, TensorLike} from '../../types';\nimport {op} from '../operation';\n\n/**\n * Split elements of `input` based on `delimiter` into a SparseTensor .\n *\n * Let N be the size of source (typically N will be the batch size). Split each\n * element of `input` based on `delimiter` and return a SparseTensor containing\n * the splitted tokens. Empty tokens are ignored if `skipEmpty` is set to True.\n *\n * `delimiter` can be empty, or a string of split characters. If `delimiter` is\n * an empty string, each element of `input` is split into individual\n * character strings. Otherwise every character of `delimiter` is a potential\n * split point.\n *\n * ```js\n * const result = tf.string.stringSplit(['hello world',  'a b c'], ' ');\n * result['indices'].print(); // [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2]]\n * result['values'].print(); // ['hello', 'world', 'a', 'b', 'c']\n * result['shape'].print(); // [2, 3]\n * ```\n * @param input: 1-D. Strings to split.\n * @param delimiter: 0-D. Delimiter characters, or empty string.\n * @param skipEmpty: Optional. If true, skip the empty strings from the result.\n *     Defaults to true.\n * @return A map with the following properties:\n *     - indices: A dense matrix of int32 representing the indices of the sparse\n *       tensor.\n *     - values: A vector of strings corresponding to the splited values.\n *     - shape: a length-2 vector of int32 representing the shape of the sparse\n * tensor, where the first value is N and the second value is the maximum number\n * of tokens in a single input entry.\n *\n * @doc {heading: 'Operations', subheading: 'String'}\n */\nfunction stringSplit_(\n    input: Tensor1D|TensorLike, delimiter: Scalar|ScalarLike,\n    skipEmpty = true): NamedTensorMap {\n  const $input = convertToTensor(input, 'input', 'stringSplit', 'string');\n  const $delimiter =\n      convertToTensor(delimiter, 'delimiter', 'stringSplit', 'string');\n\n  if ($input.rank !== 1) {\n    throw new Error(\n        `Input should be Tensor1D but received shape ${$input.shape}`);\n  }\n  if ($delimiter.rank !== 0) {\n    throw new Error(\n        `Delimiter should be a scalar but received shape ${$delimiter.shape}`);\n  }\n\n  const attrs: StringSplitAttrs = {skipEmpty};\n  const inputs: StringSplitInputs = {input: $input, delimiter: $delimiter};\n  const result: Tensor[] =\n      ENGINE.runKernel(StringSplit, inputs as {}, attrs as {});\n  return {indices: result[0], values: result[1], shape: result[2]};\n}\n\nexport const stringSplit = op({stringSplit_});\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {StringToHashBucketFast, StringToHashBucketFastAttrs, StringToHashBucketFastInputs} from '../../kernel_names';\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {op} from '../operation';\n\n/**\n * Converts each string in the input Tensor to its hash mod by a number of\n * buckets.\n *\n * The hash function is deterministic on the content of the string within the\n * process and will never change. However, it is not suitable for cryptography.\n * This function may be used when CPU time is scarce and inputs are trusted or\n * unimportant. There is a risk of adversaries constructing inputs that all hash\n * to the same bucket.\n *\n * ```js\n * const result = tf.string.stringToHashBucketFast(\n *   ['Hello', 'TensorFlow', '2.x'], 3);\n * result.print(); // [0, 2, 2]\n * ```\n * @param input: The strings to assign a hash bucket.\n * @param numBuckets: The number of buckets.\n * @return A Tensor of the same shape as the input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'String'}\n */\nfunction stringToHashBucketFast_(\n    input: Tensor|TensorLike, numBuckets: number): Tensor {\n  const $input =\n      convertToTensor(input, 'input', 'stringToHashBucketFast', 'string');\n  const attrs: StringToHashBucketFastAttrs = {numBuckets};\n\n  if (numBuckets <= 0) {\n    throw new Error(`Number of buckets must be at least 1`);\n  }\n\n  const inputs: StringToHashBucketFastInputs = {input: $input};\n  return ENGINE.runKernel(StringToHashBucketFast, inputs as {}, attrs as {});\n}\n\nexport const stringToHashBucketFast = op({stringToHashBucketFast_});\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Modularized ops.\nexport {abs} from './abs';\nexport {acos} from './acos';\nexport {acosh} from './acosh';\nexport {add} from './add';\nexport {addN} from './add_n';\nexport {all} from './all';\nexport {any} from './any';\nexport {argMax} from './arg_max';\nexport {argMin} from './arg_min';\nexport {asin} from './asin';\nexport {asinh} from './asinh';\nexport {atan} from './atan';\nexport {atan2} from './atan2';\nexport {atanh} from './atanh';\nexport {avgPool} from './avg_pool';\nexport {avgPool3d} from './avg_pool_3d';\nexport {basicLSTMCell} from './basic_lstm_cell';\nexport {batchToSpaceND} from './batch_to_space_nd';\nexport {batchNorm} from './batchnorm';\nexport {batchNorm2d} from './batchnorm2d';\nexport {batchNorm3d} from './batchnorm3d';\nexport {batchNorm4d} from './batchnorm4d';\nexport {bincount} from './bincount';\nexport {broadcastTo} from './broadcast_to';\nexport {buffer} from './buffer';\nexport {cast} from './cast';\nexport {ceil} from './ceil';\nexport {clipByValue} from './clip_by_value';\nexport {clone} from './clone';\nexport {complex} from './complex';\nexport {concat} from './concat';\nexport {concat1d} from './concat_1d';\nexport {concat2d} from './concat_2d';\nexport {concat3d} from './concat_3d';\nexport {concat4d} from './concat_4d';\nexport {conv1d} from './conv1d';\nexport {conv2d} from './conv2d';\nexport {conv2dTranspose} from './conv2d_transpose';\nexport {conv3d} from './conv3d';\nexport {conv3dTranspose} from './conv3d_transpose';\nexport {cos} from './cos';\nexport {cosh} from './cosh';\nexport {cumsum} from './cumsum';\nexport {denseBincount} from './dense_bincount';\nexport {depthToSpace} from './depth_to_space';\nexport {depthwiseConv2d} from './depthwise_conv2d';\nexport {diag} from './diag';\nexport {dilation2d} from './dilation2d';\nexport {div} from './div';\nexport {divNoNan} from './div_no_nan';\nexport {dot} from './dot';\nexport {einsum} from './einsum';\nexport {elu} from './elu';\nexport {equal} from './equal';\nexport {erf} from './erf';\nexport {exp} from './exp';\nexport {expandDims} from './expand_dims';\nexport {expm1} from './expm1';\nexport {eye} from './eye';\nexport {fill} from './fill';\nexport {floor} from './floor';\nexport {floorDiv} from './floorDiv';\nexport {gather} from './gather';\nexport {greater} from './greater';\nexport {greaterEqual} from './greater_equal';\nexport {imag} from './imag';\nexport {isFinite} from './is_finite';\nexport {isInf} from './is_inf';\nexport {isNaN} from './is_nan';\nexport {leakyRelu} from './leaky_relu';\nexport {less} from './less';\nexport {lessEqual} from './less_equal';\nexport {linspace} from './linspace';\nexport {localResponseNormalization} from './local_response_normalization';\nexport {log} from './log';\nexport {log1p} from './log1p';\nexport {logSigmoid} from './log_sigmoid';\nexport {logSoftmax} from './log_softmax';\nexport {logSumExp} from './log_sum_exp';\nexport {logicalAnd} from './logical_and';\nexport {logicalNot} from './logical_not';\nexport {logicalOr} from './logical_or';\nexport {logicalXor} from './logical_xor';\nexport {matMul} from './mat_mul';\nexport {max} from './max';\nexport {maxPool} from './max_pool';\nexport {maxPool3d} from './max_pool_3d';\nexport {maxPoolWithArgmax} from './max_pool_with_argmax';\nexport {maximum} from './maximum';\nexport {mean} from './mean';\nexport {meshgrid} from './meshgrid';\nexport {min} from './min';\nexport {minimum} from './minimum';\nexport {mirrorPad} from './mirror_pad';\nexport {mod} from './mod';\nexport {moments} from './moments';\nexport {mul} from './mul';\nexport {LSTMCellFunc, multiRNNCell} from './multi_rnn_cell';\nexport {multinomial} from './multinomial';\nexport {neg} from './neg';\nexport {notEqual} from './not_equal';\nexport {oneHot} from './one_hot';\nexport {ones} from './ones';\nexport {onesLike} from './ones_like';\nexport {outerProduct} from './outer_product';\nexport {pad} from './pad';\nexport {pad1d} from './pad1d';\nexport {pad2d} from './pad2d';\nexport {pad3d} from './pad3d';\nexport {pad4d} from './pad4d';\nexport {pool} from './pool';\nexport {pow} from './pow';\nexport {prelu} from './prelu';\nexport {print} from './print';\nexport {prod} from './prod';\nexport {rand} from './rand';\nexport {randomGamma} from './random_gamma';\nexport {randomNormal} from './random_normal';\nexport {randomUniform} from './random_uniform';\nexport {range} from './range';\nexport {real} from './real';\nexport {reciprocal} from './reciprocal';\nexport {relu} from './relu';\nexport {relu6} from './relu6';\nexport {reshape} from './reshape';\nexport {reverse} from './reverse';\nexport {reverse1d} from './reverse_1d';\nexport {reverse2d} from './reverse_2d';\nexport {reverse3d} from './reverse_3d';\nexport {reverse4d} from './reverse_4d';\nexport {round} from './round';\nexport {rsqrt} from './rsqrt';\nexport {scalar} from './scalar';\nexport {selu} from './selu';\nexport {separableConv2d} from './separable_conv2d';\nexport {setdiff1dAsync} from './setdiff1d_async';\nexport {sigmoid} from './sigmoid';\nexport {sign} from './sign';\nexport {sin} from './sin';\nexport {sinh} from './sinh';\nexport {slice} from './slice';\nexport {slice1d} from './slice1d';\nexport {slice2d} from './slice2d';\nexport {slice3d} from './slice3d';\nexport {slice4d} from './slice4d';\nexport {softmax} from './softmax';\nexport {softplus} from './softplus';\nexport {spaceToBatchND} from './space_to_batch_nd';\nexport {fft} from './spectral/fft';\nexport {ifft} from './spectral/ifft';\nexport {irfft} from './spectral/irfft';\nexport {rfft} from './spectral/rfft';\nexport {split} from './split';\nexport {sqrt} from './sqrt';\nexport {square} from './square';\nexport {squaredDifference} from './squared_difference';\nexport {squeeze} from './squeeze';\nexport {stack} from './stack';\nexport {step} from './step';\nexport {stridedSlice} from './strided_slice';\nexport {sub} from './sub';\nexport {sum} from './sum';\nexport {tan} from './tan';\nexport {tanh} from './tanh';\nexport {tensor} from './tensor';\nexport {tensor1d} from './tensor1d';\nexport {tensor2d} from './tensor2d';\nexport {tensor3d} from './tensor3d';\nexport {tensor4d} from './tensor4d';\nexport {tensor5d} from './tensor5d';\nexport {tensor6d} from './tensor6d';\nexport {tile} from './tile';\nexport {topk} from './topk';\nexport {truncatedNormal} from './truncated_normal';\nexport {unique} from './unique';\nexport {unsortedSegmentSum} from './unsorted_segment_sum';\nexport {unstack} from './unstack';\nexport {variable} from './variable';\nexport {where} from './where';\nexport {whereAsync} from './where_async';\nexport {zeros} from './zeros';\nexport {zerosLike} from './zeros_like';\n\nexport * from './boolean_mask';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\n\nexport {op, OP_SCOPE_SUFFIX} from './operation';\n\nimport {rfft} from './spectral/rfft';\nimport {fft} from './spectral/fft';\nimport {ifft} from './spectral/ifft';\nimport {irfft} from './spectral/irfft';\nconst spectral = {\n  fft,\n  ifft,\n  rfft,\n  irfft\n};\n\nimport * as fused from './fused_ops';\n\nimport {hammingWindow} from './signal/hamming_window';\nimport {hannWindow} from './signal/hann_window';\nimport {frame} from './signal/frame';\nimport {stft} from './signal/stft';\nconst signal = {\n  hammingWindow,\n  hannWindow,\n  frame,\n  stft,\n};\n\n// Image Ops namespace\nimport {cropAndResize} from './image/crop_and_resize';\nimport {flipLeftRight} from './image/flip_left_right';\nimport {rotateWithOffset} from './image/rotate_with_offset';\nimport {nonMaxSuppression} from './image/non_max_suppression';\nimport {nonMaxSuppressionAsync} from './image/non_max_suppression_async';\nimport {nonMaxSuppressionWithScore} from './image/non_max_suppression_with_score';\nimport {nonMaxSuppressionWithScoreAsync} from './image/non_max_suppression_with_score_async';\nimport {nonMaxSuppressionPadded} from './image/non_max_suppression_padded';\nimport {nonMaxSuppressionPaddedAsync} from './image/non_max_suppression_padded_async';\nimport {resizeBilinear} from './image/resize_bilinear';\nimport {resizeNearestNeighbor} from './image/resize_nearest_neighbor';\nimport {threshold} from './image/threshold';\nimport {transform} from './image/transform';\nconst image = {\n  flipLeftRight,\n  resizeNearestNeighbor,\n  resizeBilinear,\n  rotateWithOffset,\n  cropAndResize,\n  nonMaxSuppression,\n  nonMaxSuppressionAsync,\n  nonMaxSuppressionWithScore,\n  nonMaxSuppressionWithScoreAsync,\n  nonMaxSuppressionPadded,\n  nonMaxSuppressionPaddedAsync,\n  threshold,\n  transform\n};\n\n// linalg namespace\nimport {bandPart} from './linalg/band_part';\nimport {gramSchmidt} from './linalg/gram_schmidt';\nimport {qr} from './linalg/qr';\nconst linalg = {\n  bandPart,\n  gramSchmidt,\n  qr\n};\n\n// losses namespace;\nimport {absoluteDifference} from './losses/absolute_difference';\nimport {computeWeightedLoss} from './losses/compute_weighted_loss';\nimport {cosineDistance} from './losses/cosine_distance';\nimport {hingeLoss} from './losses/hinge_loss';\nimport {huberLoss} from './losses/huber_loss';\nimport {logLoss} from './losses/log_loss';\nimport {meanSquaredError} from './losses/mean_squared_error';\nimport {sigmoidCrossEntropy} from './losses/sigmoid_cross_entropy';\nimport {softmaxCrossEntropy} from './losses/softmax_cross_entropy';\nconst losses = {\n  absoluteDifference,\n  computeWeightedLoss,\n  cosineDistance,\n  hingeLoss,\n  huberLoss,\n  logLoss,\n  meanSquaredError,\n  sigmoidCrossEntropy,\n  softmaxCrossEntropy\n};\n\nimport {sparseFillEmptyRows} from './sparse/sparse_fill_empty_rows';\nimport {sparseReshape} from './sparse/sparse_reshape';\nimport {sparseSegmentMean} from './sparse/sparse_segment_mean';\nimport {sparseSegmentSum} from './sparse/sparse_segment_sum';\nconst sparse = {\n  sparseFillEmptyRows,\n  sparseReshape,\n  sparseSegmentMean,\n  sparseSegmentSum\n};\n\nimport {stringNGrams} from './string/string_n_grams';\nimport {stringSplit} from './string/string_split';\nimport {stringToHashBucketFast} from './string/string_to_hash_bucket_fast';\n// tslint:disable-next-line:variable-name\nconst string = {\n  stringNGrams,\n  stringSplit,\n  stringToHashBucketFast\n};\n\n// Second level exports.\nexport {image, linalg, losses, spectral, fused, signal, sparse, string};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {dispose} from '../globals';\nimport {variableGrads} from '../gradients';\nimport {scalar} from '../ops/ops';\nimport {Serializable} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedTensor, NamedTensorMap} from '../tensor_types';\n\n/**\n * A variable that belongs to an optimizer.\n *\n * The `originalName` field is required for keeping track of the canonical\n * name of the variable, which is usually the name of the model weight that\n * the variable is related to plus a suffix, e.g., 'dense1/kernel/momentum'.\n * The name of the `Variable` object itself cannot be used directly due to\n * possible deduplication: Every `Variable` must have a unique name but more\n * than one optimizer objects of the same type may be created for the same model\n * or the same `Variable`.\n */\nexport interface OptimizerVariable {\n  originalName: string;\n  variable: Variable;\n}\n\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport abstract class Optimizer extends Serializable {\n  protected iterations_: number;\n\n  /**\n   * Executes `f()` and minimizes the scalar output of `f()` by computing\n   * gradients of y with respect to the list of trainable variables provided by\n   * `varList`. If no list is provided, it defaults to all trainable variables.\n   *\n   * @param f The function to execute and whose output to minimize.\n   * @param returnCost Whether to return the scalar cost value produced by\n   * executing `f()`.\n   * @param varList An optional list of variables to update. If specified, only\n   * the trainable variables in varList will be updated by minimize. Defaults to\n   * all trainable variables.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers'}\n   */\n  minimize(f: () => Scalar, returnCost = false, varList?: Variable[]): Scalar\n      |null {\n    const {value, grads} = this.computeGradients(f, varList);\n\n    if (varList != null) {\n      const gradArray: NamedTensor[] =\n          varList.map(v => ({name: v.name, tensor: grads[v.name]}));\n      this.applyGradients(gradArray);\n    } else {\n      this.applyGradients(grads);\n    }\n\n    // Dispose gradients.\n    dispose(grads);\n\n    if (returnCost) {\n      return value;\n    } else {\n      value.dispose();\n      return null;\n    }\n  }\n\n  /**\n   * The number of iterations that this optimizer instance has been invoked for.\n   */\n  get iterations(): number {\n    if (this.iterations_ == null) {\n      this.iterations_ = 0;\n    }\n    return this.iterations_;\n  }\n\n  protected incrementIterations() {\n    this.iterations_ = this.iterations + 1;\n  }\n\n  /**\n   * Executes f() and computes the gradient of the scalar output of f() with\n   * respect to the list of trainable variables provided by `varList`. If no\n   * list is provided, it defaults to all trainable variables.\n   *\n   * @param f The function to execute and whose output to use for computing\n   * gradients with respect to variables.\n   * @param varList An optional list of variables to compute gradients with\n   * respect to. If specified, only the trainable variables in varList will have\n   * gradients computed with respect to. Defaults to all trainable variables.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers'}\n   */\n  computeGradients(f: () => Scalar, varList?: Variable[]):\n      {value: Scalar, grads: NamedTensorMap} {\n    return variableGrads(f, varList);\n  }\n\n  /**\n   * Updates variables by using the computed gradients.\n   *\n   * @param variableGradients A mapping of variable name to its gradient value.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers'}\n   */\n  abstract applyGradients(variableGradients: NamedTensorMap|\n                          NamedTensor[]): void;\n\n  /**\n   * Dispose the variables (if any) owned by this optimizer instance.\n   */\n  dispose(): void {\n    if (this.iterations_ != null) {\n      dispose(this.iterations_);\n    }\n  }\n\n  async saveIterations(): Promise<NamedTensor> {\n    if (this.iterations_ == null) {\n      this.iterations_ = 0;\n    }\n    return {\n      name: 'iter',  // Named for Python compatibility.\n      // TODO(cais): Use 'int64' type when available.\n      tensor: scalar(this.iterations_, 'int32')\n    };\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    throw new Error('getWeights() is not implemented for this optimizer yet.');\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    throw new Error(\n        `setWeights() is not implemented for this optimizer class ` +\n        `${this.getClassName()}`);\n  }\n\n  /**\n   * Extract the first element of the weight values and set it\n   * as the iterations counter variable of this instance of optimizer.\n   *\n   * @param weightValues\n   * @returns Weight values with the first element consumed and excluded.\n   */\n  protected async extractIterations(weightValues: NamedTensor[]):\n      Promise<NamedTensor[]> {\n    this.iterations_ = (await weightValues[0].tensor.data())[0];\n    return weightValues.slice(1);\n  }\n}\n\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n  value: (instance: Optimizer) => {\n    return instance.minimize != null && instance.computeGradients != null &&\n        instance.applyGradients != null;\n  }\n});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {sqrt} from '../ops/ops';\nimport {square} from '../ops/square';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'Adadelta';  // Name matters for Python compatibility.\n  private accumulatedGrads: OptimizerVariable[] = [];\n  private accumulatedUpdates: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, protected rho: number,\n      protected epsilon: number = null) {\n    super();\n\n    if (epsilon == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n\n  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {\n    const variableNames = Array.isArray(variableGradients) ?\n        variableGradients.map(item => item.name) :\n        Object.keys(variableGradients);\n\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      const trainable = false;\n      if (this.accumulatedGrads[i] == null) {\n        this.accumulatedGrads[i] = {\n          originalName: `${name}/accum_grad`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedUpdates[i] == null) {\n        this.accumulatedUpdates[i] = {\n          originalName: `${name}/accum_var`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n\n      const gradient = Array.isArray(variableGradients) ?\n          variableGradients[i].tensor :\n          variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n\n      const accumulatedGrad = this.accumulatedGrads[i].variable;\n      const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n\n      tidy(() => {\n        const newAccumulatedGrad =\n            add(mul(accumulatedGrad, this.rho),\n                mul(square(gradient), 1 - this.rho));\n\n        const updates =\n            mul(div(sqrt(add(accumulatedUpdate, this.epsilon)),\n                    sqrt(add(accumulatedGrad, this.epsilon))),\n                gradient);\n\n        const newAccumulatedUpdate =\n            add(mul(accumulatedUpdate, this.rho),\n                mul(square(updates), 1 - this.rho));\n\n        accumulatedGrad.assign(newAccumulatedGrad);\n        accumulatedUpdate.assign(newAccumulatedUpdate);\n\n        const newValue = add(mul(updates, -this.learningRate), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n\n  dispose(): void {\n    if (this.accumulatedUpdates != null) {\n      dispose(this.accumulatedGrads.map(v => v.variable));\n      dispose(this.accumulatedUpdates.map(v => v.variable));\n    }\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    const variables: OptimizerVariable[] =\n        [...this.accumulatedGrads, ...this.accumulatedUpdates];\n    return [await this.saveIterations()].concat(\n        variables.map(v => ({name: v.originalName, tensor: v.variable})));\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    const variableCount = weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedGrads =\n        weightValues.slice(0, variableCount).map(v => ({\n                                                   originalName: v.name,\n                                                   variable: v.tensor.variable(\n                                                       trainable)\n                                                 }));\n    this.accumulatedUpdates =\n        weightValues.slice(variableCount, variableCount * 2)\n            .map(v => ({\n                   originalName: v.name,\n                   variable: v.tensor.variable(trainable)\n                 }));\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'rho': this.rho,\n      'epsilon': this.epsilon\n    };\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config['learningRate'], config['rho'], config['epsilon']);\n  }\n}\nregisterClass(AdadeltaOptimizer);\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {fill} from '../ops/fill';\nimport {mul} from '../ops/mul';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'Adagrad';  // Note: Name matters for Python compatibility.\n\n  private accumulatedGrads: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, private initialAccumulatorValue = 0.1) {\n    super();\n  }\n\n  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {\n    const variableNames = Array.isArray(variableGradients) ?\n        variableGradients.map(item => item.name) :\n        Object.keys(variableGradients);\n\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      if (this.accumulatedGrads[i] == null) {\n        const trainable = false;\n        this.accumulatedGrads[i] = {\n          originalName: `${name}/accumulator`,\n          variable: tidy(\n              () => fill(value.shape, this.initialAccumulatorValue)\n                        .variable(trainable))\n        };\n      }\n\n      const gradient = Array.isArray(variableGradients) ?\n          variableGradients[i].tensor :\n          variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n\n      const accumulatedGrad = this.accumulatedGrads[i].variable;\n\n      tidy(() => {\n        const newAccumulatedGrad = add(accumulatedGrad, square(gradient));\n        accumulatedGrad.assign(newAccumulatedGrad);\n\n        const newValue = add(\n            mul(div(gradient,\n                    sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))),\n                -this.learningRate),\n            value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n\n  dispose(): void {\n    if (this.accumulatedGrads != null) {\n      dispose(this.accumulatedGrads.map(v => v.variable));\n    }\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    return [await this.saveIterations()].concat(this.accumulatedGrads.map(\n        v => ({name: v.originalName, tensor: v.variable})));\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    const trainable = false;\n    this.accumulatedGrads = weightValues.map(\n        v => ({originalName: v.name, variable: v.tensor.variable(trainable)}));\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'initialAccumulatorValue': this.initialAccumulatorValue,\n    };\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config['learningRate'], config['initialAccumulatorValue']);\n  }\n}\nregisterClass(AdagradOptimizer);\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {pow} from '../ops/pow';\nimport {scalar} from '../ops/scalar';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\nimport {sub} from '../ops/sub';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Variable} from '../tensor';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\nexport class AdamOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'Adam';  // Note: Name matters for Python compatibility.\n  private accBeta1: Variable;\n  private accBeta2: Variable;\n\n  private accumulatedFirstMoment: OptimizerVariable[] = [];\n  private accumulatedSecondMoment: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, protected beta1: number,\n      protected beta2: number, protected epsilon: number = null) {\n    super();\n    tidy(() => {\n      // accB* will be updated by batch.\n      this.accBeta1 = scalar(beta1).variable();\n      this.accBeta2 = scalar(beta2).variable();\n    });\n\n    if (epsilon == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n\n  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {\n    const varNames = Array.isArray(variableGradients) ?\n        variableGradients.map(v => v.name) :\n        Object.keys(variableGradients);\n    tidy(() => {\n      const oneMinusAccBeta1 = sub(1, this.accBeta1);\n      const oneMinusAccBeta2 = sub(1, this.accBeta2);\n\n      varNames.forEach((name, i) => {\n        const value = ENGINE.registeredVariables[name];\n        const trainable = false;\n        if (this.accumulatedFirstMoment[i] == null) {\n          this.accumulatedFirstMoment[i] = {\n            originalName: `${name}/m`,\n            variable: tidy(() => zerosLike(value).variable(trainable))\n          };\n        }\n        if (this.accumulatedSecondMoment[i] == null) {\n          this.accumulatedSecondMoment[i] = {\n            originalName: `${name}/v`,\n            variable: tidy(() => zerosLike(value).variable(trainable))\n          };\n        }\n\n        const gradient = Array.isArray(variableGradients) ?\n            variableGradients[i].tensor :\n            variableGradients[name];\n        if (gradient == null) {\n          return;\n        }\n\n        const firstMoment = this.accumulatedFirstMoment[i].variable;\n        const secondMoment = this.accumulatedSecondMoment[i].variable;\n\n        const newFirstMoment =\n            add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n        const newSecondMoment =\n            add(mul(secondMoment, this.beta2),\n                mul(square(gradient), 1 - this.beta2));\n\n        const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n        const biasCorrectedSecondMoment =\n            div(newSecondMoment, oneMinusAccBeta2);\n\n        firstMoment.assign(newFirstMoment);\n        secondMoment.assign(newSecondMoment);\n\n        const newValue =\n            add(mul(div(biasCorrectedFirstMoment,\n                        add(sqrt(biasCorrectedSecondMoment), this.epsilon)),\n                    -this.learningRate),\n                value);\n        value.assign(newValue);\n      });\n\n      this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n      this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n    });\n    this.incrementIterations();\n  }\n\n  dispose(): void {\n    this.accBeta1.dispose();\n    this.accBeta2.dispose();\n\n    if (this.accumulatedFirstMoment != null) {\n      dispose(this.accumulatedFirstMoment.map(v => v.variable));\n    }\n    if (this.accumulatedSecondMoment != null) {\n      dispose(this.accumulatedSecondMoment.map(v => v.variable));\n    }\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    const variables: OptimizerVariable[] =\n        [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n    return [await this.saveIterations()].concat(\n        variables.map(v => ({name: v.originalName, tensor: v.variable})));\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    tidy(() => {\n      this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n      this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n    });\n\n    const variableCount = weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedFirstMoment =\n        weightValues.slice(0, variableCount).map(v => ({\n                                                   originalName: v.name,\n                                                   variable: v.tensor.variable(\n                                                       trainable)\n                                                 }));\n    this.accumulatedSecondMoment =\n        weightValues.slice(variableCount, variableCount * 2)\n            .map(v => ({\n                   originalName: v.name,\n                   variable: v.tensor.variable(trainable)\n                 }));\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'beta1': this.beta1,\n      'beta2': this.beta2,\n      'epsilon': this.epsilon,\n    };\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config['learningRate'], config['beta1'], config['beta2'],\n        config['epsilon']);\n  }\n}\nregisterClass(AdamOptimizer);\n","﻿/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {abs} from '../ops/abs';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {maximum} from '../ops/maximum';\nimport {mul} from '../ops/mul';\nimport {scalar} from '../ops/scalar';\nimport {sub} from '../ops/sub';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Variable} from '../tensor';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\nexport class AdamaxOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'Adamax';  // Note: Name matters for Python compatbility.\n  private accBeta1: Variable;\n  private iteration: Variable;\n\n  private accumulatedFirstMoment: OptimizerVariable[] = [];\n  private accumulatedWeightedInfNorm: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, protected beta1: number,\n      protected beta2: number, protected epsilon: number = null,\n      protected decay = 0.0) {\n    super();\n\n    tidy(() => {\n      this.iteration = scalar(0).variable();\n      this.accBeta1 = scalar(beta1).variable();\n    });\n\n    if (epsilon == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n\n  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {\n    const variableNames = Array.isArray(variableGradients) ?\n        variableGradients.map(item => item.name) :\n        Object.keys(variableGradients);\n\n    tidy(() => {\n      const oneMinusAccBeta1 = sub(1, this.accBeta1);\n      const lr =\n          div(-this.learningRate, add(mul(this.iteration, this.decay), 1));\n\n      variableNames.forEach((name, i) => {\n        const value = ENGINE.registeredVariables[name];\n        const trainable = false;\n        if (this.accumulatedFirstMoment[i] == null) {\n          this.accumulatedFirstMoment[i] = {\n            originalName: `${name}/m`,\n            variable: zerosLike(value).variable(trainable)\n          };\n        }\n        if (this.accumulatedWeightedInfNorm[i] == null) {\n          this.accumulatedWeightedInfNorm[i] = {\n            originalName: `${name}/v`,\n            variable: zerosLike(value).variable(trainable)\n          };\n        }\n\n        const gradient = Array.isArray(variableGradients) ?\n            variableGradients[i].tensor :\n            variableGradients[name];\n        if (gradient == null) {\n          return;\n        }\n\n        const firstMoment = this.accumulatedFirstMoment[i].variable;\n        const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n\n        const newFirstMoment =\n            add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n\n        const ut0 = mul(weightedInfNorm, this.beta2);\n        const ut1 = abs(gradient);\n\n        const newWeightedInfNorm = maximum(ut0, ut1);\n\n        firstMoment.assign(newFirstMoment);\n        weightedInfNorm.assign(newWeightedInfNorm);\n\n        const newValue =\n            add(mul(div(lr, oneMinusAccBeta1),\n                    div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))),\n                value);\n\n        value.assign(newValue);\n      });\n\n      this.iteration.assign(add(this.iteration, 1));\n      this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n    });\n    this.incrementIterations();\n  }\n\n  dispose(): void {\n    this.accBeta1.dispose();\n    this.iteration.dispose();\n\n    if (this.accumulatedFirstMoment != null) {\n      dispose(this.accumulatedFirstMoment.map(v => v.variable));\n    }\n    if (this.accumulatedWeightedInfNorm != null) {\n      dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));\n    }\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    throw new Error('getWeights() is not implemented for Adamax yet.');\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    throw new Error('setWeights() is not implemented for Adamax yet.');\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'beta1': this.beta1,\n      'beta2': this.beta2,\n      'epsilon': this.epsilon,\n      'decay': this.decay\n    };\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config['learningRate'], config['beta1'], config['beta2'],\n        config['epsilon'], config['decay']);\n  }\n}\nregisterClass(AdamaxOptimizer);\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {keep, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {mul} from '../ops/mul';\nimport {scalar} from '../ops/scalar';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedTensor, NamedTensorMap} from '../tensor_types';\n\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'SGD';  // Note: Name matters for Python compatibility.\n  protected c: Scalar;\n\n  constructor(protected learningRate: number) {\n    super();\n    this.setLearningRate(learningRate);\n  }\n\n  applyGradients(variableGradients: NamedTensorMap|NamedTensor[]) {\n    const varNames = Array.isArray(variableGradients) ?\n        variableGradients.map(v => v.name) :\n        Object.keys(variableGradients);\n    varNames.forEach((name, i) => {\n      const gradient = Array.isArray(variableGradients) ?\n          variableGradients[i].tensor :\n          variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const value = ENGINE.registeredVariables[name];\n      tidy(() => {\n        const newValue = add(mul(this.c, gradient), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n\n  /**\n   * Sets the learning rate of the optimizer.\n   */\n  setLearningRate(learningRate: number) {\n    this.learningRate = learningRate;\n    if (this.c != null) {\n      this.c.dispose();\n    }\n    this.c = keep(scalar(-learningRate));\n  }\n\n  dispose() {\n    this.c.dispose();\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    return [await this.saveIterations()];\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    if (weightValues.length !== 0) {\n      throw new Error('SGD optimizer does not have settable weights.');\n    }\n  }\n\n  getConfig(): ConfigDict {\n    return {'learningRate': this.learningRate};\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config['learningRate']);\n  }\n}\nregisterClass(SGDOptimizer);\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {mul} from '../ops/mul';\nimport {scalar} from '../ops/scalar';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Tensor} from '../tensor';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {OptimizerVariable} from './optimizer';\nimport {SGDOptimizer} from './sgd_optimizer';\n\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n  /** @nocollapse */\n  static className = 'Momentum';  // Name matters for Python compatibility.\n  private m: Scalar;\n  private accumulations: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, private momentum: number,\n      private useNesterov = false) {\n    super(learningRate);\n    this.m = scalar(this.momentum);\n  }\n\n  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {\n    const variableNames = Array.isArray(variableGradients) ?\n        variableGradients.map(item => item.name) :\n        Object.keys(variableGradients);\n\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      if (this.accumulations[i] == null) {\n        const trainable = false;\n        this.accumulations[i] = {\n          originalName: `${name}/momentum`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n\n      const accumulation = this.accumulations[i].variable;\n      const gradient = Array.isArray(variableGradients) ?\n          variableGradients[i].tensor :\n          variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n\n      tidy(() => {\n        let newValue: Tensor;\n        const newAccumulation = add(mul(this.m, accumulation), gradient);\n        if (this.useNesterov) {\n          newValue = add(\n              mul(this.c, add(gradient, mul(newAccumulation, this.m))), value);\n        } else {\n          newValue = add(mul(this.c, newAccumulation), value);\n        }\n        accumulation.assign(newAccumulation);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n\n  dispose(): void {\n    this.m.dispose();\n    if (this.accumulations != null) {\n      dispose(this.accumulations.map(v => v.variable));\n    }\n  }\n\n  /**\n   * Sets the momentum of the optimizer.\n   *\n   * @param momentum\n   */\n  setMomentum(momentum: number) {\n    this.momentum = momentum;\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    return [await this.saveIterations()].concat(this.accumulations.map(\n        v => ({name: v.originalName, tensor: v.variable})));\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    const trainable = false;\n    this.accumulations = weightValues.map(\n        v => ({originalName: v.name, variable: v.tensor.variable(trainable)}));\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'momentum': this.momentum,\n      'useNesterov': this.useNesterov\n    };\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config['learningRate'], config['momentum'], config['useNesterov']);\n  }\n}\nregisterClass(MomentumOptimizer);\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\nimport {sub} from '../ops/sub';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {NamedTensor, NamedTensorMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'RMSProp';  // Note: Name matters for Python compatibility.\n  private centered: boolean;\n\n  private accumulatedMeanSquares: OptimizerVariable[] = [];\n  private accumulatedMoments: OptimizerVariable[] = [];\n  private accumulatedMeanGrads: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, protected decay = 0.9,\n      protected momentum = 0.0, protected epsilon: number = null,\n      centered = false) {\n    super();\n\n    this.centered = centered;\n\n    if (epsilon == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n    if (learningRate == null) {\n      throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n    }\n  }\n\n  applyGradients(variableGradients: NamedTensorMap|NamedTensor[]) {\n    const variableNames = Array.isArray(variableGradients) ?\n        variableGradients.map(item => item.name) :\n        Object.keys(variableGradients);\n\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      const trainable = false;\n      if (this.accumulatedMeanSquares[i] == null) {\n        this.accumulatedMeanSquares[i] = {\n          originalName: `${name}/rms`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedMoments[i] == null) {\n        this.accumulatedMoments[i] = {\n          originalName: `${name}/momentum`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedMeanGrads[i] == null && this.centered) {\n        this.accumulatedMeanGrads[i] = {\n          originalName: `${name}/mg`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n\n      const gradient = Array.isArray(variableGradients) ?\n          variableGradients[i].tensor :\n          variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n\n      const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n      const accumulatedMoments = this.accumulatedMoments[i].variable;\n      tidy(() => {\n        const newAccumulatedMeanSquare =\n            add(mul(accumulatedMeanSquare, this.decay),\n                mul(square(gradient), 1 - this.decay));\n\n        if (this.centered) {\n          const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n          // Centered gradient\n          const newAccumulatedMeanGrad =\n              add(mul(accumulatedMeanGrad, this.decay),\n                  mul(gradient, 1 - this.decay));\n\n          const gradContribution =\n              div(mul(gradient, this.learningRate),\n                  sqrt(\n                      sub(newAccumulatedMeanSquare,\n                          add(square(newAccumulatedMeanGrad), this.epsilon))));\n          const newAccumulatedMoments =\n              add(mul(accumulatedMoments, this.momentum), gradContribution);\n\n          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n          accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n          accumulatedMoments.assign(newAccumulatedMoments);\n\n          const newValue = sub(value, newAccumulatedMoments);\n          value.assign(newValue);\n        } else {\n          // Plain gradient\n          const newAccumulatedMeanSquare =\n              add(mul(accumulatedMeanSquare, this.decay),\n                  mul(square(gradient), 1 - this.decay));\n\n          const newAccumulatedMoments =\n              add(mul(accumulatedMoments, this.momentum),\n                  div(mul(gradient, this.learningRate),\n                      sqrt(add(newAccumulatedMeanSquare, this.epsilon))));\n\n          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n          accumulatedMoments.assign(newAccumulatedMoments);\n\n          const newValue = sub(value, newAccumulatedMoments);\n          value.assign(newValue);\n        }\n      });\n    });\n    this.incrementIterations();\n  }\n\n  dispose(): void {\n    if (this.accumulatedMeanSquares != null) {\n      dispose(this.accumulatedMeanSquares.map(v => v.variable));\n    }\n    if (this.accumulatedMeanGrads != null && this.centered) {\n      dispose(this.accumulatedMeanGrads.map(v => v.variable));\n    }\n    if (this.accumulatedMoments != null) {\n      dispose(this.accumulatedMoments.map(v => v.variable));\n    }\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    const variables: OptimizerVariable[] =\n        [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n    if (this.centered) {\n      variables.push(...this.accumulatedMeanGrads);\n    }\n    return [await this.saveIterations()].concat(\n        variables.map(v => ({name: v.originalName, tensor: v.variable})));\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    const variableCount =\n        this.centered ? weightValues.length / 3 : weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedMeanSquares =\n        weightValues.slice(0, variableCount).map(v => ({\n                                                   originalName: v.name,\n                                                   variable: v.tensor.variable(\n                                                       trainable)\n                                                 }));\n    this.accumulatedMoments =\n        weightValues.slice(variableCount, variableCount * 2)\n            .map(v => ({\n                   originalName: v.name,\n                   variable: v.tensor.variable(trainable)\n                 }));\n    if (this.centered) {\n      this.accumulatedMeanGrads =\n          weightValues.slice(variableCount * 2, variableCount * 3)\n              .map(v => ({\n                     originalName: v.name,\n                     variable: v.tensor.variable(trainable)\n                   }));\n    }\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'decay': this.decay,\n      'momentum': this.momentum,\n      'epsilon': this.epsilon,\n      'centered': this.centered\n    };\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config['learningRate'], config['decay'], config['momentum'],\n        config['epsilon'], config['centered']);\n  }\n}\nregisterClass(RMSPropOptimizer);\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AdadeltaOptimizer} from './adadelta_optimizer';\nimport {AdagradOptimizer} from './adagrad_optimizer';\nimport {AdamOptimizer} from './adam_optimizer';\nimport {AdamaxOptimizer} from './adamax_optimizer';\nimport {MomentumOptimizer} from './momentum_optimizer';\nimport {RMSPropOptimizer} from './rmsprop_optimizer';\nimport {SGDOptimizer} from './sgd_optimizer';\n\nexport class OptimizerConstructors {\n  /**\n   * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n   *\n   * ```js\n   * // Fit a quadratic function by learning the coefficients a, b, c.\n   * const xs = tf.tensor1d([0, 1, 2, 3]);\n   * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n   *\n   * const a = tf.scalar(Math.random()).variable();\n   * const b = tf.scalar(Math.random()).variable();\n   * const c = tf.scalar(Math.random()).variable();\n   *\n   * // y = a * x^2 + b * x + c.\n   * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n   * const loss = (pred, label) => pred.sub(label).square().mean();\n   *\n   * const learningRate = 0.01;\n   * const optimizer = tf.train.sgd(learningRate);\n   *\n   * // Train the model.\n   * for (let i = 0; i < 10; i++) {\n   *   optimizer.minimize(() => loss(f(xs), ys));\n   * }\n   *\n   * // Make predictions.\n   * console.log(\n   *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n   * const preds = f(xs).dataSync();\n   * preds.forEach((pred, i) => {\n   *   console.log(`x: ${i}, pred: ${pred}`);\n   * });\n   * ```\n   *\n   * @param learningRate The learning rate to use for the SGD algorithm.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static sgd(learningRate: number): SGDOptimizer {\n    return new SGDOptimizer(learningRate);\n  }\n\n  /**\n   * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n   * descent.\n   *\n   * See\n   * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n   * http://proceedings.mlr.press/v28/sutskever13.pdf)\n   *\n   * @param learningRate The learning rate to use for the Momentum gradient\n   * descent algorithm.\n   * @param momentum The momentum to use for the momentum gradient descent\n   * algorithm.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static momentum(learningRate: number, momentum: number, useNesterov = false):\n      MomentumOptimizer {\n    return new MomentumOptimizer(learningRate, momentum, useNesterov);\n  }\n\n  /**\n   * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n   * descent. This implementation uses plain momentum and is not centered\n   * version of RMSProp.\n   *\n   * See\n   * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n   * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n   *\n   * @param learningRate The learning rate to use for the RMSProp gradient\n   * descent algorithm.\n   * @param decay The discounting factor for the history/coming gradient.\n   * @param momentum The momentum to use for the RMSProp gradient descent\n   * algorithm.\n   * @param epsilon Small value to avoid zero denominator.\n   * @param centered If true, gradients are normalized by the estimated\n   * variance of the gradient.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static rmsprop(\n      learningRate: number, decay = .9, momentum = 0.0, epsilon: number = null,\n      centered = false): RMSPropOptimizer {\n    return new RMSPropOptimizer(\n        learningRate, decay, momentum, epsilon, centered);\n  }\n\n  /**\n   * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n   * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n   *\n   * @param learningRate The learning rate to use for the Adam gradient\n   * descent algorithm.\n   * @param beta1 The exponential decay rate for the 1st moment estimates.\n   * @param beta2 The exponential decay rate for the 2nd moment estimates.\n   * @param epsilon A small constant for numerical stability.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adam(\n      learningRate = 0.001, beta1 = 0.9, beta2 = 0.999,\n      epsilon: number = null): AdamOptimizer {\n    return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n  }\n\n  /**\n   * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n   * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n   *\n   * @param learningRate The learning rate to use for the Adadelta gradient\n   * descent algorithm.\n   * @param rho The learning rate decay over each update.\n   * @param epsilon A constant epsilon used to better condition the grad\n   * update.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adadelta(learningRate = .001, rho = .95, epsilon: number = null):\n      AdadeltaOptimizer {\n    return new AdadeltaOptimizer(learningRate, rho, epsilon);\n  }\n\n  /**\n   * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n   * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n   *\n   * @param learningRate The learning rate to use for the Adamax gradient\n   * descent algorithm.\n   * @param beta1 The exponential decay rate for the 1st moment estimates.\n   * @param beta2 The exponential decay rate for the 2nd moment estimates.\n   * @param epsilon A small constant for numerical stability.\n   * @param decay The learning rate decay over each update.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adamax(\n      learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon: number = null,\n      decay = 0.0): AdamaxOptimizer {\n    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n  }\n\n  /**\n   * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n   * See\n   * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n   * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n   * or\n   * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n   * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n   *\n   * @param learningRate The learning rate to use for the Adagrad gradient\n   * descent algorithm.\n   * @param initialAccumulatorValue Starting value for the accumulators, must be\n   * positive.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adagrad(learningRate: number, initialAccumulatorValue = 0.1):\n      AdagradOptimizer {\n    return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// So typings can propagate.\nimport {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';\nimport {AdagradOptimizer} from './optimizers/adagrad_optimizer';\nimport {AdamOptimizer} from './optimizers/adam_optimizer';\nimport {AdamaxOptimizer} from './optimizers/adamax_optimizer';\nimport {MomentumOptimizer} from './optimizers/momentum_optimizer';\nimport {OptimizerConstructors} from './optimizers/optimizer_constructors';\nimport {RMSPropOptimizer} from './optimizers/rmsprop_optimizer';\nimport {SGDOptimizer} from './optimizers/sgd_optimizer';\n\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\n\nexport const train = {\n  sgd: OptimizerConstructors.sgd,\n  momentum: OptimizerConstructors.momentum,\n  adadelta: OptimizerConstructors.adadelta,\n  adagrad: OptimizerConstructors.adagrad,\n  rmsprop: OptimizerConstructors.rmsprop,\n  adamax: OptimizerConstructors.adamax,\n  adam: OptimizerConstructors.adam\n};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nconst delayCallback: Function = (() => {\n  if (typeof requestAnimationFrame !== 'undefined') {\n    return requestAnimationFrame;\n  } else if (typeof setImmediate !== 'undefined') {\n    return setImmediate;\n  }\n  return (f: Function) => f();  // no delays\n})();\n\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction nextFrame(): Promise<void> {\n  return new Promise<void>(resolve => delayCallback(() => resolve()));\n}\n\nexport {nextFrame};\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\nexport function assertParamsConsistent(shapes: number[][], axis: number) {\n  const rank = shapes[0].length;\n  shapes.forEach((shape, i) => {\n    util.assert(\n        shape.length === rank,\n        () =>\n            `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n  });\n\n  util.assert(\n      axis >= 0 && axis < rank,\n      () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n\n  const firstShape = shapes[0];\n  shapes.forEach((shape, i) => {\n    for (let r = 0; r < rank; r++) {\n      util.assert(\n          (r === axis) || (shape[r] === firstShape[r]),\n          () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n              `does not match the shape of the rest (${firstShape}) ` +\n              `along the non-concatenated axis ${i}.`);\n    }\n  });\n}\n\nexport function computeOutShape(shapes: number[][], axis: number): number[] {\n  const outputShape = shapes[0].slice();\n  for (let i = 1; i < shapes.length; i++) {\n    outputShape[axis] += shapes[i][axis];\n  }\n  return outputShape;\n}\n","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport {nearestDivisor} from '../util';\n\nexport const PARALLELIZE_THRESHOLD = 30;\n\nexport interface ReduceInfo {\n  windowSize: number;\n  batchSize: number;\n  inSize: number;\n  outSize: number;\n}\n\nexport function computeOptimalWindowSize(inSize: number): number {\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    return inSize;\n  }\n  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Returns the image center in pixels.\nexport function getImageCenter(\n    center: number|[number, number], imageHeight: number,\n    imageWidth: number): [number, number] {\n  const centerX =\n      imageWidth * (typeof center === 'number' ? center : center[0]);\n  const centerY =\n      imageHeight * (typeof center === 'number' ? center : center[1]);\n  return [centerX, centerY];\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(\n    inputShape: number[], blockShape: number[], prod: number,\n    batchToSpace = true): number[] {\n  let reshaped: number[] = [];\n  if (batchToSpace) {\n    reshaped = reshaped.concat(blockShape.slice(0));\n    reshaped.push(inputShape[0] / prod);\n    reshaped = reshaped.concat(inputShape.slice(1));\n  } else {\n    reshaped = reshaped.concat(inputShape[0]);\n    const spatialLength = blockShape.length;\n    for (let i = 0; i < spatialLength; ++i) {\n      reshaped =\n          reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n    }\n    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n  }\n  return reshaped;\n}\n\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(\n    reshapedRank: number, blockShapeRank: number,\n    batchToSpace = true): number[] {\n  const permuted = [];\n  if (batchToSpace) {\n    permuted.push(blockShapeRank);\n    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n      if (i <= 2 * blockShapeRank) {\n        permuted.push(i);\n        permuted.push(i - (blockShapeRank + 1));\n      } else {\n        permuted.push(i);\n      }\n    }\n  } else {\n    const permutedBeforeBatch = [];\n    const permutedAfterBatch = [];\n    for (let i = 1; i < reshapedRank; ++i) {\n      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n        permutedAfterBatch.push(i);\n      } else {\n        permutedBeforeBatch.push(i);\n      }\n    }\n    permuted.push(...permutedBeforeBatch);\n    permuted.push(0);\n    permuted.push(...permutedAfterBatch);\n  }\n  return permuted;\n}\n\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(\n    inputShape: number[], blockShape: number[], prod: number,\n    batchToSpace = true): number[] {\n  const reshapedPermuted = [];\n\n  if (batchToSpace) {\n    reshapedPermuted.push(inputShape[0] / prod);\n  } else {\n    reshapedPermuted.push(inputShape[0] * prod);\n  }\n\n  for (let i = 1; i < inputShape.length; ++i) {\n    if (i <= blockShape.length) {\n      if (batchToSpace) {\n        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n      } else {\n        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n      }\n    } else {\n      reshapedPermuted.push(inputShape[i]);\n    }\n  }\n\n  return reshapedPermuted;\n}\n\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(\n    crops: number[][], blockShape: number): number[] {\n  const sliceBeginCoords = [0];\n  for (let i = 0; i < blockShape; ++i) {\n    sliceBeginCoords.push(crops[i][0]);\n  }\n  return sliceBeginCoords;\n}\n\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(\n    uncroppedShape: number[], crops: number[][], blockShape: number): number[] {\n  const sliceSize = uncroppedShape.slice(0, 1);\n  for (let i = 0; i < blockShape; ++i) {\n    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n  }\n\n  return sliceSize;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from './environment';\n\nexport function warn(...msg: Array<{}>): void {\n  if (!env().getBool('IS_TEST')) {\n    console.warn(...msg);\n  }\n}\n\nexport function log(...msg: Array<{}>): void {\n  if (!env().getBool('IS_TEST')) {\n    console.log(...msg);\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TypedArray} from '../types';\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(\n    real: Float32Array, imag: Float32Array): Float32Array {\n  if (real.length !== imag.length) {\n    throw new Error(\n        `Cannot merge real and imag arrays of different lengths. real:` +\n        `${real.length}, imag: ${imag.length}.`);\n  }\n  const result = new Float32Array(real.length * 2);\n  for (let i = 0; i < result.length; i += 2) {\n    result[i] = real[i / 2];\n    result[i + 1] = imag[i / 2];\n  }\n  return result;\n}\n\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const real = new Float32Array(complex.length / 2);\n  const imag = new Float32Array(complex.length / 2);\n  for (let i = 0; i < complex.length; i += 2) {\n    real[i / 2] = complex[i];\n    imag[i / 2] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const len = Math.ceil(complex.length / 4);\n  const real = new Float32Array(len);\n  const imag = new Float32Array(len);\n  for (let i = 0; i < complex.length; i += 4) {\n    real[Math.floor(i / 4)] = complex[i];\n    imag[Math.floor(i / 4)] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const len = Math.floor(complex.length / 4);\n  const real = new Float32Array(len);\n  const imag = new Float32Array(len);\n  for (let i = 2; i < complex.length; i += 4) {\n    real[Math.floor(i / 4)] = complex[i];\n    imag[Math.floor(i / 4)] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(\n    complex: Float32Array, index: number): {real: number, imag: number} {\n  const real = complex[index * 2];\n  const imag = complex[index * 2 + 1];\n  return {real, imag};\n}\n\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(\n    data: TypedArray, real: number, imag: number, index: number) {\n  data[index * 2] = real;\n  data[index * 2 + 1] = imag;\n}\n\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(\n    n: number, inverse: boolean): {real: Float32Array, imag: Float32Array} {\n  const real = new Float32Array(n / 2);\n  const imag = new Float32Array(n / 2);\n  for (let i = 0; i < Math.ceil(n / 2); i++) {\n    const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n    real[i] = Math.cos(x);\n    imag[i] = Math.sin(x);\n  }\n  return {real, imag};\n}\n\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(\n    k: number, n: number, inverse: boolean): {real: number, imag: number} {\n  const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n  const real = Math.cos(x);\n  const imag = Math.sin(x);\n  return {real, imag};\n}\n","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Utility functions for computing einsum (tensor contraction and summation\n * based on Einstein summation.)\n */\n\nimport {Tensor} from '../tensor';\nimport {assert} from '../util_base';\n\nconst ARROW = '->';\nconst ARROW_REGEX = /->/g;\nconst COMMA = ',';\nconst ELLIPSIS = '...';\n\n/**\n * Parse an equation for einsum.\n *\n * @param equation The einsum equation (e.g., \"ij,jk->ik\").\n * @param numTensors Number of tensors provided along with `equation`. Used to\n *   check matching number of input tensors.\n * @returns An object consisting of the following fields:\n *   - allDims: all dimension names as strings.\n *   - summedDims: a list of all dimensions being summed over, as indices to\n *     the elements of `allDims`.\n *   - idDims: indices of the dimensions in each input tensor, as indices to\n *     the elements of `allDims.\n */\nexport function decodeEinsumEquation(equation: string, numTensors: number): {\n  allDims: string[],\n  summedDims: number[],\n  idDims: number[][],\n} {\n  equation = equation.replace(/\\s/g, '');  // Remove witespace in equation.\n  const numArrows =\n      (equation.length - equation.replace(ARROW_REGEX, '').length) /\n      ARROW.length;\n  if (numArrows < 1) {\n    throw new Error('Equations without an arrow are not supported.');\n  } else if (numArrows > 1) {\n    throw new Error(`Equation must contain exactly one arrow (\"${ARROW}\").`);\n  }\n  const [inputString, outputString] = equation.split(ARROW);\n  assert(\n      inputString.indexOf(ELLIPSIS) === -1,\n      () => `The ellipsis notation (\"${ELLIPSIS}\") is not supported yet.`);\n  const inputTerms = inputString.split(COMMA);\n  const numInputs = inputTerms.length;\n  if (numTensors !== numInputs) {\n    throw new Error(\n        `Expected ${numInputs} input tensors, received ${numTensors}`);\n  }\n  if (numInputs > 2) {\n    throw new Error(\n        'Support for more than 2 input tensors is not implemented yet.');\n  }\n\n  const allDims: string[] = [];\n  for (let i = 0; i < outputString.length; ++i) {\n    const dimName = outputString[i];\n    if (!inputTerms.some(inputTerm => inputTerm.indexOf(dimName) !== -1)) {\n      throw new Error(\n          `Output subscripts contain the label ${dimName} ` +\n          `not present in the input subscripts.`);\n    }\n    if (allDims.indexOf(dimName) === -1) {\n      allDims.push(dimName);\n    }\n  }\n  for (let i = 0; i < inputString.length; ++i) {\n    const dimName = inputString[i];\n    if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {\n      allDims.push(dimName);\n    }\n  }\n\n  const idDims: number[][] = new Array<number[]>(inputTerms.length);\n  for (let i = 0; i < numInputs; ++i) {\n    if (new Set(inputTerms[i].split('')).size !== inputTerms[i].length) {\n      throw new Error(\n          `Found duplicate axes in input component ${inputTerms[i]}. ` +\n          `Support for duplicate axes in input is not implemented yet.`);\n    }\n    idDims[i] = [];\n    for (let j = 0; j < inputTerms[i].length; ++j) {\n      idDims[i].push(allDims.indexOf(inputTerms[i][j]));\n    }\n  }\n\n  const numDims = allDims.length;          // Number of unique dimensions.\n  const numOutDims = outputString.length;  // Number of output dimensions.\n  const summedDims: number[] = [];         // Dimensions being summed over.\n  for (let i = numOutDims; i < numDims; ++i) {\n    summedDims.push(i);\n  }\n  return {allDims, summedDims, idDims};\n}\n\n/**\n * Get the permutation for a given input tensor.\n *\n * @param nDims Total number of dimension of all tensors involved in the einsum\n *   operation.\n * @param idDims Dimension indices involve in the tensor in question.\n * @returns An object consisting of the following fields:\n *   - permutationIndices: Indices to permute the axes of the tensor with.\n *   - expandDims: Indices to the dimension that need to be expanded from the\n *     tensor after permutation.\n */\nexport function getEinsumPermutation(nDims: number, idDims: number[]):\n    {permutationIndices: number[], expandDims: number[]} {\n  let permutationIndices: number[] = new Array<number>(nDims);\n  permutationIndices.fill(-1);\n  for (let i = 0; i < idDims.length; ++i) {\n    permutationIndices[idDims[i]] = i;\n  }\n  const expandDims: number[] = [];\n  for (let i = 0; i < nDims; ++i) {\n    if (permutationIndices[i] === -1) {\n      expandDims.push(i);\n    }\n  }\n  permutationIndices = permutationIndices.filter(d => d !== -1);\n  return {permutationIndices, expandDims};\n}\n\n/**\n * Checks that the dimension sizes from different input tensors match the\n * equation.\n */\nexport function checkEinsumDimSizes(\n    nDims: number, idDims: number[][], tensors: Tensor[]) {\n  const dimSizes: number[] = new Array<number>(nDims);\n  for (let i = 0; i < tensors.length; ++i) {\n    const shape: number[] = tensors[i].shape;\n    for (let j = 0; j < idDims[i].length; ++j) {\n      if (dimSizes[idDims[i][j]] === undefined) {\n        dimSizes[idDims[i][j]] = shape[j];\n      } else {\n        assert(\n            dimSizes[idDims[i][j]] === shape[j],\n            () => `Expected dimension ${dimSizes[idDims[i][j]]} at axis ${j} ` +\n                `of input shaped ${JSON.stringify(shape)}, ` +\n                `but got dimension ${shape[j]}`);\n      }\n    }\n  }\n}\n\n/**\n * Gets path of computation for einsum.\n *\n * @param summedDims indices to the dimensions being summed over.\n * @param idDims A look up table for the dimensions present in each input\n *     tensor. Each consituent array contains indices for the dimensions in the\n *     corresponding input tensor.\n *\n * @return A map with two fields:\n *   - path: The path of computation, with each element indicating the dimension\n *     being summed over after the element-wise multiplication in that step.\n *   - steps: With the same length as `path`. Each element contains the indices\n *     to the input tensors being used for element-wise multiplication in the\n *     corresponding step.\n */\nexport function getEinsumComputePath(summedDims: number[], idDims: number[][]):\n    {path: number[], steps: number[][]} {\n  const path: number[] = summedDims;\n  const steps: number[][] = [];\n  let nSteps = 0;\n  if (summedDims.length === 0) {\n    // Einsum that involes no summing: e.g., transpose and outer product.\n    path.push(-1);\n  }\n  nSteps = summedDims.length + 1;\n  for (let i = 0; i < nSteps; ++i) {\n    steps.push([]);\n  }\n  const computedTermIndices: number[] = [];\n  for (let i = 0; i < path.length; ++i) {\n    const summedDim = path[i];\n    const termIndices = findTermsWithDim(idDims, summedDim);\n    for (const termIndex of termIndices) {\n      if (computedTermIndices.indexOf(termIndex) === -1) {\n        steps[i].push(termIndex);\n        computedTermIndices.push(termIndex);\n      }\n    }\n  }\n  return {path, steps};\n}\n\n/** Determines if an axes permutation is the identity permutation. */\nexport function isIdentityPermutation(perm: number[]): boolean {\n  return perm.every((dim: number, index: number) => dim === index);\n}\n\nfunction findTermsWithDim(idDims: number[][], dim: number): number[] {\n  const termIndices: number[] = [];\n  for (let i = 0; i < idDims.length; ++i) {\n    if (idDims[i].length === 0 || idDims[i].indexOf(dim) !== -1 || dim === -1) {\n      termIndices.push(i);\n    }\n  }\n  return termIndices;\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {TensorInfo} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {assert} from '../util';\n\n/**\n * Prepare the split size array. When the input is a number, the axis is evenly\n * divided among the split size. When the input contains the negative value, the\n * rest of the axis is allocated toward that.\n */\nexport function prepareSplitSize(\n    x: Tensor|TensorInfo, numOrSizeSplits: number[]|number,\n    axis = 0): number[] {\n  let splitSizes = [];\n  if (typeof (numOrSizeSplits) === 'number') {\n    assert(\n        x.shape[axis] % numOrSizeSplits === 0,\n        () => 'Number of splits must evenly divide the axis.');\n    splitSizes =\n        new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n  } else {\n    const numOfNegs = numOrSizeSplits.reduce((count, value) => {\n      if (value === -1) {\n        count += 1;\n      }\n      return count;\n    }, 0);\n    assert(\n        numOfNegs <= 1,\n        () => 'There should be only one negative value in split array.');\n    const negIndex = numOrSizeSplits.indexOf(-1);\n    // Allow the number of split array to be -1, which indicates the rest\n    // of dimension is allocated to that split.\n    if (negIndex !== -1) {\n      const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n      numOrSizeSplits[negIndex] = x.shape[axis] - total;\n    }\n    assert(\n        x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b),\n        () => 'The sum of sizes must match the size of the axis dimension.');\n    splitSizes = numOrSizeSplits;\n  }\n\n  return splitSizes;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorInfo} from '../kernel_registry';\nimport {nearestDivisor} from '../util';\n\nimport {PARALLELIZE_THRESHOLD} from './reduce_util';\n\nexport interface SegOpInfo {\n  windowSize: number;\n  batchSize: number;\n  inSize: number;\n  numSegments: number;\n}\n\nexport function segOpComputeOptimalWindowSize(\n    inSize: number, numSegments: number): number {\n  let done = false;\n  let res;\n\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    res = inSize;\n    done = true;\n  } else {\n    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n  }\n\n  while (!done) {\n    if (res > numSegments || res === inSize) {\n      done = true;\n    } else {\n      res = nearestDivisor(inSize, res + 1);\n    }\n  }\n  return res;\n}\n\nexport function computeOutShape(\n    aShape: number[], axis: number, numSegments: number): number[] {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (dim !== axis) {\n      outShape.push(aShape[dim]);\n    } else {\n      outShape.push(numSegments);\n    }\n  }\n  return outShape;\n}\n\nexport interface GatherOpShapeInfo {\n  batchSize: number;\n  sliceSize: number;\n  outerSize: number;\n  dimSize: number;\n  outputShape: number[];\n}\n\nexport function collectGatherOpShapeInfo(\n    x: TensorInfo, indices: TensorInfo, axis: number,\n    batchDims: number): GatherOpShapeInfo {\n  const indicesRank = indices.shape.length;\n  const xRank = x.shape.length;\n\n  if (batchDims !== 0) {\n    if (batchDims < -indicesRank || batchDims > indicesRank) {\n      throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${\n          indicesRank}], but got ${batchDims}`);\n    }\n  }\n\n  if (batchDims < 0) {\n    batchDims += indicesRank;\n  }\n\n  if (batchDims > xRank) {\n    throw new Error(`batchDims (${batchDims}) must be less than rank(x) (\n    ${xRank}).`);\n  }\n\n  if (axis < batchDims) {\n    throw new Error(`batchDims (${\n        batchDims}) must be less than or equal to axis (${axis}).`);\n  }\n\n  for (let i = 0; i < batchDims; ++i) {\n    if (x.shape[i] !== indices.shape[i]) {\n      throw new Error(\n          `x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${\n              i}]: ${indices.shape[i]}.`);\n    }\n  }\n  const dimSize = x.shape[axis];\n\n  const outputShape: number[] = [];\n  let batchSize = 1;\n  let outerSize = 1;\n  let sliceSize = 1;\n\n  for (let i = 0; i < batchDims; ++i) {\n    outputShape.push(x.shape[i]);\n    batchSize *= x.shape[i];\n  }\n\n  for (let i = batchDims; i < axis; i++) {\n    outputShape.push(x.shape[i]);\n    outerSize *= x.shape[i];\n  }\n\n  for (let i = batchDims; i < indicesRank; i++) {\n    outputShape.push(indices.shape[i]);\n  }\n\n  for (let i = axis + 1; i < xRank; i++) {\n    outputShape.push(x.shape[i]);\n    sliceSize *= x.shape[i];\n  }\n\n  return {batchSize, sliceSize, outerSize, dimSize, outputShape};\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {decodeString, encodeString} from '../util';\n\n// Utilities needed by backend consumers of tf-core.\nexport * from '../ops/axis_util';\nexport * from '../ops/broadcast_util';\nexport * from '../ops/concat_util';\nexport * from '../ops/conv_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/fused_types';\nexport * from '../ops/reduce_util';\n\nimport * as slice_util from '../ops/slice_util';\nexport {slice_util};\n\nexport {BackendValues, TypedArray, upcastType, PixelData} from '../types';\nexport {MemoryInfo, TimingInfo} from '../engine';\nexport * from '../ops/rotate_util';\nexport * from '../ops/array_ops_util';\nexport * from '../ops/gather_nd_util';\nexport * from '../ops/scatter_nd_util';\nexport * from '../ops/selu_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/erf_util';\nexport * from '../log';\nexport * from '../backends/complex_util';\nexport * from '../backends/einsum_util';\nexport * from '../ops/split_util';\n\nimport * as segment_util from '../ops/segment_util';\nexport {segment_util};\n\nexport function fromUint8ToStringArray(vals: Uint8Array[]) {\n  try {\n    // Decode the bytes into string.\n    return vals.map(val => decodeString(val));\n  } catch (err) {\n    throw new Error(\n        `Failed to decode encoded string bytes into utf-8, error: ${err}`);\n  }\n}\n\nexport function fromStringArrayToUint8(strings: string[]) {\n  return strings.map(s => encodeString(s));\n}\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport {nonMaxSuppressionV3Impl, nonMaxSuppressionV4Impl, nonMaxSuppressionV5Impl} from './non_max_suppression_impl';\nexport {whereImpl} from 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\ No newline at end of file
diff --git a/webrtc.js b/webrtc.js
index 8e920eb..08cbfd9 100644
--- a/webrtc.js
+++ b/webrtc.js
@@ -6,4 +6,4 @@
 *  No warranty, explicit or implicit, provided.
 *
 */
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_0x55c760=![];_0xf0f5da[_0x3fe766(0x3ea)][0x0][_0x3fe766(0x34a)]=0xa,_0xd746b9[_0x3fe766(0x170)](_0xf0f5da)['then'](()=>{var _0x30b577=_0x3fe766;log(_0x30b577(0x355));var _0x5df7dd=_0x4bb952[_0x30b577(0x347)][_0x5e97b3][_0x30b577(0x513)],_0x2a345d=0x64;!_0x5df7dd&&(_0x5df7dd=_0x2a345d);_0x4bb952[_0x30b577(0x347)][_0x5e97b3][_0x30b577(0x2ee)]&&(_0x2a345d=_0x4bb952[_0x30b577(0x347)][_0x5e97b3]['scale']);_0x2a345d>_0x5df7dd&&(_0x2a345d=_0x5df7dd);_0x4bb952['pcs'][_0x5e97b3][_0x30b577(0x513)]&&(_0x4bb952[_0x30b577(0x347)][_0x5e97b3][_0x30b577(0x513)]<_0x2a345d&&(_0x2a345d=_0x4bb952[_0x30b577(0x347)][_0x5e97b3][_0x30b577(0x513)]));_0x4bb952[_0x30b577(0x347)][_0x5e97b3][_0x30b577(0x5c1)]&&_0x4bb952[_0x30b577(0x347)][_0x5e97b3]['scaleDueToBitrate']<_0x2a345d&&(_0x2a345d=_0x4bb952['pcs'][_0x5e97b3][_0x30b577(0x5c1)]);log('pre\x20pixel\x20fix\x20'+_0x2a345d),_0x2a345d=_0x4bb952[_0x30b577(0x3d9)](_0x2a345d);var 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_0x67994a=_0x2b0d2d[_0x2b6552(0x584)]();(!_0x67994a['encodings']||_0x67994a[_0x2b6552(0x3ea)][_0x2b6552(0x28f)]==0x0)&&(_0x67994a['encodings']=[{}]);try{_0x67994a[_0x2b6552(0x3ea)][0x0][_0x2b6552(0x2e6)]=_0x2b6552(0x371),_0x67994a['encodings'][0x0][_0x2b6552(0x501)]='high',_0x67994a[_0x2b6552(0x3ea)][0x0][_0x2b6552(0x1e6)]=!![],_0x2b0d2d[_0x2b6552(0x170)](_0x67994a)[_0x2b6552(0x3f8)](()=>{var _0x48664e=_0x2b6552;log(_0x48664e(0x58c)),pokeIframeAPI(_0x48664e(0x285),!![],_0x4902cf);});}catch(_0xb4b7ba){errorlog(_0xb4b7ba);}},_0x4bb952[_0x41b841(0x267)]=function(_0x2781f8,_0x2b8747=0x7d00,_0x54fde4=0x3e8){var _0x42b9e2=_0x41b841;log('encodering\x20being\x20kicked');var _0x31c39b=_0x4bb952[_0x42b9e2(0x347)][_0x2781f8][_0x42b9e2(0x375)]()[_0x42b9e2(0x52f)](function(_0x3b8854){var _0x325fd3=_0x42b9e2;return _0x3b8854[_0x325fd3(0x5b3)]&&_0x3b8854[_0x325fd3(0x5b3)][_0x325fd3(0x20c)]==_0x325fd3(0x169);});if(!_0x31c39b)return log('no\x20audio\x20track\x20to\x20poke'),![];var _0x2d8359=_0x31c39b[_0x42b9e2(0x584)]();(!_0x2d8359['encodings']||_0x2d8359[_0x42b9e2(0x3ea)][_0x42b9e2(0x28f)]==0x0)&&(_0x42b9e2(0x16d)!=='uhRCx'?_0x2d8359[_0x42b9e2(0x3ea)]=[{}]:(_0x2e3834[_0x42b9e2(0x2c9)][_0x28313a]['imageElement'][_0x42b9e2(0x5dc)]=!![],_0x20e8ca[_0x42b9e2(0x2c9)][_0x52d98f][_0x42b9e2(0x220)]['style'][_0x42b9e2(0x202)]=_0x42b9e2(0x5dc))),_0x2d8359[_0x42b9e2(0x3ea)][0x0][_0x42b9e2(0x484)]=_0x2b8747,_0x31c39b['setParameters'](_0x2d8359)['then'](()=>{var _0x33110d=_0x42b9e2;pokeIframeAPI(_0x33110d(0x3cf),_0x2b8747,_0x2781f8),_0x54fde4>0x0&&setTimeout(function(){var _0x5dfe7b=_0x33110d;try{if(_0x2781f8 in _0x4bb952[_0x5dfe7b(0x347)])var _0x4c6b26=_0x4bb952['pcs'][_0x2781f8][_0x5dfe7b(0x375)]()['find'](function(_0x1a7eb0){var _0x4456ae=_0x5dfe7b;return _0x1a7eb0['track']&&_0x1a7eb0[_0x4456ae(0x5b3)][_0x4456ae(0x20c)]==_0x4456ae(0x169);});else return![];if(!_0x4c6b26)return log(_0x5dfe7b(0x46b)),![];var _0x49df97=_0x4c6b26[_0x5dfe7b(0x584)]();delete _0x49df97[_0x5dfe7b(0x3ea)][0x0][_0x5dfe7b(0x484)],_0x4c6b26[_0x5dfe7b(0x170)](_0x49df97)[_0x5dfe7b(0x3f8)](()=>{var _0x3d8bca=_0x5dfe7b;log(_0x3d8bca(0x58c));});}catch(_0x8c29e1){errorlog(_0x8c29e1);}},_0x54fde4,_0x2781f8);});},_0x4bb952[_0x41b841(0x51b)]=function(_0x2c9ace,_0x38d0a2){var _0x16a8a2=_0x41b841;_0x38d0a2=parseInt(_0x38d0a2);try{var _0x256a59=_0x4bb952[_0x16a8a2(0x347)][_0x2c9ace][_0x16a8a2(0x375)]()[_0x16a8a2(0x52f)](function(_0x86fa11){var _0x24622c=_0x16a8a2;return _0x86fa11[_0x24622c(0x5b3)]&&_0x86fa11['track'][_0x24622c(0x20c)]==_0x24622c(0x169);});if(!_0x256a59){warnlog(_0x16a8a2(0x426));return;}var 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_0x163571[_0x16a8a2(0x2c9)][_0x210493]['stats'][_0x16a8a2(0x531)]&&(_0x273072[_0x16a8a2(0x2c9)][_0x90b670]['stats'][_0x16a8a2(0x531)][_0x16a8a2(0x288)]&&(_0x561178['timestamp']&&(_0x13a1d4[_0x16a8a2(0x2c9)][_0x55157b][_0x16a8a2(0x5c3)][_0x16a8a2(0x531)][_0x16a8a2(0x5e8)]=_0x5d6d34(0x8*(_0x35448e[_0x16a8a2(0x571)]-_0x1ed398[_0x16a8a2(0x2c9)][_0x44bfc1][_0x16a8a2(0x5c3)][_0x16a8a2(0x531)][_0x16a8a2(0x231)])/(_0x52de72[_0x16a8a2(0x1c6)]-_0x129839['rpcs'][_0x2d72a4][_0x16a8a2(0x5c3)][_0x16a8a2(0x531)][_0x16a8a2(0x288)]))))),_0x1e234a[_0x16a8a2(0x2c9)][_0xac6a92][_0x16a8a2(0x5c3)][_0x16a8a2(0x531)][_0x16a8a2(0x231)]=_0x2fdf26[_0x16a8a2(0x571)])),_0x1fd2f9[_0x16a8a2(0x3ea)][0x0][_0x16a8a2(0x484)]=_0x38d0a2*0x400);_0x256a59[_0x16a8a2(0x170)](_0x1fd2f9)[_0x16a8a2(0x3f8)](()=>{var _0x3b9af9=_0x16a8a2;pokeIframeAPI(_0x3b9af9(0x3cf),_0x38d0a2,_0x2c9ace),log(_0x3b9af9(0x493));})[_0x16a8a2(0x5fa)](warnlog);}catch(_0x335473){errorlog(_0x335473);}},_0x4bb952['optimizeBitrate']=function(_0x5bd7c9){var 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_0xb29bd4=_0x3c5037;_0x41636a(_0xb29bd4(0x345));})[_0x3c5037(0x5fa)](_0x38d78e);else{_0x5592d8+=_0x4bb952[_0x3c5037(0x347)][_0x2c3d53]['setBitrate']||0x9c4,warnlog(_0x5592d8),_0x28395b+=0x1;continue;}}if(_0x257da8[_0x3c5037(0x3ea)][0x0][_0x3c5037(0x5da)]==![])continue;_0x257da8[_0x3c5037(0x3ea)][0x0][_0x3c5037(0x484)]?(_0x5592d8+=parseInt(_0x257da8[_0x3c5037(0x3ea)][0x0][_0x3c5037(0x484)])/0x400,warnlog(_0x5592d8)):(_0x5592d8+=_0x4bb952[_0x3c5037(0x347)][_0x2c3d53]['setBitrate']||0x9c4,warnlog(_0x5592d8)),_0x28395b+=0x1;}if(!_0x5592d8)return![];var _0x176c05=parseFloat(_0x5592d8/_0x4bb952[_0x3c5037(0x350)]);if(_0x176c05>0x1)for(var _0x2c3d53 in _0x4bb952[_0x3c5037(0x347)]){if(_0x6df264===_0x2c3d53)continue;var _0x1f44f2=_0x4bb952[_0x3c5037(0x347)][_0x2c3d53][_0x3c5037(0x375)]()[_0x3c5037(0x52f)](function(_0x34623c){var _0x345982=_0x3c5037;return _0x34623c[_0x345982(0x5b3)]&&_0x34623c[_0x345982(0x5b3)][_0x345982(0x20c)]==_0x345982(0x195);});if(!_0x1f44f2)continue;var _0x257da8=_0x1f44f2[_0x3c5037(0x584)]();if(!_0x257da8[_0x3c5037(0x3ea)]||_0x257da8[_0x3c5037(0x3ea)]['length']==0x0){var _0x2b152c=_0x4bb952['pcs'][_0x2c3d53]['setBitrate']||0x9c4;_0x4bb952['limitBitrate'](_0x2c3d53,parseInt(_0x2b152c/_0x176c05),!![]),errorlog(parseInt(_0x2b152c/_0x176c05));continue;}if(_0x257da8[_0x3c5037(0x3ea)][0x0][_0x3c5037(0x5da)]==![])continue;if(_0x257da8[_0x3c5037(0x3ea)][0x0][_0x3c5037(0x484)])errorlog(parseInt(parseInt(_0x257da8['encodings'][0x0][_0x3c5037(0x484)])/0x400/_0x176c05)),_0x4bb952[_0x3c5037(0x1a0)](_0x2c3d53,parseInt(parseInt(_0x257da8['encodings'][0x0][_0x3c5037(0x484)])/0x400/_0x176c05),!![]);else{var _0x2b152c=_0x4bb952['pcs'][_0x2c3d53][_0x3c5037(0x50b)]||0x9c4;errorlog(parseInt(_0x2b152c/_0x176c05)),_0x4bb952[_0x3c5037(0x1a0)](_0x2c3d53,parseInt(_0x2b152c/_0x176c05),!![]);}}return parseInt(_0x175921/_0x176c05);}else return![];},_0x4bb952[_0x41b841(0x1a0)]=function(_0x8ac085,_0x27fa15,_0x1543dd=![]){var _0x483655=_0x41b841;_0x4bb952['pcs'][_0x8ac085]['bitrateTimeout']&&(clearInterval(_0x4bb952[_0x483655(0x347)][_0x8ac085][_0x483655(0x457)]),_0x4bb952[_0x483655(0x347)][_0x8ac085]['bitrateTimeout']=null);if(_0x27fa15===null){if(_0x4bb952[_0x483655(0x347)][_0x8ac085][_0x483655(0x50e)]===![]){if(_0x483655(0x268)==='rAUpx')return;else var 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_0x551ebe=Date[_0x483655(0x2c5)]()-_0x4bb952[_0x483655(0x347)][_0x8ac085][_0x483655(0x53a)];_0x551ebe<_0x4bb952[_0x483655(0x51a)]&&(_0x483655(0x262)!==_0x483655(0x262)?(_0x43299e[_0x483655(0x2c9)][_0x496d29][_0x483655(0x487)][_0x483655(0x1a2)][_0x483655(0x232)]=_0x483655(0x54d),_0x3a0a8e=!![]):(_0x27fa15=0x23,_0x4bb952[_0x483655(0x347)][_0x8ac085][_0x483655(0x457)]=setTimeout(function(_0x336a42){var _0xce126c=_0x483655;_0x4bb952[_0xce126c(0x1a0)](_0x336a42,null);},_0x551ebe,_0x8ac085)));}try{if(safariVersion()<=0xd&&(iOS||iPad)){log(_0x483655(0x1d2));if(_0x4bb952[_0x483655(0x347)][_0x8ac085][_0x483655(0x535)]==!![]&&_0x4bb952[_0x483655(0x347)][_0x8ac085][_0x483655(0x30e)]==![])return;var _0x2bd1bb=_0x4bb952[_0x483655(0x347)][_0x8ac085][_0x483655(0x375)]()[_0x483655(0x52f)](function(_0x1d8f99){var _0x47b462=_0x483655;return _0x1d8f99[_0x47b462(0x5b3)]&&_0x1d8f99[_0x47b462(0x5b3)][_0x47b462(0x20c)]==_0x47b462(0x195);});if(!_0x2bd1bb){warnlog(_0x483655(0x4fc));return;}var _0x3b71b2=_0x2bd1bb[_0x483655(0x584)]();(!_0x3b71b2[_0x483655(0x3ea)]||_0x3b71b2[_0x483655(0x3ea)][_0x483655(0x28f)]==0x0)&&(_0x3b71b2[_0x483655(0x3ea)]=[{}]);if(_0x27fa15<0x0)_0x3b71b2[_0x483655(0x3ea)][0x0][_0x483655(0x5da)]==![]&&(_0x3b71b2[_0x483655(0x3ea)][0x0][_0x483655(0x5da)]=!![]),_0x27fa15=0x9c4,_0x4bb952[_0x483655(0x476)]&&(_0x27fa15=_0x4bb952[_0x483655(0x476)]),_0x4bb952[_0x483655(0x510)]&&(_0x27fa15>_0x4bb952[_0x483655(0x510)]&&(_0x27fa15=_0x4bb952[_0x483655(0x510)])),_0x3b71b2[_0x483655(0x3ea)][0x0][_0x483655(0x484)]=_0x27fa15*0x400;else _0x27fa15===0x0?_0x3b71b2[_0x483655(0x3ea)][0x0][_0x483655(0x5da)]=![]:(_0x3b71b2[_0x483655(0x3ea)][0x0][_0x483655(0x5da)]==![]&&(_0x3b71b2[_0x483655(0x3ea)][0x0][_0x483655(0x5da)]=!![]),_0x3b71b2[_0x483655(0x3ea)][0x0][_0x483655(0x484)]=_0x27fa15*0x400);_0x2bd1bb[_0x483655(0x170)](_0x3b71b2)[_0x483655(0x3f8)](()=>{var 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_0xdc5f16=0x64;_0x4bb952[_0x483655(0x347)][_0x8ac085]['scale']&&(_0xdc5f16=_0x4bb952['pcs'][_0x8ac085]['scale']);_0x4bb952[_0x483655(0x347)][_0x8ac085][_0x483655(0x513)]&&_0x4bb952[_0x483655(0x347)][_0x8ac085]['scaleResolution']<_0xdc5f16&&(_0xdc5f16=_0x4bb952[_0x483655(0x347)][_0x8ac085]['scaleResolution']);if(_0x27fa15<0x0)_0x4bb952['pcs'][_0x8ac085]['scaleDueToBitrate']=0x64;else{if(_0x27fa15>=0x259)_0x4bb952[_0x483655(0x347)][_0x8ac085][_0x483655(0x5c1)]=0x64;else{if(_0x4bb952[_0x483655(0x347)][_0x8ac085][_0x483655(0x24c)]===![]&&_0x4bb952[_0x483655(0x266)]!==_0x8ac085&&_0x4bb952[_0x483655(0x235)])_0x4bb952[_0x483655(0x347)][_0x8ac085]['scaleDueToBitrate']=0x64;else{if(_0x27fa15>=0xc9)_0x483655(0x454)===_0x483655(0x46f)?_0x46471d[_0x483655(0x2c9)][_0x5d1c32][_0x483655(0x45f)]&&_0x5aa66d[_0x483655(0x2c9)][_0x207735][_0x483655(0x45f)][_0x483655(0x3bb)]()['forEach'](function(_0x25fa95){var 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window[_0x236ffe(0x2e5)]['subtle'][_0x236ffe(0x40f)]({'name':_0x236ffe(0x247)},_0x4bb952[_0x236ffe(0x589)][_0x25ad27][_0x236ffe(0x1a6)],_0x3ec7dc['signature'],_0x4bb952[_0x236ffe(0x2e2)]['encode'](_0x3ec7dc['data']))[_0x236ffe(0x3f8)](function(_0x44bba7){return log(_0x44bba7),_0x44bba7;})['catch'](function(_0x1924e0){var _0x5d2324=_0x236ffe;return _0x5d2324(0x548)===_0x5d2324(0x219)?_0x2b1b9b[_0x5d2324(0x412)][_0x5d2324(0x460)]({'name':_0x5d2324(0x51d),'iv':_0x2f5292},_0x47e7d0,_0x43f765(_0x12e05a))[_0x5d2324(0x3f8)](function(_0x141369){return _0x87b34e=new _0x4f72d9(_0x141369),_0x272cac=_0x51b89a(_0x3b3748),_0x39bae9=_0x46ac79(_0x470f43),[_0x37e996,_0x5cc77];},function(_0x4a6108){var _0x407c84=_0x5d2324;return _0x25542d(_0x4a6108[_0x407c84(0x3ef)]),![];}):(errorlog(_0x1924e0),![]);});},_0x4bb952[_0x41b841(0x18d)]=function(_0x5b3739){var _0x43df2b=_0x41b841;if(_0x4bb952['password']){if(_0x4bb952['hash']!==![]){if(_0x43df2b(0x185)===_0x43df2b(0x185))return 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_0x39fb7d=_0x4f251f,_0x1375a3=![];if(_0x39fb7d(0x4ec)in _0x57618e)_0x1375a3=_0x4bb952[_0x39fb7d(0x261)](_0x57618e,_0x57618e[_0x39fb7d(0x4ec)]),_0x1375a3?log(_0x39fb7d(0x5ea)):(log('sending\x20message\x20via\x20WSS\x20as\x20WebRTC\x20failed\x20to\x20send\x20message'),_0x4bb952[_0x39fb7d(0x3c0)](_0x57618e));else _0x3c095a?(_0x1375a3=_0x4bb952[_0x39fb7d(0x261)](_0x57618e),_0x1375a3?log('successfully\x20sent\x20message\x20vis\x20WebRTC\x20instead\x20of\x20WSS\x20to\x20all\x20RTC\x20Peers'):(log(_0x39fb7d(0x2c4)),_0x4bb952['sendMsg'](_0x57618e))):(_0x4bb952[_0x39fb7d(0x3c0)](_0x57618e),log(_0x39fb7d(0x36f)));},_0x4bb952[_0x4f251f(0x43d)]=function(){var _0x24cfed=_0x4f251f;if(_0x4bb952[_0x24cfed(0x47d)]===![]){var _0x4103dc={},_0x379bba=![];_0x4bb952[_0x24cfed(0x204)][_0x24cfed(0x202)]!==null&&(_0x4bb952[_0x24cfed(0x204)][_0x24cfed(0x202)]===![]&&(_0x379bba=!![]),_0x4103dc[_0x24cfed(0x1cd)]=_0x4bb952[_0x24cfed(0x204)][_0x24cfed(0x202)]);_0x4bb952[_0x24cfed(0x204)][_0x24cfed(0x1eb)]!==null&&(_0x4103dc[_0x24cfed(0x1b7)]=_0x4bb952['obsState'][_0x24cfed(0x1eb)]);_0x4bb952[_0x24cfed(0x204)][_0x24cfed(0x203)]!==null&&(_0x4103dc[_0x24cfed(0x365)]=_0x4bb952[_0x24cfed(0x204)][_0x24cfed(0x203)]);_0x4bb952[_0x24cfed(0x204)][_0x24cfed(0x50a)]!==null&&(_0x4103dc[_0x24cfed(0x283)]=_0x4bb952[_0x24cfed(0x204)]['streaming']);for(var _0x45a289 in _0x4bb952['rpcs']){if(_0x4bb952[_0x24cfed(0x230)]!==![]){var _0x2924cf=parseInt(_0x4bb952['rpcs'][_0x45a289][_0x24cfed(0x49a)]);_0x379bba&&((_0x2924cf>_0x4bb952[_0x24cfed(0x230)]||_0x2924cf<0x0)&&(_0x2924cf=_0x4bb952['optimize'])),_0x4bb952['rpcs'][_0x45a289]['bandwidth']!==_0x2924cf?(_0x4103dc[_0x24cfed(0x476)]=_0x2924cf,warnlog(_0x24cfed(0x21d)),warnlog(_0x4103dc),_0x4bb952[_0x24cfed(0x261)](_0x4103dc,_0x45a289)?_0x4bb952['rpcs'][_0x45a289][_0x24cfed(0x1c7)]=_0x2924cf:errorlog(_0x24cfed(0x186))):(warnlog(_0x24cfed(0x21d)),warnlog(_0x4103dc),_0x4bb952[_0x24cfed(0x261)](_0x4103dc,_0x45a289));}else _0x24cfed(0x428)!==_0x24cfed(0x428)?(_0x16366c(),_0xdb683d()):(warnlog(_0x24cfed(0x21d)),warnlog(_0x4103dc),_0x4bb952[_0x24cfed(0x261)](_0x4103dc,_0x45a289));}}},_0x4bb952[_0x4f251f(0x3a1)]=function(_0x2a853e){var _0x3cbc2c=_0x4f251f;if(!_0x4bb952['rpcs'][_0x2a853e][_0x3cbc2c(0x487)])return;var _0x2bb884={};_0x2bb884[_0x3cbc2c(0x40a)]=_0x4bb952[_0x3cbc2c(0x2c9)][_0x2a853e]['videoElement'][_0x3cbc2c(0x1a2)][_0x3cbc2c(0x232)]!=_0x3cbc2c(0x54d),_0x2bb884[_0x3cbc2c(0x2f3)]=_0x4bb952[_0x3cbc2c(0x2c9)][_0x2a853e][_0x3cbc2c(0x487)][_0x3cbc2c(0x3df)];if(_0x4bb952[_0x3cbc2c(0x230)]!==![]){var _0x5331d4=parseInt(_0x4bb952[_0x3cbc2c(0x2c9)][_0x2a853e]['targetBandwidth']);_0x2bb884[_0x3cbc2c(0x40a)]===![]&&((_0x5331d4>_0x4bb952[_0x3cbc2c(0x230)]||_0x5331d4<0x0)&&(_0x5331d4=_0x4bb952[_0x3cbc2c(0x230)])),_0x4bb952[_0x3cbc2c(0x2c9)][_0x2a853e][_0x3cbc2c(0x1c7)]!==_0x5331d4?(_0x2bb884[_0x3cbc2c(0x476)]=_0x5331d4,_0x4bb952[_0x3cbc2c(0x261)](_0x2bb884,_0x2a853e)?_0x4bb952['rpcs'][_0x2a853e][_0x3cbc2c(0x1c7)]=_0x5331d4:errorlog('Unable\x20to\x20set\x20update\x20OBS\x20Visibility')):_0x4bb952[_0x3cbc2c(0x261)](_0x2bb884,_0x2a853e);}else _0x4bb952[_0x3cbc2c(0x261)](_0x2bb884,_0x2a853e);},_0x4bb952[_0x4f251f(0x22f)]=function(_0x5bf1c8){var _0x44c303=_0x4f251f;if(_0x44c303(0x17f)in _0x5bf1c8){if(_0x44c303(0x58d)in _0x5bf1c8){if('scene'in _0x5bf1c8){if(_0x4bb952[_0x44c303(0x24c)]!==![]){var _0x4f4354=![];for(var _0x3dafd6 in _0x4bb952[_0x44c303(0x2c9)]){if(_0x4bb952['rpcs'][_0x3dafd6][_0x44c303(0x4e8)]===_0x5bf1c8[_0x44c303(0x58d)]){if(_0x44c303(0x554)in 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_0x5b31b1 in _0x4bb952[_0x44c303(0x2c9)]){_0x5b31b1!==_0x3dafd6&&(_0x4bb952[_0x44c303(0x2c9)][_0x5b31b1][_0x44c303(0x487)][_0x44c303(0x1a2)]['display']&&_0x4bb952[_0x44c303(0x2c9)][_0x5b31b1][_0x44c303(0x487)][_0x44c303(0x1a2)]['display']!==_0x44c303(0x54d)&&(_0x4bb952[_0x44c303(0x2c9)][_0x5b31b1][_0x44c303(0x487)][_0x44c303(0x1a2)][_0x44c303(0x232)]=_0x44c303(0x54d),_0x4f4354=!![]));}_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)]&&(_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x1a2)][_0x44c303(0x232)]&&_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x1a2)][_0x44c303(0x232)]!==_0x44c303(0x3e8)&&(_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x1a2)][_0x44c303(0x232)]=_0x44c303(0x3e8),_0x4f4354=!![]),_0x4bb952['rpcs'][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x308)]&&clearInterval(_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x308)]),_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x4e7)]=![],_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x308)]=setTimeout(showControlBar['bind'](null,_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)]),0xbb8));}}else _0x5bf1c8[_0x44c303(0x554)]==0x0?_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)]&&(_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6]['videoElement'][_0x44c303(0x1a2)][_0x44c303(0x232)]&&_0x4bb952['rpcs'][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x1a2)][_0x44c303(0x232)]!==_0x44c303(0x54d)&&(_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x1a2)][_0x44c303(0x232)]='none',_0x4f4354=!![]),_0x4bb952['rpcs'][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x3df)]=!![]):_0x44c303(0x5ae)!==_0x44c303(0x5ae)?(_0x3afb27[0x0]['value']=_0x9dd2c7,_0x9b82b8[_0x44c303(0x2c9)][_0x18ec0f]['directorVolumeState']=_0x5c18a7,_0x137413(_0x45a1a3[0x0])):_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)]&&(_0x44c303(0x588)!==_0x44c303(0x588)?_0x34f93f['pcs'][_0x15f391[_0x44c303(0x4ec)]][_0x44c303(0x50b)]=_0x3016b1:(_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x1a2)][_0x44c303(0x232)]&&_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x1a2)][_0x44c303(0x232)]!==_0x44c303(0x3e8)&&(_0x44c303(0x2c7)!==_0x44c303(0x2c7)?_0x5d53df(_0x3d3c9f):(_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x1a2)][_0x44c303(0x232)]='block',_0x4f4354=!![])),_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6]['mutedState']===null?_0x4bb952['rpcs'][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x3df)]=![]:_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)]['muted']=_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x1a1)],_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x308)]&&clearInterval(_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x308)]),_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)][_0x44c303(0x4e7)]=![],_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6]['videoElement'][_0x44c303(0x308)]=setTimeout(showControlBar['bind'](null,_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)]),0xbb8)));}}_0x4bb952[_0x44c303(0x3a1)](_0x3dafd6);}else _0x5bf1c8[_0x44c303(0x17f)]==_0x44c303(0x1f4)&&(log(parseInt(_0x5bf1c8['value'])/0x64),_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)]&&(_0x4bb952[_0x44c303(0x2c9)][_0x3dafd6][_0x44c303(0x487)]['volume']=parseInt(_0x5bf1c8['value'])/0x64,log(_0x44c303(0x4c3))));}}}}_0x4f4354&&(_0x44c303(0x2a8)!=='wRTIW'?updateMixer():(_0x127c0d[_0x44c303(0x347)][_0x1f3491][_0x44c303(0x40e)](_0x3ed414,_0x571c2d),_0x112a40(function(_0x18b64e){_0x2cd1d6['optimizeBitrate'](_0x18b64e);},_0x128e64[_0x44c303(0x51a)],_0x243742)));}}else{if(_0x5bf1c8[_0x44c303(0x17f)]==_0x44c303(0x3ca)){}else{if(_0x5bf1c8[_0x44c303(0x17f)]==_0x44c303(0x3c9)){}}}}}},_0x4bb952['ws'][_0x4f251f(0x171)]=function(_0x543e86){var _0x17562c=_0x4f251f;clearTimeout(_0x4bb952[_0x17562c(0x20b)]);var _0x10d67a=JSON[_0x17562c(0x2b5)](_0x543e86[_0x17562c(0x367)]);'streamID'in _0x10d67a&&(_0x10d67a['streamID']=_0x4bb952[_0x17562c(0x18d)](_0x10d67a['streamID']));if(_0x4bb952[_0x17562c(0x42f)]){if('UUID'in _0x10d67a){if(_0x4bb952[_0x17562c(0x4ec)]){if(_0x10d67a[_0x17562c(0x4ec)]!==_0x4bb952[_0x17562c(0x4ec)])return;}else return;delete _0x10d67a[_0x17562c(0x4ec)];}if(_0x17562c(0x1e0)in _0x10d67a){if(!_0x4bb952[_0x17562c(0x575)])return;if(_0x17562c(0x16a)in _0x10d67a){if(_0x10d67a[_0x17562c(0x16a)]===_0x17562c(0x3ca)){if(_0x17562c(0x1e0)in _0x10d67a){if(_0x17562c(0x58d)in _0x10d67a){if(_0x10d67a[_0x17562c(0x58d)]==_0x4bb952[_0x17562c(0x4ec)]){_0x10d67a[_0x17562c(0x16a)]=_0x17562c(0x2b3),_0x4bb952[_0x17562c(0x575)]=_0x10d67a[_0x17562c(0x1e0)];var _0x4ddf74={};_0x4ddf74[_0x17562c(0x16a)]=_0x17562c(0x282),_0x4ddf74[_0x17562c(0x1e0)]=_0x4bb952['roomenc'],_0x4ddf74[_0x17562c(0x4e8)]=_0x4bb952[_0x17562c(0x4e8)],_0x4bb952[_0x17562c(0x3c0)](_0x4ddf74);}else return;}else return;}else{if(_0x17562c(0x359)!==_0x17562c(0x359))_0x3a64fc('container_'+_0x2c3ac6)['parentNode'][_0x17562c(0x36e)](_0x2dd1cc('container_'+_0xbaa633));else return;}}else{if(_0x10d67a[_0x17562c(0x1e0)]!==_0x4bb952['roomenc'])return;}}else{if(_0x10d67a[_0x17562c(0x1e0)]!==_0x4bb952[_0x17562c(0x575)])return;}delete _0x10d67a['roomid'];}_0x17562c(0x18f)in _0x10d67a&&(_0x10d67a[_0x17562c(0x534)]&&(_0x4bb952[_0x17562c(0x266)]=_0x10d67a[_0x17562c(0x534)]),delete _0x10d67a[_0x17562c(0x18f)]);'from'in _0x10d67a&&(_0x10d67a[_0x17562c(0x4ec)]=_0x10d67a[_0x17562c(0x534)],delete _0x10d67a[_0x17562c(0x534)]);if(_0x17562c(0x16a)in _0x10d67a){if(_0x10d67a[_0x17562c(0x16a)]===_0x17562c(0x31f)){if(_0x17562c(0x4e8)in _0x10d67a){if(_0x10d67a[_0x17562c(0x4e8)]===_0x4bb952[_0x17562c(0x4e8)])_0x17562c(0x3a8)!==_0x17562c(0x578)?_0x10d67a[_0x17562c(0x16a)]='offerSDP':_0x199dcf['guest']==!![]&&(_0x43ffb4[_0x17562c(0x347)][_0x4ea43f][_0x17562c(0x535)]=!![]);else return;}}else{if(_0x10d67a[_0x17562c(0x16a)]===_0x17562c(0x270)){if(_0x4bb952[_0x17562c(0x176)]){if(_0x4bb952['view_set'][_0x17562c(0x5cf)](_0x10d67a[_0x17562c(0x4e8)])){play(_0x10d67a[_0x17562c(0x4e8)]);return;}else return;}else{if(_0x4bb952[_0x17562c(0x3bc)]){if(_0x4bb952['view']!==_0x10d67a['streamID'])return;else{play(_0x10d67a['streamID']);return;}}}}else{if(_0x10d67a[_0x17562c(0x16a)]===_0x17562c(0x282)){if(_0x17562c(0x4e8)in _0x10d67a){if(_0x4bb952[_0x17562c(0x176)]){if(_0x4bb952[_0x17562c(0x176)][_0x17562c(0x5cf)](_0x10d67a[_0x17562c(0x4e8)]))play(_0x10d67a['streamID']);else{}}else{if(_0x4bb952[_0x17562c(0x3bc)]){if(_0x4bb952[_0x17562c(0x3bc)]!==_0x10d67a[_0x17562c(0x4e8)]){}else play(_0x10d67a[_0x17562c(0x4e8)]);}else play(_0x10d67a['streamID']);}}_0x10d67a[_0x17562c(0x16a)]='offerSDP';}}}}}if(_0x10d67a[_0x17562c(0x16a)]){if(_0x10d67a[_0x17562c(0x16a)]==_0x17562c(0x444)){if(_0x4bb952[_0x17562c(0x45a)]){if(_0x4bb952[_0x17562c(0x266)]===_0x10d67a[_0x17562c(0x4ec)])_0x4bb952[_0x17562c(0x444)](_0x10d67a['UUID']);else _0x4bb952[_0x17562c(0x18f)]&&(_0x10d67a[_0x17562c(0x4ec)]in _0x4bb952[_0x17562c(0x2c9)]&&_0x4bb952[_0x17562c(0x444)](_0x10d67a['UUID']));}else _0x4bb952['offerSDP'](_0x10d67a[_0x17562c(0x4ec)]);}else{if(_0x10d67a['request']==_0x17562c(0x34e))log(_0x10d67a['list']),_0x17562c(0x18f)in _0x10d67a?(_0x4bb952['directorUUID']=_0x10d67a[_0x17562c(0x18f)],_0x4bb952[_0x17562c(0x266)]in _0x4bb952['pcs']&&(_0x4bb952[_0x17562c(0x347)][_0x4bb952[_0x17562c(0x266)]][_0x17562c(0x5c3)][_0x17562c(0x20f)]&&(_0x4bb952[_0x17562c(0x347)][_0x4bb952[_0x17562c(0x266)]][_0x17562c(0x5c3)][_0x17562c(0x20f)][_0x17562c(0x18f)]=!![])),_0x4bb952['directorUUID']in _0x4bb952[_0x17562c(0x2c9)]&&(_0x4bb952['rpcs'][_0x4bb952[_0x17562c(0x266)]][_0x17562c(0x5c3)][_0x17562c(0x20f)]&&(_0x17562c(0x1a4)===_0x17562c(0x18c)?(_0x31ac0a(_0x17562c(0x4dc)),_0x269527(_0x29b1e7['streamSrc']['getTracks']()),_0x5872e1=_0x162f95[_0x17562c(0x45f)]):_0x4bb952[_0x17562c(0x2c9)][_0x4bb952['directorUUID']][_0x17562c(0x5c3)][_0x17562c(0x20f)][_0x17562c(0x18f)]=!![]))):_0x4bb952[_0x17562c(0x266)]=![],_0x17562c(0x4e4)in _0x10d67a&&(_0x10d67a[_0x17562c(0x4e4)]==![]&&(!_0x4bb952[_0x17562c(0x52d)]&&(setTimeout(function(){var _0xb4414=_0x17562c;warnUser(_0xb4414(0x59d));},0x1),getById('head4')[_0x17562c(0x172)]=_0x17562c(0x2d7)))),_0x4bb952[_0x17562c(0x4d3)][_0x17562c(0x5b6)](_0x10d67a[_0x17562c(0x1d6)]);else{if(_0x10d67a[_0x17562c(0x16a)]==_0x17562c(0x2b3)){_0x4bb952[_0x17562c(0x448)]=[],_0x4bb952[_0x17562c(0x2b3)]=!![],_0x4bb952[_0x17562c(0x3dc)]=![],log(_0x17562c(0x4a7));!_0x4bb952[_0x17562c(0x18f)]&&(_0x4bb952[_0x17562c(0x45a)]=![]);log(_0x10d67a[_0x17562c(0x1d6)]);_0x17562c(0x18f)in _0x10d67a?(_0x4bb952[_0x17562c(0x266)]=_0x10d67a[_0x17562c(0x18f)],_0x4bb952[_0x17562c(0x266)]in _0x4bb952['pcs']&&(_0x4bb952['pcs'][_0x4bb952[_0x17562c(0x266)]]['stats'][_0x17562c(0x20f)]&&(_0x17562c(0x568)!=='RWkYC'?_0x4bb952[_0x17562c(0x347)][_0x4bb952['directorUUID']][_0x17562c(0x5c3)][_0x17562c(0x20f)][_0x17562c(0x18f)]=!![]:_0x4330e4(_0x2fbd4c))),_0x4bb952['directorUUID']in _0x4bb952[_0x17562c(0x2c9)]&&(_0x4bb952[_0x17562c(0x2c9)][_0x4bb952[_0x17562c(0x266)]][_0x17562c(0x5c3)][_0x17562c(0x20f)]&&(_0x4bb952['rpcs'][_0x4bb952['directorUUID']][_0x17562c(0x5c3)]['info'][_0x17562c(0x18f)]=!![]))):_0x4bb952[_0x17562c(0x266)]=![];for(_0x3b987c in _0x4bb952[_0x17562c(0x2c9)]){try{_0x4bb952[_0x17562c(0x2c9)][_0x3b987c][_0x17562c(0x5cd)](),delete _0x4bb952['rpcs'][_0x3b987c];}catch(_0x160d26){}}for(_0x3b987c in _0x4bb952[_0x17562c(0x347)]){try{_0x4bb952[_0x17562c(0x347)][_0x3b987c][_0x17562c(0x5cd)](),delete _0x4bb952[_0x17562c(0x347)][_0x3b987c],applySceneState();}catch(_0x416131){}}getChatMessage(_0x17562c(0x239),label=![],director=![],overlay=!![]),getById('head2')[_0x17562c(0x172)]=_0x17562c(0x4d7);_0x4bb952[_0x17562c(0x3c6)]?document['title']=_0x4bb952[_0x17562c(0x3c6)]+'\x20-\x20Transferred':document[_0x17562c(0x3f2)]='Transferred';_0x4bb952[_0x17562c(0x4d5)]=_0x4bb952[_0x17562c(0x585)],updateMixer(),updateUserList(),log(_0x17562c(0x4e5)),log(_0x10d67a[_0x17562c(0x1d6)]);for(var _0x3b987c in _0x10d67a[_0x17562c(0x1d6)]){if(_0x17562c(0x4ec)in _0x10d67a[_0x17562c(0x1d6)][_0x3b987c]){if(_0x17562c(0x4e9)===_0x17562c(0x4e9)){if(_0x17562c(0x4e8)in _0x10d67a[_0x17562c(0x1d6)][_0x3b987c]){if(_0x17562c(0x3ee)!==_0x17562c(0x455)){if(_0x10d67a[_0x17562c(0x1d6)][_0x3b987c][_0x17562c(0x4ec)]in _0x4bb952[_0x17562c(0x2c9)])log(_0x17562c(0x485));else{var _0x477473=_0x4bb952['desaltStreamID'](_0x10d67a[_0x17562c(0x1d6)][_0x3b987c][_0x17562c(0x4e8)]);log(_0x17562c(0x18b)+_0x477473);if(_0x4bb952[_0x17562c(0x45a)]){if(_0x17562c(0x3f7)===_0x17562c(0x1c3))_0x2f89f3(_0x17562c(0x26f));else{if(_0x4bb952[_0x17562c(0x266)]===_0x10d67a[_0x17562c(0x1d6)][_0x3b987c][_0x17562c(0x4ec)])play(_0x477473);else _0x4bb952['queueList'][_0x17562c(0x28f)]<0x1388&&(!_0x4bb952['queueList'][_0x17562c(0x5cf)](_0x477473)&&_0x4bb952[_0x17562c(0x448)][_0x17562c(0x3a5)](_0x477473));}}else play(_0x477473);}}else return![];}}else _0x5634fc(_0x513390);}}_0x4bb952[_0x17562c(0x196)]();}else{if(_0x10d67a['request']=='roomclaimed')log(_0x10d67a),_0x17562c(0x18f)in _0x10d67a?(_0x4bb952['directorUUID']=_0x10d67a['director'],_0x4bb952['directorUUID']in 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_0x4bb952[_0x397cb3(0x2b1)]==0x4&&(_0x3d2f11[_0x397cb3(0x58b)]=CodecsHandler[_0x397cb3(0x2e7)](_0x3d2f11['sdp'],{'stereo':0x2}),log('stereo\x20enabled'));}}_0x4bb952[_0x397cb3(0x347)][_0x2136e9][_0x397cb3(0x3e6)](_0x3d2f11)[_0x397cb3(0x3f8)](function(){var _0xdc0cee=_0x397cb3;log(_0xdc0cee(0x43e)+_0x2136e9);var _0x3342f6={};_0x3342f6['UUID']=_0x2136e9,_0x3342f6[_0xdc0cee(0x4e8)]=_0x4bb952[_0xdc0cee(0x4e8)],_0x3342f6[_0xdc0cee(0x1e4)]=_0x4bb952['pcs'][_0x2136e9][_0xdc0cee(0x4a2)],_0x3342f6[_0xdc0cee(0x2d6)]=_0x4bb952['pcs'][_0x2136e9][_0xdc0cee(0x2d6)];if(_0x4bb952[_0xdc0cee(0x208)]){if(_0xdc0cee(0x41c)!==_0xdc0cee(0x3f9))_0x4bb952[_0xdc0cee(0x32b)](JSON[_0xdc0cee(0x47f)](_0x3342f6['description']))[_0xdc0cee(0x3f8)](function(_0x51676a){var _0x37d599=_0xdc0cee;_0x3342f6[_0x37d599(0x1e4)]=_0x51676a[0x0],_0x3342f6[_0x37d599(0x192)]=_0x51676a[0x1],_0x4bb952[_0x37d599(0x383)](_0x3342f6);});else{var 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log(_0x1f52e5(0x333));if(_0x4bb952[_0x1f52e5(0x1b9)]!==![]){if(Object[_0x1f52e5(0x589)](_0x4bb952['pcs'])['length']>_0x4bb952[_0x1f52e5(0x1b9)]){delete _0x4bb952['pcs'][_0x22ef7b],_0x4bb952['applySoloChat'](),warnlog(_0x1f52e5(0x3b9)),applySceneState();return;}}else{if(_0x4bb952[_0x1f52e5(0x3db)]!==![]){if(_0x1f52e5(0x1c1)!==_0x1f52e5(0x36a)){if(Object[_0x1f52e5(0x589)](_0x4bb952[_0x1f52e5(0x2c9)])['length']+Object['keys'](_0x4bb952[_0x1f52e5(0x347)])[_0x1f52e5(0x28f)]>_0x4bb952[_0x1f52e5(0x3db)]){delete _0x4bb952['pcs'][_0x22ef7b],_0x4bb952['applySoloChat'](),warnlog(_0x1f52e5(0x3b3)),applySceneState();return;}}else _0xf06a6b=_0x19b9df[_0x1f52e5(0x347)][_0xbfc77d][_0x1f52e5(0x513)];}}_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b]=new RTCPeerConnection(_0x4bb952[_0x1f52e5(0x397)]);if(_0x4bb952[_0x1f52e5(0x5d6)]){if(Object[_0x1f52e5(0x589)](_0x4bb952['pcs'])['length']>0x1){delete _0x4bb952[_0x1f52e5(0x347)][_0x22ef7b],_0x4bb952['applySoloChat'](),applySceneState();return;}}_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x40a)]=null,_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x2f3)]=null,_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x1cd)]=null,_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x1b7)]=null,_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x283)]=null,_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x365)]=null,_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x517)]=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x50e)]=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x457)]=null,_0x4bb952['pcs'][_0x22ef7b][_0x1f52e5(0x50b)]=![],_0x4bb952['pcs'][_0x22ef7b][_0x1f52e5(0x2d6)]=_0x4bb952[_0x1f52e5(0x357)](),_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x5c3)]={},_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x535)]=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x42b)]=![],_0x4bb952['pcs'][_0x22ef7b][_0x1f52e5(0x5f9)]=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x30e)]=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x481)]=!![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x37b)]=!![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x251)]=!![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b]['allowMIDI']=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x430)]=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x4ec)]=_0x22ef7b,_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x2ee)]=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b]['scaleDueToBitrate']=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b]['scaleWidth']=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x4fe)]=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x513)]=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x24c)]=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x37d)]=_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x24b)](_0x1f52e5(0x37d)),_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x37d)][_0x1f52e5(0x4ec)]=_0x22ef7b,_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b]['keyframerate']=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b]['keyframeTimeout']=null,_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x3c6)]=![],_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x540)]=![],_0x4bb952['pcs'][_0x22ef7b][_0x1f52e5(0x53a)]=Date[_0x1f52e5(0x2c5)](),_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x37d)][_0x1f52e5(0x256)]=()=>{var 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_0x50b633=_0x21e79a[_0x50d413(0x557)](_0x50d413(0x323)+_0x1b09dd+'\x22]');_0x50b633[0x0]&&(_0x50b633[0x0][_0x50d413(0x216)]=_0x3c0334[_0x50d413(0x2c9)][_0x28790f][_0x50d413(0x540)]);}}else{_0x4bb952[_0x50d413(0x347)][_0x22ef7b][_0x50d413(0x16b)]=_0x210704[_0x50d413(0x584)]()[_0x50d413(0x1b4)],log(_0x4bb952['pcs'][_0x22ef7b][_0x50d413(0x16b)]);return;}}else{if(_0x210704[_0x50d413(0x5b3)][_0x50d413(0x20c)]==='audio'){_0x4bb952[_0x50d413(0x347)][_0x22ef7b][_0x50d413(0x32f)]=_0x210704[_0x50d413(0x584)]()[_0x50d413(0x1b4)],log(_0x4bb952[_0x50d413(0x347)][_0x22ef7b][_0x50d413(0x32f)]);return;}}})):'AylIN'===_0x3973ea(0x214)?(_0x5703c6('Couldn\x27t\x20re-connect'),_0x2e3f04(_0x439e62)):log(this[_0x3973ea(0x3a0)]);}}}catch(_0xb2d288){errorlog(_0xb2d288);}},_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x212)]=function(_0x5d6bdd){var _0x45a39d=_0x1f52e5;switch(_0x4bb952[_0x45a39d(0x347)][_0x22ef7b][_0x45a39d(0x4bf)]){case _0x45a39d(0x4e1):log(_0x45a39d(0x5b4));if(_0x4bb952[_0x45a39d(0x5d6)]){if(_0x45a39d(0x5b9)===_0x45a39d(0x26e))_0x2ee888[_0x45a39d(0x2c9)][_0x54f62c][_0x45a39d(0x5c3)][_0xe182a7['trackId']][_0x45a39d(0x499)]=0x0;else{if(_0x4bb952['ws']['readyState']!==0x1){if(_0x45a39d(0x41f)===_0x45a39d(0x41f)){_0x4bb952['ws'][_0x45a39d(0x5cd)]();break;}else _0x682d0['rpcs'][_0x7ac123]['remoteVideoMuteElement'][_0x45a39d(0x1a2)][_0x45a39d(0x232)]=_0x45a39d(0x54d);}_0x4bb952['ws'][_0x45a39d(0x5cd)](),setTimeout(function(){var _0x4774c4=_0x45a39d;_0x4bb952[_0x4774c4(0x52d)]!=!![]&&warnUser(_0x4774c4(0x4de));},0x1);}}break;case _0x45a39d(0x19f):log(_0x45a39d(0x5f3));break;case'failed':warnlog(_0x45a39d(0x241)),_0x4bb952[_0x45a39d(0x347)][_0x22ef7b][_0x45a39d(0x5cd)](),_0x4bb952['pcs'][_0x22ef7b]=null;_0x4bb952[_0x45a39d(0x5d6)]&&(_0x45a39d(0x40b)!==_0x45a39d(0x40b)?_0x4491d3[_0x45a39d(0x2c9)][_0x586fa6][_0x45a39d(0x5c3)]['RTC\x20Connection'][_0x45a39d(0x3fb)]=_0x51d899[_0x45a39d(0x320)]:!_0x4bb952[_0x45a39d(0x52d)]&&setTimeout(function(){var _0x4e42db=_0x45a39d;warnUser(_0x4e42db(0x330));},0x1));delete _0x4bb952['pcs'][_0x22ef7b],_0x4bb952[_0x45a39d(0x436)](),applySceneState();break;case _0x45a39d(0x23c):warnlog(_0x45a39d(0x1a8)),_0x4bb952[_0x45a39d(0x347)][_0x22ef7b]=null;_0x4bb952[_0x45a39d(0x5d6)]&&(!_0x4bb952['cleanOutput']&&setTimeout(function _0x35a87e(){warnUser('Remote\x20peer\x20disconnected.\x20Due\x20to\x20enhanced\x20security,\x20please\x20refresh\x20to\x20create\x20a\x20new\x20connection.');},0x1));delete _0x4bb952[_0x45a39d(0x347)][_0x22ef7b],_0x4bb952[_0x45a39d(0x436)](),applySceneState();break;}},_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x1ef)]=function(_0x5d4938){var _0x9c8dd8=_0x1f52e5;warnlog('WebRTC\x20Connection\x20Closed.\x20Clean\x20up.\x20657'),_0x4bb952[_0x9c8dd8(0x347)][_0x22ef7b]=null,delete _0x4bb952[_0x9c8dd8(0x347)][_0x22ef7b],_0x4bb952['applySoloChat'](),applySceneState();},_0x4bb952[_0x1f52e5(0x347)][_0x22ef7b][_0x1f52e5(0x256)]=function _0x55259f(){var _0x19b774=_0x1f52e5;_0x19b774(0x576)!==_0x19b774(0x576)?_0x5df21f(_0x19d5a4['streamID']):log('WEBRTC\x20CONNECTION\x20OPEN');};}else _0x269e47=0x20,_0x25b0e7[_0x1f52e5(0x497)]&&(_0x262f37=_0x52f0bd[_0x1f52e5(0x497)]),_0x306e66[_0x1f52e5(0x3ea)][0x0][_0x1f52e5(0x484)]=_0x5c8ca5*0x400;},_0x4bb952[_0x41b841(0x234)]=function(_0x221080){var _0x4b3d04=_0x41b841;if(_0x4bb952[_0x4b3d04(0x208)]){if(_0x4b3d04(0x5a8)==='RCpcD')_0x4b3d04(0x192)in _0x221080&&(_0x4b3d04(0x56e)===_0x4b3d04(0x48b)?_0x103ea5[_0x4b3d04(0x2c9)][_0x1a6a96][_0x4b3d04(0x487)][_0x4b3d04(0x213)]=_0x127866[_0x4b3d04(0x2c9)][_0x3c313a][_0x4b3d04(0x45f)]:_0x4bb952[_0x4b3d04(0x541)](_0x221080[_0x4b3d04(0x1e4)],_0x221080['vector'])[_0x4b3d04(0x3f8)](function(_0x4ab0b0){var _0x2ff6ca=_0x4b3d04;_0x221080[_0x2ff6ca(0x1e4)]=JSON[_0x2ff6ca(0x2b5)](_0x4ab0b0);if(_0x221080[_0x2ff6ca(0x1e4)][_0x2ff6ca(0x2e9)]==_0x2ff6ca(0x419))_0x4bb952[_0x2ff6ca(0x28b)](_0x221080),_0x4bb952[_0x2ff6ca(0x25e)](_0x221080);else{if(_0x2ff6ca(0x1b1)!=='cPNtp')_0x284be8[_0x2ff6ca(0x3a5)](_0x2e2b05);else try{var _0x402aca=_0x4bb952[_0x2ff6ca(0x510)];if((iOS||iPad)&&_0x4bb952[_0x2ff6ca(0x347)][_0x221080['UUID']][_0x2ff6ca(0x535)]==!![]&&_0x4bb952[_0x2ff6ca(0x347)][_0x221080['UUID']]['forceios']==![]){if(_0x402aca===![]||_0x402aca>_0x4bb952[_0x2ff6ca(0x4c1)]){var _0x1a2a85=Object[_0x2ff6ca(0x589)](_0x4bb952[_0x2ff6ca(0x347)])[_0x2ff6ca(0x28f)];_0x1a2a85>0x3?_0x402aca=_0x4bb952[_0x2ff6ca(0x2b2)]:_0x402aca=_0x4bb952[_0x2ff6ca(0x4c1)];}_0x221080[_0x2ff6ca(0x1e4)][_0x2ff6ca(0x58b)]=CodecsHandler['preferCodec'](_0x221080[_0x2ff6ca(0x1e4)]['sdp'],'vp8'),_0x221080[_0x2ff6ca(0x1e4)][_0x2ff6ca(0x58b)]=CodecsHandler['setVideoBitrates'](_0x221080[_0x2ff6ca(0x1e4)][_0x2ff6ca(0x58b)],{'min':parseInt(_0x402aca/0x2)||0x1,'max':_0x402aca}),_0x402aca=![];}else _0x4bb952[_0x2ff6ca(0x347)][_0x221080[_0x2ff6ca(0x4ec)]][_0x2ff6ca(0x535)]==!![]&&(_0x402aca!==![]?_0x4bb952[_0x2ff6ca(0x545)]!==![]&&(_0x2ff6ca(0x401)!==_0x2ff6ca(0x401)?(_0x432fd4[_0x2ff6ca(0x2c9)][_0x11cf4f][_0x2ff6ca(0x273)]=_0x1abc01(_0x49f688[_0x2ff6ca(0x49d)]),_0x389327[_0x2ff6ca(0x2c9)][_0x18c546][_0x2ff6ca(0x273)]['id']='iframe_'+_0x9dd885,_0x4a2f7e[_0x2ff6ca(0x2c9)][_0x191807]['iframeEle'][_0x2ff6ca(0x53e)][_0x2ff6ca(0x4ec)]=_0x3a663c,_0x45b103=!![],_0x3ea676[_0x2ff6ca(0x2c9)][_0x25a4bd][_0x2ff6ca(0x4e8)]&&(_0xbe63a1[_0x2ff6ca(0x2c9)][_0x4c8d52][_0x2ff6ca(0x273)][_0x2ff6ca(0x53e)][_0x2ff6ca(0x399)]=_0x41f195[_0x2ff6ca(0x2c9)][_0x3c1c69][_0x2ff6ca(0x4e8)])):_0x4bb952['roombitrate']<_0x402aca&&(_0x402aca=_0x4bb952[_0x2ff6ca(0x545)])):_0x402aca=_0x4bb952['roombitrate']);if(_0x402aca){var _0xf975ce=CodecsHandler[_0x2ff6ca(0x37c)](_0x221080[_0x2ff6ca(0x1e4)]['sdp'])||0x9c4;log(_0x2ff6ca(0x1a3)+_0xf975ce);if(_0xf975ce>_0x402aca){var _0x52ff12=CodecsHandler[_0x2ff6ca(0x3c5)](_0x221080[_0x2ff6ca(0x1e4)][_0x2ff6ca(0x58b)])||0x0;_0x221080[_0x2ff6ca(0x1e4)][_0x2ff6ca(0x58b)]=CodecsHandler[_0x2ff6ca(0x209)](_0x221080[_0x2ff6ca(0x1e4)]['sdp'],{'min':parseInt(_0x402aca/0xa)||0x1,'max':parseInt(_0x402aca+_0x52ff12/0x400)}),_0x4bb952[_0x2ff6ca(0x347)][_0x221080[_0x2ff6ca(0x4ec)]][_0x2ff6ca(0x50b)]=_0x402aca;}else _0x4bb952[_0x2ff6ca(0x347)][_0x221080[_0x2ff6ca(0x4ec)]]['setBitrate']=_0xf975ce;}else{if(_0x4bb952[_0x2ff6ca(0x50f)]!==![]){if(_0x2ff6ca(0x30f)!==_0x2ff6ca(0x3c3)){var _0xf975ce=CodecsHandler[_0x2ff6ca(0x37c)](_0x221080['description']['sdp']);if(_0xf975ce===![]){if(_0x2ff6ca(0x166)===_0x2ff6ca(0x166)){var _0x52ff12=CodecsHandler[_0x2ff6ca(0x3c5)](_0x221080[_0x2ff6ca(0x1e4)][_0x2ff6ca(0x58b)])||0x0;_0x221080[_0x2ff6ca(0x1e4)][_0x2ff6ca(0x58b)]=CodecsHandler[_0x2ff6ca(0x209)](_0x221080[_0x2ff6ca(0x1e4)][_0x2ff6ca(0x58b)],{'min':parseInt(_0x4bb952[_0x2ff6ca(0x50f)]/0xa)||0x1,'max':parseInt(_0x4bb952['outboundVideoBitrate']+_0x52ff12/0x400)});}else!(_0x131655['cleanOutput']||_0x47a59d[_0x2ff6ca(0x1bd)])&&_0xccae68(_0x2ff6ca(0x1b0),0x1388);}else _0x4bb952['pcs'][_0x221080['UUID']]['setBitrate']=_0xf975ce;}else _0x1df6b9[_0x2ff6ca(0x3df)]=_0x283a23[_0x2ff6ca(0x27c)];}else _0x4bb952['pcs'][_0x221080[_0x2ff6ca(0x4ec)]][_0x2ff6ca(0x50b)]=CodecsHandler[_0x2ff6ca(0x37c)](_0x221080[_0x2ff6ca(0x1e4)]['sdp']);}_0x4bb952[_0x2ff6ca(0x29b)]&&(_0x221080[_0x2ff6ca(0x1e4)]['sdp']=CodecsHandler[_0x2ff6ca(0x2e7)](_0x221080[_0x2ff6ca(0x1e4)]['sdp'],{'maxaveragebitrate':_0x4bb952[_0x2ff6ca(0x29b)]*0x400,'cbr':_0x4bb952[_0x2ff6ca(0x316)]}));if(_0x2ff6ca(0x2d6)in _0x221080&&_0x221080[_0x2ff6ca(0x2d6)]!=_0x4bb952[_0x2ff6ca(0x347)][_0x221080[_0x2ff6ca(0x4ec)]][_0x2ff6ca(0x2d6)]){errorlog(_0x2ff6ca(0x45c));return;}_0x4bb952[_0x2ff6ca(0x347)][_0x221080[_0x2ff6ca(0x4ec)]]['setRemoteDescription'](_0x221080[_0x2ff6ca(0x1e4)])[_0x2ff6ca(0x3f8)]()[_0x2ff6ca(0x5fa)](errorlog);}catch(_0x3597bf){errorlog(_0x3597bf);}}}));else{_0x5d77d6(_0x4b3d04(0x1cc));return;}}else{if(_0x221080[_0x4b3d04(0x1e4)]['type']==_0x4b3d04(0x419))_0x4bb952[_0x4b3d04(0x28b)](_0x221080),_0x4bb952['connectPeer'](_0x221080);else try{var _0x532f02=_0x4bb952[_0x4b3d04(0x510)];if((iOS||iPad)&&_0x4bb952['pcs'][_0x221080[_0x4b3d04(0x4ec)]][_0x4b3d04(0x535)]==!![]&&_0x4bb952[_0x4b3d04(0x347)][_0x221080[_0x4b3d04(0x4ec)]][_0x4b3d04(0x30e)]==![]){if(_0x532f02===![]||_0x532f02>_0x4bb952[_0x4b3d04(0x4c1)]){var _0x1c0d0d=Object[_0x4b3d04(0x589)](_0x4bb952[_0x4b3d04(0x347)])[_0x4b3d04(0x28f)];_0x1c0d0d>0x3?_0x532f02=_0x4bb952[_0x4b3d04(0x2b2)]:_0x532f02=_0x4bb952[_0x4b3d04(0x4c1)];}_0x221080['description'][_0x4b3d04(0x58b)]=CodecsHandler['preferCodec'](_0x221080[_0x4b3d04(0x1e4)][_0x4b3d04(0x58b)],'vp8'),_0x221080[_0x4b3d04(0x1e4)][_0x4b3d04(0x58b)]=CodecsHandler[_0x4b3d04(0x209)](_0x221080[_0x4b3d04(0x1e4)][_0x4b3d04(0x58b)],{'min':parseInt(_0x532f02/0x2)||0x1,'max':_0x532f02}),_0x532f02=![];}else _0x4bb952[_0x4b3d04(0x347)][_0x221080[_0x4b3d04(0x4ec)]][_0x4b3d04(0x535)]==!![]&&(_0x4b3d04(0x4cf)===_0x4b3d04(0x15f)?_0x15324c(_0x3f4486):_0x532f02!==![]?_0x4bb952['roombitrate']!==![]&&(_0x4bb952[_0x4b3d04(0x545)]<_0x532f02&&(_0x532f02=_0x4bb952[_0x4b3d04(0x545)])):_0x532f02=_0x4bb952[_0x4b3d04(0x545)]);if(_0x532f02){var _0x179e19=CodecsHandler[_0x4b3d04(0x37c)](_0x221080[_0x4b3d04(0x1e4)][_0x4b3d04(0x58b)])||0x9c4;log('BITRATE:\x20'+_0x179e19);if(_0x179e19>_0x532f02){var _0x18eb47=CodecsHandler[_0x4b3d04(0x3c5)](_0x221080[_0x4b3d04(0x1e4)][_0x4b3d04(0x58b)])||0x0;_0x221080[_0x4b3d04(0x1e4)][_0x4b3d04(0x58b)]=CodecsHandler[_0x4b3d04(0x209)](_0x221080[_0x4b3d04(0x1e4)][_0x4b3d04(0x58b)],{'min':parseInt(_0x532f02/0xa)||0x1,'max':parseInt(_0x532f02+_0x18eb47/0x400)});}}else{if(_0x4bb952[_0x4b3d04(0x50f)]!==![]){var _0x179e19=CodecsHandler[_0x4b3d04(0x37c)](_0x221080[_0x4b3d04(0x1e4)][_0x4b3d04(0x58b)]);if(_0x179e19===![]){var _0x18eb47=CodecsHandler['getOpusBitrate'](_0x221080[_0x4b3d04(0x1e4)][_0x4b3d04(0x58b)])||0x0;_0x221080[_0x4b3d04(0x1e4)][_0x4b3d04(0x58b)]=CodecsHandler['setVideoBitrates'](_0x221080['description']['sdp'],{'min':parseInt(_0x4bb952['outboundVideoBitrate']/0xa)||0x1,'max':parseInt(_0x4bb952[_0x4b3d04(0x50f)]+_0x18eb47/0x400)});}}}_0x4bb952[_0x4b3d04(0x29b)]&&(_0x221080[_0x4b3d04(0x1e4)][_0x4b3d04(0x58b)]=CodecsHandler[_0x4b3d04(0x2e7)](_0x221080[_0x4b3d04(0x1e4)][_0x4b3d04(0x58b)],{'maxaveragebitrate':_0x4bb952[_0x4b3d04(0x29b)]*0x400,'cbr':_0x4bb952[_0x4b3d04(0x316)]}));if(_0x4b3d04(0x2d6)in _0x221080&&_0x221080['session']!=_0x4bb952[_0x4b3d04(0x347)][_0x221080[_0x4b3d04(0x4ec)]][_0x4b3d04(0x2d6)]){errorlog(_0x4b3d04(0x45c));return;}_0x4bb952[_0x4b3d04(0x347)][_0x221080[_0x4b3d04(0x4ec)]]['setRemoteDescription'](_0x221080[_0x4b3d04(0x1e4)])[_0x4b3d04(0x3f8)]()[_0x4b3d04(0x5fa)](errorlog);}catch(_0x241ec2){errorlog(_0x241ec2);}}},_0x4bb952[_0x41b841(0x5de)]=function(_0x58f81e){var _0x3251d6=_0x41b841;_0x4bb952[_0x3251d6(0x208)]&&_0x3251d6(0x192)in _0x58f81e?_0x4bb952[_0x3251d6(0x541)](_0x58f81e['candidate'],_0x58f81e[_0x3251d6(0x192)])['then'](function(_0x35e446){var _0x1d409f=_0x3251d6;_0x58f81e['candidate']=JSON[_0x1d409f(0x2b5)](_0x35e446),_0x4bb952[_0x1d409f(0x418)](_0x58f81e);}):_0x4bb952[_0x3251d6(0x418)](_0x58f81e);},_0x4bb952[_0x41b841(0x418)]=function(_0x11fc29){var _0x1fcf7d=_0x41b841;if('BlZgB'!==_0x1fcf7d(0x44a)){try{if(_0x4bb952['icefilter']){if(_0x11fc29[_0x1fcf7d(0x4d1)][_0x1fcf7d(0x4d1)][_0x1fcf7d(0x4a8)](_0x4bb952[_0x1fcf7d(0x1fa)])===-0x1){if(_0x1fcf7d(0x387)!==_0x1fcf7d(0x387))_0x3a90af[_0x1fcf7d(0x2c9)][_0x547738][_0x1fcf7d(0x5c3)][_0x40f14e[_0x1fcf7d(0x1fb)]][_0x1fcf7d(0x386)]=_0x2c93d3(_0x2bd310(_0x5d3a0b[_0x1fcf7d(0x281)])*0x2710)/0x2710;else{log(_0x1fcf7d(0x3dd)),log(_0x11fc29[_0x1fcf7d(0x4d1)]);return;}}else log(_0x1fcf7d(0x2fd)),log(_0x11fc29[_0x1fcf7d(0x4d1)]);}}catch(_0x22e49b){errorlog(_0x22e49b);}if(_0x11fc29[_0x1fcf7d(0x4d1)]&&_0x1fcf7d(0x4d1)in _0x11fc29['candidate']&&_0x11fc29[_0x1fcf7d(0x4d1)][_0x1fcf7d(0x4d1)]=='')return;if(_0x11fc29[_0x1fcf7d(0x4ec)]in _0x4bb952[_0x1fcf7d(0x347)]&&_0x11fc29[_0x1fcf7d(0x2e9)]==_0x1fcf7d(0x2cd)){log(_0x1fcf7d(0x2c8));if(!(_0x11fc29['UUID']in _0x4bb952[_0x1fcf7d(0x347)])){errorlog('ICE\x20target\x20no\x20longer\x20exists?');return;}if(_0x1fcf7d(0x2d6)in _0x11fc29&&_0x4bb952[_0x1fcf7d(0x347)][_0x11fc29[_0x1fcf7d(0x4ec)]]['session']!=_0x11fc29['session']){errorlog(_0x1fcf7d(0x525));return;}_0x4bb952[_0x1fcf7d(0x347)][_0x11fc29[_0x1fcf7d(0x4ec)]]['addIceCandidate'](_0x11fc29['candidate'])[_0x1fcf7d(0x3f8)]()[_0x1fcf7d(0x5fa)](function(_0xaef96f){errorlog(_0xaef96f),errorlog(_0x11fc29);});}else{if(_0x11fc29[_0x1fcf7d(0x4ec)]in _0x4bb952['rpcs']&&_0x11fc29['type']==_0x1fcf7d(0x39d)){log(_0x1fcf7d(0x4d9));if(!(_0x11fc29[_0x1fcf7d(0x4ec)]in _0x4bb952[_0x1fcf7d(0x2c9)])){errorlog(_0x1fcf7d(0x1cc));return;}if(_0x1fcf7d(0x2d6)in _0x11fc29&&_0x4bb952['rpcs'][_0x11fc29[_0x1fcf7d(0x4ec)]][_0x1fcf7d(0x2d6)]!=_0x11fc29[_0x1fcf7d(0x2d6)]){errorlog(_0x1fcf7d(0x525));return;}if(_0x4bb952[_0x1fcf7d(0x2c9)][_0x11fc29[_0x1fcf7d(0x4ec)]]===null)return;_0x4bb952[_0x1fcf7d(0x2c9)][_0x11fc29[_0x1fcf7d(0x4ec)]]['addIceCandidate'](_0x11fc29[_0x1fcf7d(0x4d1)])[_0x1fcf7d(0x3f8)]()[_0x1fcf7d(0x5fa)](function(_0x3bf42f){errorlog(_0x3bf42f),errorlog(_0x11fc29);});}else errorlog(_0x11fc29),errorlog(_0x1fcf7d(0x16c));}}else _0x3b501b(_0x14daf2);},_0x4bb952['processIceBundle']=function(_0x58d9cc){var _0x122e13=_0x41b841;if(_0x4bb952[_0x122e13(0x208)]&&_0x122e13(0x192)in _0x58d9cc)_0x4bb952[_0x122e13(0x541)](_0x58d9cc['candidates'],_0x58d9cc[_0x122e13(0x192)])[_0x122e13(0x3f8)](function(_0x408482){var _0x2f893b=_0x122e13;_0x58d9cc[_0x2f893b(0x325)]=JSON[_0x2f893b(0x2b5)](_0x408482);var _0x7a21c9={};_0x7a21c9[_0x2f893b(0x4ec)]=_0x58d9cc[_0x2f893b(0x4ec)],_0x7a21c9['type']=_0x58d9cc[_0x2f893b(0x2e9)];for(var _0x7c4faa=0x0;_0x7c4faa<_0x58d9cc[_0x2f893b(0x325)][_0x2f893b(0x28f)];_0x7c4faa++){_0x7a21c9[_0x2f893b(0x4d1)]=_0x58d9cc['candidates'][_0x7c4faa],_0x4bb952[_0x2f893b(0x418)](_0x7a21c9);}});else{var _0x1ac4ca={};_0x1ac4ca['UUID']=_0x58d9cc[_0x122e13(0x4ec)],_0x1ac4ca[_0x122e13(0x2e9)]=_0x58d9cc[_0x122e13(0x2e9)];for(var _0x1259b6=0x0;_0x1259b6<_0x58d9cc[_0x122e13(0x325)][_0x122e13(0x28f)];_0x1259b6++){_0x1ac4ca['candidate']=_0x58d9cc[_0x122e13(0x325)][_0x1259b6],_0x4bb952[_0x122e13(0x418)](_0x1ac4ca);}}},_0x4bb952['connectPeer']=function(_0x39dabe){var _0x22e9ea=_0x41b841;_0x4bb952[_0x22e9ea(0x2c9)][_0x39dabe[_0x22e9ea(0x4ec)]]['setRemoteDescription'](_0x39dabe[_0x22e9ea(0x1e4)])['then'](function(){var _0x2e1dee=_0x22e9ea;if(_0x4bb952[_0x2e1dee(0x2c9)][_0x39dabe[_0x2e1dee(0x4ec)]]['remoteDescription'][_0x2e1dee(0x2e9)]===_0x2e1dee(0x419))_0x4bb952['rpcs'][_0x39dabe[_0x2e1dee(0x4ec)]][_0x2e1dee(0x4ca)]()[_0x2e1dee(0x3f8)](function(_0x445c5e){var _0x998a11=_0x2e1dee;if(!_0x4bb952[_0x998a11(0x18f)]&&_0x4bb952['stereo']==0x5)_0x445c5e[_0x998a11(0x58b)]=CodecsHandler[_0x998a11(0x2e7)](_0x445c5e['sdp'],{'stereo':0x1,'maxaveragebitrate':(_0x4bb952[_0x998a11(0x497)]||0x100)*0x400,'cbr':_0x4bb952[_0x998a11(0x316)],'useinbandfec':0x1,'maxptime':_0x4bb952[_0x998a11(0x3f6)],'minptime':_0x4bb952[_0x998a11(0x2b8)],'ptime':_0x4bb952['ptime']});else{if(_0x4bb952[_0x998a11(0x2b1)]==0x1||_0x4bb952[_0x998a11(0x2b1)]==0x2||_0x4bb952[_0x998a11(0x2b1)]==0x5)_0x445c5e['sdp']=CodecsHandler[_0x998a11(0x2e7)](_0x445c5e[_0x998a11(0x58b)],{'stereo':0x1,'maxaveragebitrate':(_0x4bb952[_0x998a11(0x497)]||0x100)*0x400,'cbr':_0x4bb952[_0x998a11(0x316)],'useinbandfec':0x1,'maxptime':_0x4bb952[_0x998a11(0x3f6)],'minptime':_0x4bb952[_0x998a11(0x2b8)],'ptime':_0x4bb952[_0x998a11(0x474)]});else{if(_0x4bb952[_0x998a11(0x2b1)]==0x4)_0x445c5e[_0x998a11(0x58b)]=CodecsHandler[_0x998a11(0x2e7)](_0x445c5e[_0x998a11(0x58b)],{'stereo':0x2,'maxaveragebitrate':(_0x4bb952['audiobitrate']||0x100)*0x400,'cbr':_0x4bb952[_0x998a11(0x316)],'useinbandfec':0x1,'maxptime':_0x4bb952['maxptime'],'minptime':_0x4bb952[_0x998a11(0x2b8)],'ptime':_0x4bb952[_0x998a11(0x474)]});else _0x4bb952['audiobitrate']&&(_0x445c5e[_0x998a11(0x58b)]=CodecsHandler[_0x998a11(0x2e7)](_0x445c5e[_0x998a11(0x58b)],{'maxaveragebitrate':_0x4bb952[_0x998a11(0x497)]*0x400,'cbr':_0x4bb952[_0x998a11(0x316)],'useinbandfec':0x1,'maxptime':_0x4bb952['maxptime'],'minptime':_0x4bb952[_0x998a11(0x2b8)],'ptime':_0x4bb952[_0x998a11(0x474)]}));}}_0x4bb952['codec']&&(_0x445c5e[_0x998a11(0x58b)]=CodecsHandler[_0x998a11(0x177)](_0x445c5e[_0x998a11(0x58b)],_0x4bb952[_0x998a11(0x529)]));_0x4bb952[_0x998a11(0x5ac)]&&(log(_0x445c5e[_0x998a11(0x58b)]),_0x445c5e['sdp']=CodecsHandler[_0x998a11(0x334)](_0x445c5e[_0x998a11(0x58b)]));_0x4bb952[_0x998a11(0x35b)]&&(_0x445c5e[_0x998a11(0x58b)]=CodecsHandler['disablePLI'](_0x445c5e[_0x998a11(0x58b)]));_0x4bb952[_0x998a11(0x5b0)]&&(_0x445c5e[_0x998a11(0x58b)]=CodecsHandler[_0x998a11(0x4f3)](_0x445c5e[_0x998a11(0x58b)]));if(_0x4bb952[_0x998a11(0x2c9)][_0x39dabe[_0x998a11(0x4ec)]][_0x998a11(0x168)])log(_0x998a11(0x423)),_0x445c5e[_0x998a11(0x58b)]=_0x5e4081(_0x445c5e[_0x998a11(0x58b)],_0x4bb952[_0x998a11(0x2c9)][_0x39dabe[_0x998a11(0x4ec)]][_0x998a11(0x168)]);else _0x4bb952[_0x998a11(0x476)]&&(log('bit\x20rate\x20being\x20munged'),_0x445c5e[_0x998a11(0x58b)]=_0x5e4081(_0x445c5e[_0x998a11(0x58b)],_0x4bb952[_0x998a11(0x476)]));return _0x4bb952['rpcs'][_0x39dabe['UUID']][_0x998a11(0x3e6)](_0x445c5e);})[_0x2e1dee(0x3f8)](function _0x32cd7c(){var _0x2142ea=_0x2e1dee;log(_0x2142ea(0x447));var _0x51ee81={};_0x51ee81[_0x2142ea(0x4ec)]=_0x39dabe[_0x2142ea(0x4ec)],_0x51ee81[_0x2142ea(0x1e4)]=_0x4bb952['rpcs'][_0x39dabe[_0x2142ea(0x4ec)]]['localDescription'],_0x51ee81[_0x2142ea(0x2d6)]=_0x4bb952[_0x2142ea(0x2c9)][_0x39dabe[_0x2142ea(0x4ec)]][_0x2142ea(0x2d6)],_0x4bb952[_0x2142ea(0x208)]?_0x4bb952[_0x2142ea(0x32b)](JSON[_0x2142ea(0x47f)](_0x51ee81[_0x2142ea(0x1e4)]))[_0x2142ea(0x3f8)](function(_0x2c0d57){var _0x4e4dee=_0x2142ea;_0x51ee81['description']=_0x2c0d57[0x0],_0x51ee81[_0x4e4dee(0x192)]=_0x2c0d57[0x1],_0x4bb952['anyrequest'](_0x51ee81);}):_0x4bb952[_0x2142ea(0x490)](_0x51ee81);})[_0x2e1dee(0x5fa)](errorlog);else _0x4bb952[_0x2e1dee(0x2c9)][_0x39dabe[_0x2e1dee(0x4ec)]][_0x2e1dee(0x559)][_0x2e1dee(0x2e9)]==='answer'&&errorlog(_0x2e1dee(0x5ed));})[_0x22e9ea(0x5fa)](errorlog);},_0x4bb952['setupIncoming']=function(_0x3aa018){var _0xc05fdc=_0x41b841,_0x1ba910=_0x3aa018[_0xc05fdc(0x4ec)];if(_0x1ba910 in _0x4bb952[_0xc05fdc(0x2c9)]){log(_0xc05fdc(0x49c)),log(_0x3aa018);if(_0xc05fdc(0x2d6)in _0x3aa018){if(_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x2d6)]==_0x3aa018[_0xc05fdc(0x2d6)]){log(_0xc05fdc(0x338));return;}else warnlog('SDP\x20Sessions\x20do\x20not\x20match.\x20Do\x20I\x20delete\x20an\x20accept\x20this\x20new\x20SDP?');}}else log(_0xc05fdc(0x3b0));try{for(var _0xd72e21 in _0x4bb952[_0xc05fdc(0x2c9)]){if(_0x4bb952['rpcs'][_0xd72e21]['streamID']==_0x3aa018[_0xc05fdc(0x4e8)]){_0x4bb952[_0xc05fdc(0x2c9)][_0xd72e21][_0xc05fdc(0x487)]&&(_0x4bb952['rpcs'][_0xd72e21][_0xc05fdc(0x487)][_0xc05fdc(0x1a2)][_0xc05fdc(0x232)]=_0xc05fdc(0x54d));_0x4bb952[_0xc05fdc(0x2c9)][_0xd72e21]=null,delete _0x4bb952[_0xc05fdc(0x2c9)][_0xd72e21],warnlog(_0xc05fdc(0x570));if(_0x4bb952[_0xc05fdc(0x18f)])try{document['getElementById'](_0xc05fdc(0x3ab)+_0xd72e21)&&getById('container_'+_0xd72e21)[_0xc05fdc(0x506)][_0xc05fdc(0x36e)](getById(_0xc05fdc(0x3ab)+_0xd72e21));}catch(_0x4943b5){errorlog(_0x4943b5);}}}document[_0xc05fdc(0x54a)]('mainmenu')&&document[_0xc05fdc(0x54a)](_0xc05fdc(0x33d))[_0xc05fdc(0x506)][_0xc05fdc(0x36e)](document[_0xc05fdc(0x54a)](_0xc05fdc(0x33d)));}catch(_0x173735){errorlog(_0x173735);}if(_0x4bb952[_0xc05fdc(0x39c)]!==![]){if(Object[_0xc05fdc(0x589)](_0x4bb952[_0xc05fdc(0x2c9)])[_0xc05fdc(0x28f)]>=_0x4bb952['maxpublishers']){delete _0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910],updateUserList(),warnlog(_0xc05fdc(0x3b3));return;}}else{if(_0x4bb952['maxconnections']!==![]){if(Object[_0xc05fdc(0x589)](_0x4bb952[_0xc05fdc(0x2c9)])[_0xc05fdc(0x28f)]+Object[_0xc05fdc(0x589)](_0x4bb952[_0xc05fdc(0x347)])[_0xc05fdc(0x28f)]>=_0x4bb952[_0xc05fdc(0x3db)]){delete _0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910],updateUserList(),warnlog(_0xc05fdc(0x3b3));return;}}}if(_0x4bb952[_0xc05fdc(0x45a)]){if(_0x4bb952[_0xc05fdc(0x18f)])!(_0x1ba910 in _0x4bb952[_0xc05fdc(0x347)])&&_0x4bb952[_0xc05fdc(0x444)](_0x1ba910);else{if(_0x4bb952['directorUUID']!==_0x1ba910)return;}}_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910]=new RTCPeerConnection(_0x4bb952[_0xc05fdc(0x397)]);if(_0x4bb952[_0xc05fdc(0x5d6)]){if(Object[_0xc05fdc(0x589)](_0x4bb952[_0xc05fdc(0x2c9)])[_0xc05fdc(0x28f)]>0x1){warnlog(_0xc05fdc(0x30d)),log(_0x4bb952[_0xc05fdc(0x2c9)]),delete _0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910],updateUserList();return;}else warnlog(_0xc05fdc(0x2bd));}_0x3aa018[_0xc05fdc(0x4e8)]in _0x4bb952[_0xc05fdc(0x1ad)]&&delete _0x4bb952[_0xc05fdc(0x1ad)][_0x3aa018[_0xc05fdc(0x4e8)]];_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x4e8)]=_0x3aa018[_0xc05fdc(0x4e8)];_0x3aa018[_0xc05fdc(0x2d6)]?_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910]['session']=_0x3aa018[_0xc05fdc(0x2d6)]:_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x2d6)]=null;_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x2cf)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x5c3)]={},_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x5c3)][_0xc05fdc(0x57d)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910]['bandwidth']=-0x1,_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x40c)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x49a)]=-0x1,_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x168)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x487)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x220)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x380)]=![],_0x4bb952['rpcs'][_0x1ba910][_0xc05fdc(0x55b)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x377)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x5a1)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x2ef)]=![],_0x4bb952['rpcs'][_0x1ba910][_0xc05fdc(0x1a1)]=null,_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x3d8)]={},_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x4fe)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x1da)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x45f)]=null,_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x4eb)]=0x64,_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x1ac)]=0x0,_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x434)]=0x0,_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x50d)]=0x0,_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x3c6)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x540)]=![],_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x385)]=null,_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x5fc)]=null,_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x4c2)]={},_0x4bb952['rpcs'][_0x1ba910][_0xc05fdc(0x49d)]=![],_0x4bb952['rpcs'][_0x1ba910][_0xc05fdc(0x273)]=![],_0x4bb952['rpcs'][_0x1ba910][_0xc05fdc(0x53a)]=Date[_0xc05fdc(0x2c5)]();var _0x418651='';_0x4bb952[_0xc05fdc(0x5c0)]&&(_0xc05fdc(0x331)===_0xc05fdc(0x331)?_0x418651=_0x4bb952[_0xc05fdc(0x5c0)]:_0xc5a939(_0xc05fdc(0x4de)));if(_0x4bb952['director']){var _0x1e80d2='';_0x4bb952[_0xc05fdc(0x42f)]&&(_0x4bb952[_0xc05fdc(0x42f)]!==!![]?_0x1e80d2=_0xc05fdc(0x2f1)+_0x4bb952[_0xc05fdc(0x42f)]:_0x1e80d2=_0xc05fdc(0x272));var _0xc8bd63='';_0x4bb952['password']&&(_0x4bb952[_0xc05fdc(0x1b8)]===![]&&(_0xc8bd63=_0xc05fdc(0x2db)+_0x4bb952[_0xc05fdc(0x208)])),createControlBox(_0x1ba910,_0xc05fdc(0x17e)+location[_0xc05fdc(0x42d)]+location['pathname']+_0xc05fdc(0x3b5)+_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x4e8)]+'&scene'+_0x418651+_0xc05fdc(0x4a3)+_0x4bb952[_0xc05fdc(0x1e0)]+_0xc8bd63+_0x1e80d2,_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x4e8)]);}_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x4ec)]=_0x1ba910;if(_0xc05fdc(0x4e8)in 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_0x2917d6[_0x3b3ab9(0x216)]=_0x17b45d[_0x3b3ab9(0x2c9)][_0x313a91]['label'];}}else{if(_0x4bb952[_0x3b3ab9(0x266)]){if(_0x1ba910==_0x4bb952[_0x3b3ab9(0x266)]){if(_0x4bb952[_0x3b3ab9(0x1c5)]===![])_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)]=loadIframe(_0x10948f[_0x3b3ab9(0x49d)]),_0x4bb952['rpcs'][_0x1ba910][_0x3b3ab9(0x273)]['id']=_0x3b3ab9(0x42c)+_0x1ba910,_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)]['dataset'][_0x3b3ab9(0x4ec)]=_0x1ba910,_0x512fd8=!![],_0x4bb952['broadcastIFrame']=_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)],_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x4e8)]&&(_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)]['dataset'][_0x3b3ab9(0x399)]=_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x4e8)]);else _0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x4e8)]in _0x4bb952['noiframe']&&(_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)]=loadIframe(_0x10948f[_0x3b3ab9(0x49d)]),_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)]['id']=_0x3b3ab9(0x42c)+_0x1ba910,_0x4bb952['rpcs'][_0x1ba910][_0x3b3ab9(0x273)][_0x3b3ab9(0x53e)][_0x3b3ab9(0x4ec)]=_0x1ba910,_0x512fd8=!![],_0x4bb952[_0x3b3ab9(0x3dc)]=_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)],_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x4e8)]&&(_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)][_0x3b3ab9(0x53e)][_0x3b3ab9(0x399)]=_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x4e8)]));}}}}else{if(_0x4bb952['noiframe']===![])_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)]=loadIframe(_0x10948f['iframeSrc']),_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)]['id']=_0x3b3ab9(0x42c)+_0x1ba910,_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)][_0x3b3ab9(0x53e)][_0x3b3ab9(0x4ec)]=_0x1ba910,_0x512fd8=!![],_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x4e8)]&&(_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910]['iframeEle'][_0x3b3ab9(0x53e)][_0x3b3ab9(0x399)]=_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x4e8)]);else _0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x4e8)]in _0x4bb952['noiframe']&&(_0x4bb952['rpcs'][_0x1ba910][_0x3b3ab9(0x273)]=loadIframe(_0x10948f[_0x3b3ab9(0x49d)]),_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)]['id']=_0x3b3ab9(0x42c)+_0x1ba910,_0x4bb952['rpcs'][_0x1ba910][_0x3b3ab9(0x273)][_0x3b3ab9(0x53e)][_0x3b3ab9(0x4ec)]=_0x1ba910,_0x512fd8=!![],_0x4bb952['rpcs'][_0x1ba910][_0x3b3ab9(0x4e8)]&&(_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x273)][_0x3b3ab9(0x53e)][_0x3b3ab9(0x399)]=_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910]['streamID']));}}else _0x35e943=0x0;}}catch(_0x464228){errorlog(_0x464228);}}}}if(_0x3b3ab9(0x17a)in _0x10948f){var _0x38cbf7=![],_0x40c350=![];_0x4bb952[_0x3b3ab9(0x266)]===_0x1ba910&&(_0x38cbf7=!![],_0x3b3ab9(0x5ad)in _0x10948f&&(_0x10948f[_0x3b3ab9(0x5ad)]==!![]&&(_0x40c350=!![])));if(_0x4bb952[_0x3b3ab9(0x18f)]){if(_0x10948f[_0x3b3ab9(0x17a)]==_0x3b3ab9(0x324))_0x4bb952[_0x3b3ab9(0x4f0)]&&playtone(),getById(_0x3b3ab9(0x3da)+_0x1ba910)['style'][_0x3b3ab9(0x232)]=_0x3b3ab9(0x3e8),_0x4bb952['rpcs'][_0x1ba910][_0x3b3ab9(0x294)][_0x3b3ab9(0x1a2)]['display']=_0x3b3ab9(0x466);else _0x10948f['chat']==_0x3b3ab9(0x518)&&(getById(_0x3b3ab9(0x3da)+_0x1ba910)[_0x3b3ab9(0x1a2)]['display']=_0x3b3ab9(0x54d),_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x294)][_0x3b3ab9(0x1a2)][_0x3b3ab9(0x232)]=_0x3b3ab9(0x54d));}log(_0x3b3ab9(0x3ff)+_0x38cbf7),getChatMessage(_0x10948f[_0x3b3ab9(0x17a)],_0x4bb952['rpcs'][_0x1ba910]['label'],_0x38cbf7,_0x40c350);}_0x3b3ab9(0x5e6)in _0x10948f&&(log(_0x10948f),_0x4bb952[_0x3b3ab9(0x516)]&&updateClosedCaptions(_0x10948f,_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x3c6)],_0x1ba910));'audioOptions'in _0x10948f&&(_0x4bb952[_0x3b3ab9(0x18f)]&&updateDirectorsAudio(_0x10948f[_0x3b3ab9(0x3fe)],_0x1ba910));_0x3b3ab9(0x45d)in _0x10948f&&(_0x4bb952[_0x3b3ab9(0x18f)]&&updateDirectorsVideo(_0x10948f[_0x3b3ab9(0x45d)],_0x1ba910));_0x3b3ab9(0x43a)in _0x10948f&&(_0x4bb952[_0x3b3ab9(0x18f)]&&('gOifu'!==_0x3b3ab9(0x3d6)?_0x663bb2['encodings']=[{}]:gotDevicesRemote(_0x10948f[_0x3b3ab9(0x43a)],_0x1ba910)));_0x3b3ab9(0x3af)in _0x10948f&&('jSsjR'!==_0x3b3ab9(0x57e)?_0x4740d2(_0x3b3ab9(0x3ae)):_0x4bb952[_0x3b3ab9(0x266)]===_0x1ba910&&(_0x4bb952[_0x3b3ab9(0x363)]&&lowerhand()));if('infocus'in _0x10948f){log(_0x10948f);if(_0x4bb952[_0x3b3ab9(0x266)]===_0x1ba910){if(_0x10948f[_0x3b3ab9(0x2fa)]!==![]){if(_0x10948f[_0x3b3ab9(0x2fa)]===!![])_0x4bb952[_0x3b3ab9(0x2fa)]=!![];else for(var _0x5c7053 in _0x4bb952[_0x3b3ab9(0x2c9)]){if(_0x4bb952[_0x3b3ab9(0x2c9)][_0x5c7053][_0x3b3ab9(0x4e8)]===_0x10948f[_0x3b3ab9(0x2fa)]){_0x4bb952[_0x3b3ab9(0x2fa)]=_0x5c7053;break;}}}else _0x4bb952[_0x3b3ab9(0x2fa)]=![];_0x512fd8=!![],mustUpdateuserList=!![];}}if(_0x3b3ab9(0x1d8)in _0x10948f){_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x5c3)][_0x3b3ab9(0x1d8)]={};_0x3b3ab9(0x3e3)in _0x10948f&&(_0x3b3ab9(0x577)===_0x3b3ab9(0x311)?_0x2abbe3[_0x3b3ab9(0x448)][_0x3b3ab9(0x3a5)](_0x27b3b5):_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x5c3)]['sensors'][_0x3b3ab9(0x3e3)]=_0x10948f['lin']);_0x3b3ab9(0x389)in _0x10948f&&(_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x5c3)][_0x3b3ab9(0x1d8)][_0x3b3ab9(0x389)]=_0x10948f[_0x3b3ab9(0x389)]);_0x3b3ab9(0x1d9)in _0x10948f&&(_0x4bb952['rpcs'][_0x1ba910]['stats'][_0x3b3ab9(0x1d8)][_0x3b3ab9(0x1d9)]=_0x10948f[_0x3b3ab9(0x1d9)]);_0x3b3ab9(0x46d)in _0x10948f&&(_0x4bb952['rpcs'][_0x1ba910][_0x3b3ab9(0x5c3)][_0x3b3ab9(0x1d8)][_0x3b3ab9(0x46d)]=_0x10948f['mag']);if(isIFrame){if('NfPoG'!==_0x3b3ab9(0x5ee))parent[_0x3b3ab9(0x544)]({'sensors':_0x10948f},'*');else{_0x363012(_0x3b3ab9(0x4e6),![],_0x17cbe2),_0x4a04b4[_0x3b3ab9(0x1dc)](),_0x314e22('send\x20channel\x20closed');try{_0x4a4370[_0x3b3ab9(0x347)][_0x3ee15c][_0x3b3ab9(0x5cd)](),_0x1fb3b3['pcs'][_0x54bfc0]=null,delete _0x1cc639[_0x3b3ab9(0x347)][_0x6c1ce0],_0x5d7021();}catch(_0x4e1c39){}}}}_0x3b3ab9(0x29c)in _0x10948f&&playbackMIDI(_0x10948f[_0x3b3ab9(0x29c)]);_0x3b3ab9(0x425)in _0x10948f&&(_0x3b3ab9(0x388)===_0x3b3ab9(0x388)?updateRemoteRecordButton(_0x1ba910,_0x10948f[_0x3b3ab9(0x425)]):_0x5424a7[_0x3b3ab9(0x575)]&&(_0x208be7[_0x3b3ab9(0x1e0)]=_0x400eb6[_0x3b3ab9(0x575)]));if(_0x3b3ab9(0x20f)in _0x10948f){warnlog(_0x10948f),_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x5c3)][_0x3b3ab9(0x20f)]=_0x10948f[_0x3b3ab9(0x20f)];if(_0x3b3ab9(0x3c6)in _0x10948f['info']){typeof _0x10948f[_0x3b3ab9(0x20f)][_0x3b3ab9(0x3c6)]==_0x3b3ab9(0x5d7)?_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910]['label']=sanitizeLabel(_0x10948f[_0x3b3ab9(0x20f)]['label']):_0x3b3ab9(0x211)==='OWPSO'?_0x4bb952['rpcs'][_0x1ba910][_0x3b3ab9(0x3c6)]=![]:_0x3aa361['ws'][_0x3b3ab9(0x2e0)]===_0x5e970e[_0x3b3ab9(0x40d)]&&(_0x16c51b['ws']=null,_0x41b1b5(()=>{var _0x4ad92b=_0x3b3ab9;_0x5cdf77[_0x4ad92b(0x4d0)](!![]);},0x1388));if(_0x4bb952[_0x3b3ab9(0x18f)]){var _0x3e20f5=getById(_0x3b3ab9(0x2f4)+_0x1ba910);_0x3e20f5&&(_0x3e20f5[_0x3b3ab9(0x1a2)]['float']=_0x3b3ab9(0x50c),_0x3e20f5['style'][_0x3b3ab9(0x193)]=_0x3b3ab9(0x486),_0x3e20f5['style'][_0x3b3ab9(0x1a7)]='5px',_0x3e20f5[_0x3b3ab9(0x1a2)]['position']='relative',_0x3e20f5[_0x3b3ab9(0x1a2)][_0x3b3ab9(0x1d7)]=_0x3b3ab9(0x504),_0x3e20f5[_0x3b3ab9(0x53e)][_0x3b3ab9(0x4ec)]=_0x1ba910,_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x3c6)]?_0x3e20f5[_0x3b3ab9(0x216)]=_0x4bb952[_0x3b3ab9(0x2c9)][_0x1ba910][_0x3b3ab9(0x3c6)]:_0x3e20f5[_0x3b3ab9(0x216)]=_0x3b3ab9(0x432),_0x3e20f5['onclick']=function(_0xa1b74d){var _0x1a845e=_0x3b3ab9,_0x358e8a=_0xa1b74d[_0x1a845e(0x58d)][_0x1a845e(0x216)];_0x4bb952[_0x1a845e(0x2c9)][_0xa1b74d[_0x1a845e(0x58d)][_0x1a845e(0x53e)]['UUID']][_0x1a845e(0x3c6)]===![]&&(_0x358e8a='');window[_0x1a845e(0x174)]();var 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_0x3140bd=0x0,_0x45fa0b=0x0;try{_0x3140bd=_0x4bb952[_0x52de4a(0x2c9)][_0x55a039]['stats'][_0x365c92[_0x52de4a(0x1fb)]][_0x52de4a(0x593)],_0x45fa0b=_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x365c92[_0x52de4a(0x1fb)]][_0x52de4a(0x288)];}catch(_0x37741c){}_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x365c92['trackId']][_0x52de4a(0x42a)]=parseInt(0xa*(_0x365c92[_0x52de4a(0x301)]-_0x3140bd)/(_0x365c92[_0x52de4a(0x1c6)]/0x3e8-_0x45fa0b))/0xa,_0x4bb952['rpcs'][_0x55a039][_0x52de4a(0x5c3)][_0x365c92[_0x52de4a(0x1fb)]][_0x52de4a(0x593)]=_0x365c92[_0x52de4a(0x301)],_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x365c92[_0x52de4a(0x1fb)]]['_timestamp']=_0x365c92['timestamp']/0x3e8;}}}else _0x365c92['mediaType']==_0x52de4a(0x169)&&(_0x4bb952['rpcs'][_0x55a039]['stats'][_0x365c92[_0x52de4a(0x1fb)]][_0x52de4a(0x2e9)]='Audio\x20Track',_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x365c92[_0x52de4a(0x1fb)]][_0x52de4a(0x16e)]=_0x52de4a(0x169),_0x52de4a(0x281)in _0x365c92&&(_0x4bb952['rpcs'][_0x55a039][_0x52de4a(0x5c3)][_0x365c92[_0x52de4a(0x1fb)]][_0x52de4a(0x386)]=parseInt(parseFloat(_0x365c92[_0x52de4a(0x281)])*0x2710)/0x2710));_0x52de4a(0x414)in _0x365c92&&_0x52de4a(0x25c)in _0x365c92&&(!('_packetsLost'in _0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x365c92[_0x52de4a(0x1fb)]])&&(_0x4bb952['rpcs'][_0x55a039][_0x52de4a(0x5c3)][_0x365c92[_0x52de4a(0x1fb)]][_0x52de4a(0x319)]=_0x365c92[_0x52de4a(0x414)]),!(_0x52de4a(0x527)in _0x4bb952['rpcs'][_0x55a039]['stats'][_0x365c92['trackId']])&&(_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x365c92[_0x52de4a(0x1fb)]][_0x52de4a(0x527)]=_0x365c92[_0x52de4a(0x25c)]),!(_0x52de4a(0x499)in _0x4bb952[_0x52de4a(0x2c9)][_0x55a039]['stats'][_0x365c92[_0x52de4a(0x1fb)]])&&(_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x365c92[_0x52de4a(0x1fb)]]['packetLoss_in_percentage']=0x0),_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x365c92['trackId']]['packetLoss_in_percentage']=_0x4bb952['rpcs'][_0x55a039]['stats'][_0x365c92['trackId']]['packetLoss_in_percentage']*0.35+0.65*((_0x365c92[_0x52de4a(0x414)]-_0x4bb952['rpcs'][_0x55a039][_0x52de4a(0x5c3)][_0x365c92[_0x52de4a(0x1fb)]][_0x52de4a(0x319)])*0x64)/(_0x365c92[_0x52de4a(0x25c)]-_0x4bb952['rpcs'][_0x55a039]['stats'][_0x365c92[_0x52de4a(0x1fb)]][_0x52de4a(0x527)]+(_0x365c92[_0x52de4a(0x414)]-_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x365c92[_0x52de4a(0x1fb)]][_0x52de4a(0x319)]))||0x0,_0x4bb952[_0x52de4a(0x2c9)][_0x55a039]['stats'][_0x365c92['trackId']][_0x52de4a(0x527)]=_0x365c92[_0x52de4a(0x25c)],_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x365c92[_0x52de4a(0x1fb)]][_0x52de4a(0x319)]=_0x365c92[_0x52de4a(0x414)]);}else return;}else _0x52de4a(0x3b6)in _0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)]&&_0x365c92['id']==_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)]['_codecId']&&('mimeType'in _0x365c92&&(_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x4bb952[_0x52de4a(0x2c9)][_0x55a039]['stats'][_0x52de4a(0x53c)]]?_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x52de4a(0x53c)]]['codec']=_0x365c92[_0x52de4a(0x225)]:(_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x52de4a(0x53c)]]={},_0x4bb952[_0x52de4a(0x2c9)][_0x55a039][_0x52de4a(0x5c3)][_0x4bb952['rpcs'][_0x55a039][_0x52de4a(0x5c3)]['_codecIdTrackId']][_0x52de4a(0x529)]=_0x365c92['mimeType'])),_0x52de4a(0x55e)in _0x365c92&&('frameWidth'in _0x365c92&&(_0x4bb952['rpcs'][_0x55a039][_0x52de4a(0x5c3)][_0x52de4a(0x364)]=parseInt(_0x365c92['frameWidth'])+'\x20x\x20'+parseInt(_0x365c92[_0x52de4a(0x55e)]))));}}}}}}),_0x4bb952[_0x48e64b(0x46c)]!==![]&&_0x4bb952[_0x48e64b(0x521)](_0x55a039),setTimeout(function(){var _0x20928=_0x48e64b;_0x4bb952[_0x20928(0x51f)](),_0x4bb952[_0x20928(0x24d)]();},0x0));});}catch(_0x27d8e5){errorlog(_0x27d8e5);}},_0x4bb952[_0xc05fdc(0x2c9)][_0x1ba910][_0xc05fdc(0x33b)]=_0x5478d2=>{var _0x313bd5=_0xc05fdc;log(_0x313bd5(0x31e));_0x4bb952[_0x313bd5(0x46c)]!==![]&&_0x4bb952['playoutdelay'](_0x1ba910);_0x4bb952[_0x313bd5(0x51f)](),_0x4bb952[_0x313bd5(0x24d)]();if(_0x5478d2[_0x313bd5(0x5ec)]&&_0x5478d2[_0x313bd5(0x5ec)][0x0]){_0x5478d2[_0x313bd5(0x5ec)][0x0][_0x313bd5(0x55a)]=function(_0x13c241){var _0x56110f=_0x313bd5;try{warnlog(_0x56110f(0x358)),_0x4bb952[_0x56110f(0x2c9)][_0x1ba910][_0x56110f(0x45f)][_0x56110f(0x546)]()[_0x56110f(0x35e)](_0x48acaf=>{var _0x5a1e20=_0x56110f;_0x48acaf['id']==_0x13c241[_0x5a1e20(0x5b3)]['id']&&_0x4bb952[_0x5a1e20(0x2c9)][_0x1ba910][_0x5a1e20(0x45f)][_0x5a1e20(0x5f4)](_0x48acaf);}),_0x4bb952[_0x56110f(0x2c9)][_0x1ba910][_0x56110f(0x487)]['srcObject']=_0x4bb952['rpcs'][_0x1ba910]['streamSrc'],setTimeout(function(){updateMixer();},0x1);}catch(_0x452ba8){}},_0x5478d2[_0x313bd5(0x5ec)][0x0][_0x313bd5(0x3d1)]=function(_0xbdfa06){var _0x4cc4b7=_0x313bd5;try{warnlog('track\x20made\x20inactive;\x20removing'),_0x4bb952[_0x4cc4b7(0x2c9)][_0x1ba910][_0x4cc4b7(0x45f)]['getVideoTracks']()['forEach'](_0x2b0b87=>{var _0x43c120=_0x4cc4b7;_0x2b0b87['id']==_0xbdfa06[_0x43c120(0x5b3)]['id']&&_0x4bb952['rpcs'][_0x1ba910][_0x43c120(0x45f)][_0x43c120(0x5f4)](_0x2b0b87);}),_0x4bb952[_0x4cc4b7(0x2c9)][_0x1ba910][_0x4cc4b7(0x487)]['srcObject']=_0x4bb952[_0x4cc4b7(0x2c9)][_0x1ba910][_0x4cc4b7(0x45f)];}catch(_0xc1d16f){};};var _0x3aa1ef=_0x5478d2['streams'][0x0][_0x313bd5(0x546)]()[_0x313bd5(0x28f)];_0x3aa1ef>0x1&&errorlog(_0x313bd5(0x59e));}if(_0x4bb952['rpcs'][_0x1ba910][_0x313bd5(0x487)]){if(_0x5478d2[_0x313bd5(0x5ec)]&&_0x5478d2['streams'][0x0]){if(!_0x4bb952['rpcs'][_0x1ba910][_0x313bd5(0x45f)])_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x45f)]=_0x5478d2[_0x313bd5(0x5ec)][0x0];else{var _0x116067=![];_0x5478d2[_0x313bd5(0x5ec)][0x0]['getTracks']()[_0x313bd5(0x35e)](_0x23ed83=>{var _0x5f487f=_0x313bd5;_0x116067&&(warnlog(_0x5f487f(0x492)),warnlog(_0x23ed83)),_0x23ed83[_0x5f487f(0x20c)]==_0x5f487f(0x195)&&(_0x5f487f(0x435)!==_0x5f487f(0x1dd)?(_0x4bb952[_0x5f487f(0x2c9)][_0x1ba910][_0x5f487f(0x45f)][_0x5f487f(0x546)]()[_0x5f487f(0x35e)](_0x5189df=>{var _0x3beb52=_0x5f487f;_0x4bb952[_0x3beb52(0x2c9)][_0x1ba910]['streamSrc'][_0x3beb52(0x5f4)](_0x5189df);}),_0x116067=!![]):_0x2c8d1d(_0x5f487f(0x3b0))),log('adding\x20track'),_0x4bb952[_0x5f487f(0x2c9)][_0x1ba910][_0x5f487f(0x45f)][_0x5f487f(0x40e)](_0x23ed83);});}try{_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x487)][_0x313bd5(0x213)]=_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x45f)];}catch(_0x1dcee7){errorlog('4722');}log(_0x313bd5(0x20e));}else{if(_0x5478d2[_0x313bd5(0x5b3)]){warnlog(_0x313bd5(0x335));!_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910]['streamSrc']?_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x45f)]=new MediaStream():_0x313bd5(0x199)===_0x313bd5(0x199)?_0x5478d2['track'][_0x313bd5(0x20c)]==_0x313bd5(0x195)&&_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x45f)][_0x313bd5(0x546)]()[_0x313bd5(0x35e)](_0x55ffac=>{var _0x2fd6f2=_0x313bd5;_0x4bb952['rpcs'][_0x1ba910][_0x2fd6f2(0x45f)]['removeTrack'](_0x55ffac);}):(_0x3a610e(_0x3c67f0+'\x20is\x20not\x20defined;\x20skipping.'),_0x3734b7=_0x545c18[_0x313bd5(0x190)](_0x313bd5(0x4d2)));_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x45f)]['addTrack'](_0x5478d2[_0x313bd5(0x5b3)]);try{_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x487)][_0x313bd5(0x213)]=_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x45f)];}catch(_0x4f698c){errorlog('4722');}}else errorlog(_0x313bd5(0x4b0));}setTimeout(function(){updateMixer();},0x1);}else{log(_0x313bd5(0x463));var _0x33c8f5=document[_0x313bd5(0x190)](_0x313bd5(0x195));_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910]['videoElement']=_0x33c8f5,_0x33c8f5[_0x313bd5(0x53e)][_0x313bd5(0x4ec)]=_0x1ba910,_0x33c8f5['id']=_0x313bd5(0x2ca)+_0x1ba910;_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x4e8)]&&(_0x33c8f5['dataset'][_0x313bd5(0x399)]=_0x4bb952['rpcs'][_0x1ba910]['streamID']);setupIncomingVideoTracking(_0x33c8f5,_0x1ba910);if(_0x5478d2[_0x313bd5(0x5ec)]&&_0x5478d2[_0x313bd5(0x5ec)][0x0]){var _0x4f51c3=_0x5478d2[_0x313bd5(0x5ec)][0x0];_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x45f)]=_0x5478d2[_0x313bd5(0x5ec)][0x0],_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x487)][_0x313bd5(0x213)]=_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x45f)],log(_0x313bd5(0x20e));}else _0x5478d2[_0x313bd5(0x5b3)]&&(_0x313bd5(0x4ce)!==_0x313bd5(0x4ce)?_0x498366(_0x20624c):(warnlog(_0x313bd5(0x335)),!_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910]['streamSrc']&&(_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x45f)]=new MediaStream()),_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x45f)][_0x313bd5(0x40e)](_0x5478d2['track']),_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x487)][_0x313bd5(0x213)]=_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x45f)]));if(_0x4bb952[_0x313bd5(0x2ee)]){var _0x568d75={};_0x568d75[_0x313bd5(0x2ee)]=parseInt(_0x4bb952[_0x313bd5(0x2ee)]),log(_0x568d75),_0x4bb952['sendRequest'](_0x568d75,_0x1ba910);}}if(_0x4bb952[_0x313bd5(0x27b)]===!![]){var _0x595be2=function(_0x46b501){var _0x178582=function(_0xbb20d9){var _0xae631e=_0x3a98;if(!_0x4bb952[_0xae631e(0x2c9)][_0xbb20d9]){if(_0xae631e(0x5b5)===_0xae631e(0x44d)){_0x42b7c9(_0xae631e(0x4fc));return;}else return;}var _0x5130d5=_0x4bb952[_0xae631e(0x2c9)][_0xbb20d9][_0xae631e(0x487)][_0xae631e(0x3df)];_0x4bb952[_0xae631e(0x2c9)][_0xbb20d9]['videoElement'][_0xae631e(0x3df)]=![],_0x4bb952[_0xae631e(0x2c9)][_0xbb20d9][_0xae631e(0x487)][_0xae631e(0x167)](_0xae631e(0x31f),_0x178582);_0x4bb952[_0xae631e(0x443)][_0xae631e(0x467)]==_0xae631e(0x533)&&_0x4bb952[_0xae631e(0x443)][_0xae631e(0x478)]();var _0x5b2a28=_0x4bb952[_0xae631e(0x2c9)][_0xbb20d9][_0xae631e(0x45f)][_0xae631e(0x2dd)]();if(_0x5b2a28[_0xae631e(0x28f)]){var _0x2521d3=_0x5b2a28[0x0];_0x2521d3['label']!=_0xae631e(0x3c8)&&(_0x4bb952[_0xae631e(0x2c9)][_0xbb20d9]['videoElement']['srcObject']=addAudioPipeline(_0x4bb952[_0xae631e(0x2c9)][_0xbb20d9][_0xae631e(0x45f)],_0xbb20d9,_0x2521d3));}else _0x4bb952[_0xae631e(0x2c9)][_0xbb20d9][_0xae631e(0x487)][_0xae631e(0x213)]=_0x4bb952[_0xae631e(0x2c9)][_0xbb20d9]['streamSrc'];_0x4bb952[_0xae631e(0x2c9)][_0xbb20d9][_0xae631e(0x487)][_0xae631e(0x3df)]=_0x5130d5,delete _0x4bb952[_0xae631e(0x2c9)][_0xbb20d9][_0xae631e(0x309)];if('eventPlayActive'in _0x4bb952[_0xae631e(0x2c9)][_0xbb20d9]){if(_0xae631e(0x188)!=='YWPqY')errorlog(_0xae631e(0x44b));else try{_0x4c581e[_0xae631e(0x347)][_0x358ee0][_0xae631e(0x5cd)]();}catch(_0x4cc6d7){}}};return _0x178582(_0x46b501);};_0x4bb952[_0x313bd5(0x443)][_0x313bd5(0x467)]==_0x313bd5(0x4a9)&&(_0x4bb952['firstPlayTriggered']=!![]);if(_0x313bd5(0x309)in _0x4bb952[_0x313bd5(0x2c9)][_0x1ba910]){}else _0x4bb952[_0x313bd5(0x5f7)]==![]?_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x309)]=setInterval(function(_0x11bafa){var _0x1b9bfe=_0x313bd5;log(_0x1b9bfe(0x512)),_0x4bb952[_0x1b9bfe(0x2c9)][_0x11bafa]&&(_0x4bb952[_0x1b9bfe(0x5f7)]&&(clearInterval(_0x4bb952[_0x1b9bfe(0x2c9)][_0x11bafa][_0x1b9bfe(0x309)]),_0x4bb952[_0x1b9bfe(0x2c9)][_0x11bafa]['videoElement']['addEventListener'](_0x1b9bfe(0x31f),_0x595be2(_0x11bafa))));},0x3e8,_0x1ba910):(!_0x4bb952['rpcs'][_0x1ba910][_0x313bd5(0x309)]&&(_0x4bb952['rpcs'][_0x1ba910][_0x313bd5(0x309)]=!![]),_0x4bb952[_0x313bd5(0x2c9)][_0x1ba910][_0x313bd5(0x487)]['addEventListener'](_0x313bd5(0x31f),_0x595be2(_0x1ba910)));}_0x4bb952[_0x313bd5(0x51f)](),_0x4bb952[_0x313bd5(0x24d)](),updateUserList();},log(_0xc05fdc(0x4ea));},_0x4bb952;}();
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_0x9738e[_0x1db3ba(0x57b)][0x0][_0x1db3ba(0x235)],_0x1027ed[_0x1db3ba(0x3e1)](_0x9738e)[_0x1db3ba(0x3b1)](()=>{log('done\x20clearing\x20audio');});}catch(_0x3b8f01){errorlog(_0x3b8f01);}},_0x2872b7,_0x367a4c);});},_0x54a079['limitAudioBitrate']=function(_0x2d3b57,_0x15dfd1){var _0x129af9=_0x397888;_0x15dfd1=parseInt(_0x15dfd1);try{if(_0x129af9(0x1bd)===_0x129af9(0x3b2))_0x542c18[_0x129af9(0x4a1)]===_0x240c86?_0xd3253d[_0x129af9(0x481)]():_0x434769(_0x129af9(0x44c));else{var _0x51cca2=_0x54a079[_0x129af9(0x33f)][_0x2d3b57]['getSenders']()[_0x129af9(0x553)](function(_0x4c8f6b){var _0x150dc9=_0x129af9;return _0x4c8f6b[_0x150dc9(0x5ca)]&&_0x4c8f6b[_0x150dc9(0x5ca)][_0x150dc9(0x237)]==_0x150dc9(0x388);});if(!_0x51cca2){warnlog(_0x129af9(0x3dd));return;}var _0xb36f5d=_0x51cca2[_0x129af9(0x4ba)]();(!_0xb36f5d[_0x129af9(0x57b)]||_0xb36f5d[_0x129af9(0x57b)]['length']==0x0)&&(_0xb36f5d[_0x129af9(0x57b)]=[{}]);if(_0x15dfd1<0x0)_0xb36f5d[_0x129af9(0x57b)][0x0]['active']==![]&&(_0xb36f5d[_0x129af9(0x57b)][0x0][_0x129af9(0x5d6)]=!![]),safariVersion()<=0xd&&(iOS||iPad)?(_0x15dfd1=0x20,_0x54a079[_0x129af9(0x482)]&&(_0x15dfd1=_0x54a079[_0x129af9(0x482)]),_0xb36f5d[_0x129af9(0x57b)][0x0][_0x129af9(0x235)]=_0x15dfd1*0x400):delete _0xb36f5d[_0x129af9(0x57b)][0x0]['maxBitrate'];else _0x15dfd1===0x0?_0xb36f5d[_0x129af9(0x57b)][0x0][_0x129af9(0x5d6)]=![]:(_0xb36f5d[_0x129af9(0x57b)][0x0][_0x129af9(0x5d6)]==![]&&(_0xb36f5d['encodings'][0x0][_0x129af9(0x5d6)]=!![]),_0xb36f5d['encodings'][0x0][_0x129af9(0x235)]=_0x15dfd1*0x400);_0x51cca2[_0x129af9(0x3e1)](_0xb36f5d)[_0x129af9(0x3b1)](()=>{var _0x355406=_0x129af9;pokeIframeAPI(_0x355406(0x5d3),_0x15dfd1,_0x2d3b57),log('audio\x20bandwidth\x20set!');})[_0x129af9(0x35f)](warnlog);}}catch(_0x3f8fec){if(_0x129af9(0x572)!==_0x129af9(0x266))errorlog(_0x3f8fec);else return _0x118a6a(_0x129af9(0x469)),_0x2c1e67=_0x390d85[_0x129af9(0x322)](0x0,-0x1*_0x35bee9[_0x129af9(0x60d)][_0x129af9(0x44a)]),_0x50d05f;}},_0x54a079[_0x397888(0x601)]=function(_0x167a14){var 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_0x56b164[_0x3e0f87(0x306)][_0x3e0f87(0x573)]==_0x3e0f87(0x5ac)?_0x51b533[_0x3e0f87(0x33f)][_0x5b9d27]['label']=_0x268a22(_0x94218e[_0x3e0f87(0x306)][_0x3e0f87(0x573)]):_0x47594a[_0x3e0f87(0x33f)][_0x3d2aba][_0x3e0f87(0x573)]=![];}_0xfa3211[_0x3e0f87(0x57b)][0x0][_0x3e0f87(0x235)]?(_0x222036+=parseInt(_0xfa3211[_0x3e0f87(0x57b)][0x0][_0x3e0f87(0x235)])/0x400,warnlog(_0x222036)):(_0x222036+=_0x54a079[_0x3e0f87(0x33f)][_0x2dc8bb][_0x3e0f87(0x1ac)]||0x9c4,warnlog(_0x222036)),_0x3d2f60+=0x1;}if(!_0x222036)return![];var _0x5e835e=parseFloat(_0x222036/_0x54a079['limitTotalBitrate']);if(_0x5e835e>0x1)for(var _0x2dc8bb in _0x54a079[_0x3e0f87(0x33f)]){if(_0x11292c===_0x2dc8bb)continue;var _0x259b24=_0x54a079[_0x3e0f87(0x33f)][_0x2dc8bb][_0x3e0f87(0x253)]()[_0x3e0f87(0x553)](function(_0x3b9007){var _0x5a7140=_0x3e0f87;return _0x3b9007[_0x5a7140(0x5ca)]&&_0x3b9007[_0x5a7140(0x5ca)]['kind']==_0x5a7140(0x2bc);});if(!_0x259b24)continue;var _0xfa3211=_0x259b24[_0x3e0f87(0x4ba)]();if(!_0xfa3211[_0x3e0f87(0x57b)]||_0xfa3211['encodings'][_0x3e0f87(0x44a)]==0x0){if(_0x3e0f87(0x4e8)!==_0x3e0f87(0x505)){var _0x36cafa=_0x54a079[_0x3e0f87(0x33f)][_0x2dc8bb][_0x3e0f87(0x1ac)]||0x9c4;_0x54a079[_0x3e0f87(0x516)](_0x2dc8bb,parseInt(_0x36cafa/_0x5e835e),!![]),errorlog(parseInt(_0x36cafa/_0x5e835e));continue;}else _0x8b41bb[_0x3e0f87(0x3e1)](_0x239c8b)[_0x3e0f87(0x3b1)](()=>{var _0x504b2a=_0x3e0f87;_0x45e056[_0x504b2a(0x58b)](_0x504b2a(0x5a8)+_0x231bef+'\x22]')[0x0][_0x504b2a(0x4fb)][_0x504b2a(0x1d6)](_0x504b2a(0x202));})[_0x3e0f87(0x35f)](_0x8b45d);}if(_0xfa3211[_0x3e0f87(0x57b)][0x0][_0x3e0f87(0x5d6)]==![])continue;if(_0xfa3211[_0x3e0f87(0x57b)][0x0][_0x3e0f87(0x235)])errorlog(parseInt(parseInt(_0xfa3211[_0x3e0f87(0x57b)][0x0][_0x3e0f87(0x235)])/0x400/_0x5e835e)),_0x54a079[_0x3e0f87(0x516)](_0x2dc8bb,parseInt(parseInt(_0xfa3211[_0x3e0f87(0x57b)][0x0]['maxBitrate'])/0x400/_0x5e835e),!![]);else{var _0x36cafa=_0x54a079[_0x3e0f87(0x33f)][_0x2dc8bb][_0x3e0f87(0x1ac)]||0x9c4;errorlog(parseInt(_0x36cafa/_0x5e835e)),_0x54a079['limitBitrate'](_0x2dc8bb,parseInt(_0x36cafa/_0x5e835e),!![]);}}return parseInt(_0x3e96f4/_0x5e835e);}else return![];},_0x54a079[_0x397888(0x516)]=function(_0x3fce7a,_0x18de61,_0x536f90=![]){var _0x4d58c5=_0x397888;_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x1cb)]&&(_0x4d58c5(0x55e)!==_0x4d58c5(0x55e)?_0x85f44e['roombitrate']!==![]&&(_0x37ee79[_0x4d58c5(0x5c7)]<_0x57eed5&&(_0xfabf79=_0x1b2c90[_0x4d58c5(0x5c7)])):(clearInterval(_0x54a079['pcs'][_0x3fce7a][_0x4d58c5(0x1cb)]),_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x1cb)]=null));if(_0x18de61===null){if(_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x4a9)]===![])return;_0x18de61=_0x54a079['pcs'][_0x3fce7a][_0x4d58c5(0x4a9)];}_0x18de61=parseInt(_0x18de61);_0x54a079['maxvideobitrate']&&(_0x18de61>_0x54a079[_0x4d58c5(0x625)]&&(_0x18de61=_0x54a079['maxvideobitrate']));_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a]['savedBitrate']=_0x18de61;_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x2a9)]!==![]&&(_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x333)]===![]&&(_0x18de61>_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x2a9)]&&(_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x4a9)]=_0x18de61,_0x18de61=parseInt(_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a]['optimizedBitrate'])||0x0)));_0x536f90===![]&&(_0x54a079[_0x4d58c5(0x1a9)]&&(_0x18de61=_0x54a079[_0x4d58c5(0x3a3)](_0x18de61,_0x3fce7a)));if(_0x18de61==0x0){var _0x181c47=Date['now']()-_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x338)];if(_0x181c47<_0x54a079[_0x4d58c5(0x552)]){if(_0x4d58c5(0x367)===_0x4d58c5(0x35e)){if(_0x6a3e1d[_0x4d58c5(0x1f8)]){if(_0x968290[_0x4d58c5(0x46b)][_0x4d58c5(0x46b)][_0x4d58c5(0x3fd)](_0x56a7bf[_0x4d58c5(0x1f8)])===-0x1){_0x201cfe(_0x4d58c5(0x444));return;}else _0x100615(_0x26e6d2['candidate']);}}else _0x18de61=0x23,_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x1cb)]=setTimeout(function(_0x19915e){_0x54a079['limitBitrate'](_0x19915e,null);},_0x181c47,_0x3fce7a);}}try{if(safariVersion()<=0xd&&(iOS||iPad)){log('iOS\x20devices\x20do\x20not\x20support\x20dynamic\x20bitrates\x20correctly;\x20skipping');if(_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x436)]==!![]&&_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a]['forceios']==![])return;var _0x5b39d3=_0x54a079['pcs'][_0x3fce7a][_0x4d58c5(0x253)]()[_0x4d58c5(0x553)](function(_0x45da45){var _0x2a15fe=_0x4d58c5;return _0x45da45[_0x2a15fe(0x5ca)]&&_0x45da45['track']['kind']==_0x2a15fe(0x2bc);});if(!_0x5b39d3){warnlog(_0x4d58c5(0x357));return;}var _0xf9efaa=_0x5b39d3[_0x4d58c5(0x4ba)]();(!_0xf9efaa[_0x4d58c5(0x57b)]||_0xf9efaa['encodings']['length']==0x0)&&(_0xf9efaa[_0x4d58c5(0x57b)]=[{}]);if(_0x18de61<0x0)_0xf9efaa[_0x4d58c5(0x57b)][0x0][_0x4d58c5(0x5d6)]==![]&&(_0xf9efaa[_0x4d58c5(0x57b)][0x0][_0x4d58c5(0x5d6)]=!![]),_0x18de61=0x9c4,_0x54a079[_0x4d58c5(0x2a8)]&&(_0x18de61=_0x54a079[_0x4d58c5(0x2a8)]),_0x54a079['maxvideobitrate']&&(_0x18de61>_0x54a079['maxvideobitrate']&&(_0x18de61=_0x54a079[_0x4d58c5(0x625)])),_0xf9efaa[_0x4d58c5(0x57b)][0x0][_0x4d58c5(0x235)]=_0x18de61*0x400;else{if(_0x18de61===0x0){if('qlYKz'!==_0x4d58c5(0x212)){_0x347fdc[_0x4d58c5(0x389)]=new _0x195154(_0x4fa121[_0x4d58c5(0x389)][_0x4d58c5(0x4e6)](/.{1,2}/g)['map'](_0x5e21fd=>_0x469e32(_0x5e21fd,0x10)));if(_0x535d31[_0x4d58c5(0x219)][_0x14a1e1][_0x4d58c5(0x276)])return _0x317184[_0x4d58c5(0x272)][_0x4d58c5(0x256)][_0x4d58c5(0x34d)]({'name':_0x4d58c5(0x308)},_0x199bbd[_0x4d58c5(0x219)][_0x299506][_0x4d58c5(0x276)],_0x455d63[_0x4d58c5(0x389)],_0x27eb87[_0x4d58c5(0x392)][_0x4d58c5(0x2e1)](_0xb0d60a[_0x4d58c5(0x46d)]))['then'](function(_0x188af7){return _0x11eaa1(_0x188af7),_0x188af7;})[_0x4d58c5(0x35f)](function(_0x51cef1){return _0x41c2cf(_0x51cef1),![];});}else _0xf9efaa[_0x4d58c5(0x57b)][0x0][_0x4d58c5(0x5d6)]=![];}else _0xf9efaa['encodings'][0x0][_0x4d58c5(0x5d6)]==![]&&(_0xf9efaa[_0x4d58c5(0x57b)][0x0]['active']=!![]),_0xf9efaa[_0x4d58c5(0x57b)][0x0][_0x4d58c5(0x235)]=_0x18de61*0x400;}_0x5b39d3['setParameters'](_0xf9efaa)['then'](()=>{var _0x52b594=_0x4d58c5;pokeIframeAPI('setVideoBitrate',_0x18de61,_0x3fce7a),log(_0x52b594(0x636));})[_0x4d58c5(0x35f)](warnlog);return;}else{if((iOS||iPad)&&(_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x436)]==!![]&&_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x468)]==![])){var _0x2cdd53=0x64;_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x247)]&&(_0x2cdd53=_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x247)]),_0x54a079['pcs'][_0x3fce7a][_0x4d58c5(0x1c0)]&&_0x54a079['pcs'][_0x3fce7a]['scaleResolution']<_0x2cdd53&&(_0x2cdd53=_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x1c0)]),_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x197)]=0x64/0x3,_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x197)]<_0x2cdd53&&(_0x2cdd53=_0x54a079['pcs'][_0x3fce7a][_0x4d58c5(0x197)]),_0x2cdd53=_0x54a079[_0x4d58c5(0x545)](_0x2cdd53),_0xf9efaa[_0x4d58c5(0x57b)][0x0][_0x4d58c5(0x4f5)]=0x64/_0x2cdd53,_0x5b39d3[_0x4d58c5(0x3e1)](_0xf9efaa)[_0x4d58c5(0x3b1)](()=>{var _0x49a21c=_0x4d58c5;log('iOS\x20guest\x20SCALE\x20set!'),pokeIframeAPI(_0x49a21c(0x3d3),_0x2cdd53,_0x3fce7a);})[_0x4d58c5(0x35f)](warnlog),_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x40b)][_0x4d58c5(0x480)]=parseInt(_0x2cdd53)+'%';}else{if((adapter[_0x4d58c5(0x2cc)][_0x4d58c5(0x3f6)]==='chrome'||adapter['browserDetails'][_0x4d58c5(0x3f6)]===_0x4d58c5(0x35d)||adapter[_0x4d58c5(0x2cc)][_0x4d58c5(0x3f6)]===_0x4d58c5(0x409)&&adapter[_0x4d58c5(0x2cc)][_0x4d58c5(0x5e9)]>=0x40)&&'RTCRtpSender'in window&&_0x4d58c5(0x3e1)in window[_0x4d58c5(0x23c)][_0x4d58c5(0x29a)]){var _0x5b39d3=_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a]['getSenders']()[_0x4d58c5(0x553)](function(_0x13e191){var _0x272e42=_0x4d58c5;return _0x13e191[_0x272e42(0x5ca)]&&_0x13e191[_0x272e42(0x5ca)]['kind']==_0x272e42(0x2bc);});if(!_0x5b39d3){warnlog(_0x4d58c5(0x357));return;}var _0xf9efaa=_0x5b39d3[_0x4d58c5(0x4ba)]();(!_0xf9efaa[_0x4d58c5(0x57b)]||_0xf9efaa[_0x4d58c5(0x57b)]['length']==0x0)&&(_0xf9efaa['encodings']=[{}]);if(_0x18de61<0x0)_0xf9efaa['encodings'][0x0]['active']==![]&&(_0xf9efaa['encodings'][0x0]['active']=!![]),delete _0xf9efaa[_0x4d58c5(0x57b)][0x0][_0x4d58c5(0x235)];else _0x18de61===0x0?_0xf9efaa['encodings'][0x0][_0x4d58c5(0x5d6)]=![]:(_0xf9efaa[_0x4d58c5(0x57b)][0x0][_0x4d58c5(0x5d6)]==![]&&(_0xf9efaa[_0x4d58c5(0x57b)][0x0][_0x4d58c5(0x5d6)]=!![]),_0xf9efaa[_0x4d58c5(0x57b)][0x0][_0x4d58c5(0x235)]=_0x18de61*0x400);var _0x2cdd53=0x64;_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x247)]&&(_0x2cdd53=_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x247)]);_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x1c0)]&&_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x1c0)]<_0x2cdd53&&(_0x2cdd53=_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x1c0)]);if(_0x18de61<0x0)_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x197)]=0x64;else{if(_0x18de61>=0x259)_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x197)]=0x64;else{if(_0x54a079['pcs'][_0x3fce7a][_0x4d58c5(0x478)]===![]&&_0x54a079['directorUUID']!==_0x3fce7a&&_0x54a079[_0x4d58c5(0x511)])_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a]['scaleDueToBitrate']=0x64;else{if(_0x18de61>=0xc9)_0x54a079['mobile']?_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a]['scaleDueToBitrate']=0x64/2.5:_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x197)]=0x64/0x2;else _0x18de61>=0x51?_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x197)]=0x64/0x3:_0x54a079['pcs'][_0x3fce7a][_0x4d58c5(0x197)]=0x64/0x4;}}}_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x197)]<_0x2cdd53&&(_0x2cdd53=_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x197)]);_0x2cdd53=_0x54a079[_0x4d58c5(0x545)](_0x2cdd53),_0xf9efaa[_0x4d58c5(0x57b)][0x0]['scaleResolutionDownBy']=0x64/_0x2cdd53,_0x5b39d3['setParameters'](_0xf9efaa)[_0x4d58c5(0x3b1)](()=>{var _0x433818=_0x4d58c5;log(_0x433818(0x636)),pokeIframeAPI(_0x433818(0x2cf),_0x18de61,_0x3fce7a),pokeIframeAPI('setVideoScale',_0x2cdd53,_0x3fce7a);})[_0x4d58c5(0x35f)](warnlog),_0x54a079[_0x4d58c5(0x33f)][_0x3fce7a][_0x4d58c5(0x40b)][_0x4d58c5(0x480)]=parseInt(_0x2cdd53)+'%';return;}}}}catch(_0x222914){errorlog(_0x222914);}};function _0x56d44c(_0x5d09b3,_0x4a9e91=0x2710){var _0x372ee8=_0x397888;_0x4a9e91=parseInt(_0x4a9e91);if(_0x54a079['audiobitrate'])_0x4a9e91+=_0x54a079[_0x372ee8(0x482)];else{if(_0x54a079['director']&&_0x54a079[_0x372ee8(0x2d8)]==0x5)_0x4a9e91+=0x20;else _0x54a079[_0x372ee8(0x2d8)]&&_0x54a079[_0x372ee8(0x2d8)]!=0x3?_0x372ee8(0x41d)!==_0x372ee8(0x63c)?_0x4a9e91+=0x100:_0x1fdf91['dataset'][_0x372ee8(0x24f)]=_0x22c186['rpcs'][_0x19c916][_0x372ee8(0x249)]:_0x4a9e91+=0x20;}return log(_0x372ee8(0x271)+_0x4a9e91),_0x4a9e91<0x1&&(_0x4a9e91=0x1),_0x5d09b3=CodecsHandler['setVideoBitrates'](_0x5d09b3,{'min':parseInt(_0x4a9e91/0xa)||0x1,'max':_0x4a9e91},_0x54a079[_0x372ee8(0x4d7)]),_0x5d09b3;}return _0x54a079[_0x397888(0x4b6)]=function(_0x4b47c3,_0x4dbfbd){var _0x1dc7e8=_0x397888;_0x1dc7e8(0x467)!=='AMYnz'?_0xd1f137=_0x4918e5[_0x1dc7e8(0x33f)][_0xe36167][_0x1dc7e8(0x197)]:(log(_0x4b47c3),_0x54a079[_0x1dc7e8(0x5f2)]==={}&&log('Generate\x20Some\x20Crypto\x20keys\x20first'),window[_0x1dc7e8(0x272)][_0x1dc7e8(0x256)][_0x1dc7e8(0x3ba)]({'name':_0x1dc7e8(0x308)},_0x54a079[_0x1dc7e8(0x5f2)][_0x1dc7e8(0x449)],_0x54a079[_0x1dc7e8(0x392)]['encode'](_0x4b47c3))[_0x1dc7e8(0x3b1)](function(_0x2bc47e){var _0x4170a2=_0x1dc7e8;_0x2bc47e=new Uint8Array(_0x2bc47e),_0x2bc47e=_0x2bc47e[_0x4170a2(0x443)]((_0x27e154,_0x4c568b)=>_0x27e154+_0x4c568b[_0x4170a2(0x3c7)](0x10)[_0x4170a2(0x330)](0x2,'0'),''),_0x4dbfbd(_0x4b47c3,_0x2bc47e),log(JSON[_0x4170a2(0x242)](_0x2bc47e));})[_0x1dc7e8(0x35f)](errorlog));},_0x54a079[_0x397888(0x50a)]=function(_0xdd183f,_0x361609){var _0x8d90=_0x397888;_0xdd183f[_0x8d90(0x389)]=new Uint8Array(_0xdd183f['signature'][_0x8d90(0x4e6)](/.{1,2}/g)[_0x8d90(0x402)](_0x58918c=>parseInt(_0x58918c,0x10)));if(_0x54a079[_0x8d90(0x219)][_0x361609][_0x8d90(0x276)])return window['crypto'][_0x8d90(0x256)][_0x8d90(0x34d)]({'name':_0x8d90(0x308)},_0x54a079[_0x8d90(0x219)][_0x361609][_0x8d90(0x276)],_0xdd183f[_0x8d90(0x389)],_0x54a079[_0x8d90(0x392)][_0x8d90(0x2e1)](_0xdd183f[_0x8d90(0x46d)]))[_0x8d90(0x3b1)](function(_0x1af2e0){return log(_0x1af2e0),_0x1af2e0;})[_0x8d90(0x35f)](function(_0x397269){return errorlog(_0x397269),![];});},_0x54a079['desaltStreamID']=function(_0x52647e){var _0x5356be=_0x397888;if(_0x54a079[_0x5356be(0x284)]){if(_0x54a079[_0x5356be(0x60d)]!==![]){if(_0x5356be(0x4ad)!==_0x5356be(0x571))return log('hash\x20is\x20not\x20false'),_0x52647e=_0x52647e[_0x5356be(0x322)](0x0,-0x1*_0x54a079[_0x5356be(0x60d)][_0x5356be(0x44a)]),_0x52647e;else _0x26ba1e(_0x5356be(0x643));}else return 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_0x3a208c=_0xdedec0,_0x5e0e2c=![];if(_0x3a208c(0x33b)in _0x389881)_0x5e0e2c=_0x54a079[_0x3a208c(0x1bf)](_0x389881,_0x389881[_0x3a208c(0x33b)]),_0x5e0e2c?log(_0x3a208c(0x414)):(log(_0x3a208c(0x275)),_0x54a079[_0x3a208c(0x3ed)](_0x389881));else _0x4a521f?(_0x5e0e2c=_0x54a079[_0x3a208c(0x1bf)](_0x389881),_0x5e0e2c?log(_0x3a208c(0x427)):(log('sending\x20message\x20via\x20WSS\x20as\x20WebRTC\x20failed\x20to\x20send\x20message;\x20RTC\x20peers\x20only'),_0x54a079[_0x3a208c(0x3ed)](_0x389881))):(_0x54a079[_0x3a208c(0x3ed)](_0x389881),log(_0x3a208c(0x364)));},_0x54a079[_0xdedec0(0x37f)]=function(){var _0x233b13=_0xdedec0;if(_0x54a079[_0x233b13(0x3b4)]===![]){if(_0x233b13(0x5c9)===_0x233b13(0x485))_0xb75d47(_0x3a27b7);else{var _0x5a1127={},_0x9bf816=![];_0x54a079[_0x233b13(0x5f8)][_0x233b13(0x3a4)]!==null&&(_0x54a079[_0x233b13(0x5f8)][_0x233b13(0x3a4)]===![]&&(_0x233b13(0x2ea)!==_0x233b13(0x2ea)?(_0x173ddf['rpcs'][_0x334bb9][_0x233b13(0x2b5)][_0x233b13(0x3ae)]=!![],_0x1d0d90[_0x233b13(0x56b)][_0x1fc4b2][_0x233b13(0x2b5)][_0x233b13(0x453)][_0x233b13(0x3a7)]&&_0xfb618a[_0x233b13(0x56b)][_0x3ccd18][_0x233b13(0x2b5)][_0x233b13(0x453)]['display']!==_0x233b13(0x5b1)&&(_0x9b8ef1[_0x233b13(0x56b)][_0x17c455][_0x233b13(0x2b5)][_0x233b13(0x453)][_0x233b13(0x3a7)]='none',_0x5ebc20=!![])):_0x9bf816=!![]),_0x5a1127[_0x233b13(0x333)]=_0x54a079[_0x233b13(0x5f8)][_0x233b13(0x3a4)]);_0x54a079[_0x233b13(0x5f8)]['sourceActive']!==null&&(_0x233b13(0x54b)!==_0x233b13(0x1a0)?_0x5a1127[_0x233b13(0x502)]=_0x54a079['obsState'][_0x233b13(0x5e2)]:_0xb2db1c=_0x42d036[_0x233b13(0x5c7)]);_0x54a079[_0x233b13(0x5f8)][_0x233b13(0x4cc)]!==null&&(_0x5a1127[_0x233b13(0x465)]=_0x54a079[_0x233b13(0x5f8)][_0x233b13(0x4cc)]);_0x54a079[_0x233b13(0x5f8)]['streaming']!==null&&(_0x5a1127['obsStreaming']=_0x54a079[_0x233b13(0x5f8)][_0x233b13(0x1e1)]);for(var _0x564b4f in _0x54a079[_0x233b13(0x56b)]){if(_0x54a079[_0x233b13(0x52d)]!==![]){var _0x285b9d=parseInt(_0x54a079[_0x233b13(0x56b)][_0x564b4f]['targetBandwidth']);_0x9bf816&&((_0x285b9d>_0x54a079[_0x233b13(0x52d)]||_0x285b9d<0x0)&&(_0x285b9d=_0x54a079['optimize']));if(_0x54a079[_0x233b13(0x56b)][_0x564b4f][_0x233b13(0x36a)]!==_0x285b9d){if(_0x233b13(0x2e9)===_0x233b13(0x2e9))_0x5a1127['bitrate']=_0x285b9d,warnlog('Message\x20to\x20be\x20sent:\x20'),warnlog(_0x5a1127),_0x54a079[_0x233b13(0x1bf)](_0x5a1127,_0x564b4f)?_0x54a079[_0x233b13(0x56b)][_0x564b4f][_0x233b13(0x36a)]=_0x285b9d:errorlog('Unable\x20to\x20set\x20update\x20OBS\x20Visibility');else{_0x4486a5[_0x233b13(0x33f)][_0x5dc7f6][_0x233b13(0x2da)]=_0x3024a3(_0x29914a[_0x233b13(0x2da)])||0x0;_0x345d46 in _0x40978a['rpcs']&&(_0x29fe4d[_0x233b13(0x56b)][_0x836ccb][_0x233b13(0x2da)]=_0x170f3d[_0x233b13(0x33f)][_0x1dc7ba][_0x233b13(0x2da)]);if(_0x292f14[_0x233b13(0x1f3)]){var _0x18027f=_0x411811[_0x233b13(0x58b)]('[data-action-type=\x22order-value\x22][data--u-u-i-d=\x22'+_0x3011b7+'\x22]');_0x1aebb5(_0x18027f),_0x18027f[0x0]&&(_0x18027f[0x0][_0x233b13(0x58f)]=_0x3eb8e9(_0x232e06['order'])||0x0);}_0x23f246();}}else warnlog(_0x233b13(0x62a)),warnlog(_0x5a1127),_0x54a079[_0x233b13(0x1bf)](_0x5a1127,_0x564b4f);}else warnlog(_0x233b13(0x62a)),warnlog(_0x5a1127),_0x54a079[_0x233b13(0x1bf)](_0x5a1127,_0x564b4f);}}}},_0x54a079[_0xdedec0(0x354)]=function(_0x2714a5){var _0x191cd1=_0xdedec0;if(!_0x54a079[_0x191cd1(0x56b)][_0x2714a5][_0x191cd1(0x2b5)]){if(_0x191cd1(0x1f7)!==_0x191cd1(0x1f7))_0xae2629(_0x191cd1(0x3a5));else return;}var _0x1507c3={};_0x1507c3[_0x191cd1(0x31b)]=_0x54a079[_0x191cd1(0x56b)][_0x2714a5]['videoElement'][_0x191cd1(0x453)]['display']!=_0x191cd1(0x5b1),_0x1507c3[_0x191cd1(0x5de)]=_0x54a079[_0x191cd1(0x56b)][_0x2714a5]['videoElement']['muted'];if(_0x54a079[_0x191cd1(0x52d)]!==![]){var _0x54ba6a=parseInt(_0x54a079['rpcs'][_0x2714a5][_0x191cd1(0x592)]);_0x1507c3[_0x191cd1(0x31b)]===![]&&((_0x54ba6a>_0x54a079[_0x191cd1(0x52d)]||_0x54ba6a<0x0)&&(_0x54ba6a=_0x54a079[_0x191cd1(0x52d)])),_0x54a079[_0x191cd1(0x56b)][_0x2714a5][_0x191cd1(0x36a)]!==_0x54ba6a?(_0x1507c3['bitrate']=_0x54ba6a,_0x54a079[_0x191cd1(0x1bf)](_0x1507c3,_0x2714a5)?_0x54a079[_0x191cd1(0x56b)][_0x2714a5][_0x191cd1(0x36a)]=_0x54ba6a:errorlog(_0x191cd1(0x5f6))):_0x54a079[_0x191cd1(0x1bf)](_0x1507c3,_0x2714a5);}else _0x54a079['sendRequest'](_0x1507c3,_0x2714a5);},_0x54a079[_0xdedec0(0x4b5)]=function(_0x9bcf60){var _0x54c00c=_0xdedec0;if('action'in _0x9bcf60){if(_0x54c00c(0x2b3)in _0x9bcf60){if('scene'in _0x9bcf60){if(_0x54a079[_0x54c00c(0x478)]!==![]){var _0x41abf5=![];for(var _0xed5389 in _0x54a079[_0x54c00c(0x56b)]){if(_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x249)]===_0x9bcf60[_0x54c00c(0x2b3)]){if(_0x54c00c(0x39c)in 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_0x5045f9 in _0x54a079[_0x54c00c(0x56b)]){_0x5045f9!==_0xed5389&&(_0x54a079[_0x54c00c(0x56b)][_0x5045f9][_0x54c00c(0x2b5)][_0x54c00c(0x453)][_0x54c00c(0x3a7)]&&_0x54a079[_0x54c00c(0x56b)][_0x5045f9]['videoElement'][_0x54c00c(0x453)][_0x54c00c(0x3a7)]!=='none'&&(_0x54a079[_0x54c00c(0x56b)][_0x5045f9][_0x54c00c(0x2b5)]['style']['display']=_0x54c00c(0x5b1),_0x41abf5=!![]));}_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)]&&(_0x54c00c(0x5fc)!==_0x54c00c(0x5fc)?(_0x3a3e8d['rpcs'][_0x1361f5][_0x54c00c(0x396)]=_0x6831e2,_0x223c1a=!![]):(_0x54a079['rpcs'][_0xed5389]['videoElement']['style'][_0x54c00c(0x3a7)]&&_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x453)][_0x54c00c(0x3a7)]!=='block'&&(_0x54a079['rpcs'][_0xed5389][_0x54c00c(0x2b5)]['style'][_0x54c00c(0x3a7)]=_0x54c00c(0x57c),_0x41abf5=!![]),_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x38a)]&&clearInterval(_0x54a079['rpcs'][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x38a)]),_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x5c6)]=![],_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x38a)]=setTimeout(showControlBar['bind'](null,_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)]),0xbb8)));}}else _0x9bcf60[_0x54c00c(0x39c)]==0x0?_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)]&&(_0x54a079['rpcs'][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x453)][_0x54c00c(0x3a7)]&&_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x453)][_0x54c00c(0x3a7)]!==_0x54c00c(0x5b1)&&(_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x453)][_0x54c00c(0x3a7)]=_0x54c00c(0x5b1),_0x41abf5=!![]),_0x54a079['rpcs'][_0xed5389]['videoElement'][_0x54c00c(0x3ae)]=!![]):_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)]&&(_0x54a079[_0x54c00c(0x56b)][_0xed5389]['videoElement'][_0x54c00c(0x453)][_0x54c00c(0x3a7)]&&_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x453)][_0x54c00c(0x3a7)]!==_0x54c00c(0x57c)&&(_0x54a079['rpcs'][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x453)][_0x54c00c(0x3a7)]=_0x54c00c(0x57c),_0x41abf5=!![]),_0x54a079[_0x54c00c(0x56b)][_0xed5389]['mutedState']===null?_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x3ae)]=![]:_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)]['muted']=_0x54a079['rpcs'][_0xed5389]['mutedState'],_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x38a)]&&clearInterval(_0x54a079['rpcs'][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x38a)]),_0x54a079[_0x54c00c(0x56b)][_0xed5389]['videoElement'][_0x54c00c(0x5c6)]=![],_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)]['controlTimer']=setTimeout(showControlBar[_0x54c00c(0x3e8)](null,_0x54a079['rpcs'][_0xed5389][_0x54c00c(0x2b5)]),0xbb8));}}_0x54a079[_0x54c00c(0x354)](_0xed5389);}else _0x9bcf60[_0x54c00c(0x298)]==_0x54c00c(0x1a1)&&(log(parseInt(_0x9bcf60['value'])/0x64),_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)]&&(_0x54c00c(0x2af)!==_0x54c00c(0x313)?(_0x54a079[_0x54c00c(0x56b)][_0xed5389][_0x54c00c(0x2b5)][_0x54c00c(0x1a1)]=parseInt(_0x9bcf60[_0x54c00c(0x39c)])/0x64,log(_0x54c00c(0x241))):_0x18b121=_0x54c00c(0x376)));}}}}_0x41abf5&&updateMixer();}}else{if(_0x9bcf60[_0x54c00c(0x298)]==_0x54c00c(0x2ec)){}else{if(_0x9bcf60[_0x54c00c(0x298)]==_0x54c00c(0x262)){}}}}}},_0x54a079['ws'][_0xdedec0(0x230)]=function(_0x55b837){var _0x619a30=_0xdedec0;clearTimeout(_0x54a079[_0x619a30(0x4b1)]);var _0x90fb2=JSON[_0x619a30(0x52b)](_0x55b837[_0x619a30(0x46d)]);_0x619a30(0x249)in _0x90fb2&&(_0x90fb2[_0x619a30(0x249)]=_0x54a079[_0x619a30(0x5bf)](_0x90fb2[_0x619a30(0x249)]));if(_0x54a079[_0x619a30(0x2e2)]){if('UUID'in _0x90fb2){if(_0x54a079[_0x619a30(0x33b)]){if(_0x90fb2[_0x619a30(0x33b)]!==_0x54a079['UUID'])return;}else return;delete _0x90fb2[_0x619a30(0x33b)];}if(_0x619a30(0x2aa)in _0x90fb2){if(!_0x54a079[_0x619a30(0x21d)])return;if(_0x619a30(0x525)in _0x90fb2){if(_0x90fb2['request']===_0x619a30(0x2ec)){if(_0x619a30(0x2aa)in _0x90fb2){if(_0x619a30(0x2b3)in _0x90fb2){if(_0x90fb2[_0x619a30(0x2b3)]==_0x54a079['UUID']){_0x90fb2[_0x619a30(0x525)]=_0x619a30(0x301),_0x54a079[_0x619a30(0x21d)]=_0x90fb2[_0x619a30(0x2aa)];var _0x558a6d={};_0x558a6d[_0x619a30(0x525)]='joinroom',_0x558a6d['roomid']=_0x54a079[_0x619a30(0x21d)],_0x558a6d['streamID']=_0x54a079[_0x619a30(0x249)],_0x54a079[_0x619a30(0x3ed)](_0x558a6d);}else return;}else return;}else return;}else{if(_0x90fb2[_0x619a30(0x2aa)]!==_0x54a079['roomenc'])return;}}else{if(_0x90fb2[_0x619a30(0x2aa)]!==_0x54a079[_0x619a30(0x21d)])return;}delete _0x90fb2[_0x619a30(0x2aa)];}if('streamID'in _0x90fb2){if(_0x54a079[_0x619a30(0x2c0)]){if(_0x54a079[_0x619a30(0x2c0)][_0x619a30(0x18b)](_0x90fb2[_0x619a30(0x249)])){}else return;}else{if(_0x54a079[_0x619a30(0x617)]){if(_0x54a079[_0x619a30(0x617)]!==_0x90fb2['streamID'])return;else{}}}}_0x619a30(0x1f3)in _0x90fb2&&(_0x90fb2[_0x619a30(0x543)]&&(_0x54a079['directorUUID']=_0x90fb2[_0x619a30(0x543)]),delete _0x90fb2[_0x619a30(0x1f3)]);_0x619a30(0x543)in _0x90fb2&&(_0x90fb2[_0x619a30(0x33b)]=_0x90fb2[_0x619a30(0x543)],delete _0x90fb2[_0x619a30(0x543)]);if(_0x619a30(0x525)in _0x90fb2){if(_0x90fb2[_0x619a30(0x525)]===_0x619a30(0x5ef)){if('streamID'in _0x90fb2){if(_0x90fb2[_0x619a30(0x249)]===_0x54a079[_0x619a30(0x249)])_0x90fb2[_0x619a30(0x525)]=_0x619a30(0x5ae);else return;}}else{if(_0x90fb2['request']===_0x619a30(0x3e5)){if(_0x54a079[_0x619a30(0x2c0)]){if(_0x54a079['view_set'][_0x619a30(0x18b)](_0x90fb2[_0x619a30(0x249)])){play(_0x90fb2[_0x619a30(0x249)]);return;}else return;}else{if(_0x54a079['view']){if(_0x54a079['view']!==_0x90fb2[_0x619a30(0x249)])return;else{play(_0x90fb2[_0x619a30(0x249)]);return;}}}}else{if(_0x90fb2[_0x619a30(0x525)]===_0x619a30(0x52c)){if(_0x619a30(0x535)===_0x619a30(0x57e))return;else{if('streamID'in _0x90fb2){if(_0x54a079[_0x619a30(0x2c0)]){if(_0x54a079[_0x619a30(0x2c0)][_0x619a30(0x18b)](_0x90fb2['streamID']))play(_0x90fb2[_0x619a30(0x249)]);else{}}else{if(_0x54a079[_0x619a30(0x617)]){if(_0x54a079['view']!==_0x90fb2['streamID']){}else play(_0x90fb2[_0x619a30(0x249)]);}else play(_0x90fb2[_0x619a30(0x249)]);}}_0x90fb2['request']=_0x619a30(0x5ae);}}}}}}if(_0x90fb2[_0x619a30(0x525)]){if(_0x90fb2[_0x619a30(0x525)]==_0x619a30(0x5ae)){if(_0x54a079[_0x619a30(0x39e)]){if(_0x54a079['directorUUID']===_0x90fb2[_0x619a30(0x33b)])_0x54a079[_0x619a30(0x5ae)](_0x90fb2[_0x619a30(0x33b)]);else _0x54a079[_0x619a30(0x1f3)]&&(_0x90fb2[_0x619a30(0x33b)]in _0x54a079[_0x619a30(0x56b)]&&_0x54a079[_0x619a30(0x5ae)](_0x90fb2[_0x619a30(0x33b)]));}else _0x54a079[_0x619a30(0x5ae)](_0x90fb2[_0x619a30(0x33b)]);}else{if(_0x90fb2[_0x619a30(0x525)]==_0x619a30(0x1fe))log(_0x90fb2[_0x619a30(0x640)]),_0x619a30(0x1f3)in _0x90fb2?(_0x54a079[_0x619a30(0x4a1)]=_0x90fb2['director'],_0x54a079[_0x619a30(0x4a1)]in _0x54a079[_0x619a30(0x33f)]&&(_0x54a079[_0x619a30(0x33f)][_0x54a079[_0x619a30(0x4a1)]][_0x619a30(0x40b)]['info']&&(_0x54a079[_0x619a30(0x33f)][_0x54a079[_0x619a30(0x4a1)]][_0x619a30(0x40b)][_0x619a30(0x306)][_0x619a30(0x1f3)]=!![])),_0x54a079[_0x619a30(0x4a1)]in _0x54a079[_0x619a30(0x56b)]&&(_0x54a079[_0x619a30(0x56b)][_0x54a079[_0x619a30(0x4a1)]][_0x619a30(0x40b)]['info']&&(_0x54a079[_0x619a30(0x56b)][_0x54a079[_0x619a30(0x4a1)]][_0x619a30(0x40b)][_0x619a30(0x306)][_0x619a30(0x1f3)]=!![]))):_0x54a079['directorUUID']=![],'claim'in _0x90fb2&&(_0x90fb2[_0x619a30(0x2f5)]==![]&&(!_0x54a079[_0x619a30(0x2f6)]&&(setTimeout(function(){var _0x4f781b=_0x619a30;warnUser(_0x4f781b(0x385));},0x1),getById(_0x619a30(0x56e))['innerHTML']=_0x619a30(0x41b)))),_0x54a079[_0x619a30(0x4a2)][_0x619a30(0x5f9)](_0x90fb2[_0x619a30(0x640)]);else{if(_0x90fb2['request']==_0x619a30(0x301)){_0x54a079[_0x619a30(0x459)]=[],_0x54a079[_0x619a30(0x301)]=!![],_0x54a079[_0x619a30(0x455)]=![],log(_0x619a30(0x558));!_0x54a079[_0x619a30(0x1f3)]&&(_0x54a079[_0x619a30(0x39e)]=![]);log(_0x90fb2[_0x619a30(0x640)]);_0x619a30(0x1f3)in _0x90fb2?(_0x54a079['directorUUID']=_0x90fb2[_0x619a30(0x1f3)],_0x54a079[_0x619a30(0x4a1)]in _0x54a079[_0x619a30(0x33f)]&&(_0x619a30(0x416)==='SSKlT'?(_0x3031e0[_0x619a30(0x37e)](),_0x337c00[_0x619a30(0x5e8)]()):_0x54a079[_0x619a30(0x33f)][_0x54a079[_0x619a30(0x4a1)]]['stats'][_0x619a30(0x306)]&&(_0x54a079[_0x619a30(0x33f)][_0x54a079[_0x619a30(0x4a1)]]['stats'][_0x619a30(0x306)]['director']=!![])),_0x54a079[_0x619a30(0x4a1)]in _0x54a079['rpcs']&&(_0x54a079['rpcs'][_0x54a079[_0x619a30(0x4a1)]]['stats'][_0x619a30(0x306)]&&(_0x54a079[_0x619a30(0x56b)][_0x54a079['directorUUID']][_0x619a30(0x40b)][_0x619a30(0x306)]['director']=!![]))):_0x54a079[_0x619a30(0x4a1)]=![];for(_0x36908e in _0x54a079['rpcs']){try{_0x54a079[_0x619a30(0x56b)][_0x36908e][_0x619a30(0x518)](),delete _0x54a079[_0x619a30(0x56b)][_0x36908e];}catch(_0x59a5e7){}}for(_0x36908e in _0x54a079[_0x619a30(0x33f)]){try{_0x54a079[_0x619a30(0x33f)][_0x36908e][_0x619a30(0x518)](),delete _0x54a079[_0x619a30(0x33f)][_0x36908e],applySceneState();}catch(_0x56ff45){}}getChatMessage(_0x619a30(0x47f),label=![],director=![],overlay=!![]),getById(_0x619a30(0x607))[_0x619a30(0x22f)]=_0x619a30(0x425);_0x54a079[_0x619a30(0x573)]?document[_0x619a30(0x3cd)]=_0x54a079[_0x619a30(0x573)]+_0x619a30(0x3d5):document[_0x619a30(0x3cd)]=_0x619a30(0x408);_0x54a079[_0x619a30(0x281)]=_0x54a079[_0x619a30(0x590)],updateMixer(),updateUserList(),log(_0x619a30(0x1ba)),log(_0x90fb2[_0x619a30(0x640)]);for(var _0x36908e in _0x90fb2[_0x619a30(0x640)]){if(_0x619a30(0x33b)in _0x90fb2[_0x619a30(0x640)][_0x36908e]){if(_0x619a30(0x249)in _0x90fb2[_0x619a30(0x640)][_0x36908e]){if(_0x90fb2[_0x619a30(0x640)][_0x36908e][_0x619a30(0x33b)]in _0x54a079[_0x619a30(0x56b)])_0x619a30(0x454)===_0x619a30(0x454)?log(_0x619a30(0x386)):(_0x3fb1f9[_0x619a30(0x57b)][0x0][_0x619a30(0x5d6)]==![]&&(_0x50d55d[_0x619a30(0x57b)][0x0][_0x619a30(0x5d6)]=!![]),_0x1ff99b[_0x619a30(0x57b)][0x0][_0x619a30(0x235)]=_0x821093*0x400);else{var _0x1df8ee=_0x54a079[_0x619a30(0x5bf)](_0x90fb2[_0x619a30(0x640)][_0x36908e][_0x619a30(0x249)]);log(_0x619a30(0x347)+_0x1df8ee);if(_0x54a079[_0x619a30(0x39e)]){if(_0x619a30(0x490)!==_0x619a30(0x490))_0x3ec155&&(_0x19d5ec(_0x619a30(0x44f)),_0x5bd0f6(_0x5263fe)),_0x29f4cb[_0x619a30(0x237)]==_0x619a30(0x2bc)&&(_0xe9385e[_0x619a30(0x56b)][_0x4e740b][_0x619a30(0x25b)][_0x619a30(0x38c)]()[_0x619a30(0x59b)](_0x2e7a6f=>{var _0x23d4b1=_0x619a30;_0x427296['rpcs'][_0x43b989][_0x23d4b1(0x25b)]['removeTrack'](_0x2e7a6f);}),_0x1273fb=!![]),_0x240f4e(_0x619a30(0x494)),_0x3e42b6['rpcs'][_0x290534][_0x619a30(0x25b)][_0x619a30(0x561)](_0x35f9e1);else{if(_0x54a079[_0x619a30(0x4a1)]===_0x90fb2['list'][_0x36908e][_0x619a30(0x33b)])play(_0x1df8ee);else{if(_0x54a079[_0x619a30(0x459)][_0x619a30(0x44a)]<0x1388){if(!_0x54a079[_0x619a30(0x459)][_0x619a30(0x18b)](_0x1df8ee)){if(_0x619a30(0x305)!==_0x619a30(0x305)){_0x1d6d61(_0x619a30(0x4cf));if(!(_0x22d167[_0x619a30(0x33b)]in _0x36dc1d[_0x619a30(0x33f)])){_0x10a3e8(_0x619a30(0x646));return;}if('session'in _0x166438&&_0x117eca['pcs'][_0x56dd33[_0x619a30(0x33b)]][_0x619a30(0x644)]!=_0x4c58df[_0x619a30(0x644)]){_0x1c96b2(_0x619a30(0x1d2));return;}_0x1b5cba['pcs'][_0x675b68['UUID']][_0x619a30(0x2de)](_0x3f4b14[_0x619a30(0x46b)])[_0x619a30(0x3b1)]()[_0x619a30(0x35f)](function(_0x81f43){_0xc3e7bc(_0x81f43),_0x23ce5e(_0x11b8fe);});}else _0x54a079['queueList'][_0x619a30(0x5d7)](_0x1df8ee);}}}}}else play(_0x1df8ee);}}}}_0x54a079[_0x619a30(0x2ee)]();}else{if(_0x90fb2[_0x619a30(0x525)]==_0x619a30(0x2ff))log(_0x90fb2),_0x619a30(0x1f3)in _0x90fb2?(_0x54a079[_0x619a30(0x4a1)]=_0x90fb2['director'],_0x54a079[_0x619a30(0x4a1)]in _0x54a079[_0x619a30(0x33f)]&&(_0x54a079[_0x619a30(0x33f)][_0x54a079[_0x619a30(0x4a1)]][_0x619a30(0x40b)][_0x619a30(0x306)]&&(_0x54a079[_0x619a30(0x33f)][_0x54a079[_0x619a30(0x4a1)]]['stats']['info'][_0x619a30(0x1f3)]=!![])),_0x54a079[_0x619a30(0x4a1)]in _0x54a079['rpcs']&&(_0x54a079[_0x619a30(0x56b)][_0x54a079['directorUUID']][_0x619a30(0x40b)]['info']&&(_0x54a079[_0x619a30(0x56b)][_0x54a079[_0x619a30(0x4a1)]][_0x619a30(0x40b)][_0x619a30(0x306)][_0x619a30(0x1f3)]=!![]))):(_0x54a079[_0x619a30(0x4a1)]=![],errorlog(_0x619a30(0x283))),updateUserList();else{if(_0x90fb2[_0x619a30(0x525)]==_0x619a30(0x315)){log(_0x619a30(0x31c)),log(_0x90fb2);try{_0x619a30(0x1f3)in 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log('PUBLISHER\x27s\x20RTC\x20Connection\x20seems\x20to\x20be\x20dead?\x20'),![];}},_0x54a079[_0x397888(0x370)]=function(){setTimeout(function(){_0x54a079['localstats'];},0xbb8);},_0x54a079[_0x397888(0x264)]=function(_0x2221c9){var _0xa49b49=_0x397888;try{var _0x602d62=_0x54a079[_0xa49b49(0x25b)]['getVideoTracks']();_0x602d62=_0x602d62[0x0];if(_0x602d62[_0xa49b49(0x234)]){var _0x2e00e6=_0x602d62[_0xa49b49(0x234)]();_0x54a079[_0xa49b49(0x5b0)]==![]&&(_0x54a079[_0xa49b49(0x5b0)]=_0x2e00e6[_0xa49b49(0x5b0)]['min']);_0x54a079[_0xa49b49(0x5b0)]+=_0x2221c9;if(_0x54a079[_0xa49b49(0x5b0)]>_0x2e00e6[_0xa49b49(0x5b0)]['max'])_0x54a079[_0xa49b49(0x5b0)]=_0x2e00e6['zoom']['max'];else _0x54a079['zoom']<_0x2e00e6['zoom'][_0xa49b49(0x5ba)]&&(_0x54a079['zoom']=_0x2e00e6['zoom'][_0xa49b49(0x5ba)]);_0x602d62['applyConstraints']({'advanced':[{'zoom':_0x54a079['zoom']}]});}}catch(_0x22f6db){errorlog(_0x22f6db);}},_0x54a079[_0x397888(0x262)]=function(){var 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_0x2c1c85=_0x397888;_0x54a079['taintedSession']=!![],_0x54a079[_0x2c1c85(0x511)]=![],warnlog('hanging\x20up');try{_0x54a079[_0x2c1c85(0x2b5)][_0x2c1c85(0x186)][_0x2c1c85(0x645)]()[_0x2c1c85(0x59b)](function(_0x5ae706){var _0x5c502f=_0x2c1c85;_0x5ae706['stop'](),_0x54a079['videoElement'][_0x5c502f(0x186)][_0x5c502f(0x611)](_0x5ae706),log(_0x5c502f(0x549));}),_0x54a079[_0x2c1c85(0x25b)][_0x2c1c85(0x645)]()[_0x2c1c85(0x59b)](function(_0xf757e0){var _0x227bd8=_0x2c1c85;_0xf757e0[_0x227bd8(0x248)](),_0x54a079[_0x227bd8(0x25b)][_0x227bd8(0x611)](_0xf757e0),log(_0x227bd8(0x549));});for(UUID in _0x54a079[_0x2c1c85(0x33f)]){if(_0x2c1c85(0x5a7)!=='kSGHg'){var _0x3fc575=_0x4c6060[_0x2c1c85(0x33f)][_0x28e68d][_0x2c1c85(0x2a9)];_0x36bf42[_0x2c1c85(0x33f)][_0x1526a9][_0x2c1c85(0x4a9)]&&_0x240ecd[_0x2c1c85(0x33f)][_0x3d2985][_0x2c1c85(0x4a9)]>0x0&&(_0x16a218[_0x2c1c85(0x33f)][_0x16e500]['savedBitrate']<_0xd84055[_0x2c1c85(0x33f)][_0x2596cc][_0x2c1c85(0x2a9)]&&(_0x3fc575=_0x49ff84[_0x2c1c85(0x33f)][_0x114f42]['savedBitrate'])),_0x224450[_0x2c1c85(0x516)](_0x2c3c89,_0x3fc575),_0x12bcdd['pcs'][_0x1466af]['optimizedBitrate']===0x0&&_0x1020a2[_0x2c1c85(0x1d9)](_0x53e3fe,0x0);}else{var _0x6bf9e2=_0x54a079[_0x2c1c85(0x33f)][UUID][_0x2c1c85(0x253)]();_0x6bf9e2[_0x2c1c85(0x59b)](_0x35f886=>{var _0x1dae3c=_0x2c1c85;if(_0x35f886['track']){if(_0x1dae3c(0x3c2)!==_0x1dae3c(0x3c2)){if(_0x581a16){var _0x352508=_0x5c79fb[_0x1dae3c(0x192)](_0x12a27d,arguments);return _0x56ec3d=null,_0x352508;}}else _0x35f886[_0x1dae3c(0x5ca)]['enabled']=![];}});}}var _0x26eceb={};_0x26eceb[_0x2c1c85(0x557)]=!![],_0x54a079[_0x2c1c85(0x1f1)](_0x26eceb);}catch(_0x435ec5){errorlog(_0x2c1c85(0x2c6));}log('HANG\x20UP\x202\x20COMPLETE');},_0x54a079[_0x397888(0x318)]=function(_0x23900b){var _0x62076a=_0x397888;try{if('NkQhu'===_0x62076a(0x263))return _0x20f2de(_0x62076a(0x60c)),![];else{var _0x197070=_0x54a079[_0x62076a(0x25b)][_0x62076a(0x38c)]();_0x197070=_0x197070[0x0];if(_0x197070[_0x62076a(0x234)]){var _0x106c71=_0x197070[_0x62076a(0x234)]();_0x54a079['focusDistance']==![]&&(_0x54a079[_0x62076a(0x4ff)]=_0x106c71[_0x62076a(0x4ff)]['min']);_0x54a079[_0x62076a(0x4ff)]+=_0x23900b;if(_0x54a079[_0x62076a(0x4ff)]>_0x106c71[_0x62076a(0x4ff)][_0x62076a(0x29b)])_0x54a079[_0x62076a(0x4ff)]=_0x106c71[_0x62076a(0x4ff)][_0x62076a(0x29b)];else _0x54a079['focusDistance']<_0x106c71[_0x62076a(0x4ff)][_0x62076a(0x5ba)]&&(_0x54a079[_0x62076a(0x4ff)]=_0x106c71[_0x62076a(0x4ff)][_0x62076a(0x5ba)]);_0x197070[_0x62076a(0x551)]({'advanced':[{'focusMode':_0x62076a(0x38b),'focusDistance':_0x54a079[_0x62076a(0x4ff)]}]});}}}catch(_0x15835d){errorlog(_0x15835d);}},_0x54a079[_0x397888(0x4ac)]=function(_0x34e629){var _0x193075=_0x397888;_0x54a079[_0x193075(0x33f)][_0x34e629][_0x193075(0x4ac)]()[_0x193075(0x3b1)](_0x545417=>{var _0x4ecb7d=_0x193075;log('create\x20offer\x20worked');if(safariVersion()<=0xd&&(iOS||iPad)){}else{if(_0x54a079[_0x4ecb7d(0x2d8)]==0x3||_0x54a079[_0x4ecb7d(0x2d8)]==0x5||_0x54a079[_0x4ecb7d(0x2d8)]==0x1)_0x545417[_0x4ecb7d(0x456)]=CodecsHandler[_0x4ecb7d(0x24e)](_0x545417[_0x4ecb7d(0x456)],{'stereo':0x1}),log('stereo\x20enabled');else{if(iOS||iPad){}else _0x54a079[_0x4ecb7d(0x2d8)]==0x4&&(_0x545417[_0x4ecb7d(0x456)]=CodecsHandler[_0x4ecb7d(0x24e)](_0x545417[_0x4ecb7d(0x456)],{'stereo':0x2}),log(_0x4ecb7d(0x1c1)));}}_0x54a079[_0x4ecb7d(0x33f)][_0x34e629][_0x4ecb7d(0x394)](_0x545417)[_0x4ecb7d(0x3b1)](function(){var _0xcd5cfa=_0x4ecb7d;log(_0xcd5cfa(0x488)+_0x34e629);var _0x4bcfc8={};_0x4bcfc8[_0xcd5cfa(0x33b)]=_0x34e629,_0x4bcfc8['streamID']=_0x54a079[_0xcd5cfa(0x249)],_0x4bcfc8['description']=_0x54a079[_0xcd5cfa(0x33f)][_0x34e629][_0xcd5cfa(0x391)],_0x4bcfc8[_0xcd5cfa(0x644)]=_0x54a079[_0xcd5cfa(0x33f)][_0x34e629][_0xcd5cfa(0x644)],_0x54a079[_0xcd5cfa(0x2e2)]&&(_0x4bcfc8[_0xcd5cfa(0x58c)]=_0x54a079['scene']),_0x54a079[_0xcd5cfa(0x284)]?_0x54a079[_0xcd5cfa(0x3f1)](JSON[_0xcd5cfa(0x242)](_0x4bcfc8[_0xcd5cfa(0x1b5)]))['then'](function(_0x29ae9b){var _0x26751e=_0xcd5cfa;_0x4bcfc8['description']=_0x29ae9b[0x0],_0x4bcfc8[_0x26751e(0x300)]=_0x29ae9b[0x1],_0x54a079[_0x26751e(0x24d)](_0x4bcfc8);}):_0x54a079[_0xcd5cfa(0x24d)](_0x4bcfc8);})[_0x4ecb7d(0x35f)](errorlog);})[_0x193075(0x35f)](errorlog);},_0x54a079[_0x397888(0x481)]=function(){var _0x216566=_0x397888;for(var _0x39a871 in _0x54a079[_0x216566(0x33f)]){_0x54a079[_0x216566(0x33f)][_0x39a871][_0x216566(0x478)]!==![]?(_0x54a079[_0x216566(0x3aa)](_0x39a871),log('FORCE\x20KEYFRAME\x20FOR\x20SCENE')):log(_0x216566(0x3e3));}},_0x54a079['offerSDP']=function(_0x61b103){var _0x260aa4=_0x397888;if(_0x61b103 in _0x54a079[_0x260aa4(0x33f)]){errorlog('PROBLEM!\x20RESENDING\x20SDP\x20OFFER\x20SHOULD\x20NOT\x20HAPPEN');try{'snTGx'!==_0x260aa4(0x307)?(_0x31ac0e['rpcs'][_0xf63e82][_0x260aa4(0x412)][_0x260aa4(0x633)]=!![],_0x5f0232[_0x260aa4(0x56b)][_0x5b327d][_0x260aa4(0x412)][_0x260aa4(0x453)]['visibility']=_0x260aa4(0x633)):_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x518)]();}catch(_0x5c178f){if(_0x260aa4(0x28d)!==_0x260aa4(0x28d))try{_0x260aa4(0x2cd)in _0x3bb310['rpcs'][_0xf0efdf][_0x260aa4(0x2b5)]&&_0x3b934c[_0x260aa4(0x56b)][_0x58aef2]['videoElement'][_0x260aa4(0x2cd)][_0x260aa4(0x248)]();}catch(_0x28186b){_0x3ee290(_0x28186b);}else errorlog(_0x260aa4(0x48b));}_0x54a079[_0x260aa4(0x33f)][_0x61b103]=null,delete _0x54a079[_0x260aa4(0x33f)][_0x61b103],_0x54a079[_0x260aa4(0x50c)](),applySceneState();}else log('Create\x20a\x20new\x20RTC\x20connection;\x20offering\x20SDP\x20on\x20request');if(_0x54a079[_0x260aa4(0x1cd)]!==![]){if(Object['keys'](_0x54a079[_0x260aa4(0x33f)])[_0x260aa4(0x44a)]>_0x54a079[_0x260aa4(0x1cd)]){delete _0x54a079[_0x260aa4(0x33f)][_0x61b103],_0x54a079[_0x260aa4(0x50c)](),warnlog('Viewer\x20will\x20be\x20ignored\x20due\x20to\x20max\x20connections\x20already\x20hit'),applySceneState();return;}}else{if(_0x54a079[_0x260aa4(0x48f)]!==![]){if(Object[_0x260aa4(0x219)](_0x54a079[_0x260aa4(0x56b)])[_0x260aa4(0x44a)]+Object[_0x260aa4(0x219)](_0x54a079[_0x260aa4(0x33f)])[_0x260aa4(0x44a)]>_0x54a079[_0x260aa4(0x48f)]){delete _0x54a079['pcs'][_0x61b103],_0x54a079[_0x260aa4(0x50c)](),warnlog('Publisher\x20will\x20be\x20ignored\x20due\x20to\x20max\x20connections\x20already\x20hit'),applySceneState();return;}}}_0x54a079[_0x260aa4(0x33f)][_0x61b103]=new RTCPeerConnection(_0x54a079[_0x260aa4(0x1d4)]);if(_0x54a079[_0x260aa4(0x3f0)]){if(Object[_0x260aa4(0x219)](_0x54a079['pcs'])[_0x260aa4(0x44a)]>0x1){delete _0x54a079[_0x260aa4(0x33f)][_0x61b103],_0x54a079[_0x260aa4(0x50c)](),applySceneState();return;}}_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x31b)]=null,_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x5de)]=null,_0x54a079['pcs'][_0x61b103][_0x260aa4(0x333)]=null,_0x54a079[_0x260aa4(0x33f)][_0x61b103]['obsSourceActive']=null,_0x54a079['pcs'][_0x61b103][_0x260aa4(0x2f8)]=null,_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x465)]=null,_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x2a9)]=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x4a9)]=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103]['bitrateTimeout']=null,_0x54a079['pcs'][_0x61b103][_0x260aa4(0x1ac)]=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x644)]=_0x54a079[_0x260aa4(0x38e)](),_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x40b)]={},_0x54a079['pcs'][_0x61b103][_0x260aa4(0x436)]=![],_0x54a079['pcs'][_0x61b103][_0x260aa4(0x484)]=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x246)]=![],_0x54a079['pcs'][_0x61b103][_0x260aa4(0x468)]=![],_0x54a079['pcs'][_0x61b103][_0x260aa4(0x2e8)]=!![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x57d)]=!![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x28b)]=!![],_0x54a079[_0x260aa4(0x33f)][_0x61b103]['allowMIDI']=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x3eb)]=![],_0x54a079['pcs'][_0x61b103][_0x260aa4(0x33b)]=_0x61b103,_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x247)]=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x197)]=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x396)]=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x4f2)]=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x1c0)]=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x478)]=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x250)]=_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x23b)](_0x260aa4(0x250)),_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x250)][_0x260aa4(0x33b)]=_0x61b103,_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x53e)]=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x5b4)]=null,_0x54a079['pcs'][_0x61b103][_0x260aa4(0x573)]=![],_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x2da)]=![],_0x54a079['pcs'][_0x61b103][_0x260aa4(0x338)]=Date[_0x260aa4(0x5da)](),_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x250)][_0x260aa4(0x377)]=()=>{var _0x5ac45f=_0x260aa4;log(_0x5ac45f(0x3bd)),msg={},msg['info']={},msg[_0x5ac45f(0x306)][_0x5ac45f(0x573)]=_0x54a079[_0x5ac45f(0x573)],msg[_0x5ac45f(0x306)]['order']=_0x54a079[_0x5ac45f(0x2da)],msg[_0x5ac45f(0x306)][_0x5ac45f(0x3ae)]=_0x54a079[_0x5ac45f(0x3ae)];_0x54a079[_0x5ac45f(0x52e)]&&_0x54a079[_0x5ac45f(0x52e)]!==_0x61b103&&(msg[_0x5ac45f(0x306)]['muted']=!![]);msg[_0x5ac45f(0x306)][_0x5ac45f(0x623)]=_0x54a079[_0x5ac45f(0x623)],msg[_0x5ac45f(0x306)][_0x5ac45f(0x591)]=_0x54a079['directorDisplayMuted'],msg[_0x5ac45f(0x306)][_0x5ac45f(0x461)]=_0x54a079[_0x5ac45f(0x461)],msg[_0x5ac45f(0x306)][_0x5ac45f(0x22d)]=_0x54a079[_0x5ac45f(0x557)];_0x54a079[_0x5ac45f(0x1f3)]&&(_0x5ac45f(0x22a)===_0x5ac45f(0x22a)?(msg[_0x5ac45f(0x53c)]={},msg[_0x5ac45f(0x53c)][_0x5ac45f(0x281)]=_0x54a079['totalRoomBitrate']):_0x2837eb(_0x5ac45f(0x4d6)));msg[_0x5ac45f(0x306)][_0x5ac45f(0x261)]=_0x54a079[_0x5ac45f(0x1fd)],msg[_0x5ac45f(0x306)]['height_url']=_0x54a079[_0x5ac45f(0x51c)],msg[_0x5ac45f(0x306)]['quality_url']=_0x54a079[_0x5ac45f(0x424)],msg[_0x5ac45f(0x306)][_0x5ac45f(0x503)]=_0x54a079[_0x5ac45f(0x625)],msg[_0x5ac45f(0x306)]['maxviewers_url']=_0x54a079['maxviewers'],msg[_0x5ac45f(0x306)][_0x5ac45f(0x404)]=_0x54a079[_0x5ac45f(0x2d8)],msg[_0x5ac45f(0x306)][_0x5ac45f(0x612)]=_0x54a079[_0x5ac45f(0x2a5)],msg['info']['agc_url']=_0x54a079[_0x5ac45f(0x1ef)],msg[_0x5ac45f(0x306)][_0x5ac45f(0x2c3)]=_0x54a079[_0x5ac45f(0x390)],msg['info']['version']=_0x54a079[_0x5ac45f(0x5e9)],msg[_0x5ac45f(0x306)][_0x5ac45f(0x32b)]=_0x54a079[_0x5ac45f(0x3f5)],msg[_0x5ac45f(0x306)][_0x5ac45f(0x31a)]=_0x54a079[_0x5ac45f(0x4d5)],msg[_0x5ac45f(0x306)]['recording_audio_ctx_latency']=_0x54a079[_0x5ac45f(0x1b3)],msg[_0x5ac45f(0x306)][_0x5ac45f(0x4c9)]=!_0x54a079[_0x5ac45f(0x1c2)],msg['info'][_0x5ac45f(0x528)]=_0x54a079[_0x5ac45f(0x407)],msg['info'][_0x5ac45f(0x2db)]=_0x54a079['sampleRate'],msg[_0x5ac45f(0x306)][_0x5ac45f(0x358)]=_0x54a079[_0x5ac45f(0x35b)];try{if(navigator[_0x5ac45f(0x40f)]){var _0x20abf5,_0x309eaa=navigator['userAgent'];msg['info'][_0x5ac45f(0x37b)]=_0x309eaa;}navigator[_0x5ac45f(0x217)]&&(msg[_0x5ac45f(0x306)][_0x5ac45f(0x217)]=navigator['platform']);if(safariVersion())msg[_0x5ac45f(0x306)][_0x5ac45f(0x351)]=_0x5ac45f(0x426);else{if(getChromeVersion()>0x3c)msg['info'][_0x5ac45f(0x351)]='Chromium-based\x20v'+getChromeVersion();else _0x309eaa[_0x5ac45f(0x3fd)](_0x5ac45f(0x534))&&(msg[_0x5ac45f(0x306)][_0x5ac45f(0x351)]=_0x5ac45f(0x534));}}catch(_0x43c40b){};_0x54a079[_0x5ac45f(0x1f1)](msg,_0x61b103),pokeIframeAPI(_0x5ac45f(0x495),!![],_0x61b103),updateUserList();},_0x54a079[_0x260aa4(0x33f)][_0x61b103][_0x260aa4(0x250)][_0x260aa4(0x30a)]=()=>{var _0x4c4437=_0x260aa4;pokeIframeAPI('new-push-connection',![],_0x61b103),_0x54a079[_0x4c4437(0x188)](),warnlog(_0x4c4437(0x60e));try{_0x54a079[_0x4c4437(0x33f)][_0x61b103][_0x4c4437(0x518)](),_0x54a079[_0x4c4437(0x33f)][_0x61b103]=null,delete 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_0x178350=_0x5e3e29;_0x5d7ff8[_0x178350(0x1b5)]=JSON[_0x178350(0x52b)](_0x58e2d1);if(_0x5d7ff8[_0x178350(0x1b5)][_0x178350(0x2ac)]==_0x178350(0x286))_0x178350(0x1fb)!==_0x178350(0x1fb)?(_0x5b697e[_0x178350(0x2a8)]=_0x3f9fe0,_0x5eab27[_0x178350(0x1bf)](_0x2983de,_0x2dfc15)?_0x472dba[_0x178350(0x56b)][_0x4594be][_0x178350(0x36a)]=_0x58ed6b:_0x1fc947(_0x178350(0x5f6))):(_0x54a079[_0x178350(0x198)](_0x5d7ff8),_0x54a079[_0x178350(0x576)](_0x5d7ff8));else{if(_0x178350(0x628)!==_0x178350(0x628))_0x3efeb8['pcs'][_0x17fa60][_0x178350(0x518)](),_0x4707f1[_0x178350(0x33f)][_0x169d6a]=null,delete _0x18665f[_0x178350(0x33f)][_0x13a250],_0x353057();else try{if(_0x178350(0x539)==='MSeku'){var _0x3de9cd=_0x54a079[_0x178350(0x625)];if((iOS||iPad)&&_0x54a079['pcs'][_0x5d7ff8[_0x178350(0x33b)]]['guest']==!![]&&_0x54a079[_0x178350(0x33f)][_0x5d7ff8[_0x178350(0x33b)]][_0x178350(0x468)]==![]){if(_0x3de9cd===![]||_0x3de9cd>_0x54a079[_0x178350(0x2e0)]){if(_0x178350(0x345)===_0x178350(0x228))_0x2786ab[_0x178350(0x56b)][this[_0x178350(0x33b)]]=null,delete _0x18e4f6[_0x178350(0x56b)][this[_0x178350(0x33b)]];else{var _0x200ccf=Object['keys'](_0x54a079[_0x178350(0x33f)])[_0x178350(0x44a)];_0x200ccf>0x3?_0x3de9cd=_0x54a079[_0x178350(0x374)]:_0x3de9cd=_0x54a079['maxiosbitrate'];}}_0x5d7ff8[_0x178350(0x1b5)][_0x178350(0x456)]=CodecsHandler[_0x178350(0x5ce)](_0x5d7ff8[_0x178350(0x1b5)]['sdp'],_0x178350(0x18a)),_0x5d7ff8[_0x178350(0x1b5)][_0x178350(0x456)]=CodecsHandler['setVideoBitrates'](_0x5d7ff8[_0x178350(0x1b5)][_0x178350(0x456)],{'min':parseInt(_0x3de9cd/0x2)||0x1,'max':_0x3de9cd}),_0x3de9cd=![];}else _0x54a079[_0x178350(0x33f)][_0x5d7ff8[_0x178350(0x33b)]][_0x178350(0x436)]==!![]&&(_0x3de9cd!==![]?_0x54a079['roombitrate']!==![]&&(_0x178350(0x323)===_0x178350(0x323)?_0x54a079[_0x178350(0x5c7)]<_0x3de9cd&&(_0x3de9cd=_0x54a079[_0x178350(0x5c7)]):_0x2c006c(_0x178350(0x48b))):_0x3de9cd=_0x54a079[_0x178350(0x5c7)]);if(_0x3de9cd){var _0x48f8c2=CodecsHandler[_0x178350(0x356)](_0x5d7ff8[_0x178350(0x1b5)][_0x178350(0x456)])||0x9c4;log(_0x178350(0x4c6)+_0x48f8c2);if(_0x48f8c2>_0x3de9cd){var _0x3f579b=CodecsHandler[_0x178350(0x4ab)](_0x5d7ff8[_0x178350(0x1b5)][_0x178350(0x456)])||0x0;_0x5d7ff8[_0x178350(0x1b5)][_0x178350(0x456)]=CodecsHandler[_0x178350(0x5eb)](_0x5d7ff8[_0x178350(0x1b5)]['sdp'],{'min':parseInt(_0x3de9cd/0xa)||0x1,'max':parseInt(_0x3de9cd+_0x3f579b/0x400)}),_0x54a079[_0x178350(0x33f)][_0x5d7ff8['UUID']][_0x178350(0x1ac)]=_0x3de9cd;}else _0x54a079[_0x178350(0x33f)][_0x5d7ff8[_0x178350(0x33b)]][_0x178350(0x1ac)]=_0x48f8c2;}else{if(_0x54a079[_0x178350(0x32d)]!==![]){var _0x48f8c2=CodecsHandler[_0x178350(0x356)](_0x5d7ff8['description'][_0x178350(0x456)]);if(_0x48f8c2===![]){var _0x3f579b=CodecsHandler[_0x178350(0x4ab)](_0x5d7ff8[_0x178350(0x1b5)][_0x178350(0x456)])||0x0;_0x5d7ff8[_0x178350(0x1b5)][_0x178350(0x456)]=CodecsHandler[_0x178350(0x5eb)](_0x5d7ff8[_0x178350(0x1b5)]['sdp'],{'min':parseInt(_0x54a079[_0x178350(0x32d)]/0xa)||0x1,'max':parseInt(_0x54a079[_0x178350(0x32d)]+_0x3f579b/0x400)});}else _0x54a079[_0x178350(0x33f)][_0x5d7ff8['UUID']][_0x178350(0x1ac)]=_0x48f8c2;}else _0x54a079[_0x178350(0x33f)][_0x5d7ff8[_0x178350(0x33b)]][_0x178350(0x1ac)]=CodecsHandler[_0x178350(0x356)](_0x5d7ff8[_0x178350(0x1b5)][_0x178350(0x456)]);}_0x54a079[_0x178350(0x19f)]&&(_0x5d7ff8[_0x178350(0x1b5)][_0x178350(0x456)]=CodecsHandler[_0x178350(0x24e)](_0x5d7ff8[_0x178350(0x1b5)][_0x178350(0x456)],{'maxaveragebitrate':_0x54a079[_0x178350(0x19f)]*0x400,'cbr':_0x54a079[_0x178350(0x5e3)]}));if(_0x178350(0x644)in _0x5d7ff8&&_0x5d7ff8['session']!=_0x54a079[_0x178350(0x33f)][_0x5d7ff8['UUID']][_0x178350(0x644)]){errorlog(_0x178350(0x4bc));return;}_0x54a079[_0x178350(0x33f)][_0x5d7ff8[_0x178350(0x33b)]][_0x178350(0x26f)](_0x5d7ff8['description'])['then']()[_0x178350(0x35f)](errorlog);}else _0x245526();}catch(_0x8f97b3){errorlog(_0x8f97b3);}}});else{if(_0x5d7ff8[_0x5e3e29(0x1b5)][_0x5e3e29(0x2ac)]=='offer')_0x5e3e29(0x62b)==='iAfmF'?_0x3b1bab['displayMute']?(_0x5e945c[_0x5e3e29(0x591)]=!![],_0x3e8e55[_0x5e3e29(0x5e8)]()):(_0x5224bb[_0x5e3e29(0x591)]=![],_0x21cfd9[_0x5e3e29(0x5e8)]()):(_0x54a079[_0x5e3e29(0x198)](_0x5d7ff8),_0x54a079[_0x5e3e29(0x576)](_0x5d7ff8));else try{var _0x30bd75=_0x54a079[_0x5e3e29(0x625)];if((iOS||iPad)&&_0x54a079[_0x5e3e29(0x33f)][_0x5d7ff8[_0x5e3e29(0x33b)]]['guest']==!![]&&_0x54a079[_0x5e3e29(0x33f)][_0x5d7ff8[_0x5e3e29(0x33b)]][_0x5e3e29(0x468)]==![]){if(_0x30bd75===![]||_0x30bd75>_0x54a079['maxiosbitrate']){var _0x32d544=Object[_0x5e3e29(0x219)](_0x54a079[_0x5e3e29(0x33f)])['length'];_0x32d544>0x3?_0x30bd75=_0x54a079[_0x5e3e29(0x374)]:_0x30bd75=_0x54a079[_0x5e3e29(0x2e0)];}_0x5d7ff8[_0x5e3e29(0x1b5)][_0x5e3e29(0x456)]=CodecsHandler['preferCodec'](_0x5d7ff8[_0x5e3e29(0x1b5)]['sdp'],'vp8'),_0x5d7ff8[_0x5e3e29(0x1b5)][_0x5e3e29(0x456)]=CodecsHandler['setVideoBitrates'](_0x5d7ff8[_0x5e3e29(0x1b5)][_0x5e3e29(0x456)],{'min':parseInt(_0x30bd75/0x2)||0x1,'max':_0x30bd75}),_0x30bd75=![];}else _0x54a079[_0x5e3e29(0x33f)][_0x5d7ff8[_0x5e3e29(0x33b)]][_0x5e3e29(0x436)]==!![]&&(_0x30bd75!==![]?_0x54a079['roombitrate']!==![]&&(_0x5e3e29(0x3f8)===_0x5e3e29(0x4cd)?_0x4c9865(_0x463434(_0x41d272[_0x5e3e29(0x39c)]),_0x1a72ca[_0x5e3e29(0x5ca)]):_0x54a079[_0x5e3e29(0x5c7)]<_0x30bd75&&(_0x30bd75=_0x54a079[_0x5e3e29(0x5c7)])):_0x30bd75=_0x54a079[_0x5e3e29(0x5c7)]);if(_0x30bd75){if(_0x5e3e29(0x20a)!==_0x5e3e29(0x3c0)){var _0x1bd551=CodecsHandler[_0x5e3e29(0x356)](_0x5d7ff8['description'][_0x5e3e29(0x456)])||0x9c4;log(_0x5e3e29(0x4c6)+_0x1bd551);if(_0x1bd551>_0x30bd75){var _0x40355e=CodecsHandler[_0x5e3e29(0x4ab)](_0x5d7ff8[_0x5e3e29(0x1b5)][_0x5e3e29(0x456)])||0x0;_0x5d7ff8['description'][_0x5e3e29(0x456)]=CodecsHandler[_0x5e3e29(0x5eb)](_0x5d7ff8['description'][_0x5e3e29(0x456)],{'min':parseInt(_0x30bd75/0xa)||0x1,'max':parseInt(_0x30bd75+_0x40355e/0x400)});}}else return;}else{if(_0x54a079[_0x5e3e29(0x32d)]!==![]){var _0x1bd551=CodecsHandler[_0x5e3e29(0x356)](_0x5d7ff8[_0x5e3e29(0x1b5)][_0x5e3e29(0x456)]);if(_0x1bd551===![]){var _0x40355e=CodecsHandler[_0x5e3e29(0x4ab)](_0x5d7ff8[_0x5e3e29(0x1b5)][_0x5e3e29(0x456)])||0x0;_0x5d7ff8['description'][_0x5e3e29(0x456)]=CodecsHandler[_0x5e3e29(0x5eb)](_0x5d7ff8[_0x5e3e29(0x1b5)][_0x5e3e29(0x456)],{'min':parseInt(_0x54a079[_0x5e3e29(0x32d)]/0xa)||0x1,'max':parseInt(_0x54a079[_0x5e3e29(0x32d)]+_0x40355e/0x400)});}}}_0x54a079[_0x5e3e29(0x19f)]&&(_0x5d7ff8[_0x5e3e29(0x1b5)]['sdp']=CodecsHandler[_0x5e3e29(0x24e)](_0x5d7ff8[_0x5e3e29(0x1b5)][_0x5e3e29(0x456)],{'maxaveragebitrate':_0x54a079['outboundAudioBitrate']*0x400,'cbr':_0x54a079[_0x5e3e29(0x5e3)]}));if(_0x5e3e29(0x644)in _0x5d7ff8&&_0x5d7ff8[_0x5e3e29(0x644)]!=_0x54a079['pcs'][_0x5d7ff8[_0x5e3e29(0x33b)]][_0x5e3e29(0x644)]){errorlog(_0x5e3e29(0x4bc));return;}_0x54a079[_0x5e3e29(0x33f)][_0x5d7ff8[_0x5e3e29(0x33b)]]['setRemoteDescription'](_0x5d7ff8[_0x5e3e29(0x1b5)])[_0x5e3e29(0x3b1)]()[_0x5e3e29(0x35f)](errorlog);}catch(_0x1ea524){errorlog(_0x1ea524);}}},_0x54a079[_0x397888(0x37d)]=function(_0x3213ee){var _0x426560=_0x397888;_0x54a079[_0x426560(0x284)]&&_0x426560(0x300)in _0x3213ee?_0x54a079[_0x426560(0x63d)](_0x3213ee[_0x426560(0x46b)],_0x3213ee[_0x426560(0x300)])[_0x426560(0x3b1)](function(_0x1b8d5a){var _0x5b0f4d=_0x426560;_0x3213ee[_0x5b0f4d(0x46b)]=JSON[_0x5b0f4d(0x52b)](_0x1b8d5a),_0x54a079[_0x5b0f4d(0x34a)](_0x3213ee);}):_0x54a079['processIce2'](_0x3213ee);},_0x54a079[_0x397888(0x34a)]=function(_0x322c0f){var _0x34706d=_0x397888;try{if(_0x34706d(0x201)===_0x34706d(0x201)){if(_0x54a079[_0x34706d(0x1f8)]){if(_0x322c0f[_0x34706d(0x46b)]['candidate']['indexOf'](_0x54a079[_0x34706d(0x1f8)])===-0x1){log('dropped\x20candidate\x20due\x20to\x20filter'),log(_0x322c0f[_0x34706d(0x46b)]);return;}else log(_0x34706d(0x26a)),log(_0x322c0f[_0x34706d(0x46b)]);}}else _0x59803f[_0x34706d(0x56b)][_0x1e018c][_0x34706d(0x33e)]?_0x477702[_0x34706d(0x56b)][_0x2b1667][_0x34706d(0x393)]['style'][_0x34706d(0x3a7)]='block':_0x43dd75[_0x34706d(0x56b)][_0x5cb694][_0x34706d(0x393)][_0x34706d(0x453)][_0x34706d(0x3a7)]='none';}catch(_0x465872){errorlog(_0x465872);}if(_0x322c0f[_0x34706d(0x46b)]&&_0x34706d(0x46b)in _0x322c0f[_0x34706d(0x46b)]&&_0x322c0f[_0x34706d(0x46b)][_0x34706d(0x46b)]=='')return;if(_0x322c0f[_0x34706d(0x33b)]in _0x54a079[_0x34706d(0x33f)]&&_0x322c0f[_0x34706d(0x2ac)]=='remote'){if(_0x34706d(0x616)!==_0x34706d(0x616))_0x393e30[_0x34706d(0x56b)][_0x1bd1c1][_0x34706d(0x40b)][_0x34706d(0x635)][_0x34706d(0x5c4)]=_0xfe0d4a[_0x34706d(0x5b2)],!_0x324cd5[_0x34706d(0x56b)][_0x15d40e]['stats']['RTC\x20Connection']['_timestampStart']?_0x1d6714[_0x34706d(0x56b)][_0x1201dc][_0x34706d(0x40b)][_0x34706d(0x635)]['_timestampStart']=_0x4d5ba7[_0x34706d(0x5b2)]:_0x37f764[_0x34706d(0x56b)][_0x5ca7c0][_0x34706d(0x40b)][_0x34706d(0x635)][_0x34706d(0x5b7)]=_0x4d0550((_0x25cbff[_0x34706d(0x5b2)]-_0x48f73f[_0x34706d(0x56b)][_0x38f441]['stats'][_0x34706d(0x635)][_0x34706d(0x632)])/0x258)/0x64;else{log(_0x34706d(0x4cf));if(!(_0x322c0f['UUID']in _0x54a079[_0x34706d(0x33f)])){errorlog(_0x34706d(0x646));return;}if(_0x34706d(0x644)in _0x322c0f&&_0x54a079[_0x34706d(0x33f)][_0x322c0f[_0x34706d(0x33b)]]['session']!=_0x322c0f[_0x34706d(0x644)]){errorlog('Incoming\x20Ice\x20Offer\x20does\x20not\x20match\x20Session');return;}_0x54a079[_0x34706d(0x33f)][_0x322c0f['UUID']]['addIceCandidate'](_0x322c0f[_0x34706d(0x46b)])[_0x34706d(0x3b1)]()[_0x34706d(0x35f)](function(_0x27f20c){errorlog(_0x27f20c),errorlog(_0x322c0f);});}}else{if(_0x322c0f[_0x34706d(0x33b)]in _0x54a079[_0x34706d(0x56b)]&&_0x322c0f[_0x34706d(0x2ac)]==_0x34706d(0x225)){log(_0x34706d(0x30d));if(!(_0x322c0f[_0x34706d(0x33b)]in _0x54a079['rpcs'])){errorlog(_0x34706d(0x646));return;}if('session'in _0x322c0f&&_0x54a079['rpcs'][_0x322c0f['UUID']][_0x34706d(0x644)]!=_0x322c0f[_0x34706d(0x644)]){errorlog(_0x34706d(0x1d2));return;}if(_0x54a079[_0x34706d(0x56b)][_0x322c0f[_0x34706d(0x33b)]]===null)return;_0x54a079[_0x34706d(0x56b)][_0x322c0f[_0x34706d(0x33b)]][_0x34706d(0x2de)](_0x322c0f[_0x34706d(0x46b)])[_0x34706d(0x3b1)]()[_0x34706d(0x35f)](function(_0xbd5163){var _0x3483e2=_0x34706d;if(_0x3483e2(0x328)!==_0x3483e2(0x4f6))errorlog(_0xbd5163),errorlog(_0x322c0f);else return _0x26c347('no\x20audio\x20track\x20to\x20poke'),![];});}else errorlog(_0x322c0f),errorlog('ICE\x20DID\x20NOT\x20FIND\x20A\x20PC\x20OPTION?');}},_0x54a079[_0x397888(0x59e)]=function(_0x5ea0b0){var _0x435f75=_0x397888;if('IiROe'!==_0x435f75(0x27b))_0x1fa229['rpcs'][_0x1dc616]['remoteVideoMuteElement'][_0x435f75(0x453)][_0x435f75(0x3a7)]=_0x435f75(0x610);else{if(_0x54a079[_0x435f75(0x284)]&&_0x435f75(0x300)in _0x5ea0b0)_0x54a079['decryptMessage'](_0x5ea0b0['candidates'],_0x5ea0b0[_0x435f75(0x300)])[_0x435f75(0x3b1)](function(_0x1f32fd){var _0x5b2dc4=_0x435f75;_0x5ea0b0[_0x5b2dc4(0x216)]=JSON[_0x5b2dc4(0x52b)](_0x1f32fd);var _0xb0293d={};_0xb0293d[_0x5b2dc4(0x33b)]=_0x5ea0b0['UUID'],_0xb0293d[_0x5b2dc4(0x2ac)]=_0x5ea0b0[_0x5b2dc4(0x2ac)];for(var _0x339840=0x0;_0x339840<_0x5ea0b0[_0x5b2dc4(0x216)][_0x5b2dc4(0x44a)];_0x339840++){_0xb0293d[_0x5b2dc4(0x46b)]=_0x5ea0b0[_0x5b2dc4(0x216)][_0x339840],_0x54a079['processIce2'](_0xb0293d);}});else{var _0x53c38f={};_0x53c38f[_0x435f75(0x33b)]=_0x5ea0b0[_0x435f75(0x33b)],_0x53c38f[_0x435f75(0x2ac)]=_0x5ea0b0[_0x435f75(0x2ac)];for(var _0x4243ce=0x0;_0x4243ce<_0x5ea0b0[_0x435f75(0x216)][_0x435f75(0x44a)];_0x4243ce++){_0x53c38f['candidate']=_0x5ea0b0[_0x435f75(0x216)][_0x4243ce],_0x54a079[_0x435f75(0x34a)](_0x53c38f);}}}},_0x54a079[_0x397888(0x576)]=function(_0x4d8715){var _0x12acd7=_0x397888;_0x54a079[_0x12acd7(0x56b)][_0x4d8715[_0x12acd7(0x33b)]][_0x12acd7(0x26f)](_0x4d8715['description'])[_0x12acd7(0x3b1)](function(){var _0x37e853=_0x12acd7;if(_0x54a079['rpcs'][_0x4d8715['UUID']][_0x37e853(0x26c)][_0x37e853(0x2ac)]==='offer')_0x54a079[_0x37e853(0x56b)][_0x4d8715[_0x37e853(0x33b)]][_0x37e853(0x189)]()[_0x37e853(0x3b1)](function(_0x1de740){var _0x4b350b=_0x37e853;if(!_0x54a079[_0x4b350b(0x1f3)]&&_0x54a079[_0x4b350b(0x2d8)]==0x5)_0x4b350b(0x42d)===_0x4b350b(0x48a)?(_0x1e4e2e[_0x4b350b(0x260)](_0x428ebd),_0x14a8c3[_0x4b350b(0x56b)][_0x55fb3d][_0x4b350b(0x40b)][_0x5edcce[_0x4b350b(0x3c5)]]['nackTrigger']=0x0,_0x1c1232(_0x4b350b(0x33d))):_0x1de740['sdp']=CodecsHandler[_0x4b350b(0x24e)](_0x1de740[_0x4b350b(0x456)],{'stereo':0x1,'maxaveragebitrate':(_0x54a079[_0x4b350b(0x482)]||0x100)*0x400,'cbr':_0x54a079[_0x4b350b(0x5e3)],'useinbandfec':0x1,'maxptime':_0x54a079[_0x4b350b(0x366)],'minptime':_0x54a079[_0x4b350b(0x533)],'ptime':_0x54a079[_0x4b350b(0x223)]});else{if(_0x54a079[_0x4b350b(0x2d8)]==0x1||_0x54a079[_0x4b350b(0x2d8)]==0x2||_0x54a079[_0x4b350b(0x2d8)]==0x5)_0x1de740['sdp']=CodecsHandler['setOpusAttributes'](_0x1de740[_0x4b350b(0x456)],{'stereo':0x1,'maxaveragebitrate':(_0x54a079['audiobitrate']||0x100)*0x400,'cbr':_0x54a079[_0x4b350b(0x5e3)],'useinbandfec':0x1,'maxptime':_0x54a079[_0x4b350b(0x366)],'minptime':_0x54a079[_0x4b350b(0x533)],'ptime':_0x54a079['ptime']});else{if(_0x54a079[_0x4b350b(0x2d8)]==0x4)_0x1de740[_0x4b350b(0x456)]=CodecsHandler[_0x4b350b(0x24e)](_0x1de740['sdp'],{'stereo':0x2,'maxaveragebitrate':(_0x54a079['audiobitrate']||0x100)*0x400,'cbr':_0x54a079['cbr'],'useinbandfec':0x1,'maxptime':_0x54a079[_0x4b350b(0x366)],'minptime':_0x54a079[_0x4b350b(0x533)],'ptime':_0x54a079[_0x4b350b(0x223)]});else _0x54a079[_0x4b350b(0x482)]&&(_0x1de740[_0x4b350b(0x456)]=CodecsHandler[_0x4b350b(0x24e)](_0x1de740[_0x4b350b(0x456)],{'maxaveragebitrate':_0x54a079[_0x4b350b(0x482)]*0x400,'cbr':_0x54a079[_0x4b350b(0x5e3)],'useinbandfec':0x1,'maxptime':_0x54a079[_0x4b350b(0x366)],'minptime':_0x54a079[_0x4b350b(0x533)],'ptime':_0x54a079[_0x4b350b(0x223)]}));}}_0x54a079[_0x4b350b(0x4d7)]&&(_0x1de740[_0x4b350b(0x456)]=CodecsHandler['preferCodec'](_0x1de740[_0x4b350b(0x456)],_0x54a079['codec']));_0x54a079[_0x4b350b(0x42a)]&&(log(_0x1de740[_0x4b350b(0x456)]),_0x1de740['sdp']=CodecsHandler[_0x4b350b(0x1d1)](_0x1de740[_0x4b350b(0x456)]));_0x54a079['noPLIs']&&(_0x1de740[_0x4b350b(0x456)]=CodecsHandler[_0x4b350b(0x433)](_0x1de740[_0x4b350b(0x456)]));_0x54a079[_0x4b350b(0x624)]&&(_0x1de740['sdp']=CodecsHandler[_0x4b350b(0x187)](_0x1de740[_0x4b350b(0x456)]));if(_0x54a079[_0x4b350b(0x56b)][_0x4d8715[_0x4b350b(0x33b)]][_0x4b350b(0x42c)])log(_0x4b350b(0x550)),_0x1de740[_0x4b350b(0x456)]=_0x56d44c(_0x1de740[_0x4b350b(0x456)],_0x54a079['rpcs'][_0x4d8715[_0x4b350b(0x33b)]][_0x4b350b(0x42c)]);else _0x54a079[_0x4b350b(0x2a8)]&&(log(_0x4b350b(0x550)),_0x1de740[_0x4b350b(0x456)]=_0x56d44c(_0x1de740[_0x4b350b(0x456)],_0x54a079[_0x4b350b(0x2a8)]));return _0x54a079['rpcs'][_0x4d8715[_0x4b350b(0x33b)]]['setLocalDescription'](_0x1de740);})[_0x37e853(0x3b1)](function _0x54421(){var _0x190c9a=_0x37e853;log(_0x190c9a(0x3c4));var _0x229504={};_0x229504['UUID']=_0x4d8715[_0x190c9a(0x33b)],_0x229504[_0x190c9a(0x1b5)]=_0x54a079[_0x190c9a(0x56b)][_0x4d8715[_0x190c9a(0x33b)]][_0x190c9a(0x391)],_0x229504[_0x190c9a(0x644)]=_0x54a079[_0x190c9a(0x56b)][_0x4d8715['UUID']][_0x190c9a(0x644)],_0x54a079[_0x190c9a(0x284)]?_0x54a079['encryptMessage'](JSON['stringify'](_0x229504['description']))[_0x190c9a(0x3b1)](function(_0x190238){var _0xece616=_0x190c9a;_0x229504[_0xece616(0x1b5)]=_0x190238[0x0],_0x229504[_0xece616(0x300)]=_0x190238[0x1],_0x54a079['anyrequest'](_0x229504);}):_0x54a079['anyrequest'](_0x229504);})[_0x37e853(0x35f)](errorlog);else _0x54a079[_0x37e853(0x56b)][_0x4d8715['UUID']]['remoteDescription'][_0x37e853(0x2ac)]===_0x37e853(0x4ec)&&(_0x37e853(0x4ed)==='PbIBb'?errorlog(_0x37e853(0x512)):(_0x2f3941[_0x37e853(0x56b)][_0x56eb26][_0x37e853(0x40b)][_0x50197a[_0x37e853(0x3c5)]][_0x37e853(0x2ac)]=_0x37e853(0x336),_0xf54517[_0x37e853(0x56b)][_0x30f164][_0x37e853(0x40b)][_0x40bd99[_0x37e853(0x3c5)]][_0x37e853(0x203)]=_0x37e853(0x388),_0x37e853(0x2e5)in _0x2dd08d&&(_0xd81d4c[_0x37e853(0x56b)][_0xbca8ee][_0x37e853(0x40b)][_0x22b39d[_0x37e853(0x3c5)]]['audio_level']=_0x5728c6(_0x350495(_0x516e46[_0x37e853(0x2e5)])*0x2710)/0x2710)));})[_0x12acd7(0x35f)](errorlog);},_0x54a079[_0x397888(0x198)]=function(_0x2d91ed){var _0x29f09d=_0x397888,_0x18ef0b=_0x2d91ed[_0x29f09d(0x33b)];if(_0x18ef0b in _0x54a079['rpcs']){log(_0x29f09d(0x51f)),log(_0x2d91ed);if(_0x29f09d(0x644)in _0x2d91ed){if(_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x644)]==_0x2d91ed[_0x29f09d(0x644)]){log('SDP\x20Sessions\x20Match.\x20I\x20assume\x20ADDING\x20TRACKS');return;}else warnlog(_0x29f09d(0x536));}}else log('MAKING\x20A\x20NEW\x20RTC\x20CONNECTION');try{if(_0x29f09d(0x575)!==_0x29f09d(0x575))_0x458c18['innerText']=_0x40de0f[_0x29f09d(0x573)];else{for(var _0x5c786d in _0x54a079[_0x29f09d(0x56b)]){if(_0x54a079[_0x29f09d(0x56b)][_0x5c786d][_0x29f09d(0x249)]==_0x2d91ed[_0x29f09d(0x249)]){_0x54a079[_0x29f09d(0x56b)][_0x5c786d][_0x29f09d(0x2b5)]&&(_0x54a079[_0x29f09d(0x56b)][_0x5c786d][_0x29f09d(0x2b5)][_0x29f09d(0x453)]['display']=_0x29f09d(0x5b1));_0x54a079[_0x29f09d(0x56b)][_0x5c786d]=null,delete _0x54a079[_0x29f09d(0x56b)][_0x5c786d],warnlog(_0x29f09d(0x280));if(_0x54a079[_0x29f09d(0x1f3)])try{_0x29f09d(0x1b1)!==_0x29f09d(0x1b1)?_0x5b9765(_0x284c74):document[_0x29f09d(0x45d)](_0x29f09d(0x29e)+_0x5c786d)&&getById(_0x29f09d(0x29e)+_0x5c786d)[_0x29f09d(0x1bc)][_0x29f09d(0x18d)](getById(_0x29f09d(0x29e)+_0x5c786d));}catch(_0x5a9b11){errorlog(_0x5a9b11);}}}document[_0x29f09d(0x45d)](_0x29f09d(0x31e))&&document[_0x29f09d(0x45d)](_0x29f09d(0x31e))['parentNode'][_0x29f09d(0x18d)](document[_0x29f09d(0x45d)](_0x29f09d(0x31e)));}}catch(_0x30ed46){errorlog(_0x30ed46);}if(_0x54a079[_0x29f09d(0x27c)]!==![]){if(Object[_0x29f09d(0x219)](_0x54a079[_0x29f09d(0x56b)])[_0x29f09d(0x44a)]>=_0x54a079[_0x29f09d(0x27c)]){delete _0x54a079[_0x29f09d(0x56b)][_0x18ef0b],updateUserList(),warnlog(_0x29f09d(0x442));return;}}else{if(_0x54a079[_0x29f09d(0x48f)]!==![]){if(Object['keys'](_0x54a079[_0x29f09d(0x56b)])[_0x29f09d(0x44a)]+Object[_0x29f09d(0x219)](_0x54a079['pcs'])[_0x29f09d(0x44a)]>=_0x54a079[_0x29f09d(0x48f)]){delete _0x54a079['rpcs'][_0x18ef0b],updateUserList(),warnlog(_0x29f09d(0x442));return;}}}if(_0x54a079[_0x29f09d(0x39e)]){if(_0x29f09d(0x519)!==_0x29f09d(0x519))return-0x1;else{if(_0x54a079[_0x29f09d(0x1f3)])!(_0x18ef0b in _0x54a079[_0x29f09d(0x33f)])&&_0x54a079['offerSDP'](_0x18ef0b);else{if(_0x54a079[_0x29f09d(0x4a1)]!==_0x18ef0b)return;}}}_0x54a079['rpcs'][_0x18ef0b]=new RTCPeerConnection(_0x54a079[_0x29f09d(0x1d4)]);if(_0x54a079[_0x29f09d(0x3f0)]){if(Object[_0x29f09d(0x219)](_0x54a079[_0x29f09d(0x56b)])[_0x29f09d(0x44a)]>0x1){warnlog('TOO\x20MANY\x20PUBLISHING\x20PEERS'),log(_0x54a079[_0x29f09d(0x56b)]),delete _0x54a079[_0x29f09d(0x56b)][_0x18ef0b],updateUserList();return;}else warnlog(_0x29f09d(0x5b9));}_0x2d91ed[_0x29f09d(0x249)]in _0x54a079[_0x29f09d(0x380)]&&delete _0x54a079['waitingWatchList'][_0x2d91ed[_0x29f09d(0x249)]];_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x249)]=_0x2d91ed['streamID'];_0x2d91ed[_0x29f09d(0x644)]?_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x644)]=_0x2d91ed['session']:_0x54a079[_0x29f09d(0x56b)][_0x18ef0b]['session']=null;_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x634)]=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x40b)]={},_0x54a079['rpcs'][_0x18ef0b][_0x29f09d(0x40b)][_0x29f09d(0x199)]=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x36a)]=-0x1,_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x50d)]=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b]['targetBandwidth']=-0x1,_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x42c)]=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b]['videoElement']=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x412)]=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x604)]=![],_0x54a079['rpcs'][_0x18ef0b][_0x29f09d(0x557)]=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x461)]=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b]['remoteMuteState']=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b]['remoteMuteElement']=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x1ce)]=null,_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x274)]={},_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x4f2)]=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b]['scaleWidth']=![],_0x54a079['rpcs'][_0x18ef0b][_0x29f09d(0x25b)]=null,_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x500)]=0x64,_0x54a079[_0x29f09d(0x56b)][_0x18ef0b]['directorMutedState']=0x0,_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x4ae)]=0x0,_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x36f)]=0x0,_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x573)]=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x2da)]=![],_0x54a079['rpcs'][_0x18ef0b][_0x29f09d(0x5cc)]=null,_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x613)]=null,_0x54a079['rpcs'][_0x18ef0b][_0x29f09d(0x532)]={},_0x54a079['rpcs'][_0x18ef0b][_0x29f09d(0x49e)]=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x5bd)]=![],_0x54a079[_0x29f09d(0x56b)][_0x18ef0b][_0x29f09d(0x338)]=Date['now']();var _0x36ecbb='';_0x54a079[_0x29f09d(0x2eb)]&&(_0x36ecbb=_0x54a079[_0x29f09d(0x2eb)]);if(_0x54a079[_0x29f09d(0x1f3)]){var _0x22c9e3='';_0x54a079[_0x29f09d(0x2e2)]&&(_0x54a079[_0x29f09d(0x2e2)]!==!![]?_0x22c9e3='&pie='+_0x54a079[_0x29f09d(0x2e2)]:_0x22c9e3=_0x29f09d(0x376));var _0x2303c4='';_0x54a079[_0x29f09d(0x284)]&&(_0x54a079['defaultPassword']===![]&&(_0x2303c4=_0x29f09d(0x5c8)+_0x54a079[_0x29f09d(0x284)]));if(_0x54a079['pie']&&_0x29f09d(0x58c)in _0x2d91ed&&_0x2d91ed['isScene']!==![]){}else createControlBox(_0x18ef0b,_0x29f09d(0x312)+location['host']+location[_0x29f09d(0x1c6)]+_0x29f09d(0x5af)+_0x54a079[_0x29f09d(0x56b)][_0x18ef0b]['streamID']+_0x29f09d(0x4be)+_0x36ecbb+_0x29f09d(0x378)+_0x54a079[_0x29f09d(0x2aa)]+_0x2303c4+_0x22c9e3,_0x54a079['rpcs'][_0x18ef0b][_0x29f09d(0x249)]);}_0x54a079['rpcs'][_0x18ef0b][_0x29f09d(0x33b)]=_0x18ef0b;if(_0x29f09d(0x249)in 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_0x54a079[_0x5df1d1(0x56b)][_0x18ef0b][_0x5df1d1(0x4af)]=_0x122dd6[_0x5df1d1(0x31d)],_0x54a079[_0x5df1d1(0x56b)][_0x18ef0b][_0x5df1d1(0x4af)][_0x5df1d1(0x33b)]=_0x18ef0b,_0x54a079[_0x5df1d1(0x56b)][_0x18ef0b][_0x5df1d1(0x4af)]['onopen']=_0x351182=>{var _0x14986a=_0x5df1d1,_0x1559f9={};_0x1559f9[_0x14986a(0x1af)]=![];if(_0x54a079[_0x14986a(0x355)]!==![])_0x54a079[_0x14986a(0x355)][_0x14986a(0x18b)](_0x54a079['rpcs'][_0x18ef0b]['streamID'])?_0x1559f9[_0x14986a(0x2bc)]=!![]:_0x1559f9[_0x14986a(0x2bc)]=![];else{if(_0x54a079[_0x14986a(0x1af)]!==![]){if(_0x54a079[_0x14986a(0x1af)]!==null)_0x54a079[_0x14986a(0x56b)][_0x18ef0b][_0x14986a(0x249)]===_0x54a079[_0x14986a(0x1af)]?_0x54a079[_0x14986a(0x32c)]?(_0x1559f9[_0x14986a(0x1af)]=!![],_0x1559f9[_0x14986a(0x2bc)]=![]):_0x1559f9[_0x14986a(0x2bc)]=!![]:_0x14986a(0x4c8)===_0x14986a(0x5d1)?_0x18d506[_0x14986a(0x56b)][_0x58ec44]['videoElement'][_0x14986a(0x2cd)][_0x14986a(0x248)]():_0x1559f9[_0x14986a(0x2bc)]=![];else 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_0x195c7e,_0xd29fbb=navigator[_0x14986a(0x40f)];_0x1559f9[_0x14986a(0x306)][_0x14986a(0x37b)]=_0xd29fbb;}navigator['platform']&&(_0x1559f9[_0x14986a(0x306)][_0x14986a(0x217)]=navigator[_0x14986a(0x217)]);if(window['obsstudio']){_0x1559f9[_0x14986a(0x306)][_0x14986a(0x34b)]=window['obsstudio'][_0x14986a(0x630)];if(_0x54a079[_0x14986a(0x5f8)]){var _0x5bab77=![];_0x54a079[_0x14986a(0x5f8)][_0x14986a(0x3a4)]!==null&&(_0x1559f9['obsVisibility']=_0x54a079['obsState'][_0x14986a(0x3a4)],_0x54a079[_0x14986a(0x5f8)][_0x14986a(0x3a4)]===![]&&(_0x5bab77=!![])),_0x54a079[_0x14986a(0x5f8)]['sourceActive']!==null&&(_0x1559f9[_0x14986a(0x502)]=_0x54a079[_0x14986a(0x5f8)]['sourceActive']),_0x54a079[_0x14986a(0x5f8)][_0x14986a(0x4cc)]!==null&&(_0x1559f9[_0x14986a(0x465)]=_0x54a079[_0x14986a(0x5f8)][_0x14986a(0x4cc)]),_0x54a079['obsState'][_0x14986a(0x1e1)]!==null&&(_0x1559f9[_0x14986a(0x2f8)]=_0x54a079[_0x14986a(0x5f8)]['streaming']);}_0x54a079[_0x14986a(0x52d)]!==![]&&(_0x1559f9[_0x14986a(0x2a9)]=parseInt(_0x54a079[_0x14986a(0x52d)]),_0x5bab77&&(_0x54a079[_0x14986a(0x56b)][_0x18ef0b][_0x14986a(0x36a)]=_0x1559f9[_0x14986a(0x2a9)]));}else _0x1559f9[_0x14986a(0x306)][_0x14986a(0x34b)]=![];}catch(_0x13bd3d){};_0x54a079[_0x14986a(0x226)]!==![]?_0x54a079[_0x14986a(0x226)][_0x14986a(0x18b)](_0x54a079[_0x14986a(0x56b)][_0x18ef0b][_0x14986a(0x249)])?_0x1559f9['audio']=!![]:_0x1559f9[_0x14986a(0x388)]=![]:_0x1559f9['audio']=!![];_0x1559f9[_0x14986a(0x3d6)]=![];_0x54a079[_0x14986a(0x4ee)]!==![]?_0x54a079[_0x14986a(0x4ee)][_0x14986a(0x18b)](_0x54a079['rpcs'][_0x18ef0b][_0x14986a(0x249)])?_0x1559f9[_0x14986a(0x3d6)]=!![]:_0x1559f9['iframe']=![]:_0x14986a(0x589)===_0x14986a(0x53d)?_0xb7aeba['optimizeBitrate'](_0x3180b5):_0x1559f9[_0x14986a(0x3d6)]=!![];_0x1559f9[_0x14986a(0x436)]=![],_0x1559f9[_0x14986a(0x478)]=![],_0x1559f9[_0x14986a(0x1f3)]=![],_0x1559f9[_0x14986a(0x53e)]=_0x54a079['keyframerate'],_0x1559f9[_0x14986a(0x4b4)]=![],_0x1559f9[_0x14986a(0x3ad)]=![],_0x1559f9[_0x14986a(0x468)]=![];_0x54a079[_0x14986a(0x48d)]&&(_0x1559f9[_0x14986a(0x4b4)]=!![]);if(_0x54a079[_0x14986a(0x1f3)])_0x1559f9[_0x14986a(0x1f3)]=!![],_0x1559f9[_0x14986a(0x468)]=_0x54a079['forceios'];else{if(_0x54a079[_0x14986a(0x478)]!==![])_0x1559f9[_0x14986a(0x478)]=_0x54a079[_0x14986a(0x478)];else _0x54a079[_0x14986a(0x2aa)]!==![]&&_0x54a079[_0x14986a(0x2aa)]!==''&&(_0x1559f9[_0x14986a(0x468)]=_0x54a079['forceios'],_0x1559f9[_0x14986a(0x436)]=!![]);}_0x54a079[_0x14986a(0x1bf)](_0x1559f9,_0x18ef0b)?log(_0x14986a(0x523)):errorlog(_0x14986a(0x255)),pokeIframeAPI(_0x14986a(0x527),!![],_0x18ef0b);},_0x54a079[_0x5df1d1(0x56b)][_0x18ef0b][_0x5df1d1(0x4af)][_0x5df1d1(0x230)]=_0x1f98f1=>{var _0x4fe924=_0x5df1d1;if(typeof 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[_0x23c8ad,_0x572c11]=statsMenuCreator();printViewStats(_0x572c11,_0x58a7e5),_0x23c8ad[_0x1053ea(0x4c4)]=setInterval(printViewStats,0xbb8,_0x572c11,_0x58a7e5);}return _0x4c83f9[_0x1053ea(0x2c4)](),![];}}catch(_0x192151){errorlog(_0x192151);}}),updateMixer();else _0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x412)][_0x4fe924(0x633)]&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['imageElement'][_0x4fe924(0x633)]=![],_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x412)]['style'][_0x4fe924(0x3a4)]=_0x4fe924(0x4e9));_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x412)][_0x4fe924(0x54f)]=window[_0x4fe924(0x292)][_0x4fe924(0x296)](new Blob([new Uint8Array(_0x1f98f1[_0x4fe924(0x46d)])],{'type':_0x4fe924(0x348)}));return;}var _0x594668=JSON[_0x4fe924(0x52b)](_0x1f98f1[_0x4fe924(0x46d)]);_0x594668[_0x4fe924(0x33b)]=_0x18ef0b;var _0x56035e=![],_0x3f64c1=![];_0x4fe924(0x53f)in _0x594668&&(_0x54a079[_0x4fe924(0x1f3)]&&(!(_0x54a079['cleanOutput']||_0x54a079['cleanDirector'])&&warnUser(_0x4fe924(0x3b0),0x1388)),errorlog('ACTION\x20REJECTED:\x20'+_0x594668[_0x4fe924(0x53f)]+_0x4fe924(0x63b)+_0x54a079[_0x4fe924(0x1f3)]));if(_0x4fe924(0x1b5)in _0x594668)_0x54a079[_0x4fe924(0x2d0)](_0x594668);else{if(_0x4fe924(0x46b)in _0x594668)_0x594668['UUID']=_0x18ef0b,log(_0x4fe924(0x4f1)),_0x54a079[_0x4fe924(0x37d)](_0x594668);else{if(_0x4fe924(0x216)in _0x594668)_0x4fe924(0x3be)!==_0x4fe924(0x3be)?_0x33453d+=0x100:(_0x594668[_0x4fe924(0x33b)]=_0x18ef0b,log(_0x4fe924(0x2f7)),_0x54a079[_0x4fe924(0x59e)](_0x594668));else{if(_0x4fe924(0x49e)in _0x594668)try{if('feGeK'===_0x4fe924(0x19e)){_0x123393('Answer\x20SDP\x20does\x20not\x20have\x20a\x20matching\x20session\x20ID');return;}else{_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x49e)]=_0x594668[_0x4fe924(0x49e)]||![];if(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['iframeSrc']==![]){_0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x5bd)]=![],_0x56035e=!![];if(_0x54a079[_0x4fe924(0x1af)]!==![]){if(_0x54a079[_0x4fe924(0x1af)]!==null)_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x249)]===_0x54a079['broadcast']&&(_0x54a079['broadcastIFrame']=![]);else 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_0x54a079[_0x4fe924(0x4ee)]&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['iframeEle']=loadIframe(_0x594668[_0x4fe924(0x49e)]),_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)]['id']=_0x4fe924(0x2e4)+_0x18ef0b,_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)][_0x4fe924(0x1d0)][_0x4fe924(0x33b)]=_0x18ef0b,_0x56035e=!![],_0x54a079[_0x4fe924(0x455)]=_0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x5bd)],_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x249)]&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)][_0x4fe924(0x1d0)]['sid']=_0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['streamID']));}}else{if(_0x54a079[_0x4fe924(0x4a1)]){if(_0x18ef0b==_0x54a079[_0x4fe924(0x4a1)]){if(_0x54a079['noiframe']===![])_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)]=loadIframe(_0x594668[_0x4fe924(0x49e)]),_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)]['id']=_0x4fe924(0x2e4)+_0x18ef0b,_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)][_0x4fe924(0x1d0)]['UUID']=_0x18ef0b,_0x56035e=!![],_0x54a079[_0x4fe924(0x455)]=_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)],_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x249)]&&(_0x4fe924(0x405)===_0x4fe924(0x405)?_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)][_0x4fe924(0x1d0)]['sid']=_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x249)]:_0x2c28ff['target'][_0x4fe924(0x58f)]=_0x17920b);else _0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x249)]in _0x54a079[_0x4fe924(0x4ee)]&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)]=loadIframe(_0x594668[_0x4fe924(0x49e)]),_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)]['id']=_0x4fe924(0x2e4)+_0x18ef0b,_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)][_0x4fe924(0x1d0)][_0x4fe924(0x33b)]=_0x18ef0b,_0x56035e=!![],_0x54a079['broadcastIFrame']=_0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['iframeEle'],_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x249)]&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)][_0x4fe924(0x1d0)][_0x4fe924(0x24f)]=_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x249)]));}}}}}else{if(_0x54a079['noiframe']===![])_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)]=loadIframe(_0x594668[_0x4fe924(0x49e)]),_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)]['id']=_0x4fe924(0x2e4)+_0x18ef0b,_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)][_0x4fe924(0x1d0)][_0x4fe924(0x33b)]=_0x18ef0b,_0x56035e=!![],_0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['streamID']&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)][_0x4fe924(0x1d0)][_0x4fe924(0x24f)]=_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x249)]);else{if(_0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x249)]in _0x54a079[_0x4fe924(0x4ee)]){if(_0x4fe924(0x288)===_0x4fe924(0x288))_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)]=loadIframe(_0x594668[_0x4fe924(0x49e)]),_0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x5bd)]['id']=_0x4fe924(0x2e4)+_0x18ef0b,_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)][_0x4fe924(0x1d0)][_0x4fe924(0x33b)]=_0x18ef0b,_0x56035e=!![],_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x249)]&&('VEqJt'===_0x4fe924(0x21b)?(_0x4aa045[_0x4fe924(0x459)][_0x4fe924(0x5d7)](_0x2f228a[_0x4fe924(0x249)]),_0x29ac96[_0x4fe924(0x2ee)]()):_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x5bd)][_0x4fe924(0x1d0)][_0x4fe924(0x24f)]=_0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x249)]);else return _0x56c838;}}}}}}catch(_0x3a3f64){errorlog(_0x3a3f64);}}}}if(_0x4fe924(0x4a7)in _0x594668){var _0x1ef08b=![],_0x49fcfb=![];_0x54a079[_0x4fe924(0x4a1)]===_0x18ef0b&&(_0x1ef08b=!![],_0x4fe924(0x62d)in _0x594668&&(_0x594668[_0x4fe924(0x62d)]==!![]&&(_0x49fcfb=!![])));if(_0x54a079[_0x4fe924(0x1f3)]){if(_0x594668[_0x4fe924(0x4a7)]==_0x4fe924(0x231))_0x54a079['beepToNotify']&&playtone(),getById('hands_'+_0x18ef0b)['style'][_0x4fe924(0x3a7)]=_0x4fe924(0x57c),_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x297)][_0x4fe924(0x453)][_0x4fe924(0x3a7)]=_0x4fe924(0x610);else _0x594668[_0x4fe924(0x4a7)]==_0x4fe924(0x52a)&&(getById('hands_'+_0x18ef0b)[_0x4fe924(0x453)]['display']=_0x4fe924(0x5b1),_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x297)][_0x4fe924(0x453)][_0x4fe924(0x3a7)]=_0x4fe924(0x5b1));}log(_0x4fe924(0x629)+_0x1ef08b),getChatMessage(_0x594668[_0x4fe924(0x4a7)],_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x573)],_0x1ef08b,_0x49fcfb);}_0x4fe924(0x4f8)in _0x594668&&(log(_0x594668),_0x54a079[_0x4fe924(0x5a1)]&&updateClosedCaptions(_0x594668,_0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x573)],_0x18ef0b));'audioOptions'in _0x594668&&(_0x54a079['director']&&updateDirectorsAudio(_0x594668[_0x4fe924(0x2e6)],_0x18ef0b));_0x4fe924(0x60a)in _0x594668&&(_0x54a079['director']&&(_0x4fe924(0x5e6)!==_0x4fe924(0x472)?updateDirectorsVideo(_0x594668[_0x4fe924(0x60a)],_0x18ef0b):_0x448c58(_0x2f3273[_0x4fe924(0x249)])));_0x4fe924(0x46a)in _0x594668&&(_0x54a079[_0x4fe924(0x1f3)]&&gotDevicesRemote(_0x594668[_0x4fe924(0x46a)],_0x18ef0b));if(_0x4fe924(0x28e)in _0x594668){if(_0x54a079[_0x4fe924(0x4a1)]===_0x18ef0b){if(_0x4fe924(0x462)!==_0x4fe924(0x2bb)){if(_0x54a079[_0x4fe924(0x287)]){if(_0x4fe924(0x474)!=='ShKBJ')lowerhand();else{var _0x4c916d={};_0x4c916d[_0x4fe924(0x53f)]=_0x4fe924(0x4f4),_0x3fb9df[_0x4fe924(0x1f1)](_0x4c916d,_0x235219);}}}else _0x365952(_0x30996e);}}if(_0x4fe924(0x3fc)in _0x594668){log(_0x594668);if(_0x54a079[_0x4fe924(0x4a1)]===_0x18ef0b){if(_0x594668[_0x4fe924(0x3fc)]!==![]){if(_0x594668['infocus']===!![])_0x54a079[_0x4fe924(0x3fc)]=!![];else for(var _0x2dce6b in _0x54a079['rpcs']){if(_0x54a079[_0x4fe924(0x56b)][_0x2dce6b]['streamID']===_0x594668[_0x4fe924(0x3fc)]){_0x54a079[_0x4fe924(0x3fc)]=_0x2dce6b;break;}}}else _0x54a079[_0x4fe924(0x3fc)]=![];_0x56035e=!![],mustUpdateuserList=!![];}}_0x4fe924(0x236)in _0x594668&&(_0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x40b)][_0x4fe924(0x236)]={},_0x4fe924(0x59f)in _0x594668&&('Mujpv'===_0x4fe924(0x339)?_0x181f8e[_0x4fe924(0x56b)][_0x5d7d4e][_0x4fe924(0x40b)][_0x4fe924(0x306)][_0x4fe924(0x1f3)]=!![]:_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x40b)][_0x4fe924(0x236)][_0x4fe924(0x59f)]=_0x594668[_0x4fe924(0x59f)]),_0x4fe924(0x5f0)in _0x594668&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x40b)]['sensors']['acc']=_0x594668['acc']),_0x4fe924(0x4e0)in _0x594668&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x40b)][_0x4fe924(0x236)]['gyro']=_0x594668[_0x4fe924(0x4e0)]),'mag'in _0x594668&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x40b)]['sensors'][_0x4fe924(0x4d9)]=_0x594668[_0x4fe924(0x4d9)]),isIFrame&&(_0x4fe924(0x422)!==_0x4fe924(0x254)?parent[_0x4fe924(0x29d)]({'sensors':_0x594668},'*'):_0x5d6597[_0x4fe924(0x33f)][_0x2fb478][_0x4fe924(0x436)]=!![]));_0x4fe924(0x221)in _0x594668&&playbackMIDI(_0x594668['midi']);_0x4fe924(0x2cd)in _0x594668&&updateRemoteRecordButton(_0x18ef0b,_0x594668[_0x4fe924(0x2cd)]);if(_0x4fe924(0x306)in _0x594668){warnlog(_0x594668),_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x40b)][_0x4fe924(0x306)]=_0x594668[_0x4fe924(0x306)];if(_0x4fe924(0x573)in _0x594668[_0x4fe924(0x306)]){typeof _0x594668[_0x4fe924(0x306)][_0x4fe924(0x573)]==_0x4fe924(0x5ac)?_0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['label']=sanitizeLabel(_0x594668[_0x4fe924(0x306)][_0x4fe924(0x573)]):_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x573)]=![];if(_0x54a079[_0x4fe924(0x1f3)]){var _0x390f01=getById(_0x4fe924(0x556)+_0x18ef0b);_0x390f01&&(_0x390f01[_0x4fe924(0x453)][_0x4fe924(0x22b)]='left',_0x390f01[_0x4fe924(0x453)]['top']='2px',_0x390f01[_0x4fe924(0x453)][_0x4fe924(0x1a4)]=_0x4fe924(0x4e2),_0x390f01['style'][_0x4fe924(0x285)]=_0x4fe924(0x60f),_0x390f01['style'][_0x4fe924(0x423)]=_0x4fe924(0x1f0),_0x390f01[_0x4fe924(0x1d0)][_0x4fe924(0x33b)]=_0x18ef0b,_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x573)]?_0x390f01[_0x4fe924(0x58f)]=_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x573)]:_0x390f01[_0x4fe924(0x58f)]='Add\x20a\x20label',_0x390f01[_0x4fe924(0x239)]=function(_0x4190ff){var _0x2d3aa8=_0x4fe924,_0xfe8e4c=_0x4190ff['target'][_0x2d3aa8(0x58f)];_0x54a079[_0x2d3aa8(0x56b)][_0x4190ff[_0x2d3aa8(0x2b3)][_0x2d3aa8(0x1d0)][_0x2d3aa8(0x33b)]][_0x2d3aa8(0x573)]===![]&&(_0xfe8e4c='');window[_0x2d3aa8(0x5f1)]();var _0x41cc14=prompt(_0x2d3aa8(0x2ca),_0xfe8e4c);if(_0x41cc14!==null){_0x41cc14==''?(_0x41cc14=![],_0x4190ff[_0x2d3aa8(0x2b3)][_0x2d3aa8(0x58f)]=_0x2d3aa8(0x2f0)):_0x4190ff[_0x2d3aa8(0x2b3)]['innerText']=_0x41cc14;var _0x585153={};_0x585153[_0x2d3aa8(0x33b)]=_0x4190ff[_0x2d3aa8(0x2b3)][_0x2d3aa8(0x1d0)][_0x2d3aa8(0x33b)],_0x585153[_0x2d3aa8(0x373)]=!![],_0x585153[_0x2d3aa8(0x39c)]=_0x41cc14,_0x54a079[_0x2d3aa8(0x1bf)](_0x585153,_0x585153[_0x2d3aa8(0x33b)]);}});}}if(_0x4fe924(0x2da)in _0x594668[_0x4fe924(0x306)]){_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x2da)]=parseInt(_0x594668[_0x4fe924(0x306)][_0x4fe924(0x2da)])||0x0;if(_0x54a079[_0x4fe924(0x1f3)]){var _0x10f9c8=document['querySelectorAll'](_0x4fe924(0x50e)+_0x18ef0b+'\x22]');_0x10f9c8[0x0]&&(_0x10f9c8[0x0][_0x4fe924(0x58f)]=_0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['order']);}}else _0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x2da)]=0x0;if(_0x54a079[_0x4fe924(0x1f3)]){if('recording_audio_pipeline'in _0x594668[_0x4fe924(0x306)]){if(_0x594668[_0x4fe924(0x306)][_0x4fe924(0x4c9)]==![]){var _0x10f9c8=document['querySelectorAll'](_0x4fe924(0x2a1)+_0x18ef0b+'\x22]');_0x10f9c8[0x0]&&(_0x10f9c8[0x0]['disabled']=!![],_0x10f9c8[0x0][_0x4fe924(0x3cd)]=_0x4fe924(0x517));var _0x10f9c8=document[_0x4fe924(0x58b)](_0x4fe924(0x30c)+_0x18ef0b+'\x22]');_0x10f9c8[0x0]&&(_0x10f9c8[0x0][_0x4fe924(0x21a)]=!![],_0x10f9c8[0x0]['title']=_0x4fe924(0x517),_0x10f9c8[0x0][_0x4fe924(0x453)][_0x4fe924(0x48e)]=0.2);}}if(_0x4fe924(0x32b)in _0x594668[_0x4fe924(0x306)]){if(_0x4fe924(0x3ac)!=='CPVPK'){if(_0x594668[_0x4fe924(0x306)][_0x4fe924(0x32b)]!==![]){var _0x254c58=parseInt(_0x594668[_0x4fe924(0x306)]['recording_audio_gain'])||0x0;if(_0x254c58===0x0){var _0x10f9c8=document['querySelectorAll'](_0x4fe924(0x2a1)+_0x18ef0b+'\x22]');_0x10f9c8[0x0]&&(_0x10f9c8[0x0]['dataset'][_0x4fe924(0x39c)]=0x1,_0x10f9c8[0x0][_0x4fe924(0x327)]=_0x4fe924(0x202),_0x10f9c8[0x0][_0x4fe924(0x23a)][0x1][_0x4fe924(0x22f)]=_0x4fe924(0x5cb),_0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['directorMutedState']=0x1);}else{var _0x10f9c8=document[_0x4fe924(0x58b)](_0x4fe924(0x30c)+_0x18ef0b+'\x22]');_0x10f9c8[0x0]&&(_0x4fe924(0x55a)!==_0x4fe924(0x3ec)?(_0x10f9c8[0x0][_0x4fe924(0x39c)]=_0x254c58,_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x500)]=_0x254c58,remoteVolumeUI(_0x10f9c8[0x0])):_0x39a982[_0x4fe924(0x5d7)](_0x15ab88));}}}else _0x41bf8f[_0x4fe924(0x264)](_0x17f1d0(_0x5e4b09[_0x4fe924(0x5b0)]));}}_0x4fe924(0x3ae)in _0x594668[_0x4fe924(0x306)]&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x33e)]=_0x594668[_0x4fe924(0x306)][_0x4fe924(0x3ae)],_0x54a079[_0x4fe924(0x478)]===![]&&(_0x54a079[_0x4fe924(0x2aa)]&&(!_0x54a079['cleanOutput']&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['remoteMuteElement']?_0x54a079['rpcs'][_0x18ef0b]['remoteMuteState']?_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x393)][_0x4fe924(0x453)]['display']=_0x4fe924(0x57c):_0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x393)][_0x4fe924(0x453)][_0x4fe924(0x3a7)]=_0x4fe924(0x5b1):_0x4fe924(0x2b7)===_0x4fe924(0x2b7)?(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x393)]=getById('muteStateTemplate')[_0x4fe924(0x40c)](!![]),_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x393)]['id']='remoteMuteState_'+_0x18ef0b,_0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x33e)]?_0x54a079['rpcs'][_0x18ef0b]['remoteMuteElement'][_0x4fe924(0x453)][_0x4fe924(0x3a7)]=_0x4fe924(0x57c):_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x393)]['style']['display']=_0x4fe924(0x5b1),_0x56035e=!![]):_0x47be07(_0x25812c[_0x4fe924(0x326)][_0x42ee77])))));'directorSpeakerMuted'in _0x594668[_0x4fe924(0x306)]&&(_0x54a079[_0x4fe924(0x1f3)]&&(_0x594668[_0x4fe924(0x306)]['directorSpeakerMuted']&&updateRemoteSpeakerMute(_0x18ef0b)));_0x4fe924(0x591)in _0x594668['info']&&(_0x54a079[_0x4fe924(0x1f3)]&&(_0x594668[_0x4fe924(0x306)][_0x4fe924(0x591)]&&updateRemoteDisplayMute(_0x18ef0b)));_0x4fe924(0x461)in _0x594668[_0x4fe924(0x306)]&&(_0x4fe924(0x437)===_0x4fe924(0x191)?_0x54603d[_0x4fe924(0x3ad)]==!![]&&(_0x4d237e[_0x4fe924(0x33f)][_0x78cacc][_0x4fe924(0x484)]=!![]):_0x54a079[_0x4fe924(0x1f3)]?_0x594668[_0x4fe924(0x306)][_0x4fe924(0x461)]&&updateDirectorVideoMute(_0x18ef0b):(_0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x461)]=_0x594668['info'][_0x4fe924(0x461)],_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x461)]&&(_0x4fe924(0x41e)===_0x4fe924(0x41e)?_0x18ef0b in _0x54a079[_0x4fe924(0x56b)]&&_0x54a079[_0x4fe924(0x40a)](0x0,_0x18ef0b):(_0x3bc810(_0x4fe924(0x1c3)),_0x1cca80[_0x4fe924(0x56b)][_0x21543a[_0x4fe924(0x33b)]][_0x4fe924(0x518)](),delete _0x227c59['rpcs'][_0x4a635e[_0x4fe924(0x33b)]]))));if(_0x4fe924(0x22d)in _0x594668[_0x4fe924(0x306)]){if(_0x4fe924(0x475)!==_0x4fe924(0x522))_0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['videoMuted']=_0x594668['info'][_0x4fe924(0x22d)],_0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['videoMuted']&&(_0x54a079['director']&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x41a)][_0x4fe924(0x453)][_0x4fe924(0x3a7)]=_0x4fe924(0x57c)));else{if(_0x36b2cf[_0x4fe924(0x33b)]!==_0x2e0179['UUID'])return;}}if(_0x18ef0b===_0x54a079[_0x4fe924(0x4a1)])try{_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x40b)][_0x4fe924(0x306)][_0x4fe924(0x1f3)]=!![];}catch(_0x32d329){}mustUpdateuserList=!![];}if(_0x4fe924(0x53c)in _0x594668){if(_0x54a079[_0x4fe924(0x4a1)]===_0x18ef0b){if('EwHeI'!=='EwHeI'){if(typeof _0xbd3711['ws']!==_0x4fe924(0x27e)||_0x2b074b['ws'][_0x4fe924(0x25f)]!==0x1)_0x1f4d42(_0x45e74d,_0x4fe924(0x598)),_0x3fa195[_0x4fe924(0x19b)]['push'](_0x3fcf4a);else{var _0x202c3b=_0x428933[_0x4fe924(0x242)](_0x11e658);if(_0x202c3b[_0x4fe924(0x44a)]>0x3a98){_0x37304a(_0x4fe924(0x5bb));return;}_0x5b0805['ws'][_0x4fe924(0x526)](_0x202c3b);}}else{if(_0x4fe924(0x281)in _0x594668['directorSettings']){if(_0x4fe924(0x5bc)!=='izOme'){_0x208ecd(_0x4fe924(0x5bb));return;}else _0x54a079[_0x4fe924(0x281)]=parseInt(_0x594668[_0x4fe924(0x53c)][_0x4fe924(0x281)])||0x0,_0x56035e=!![];}if(_0x594668[_0x4fe924(0x53c)][_0x4fe924(0x2a7)]){if(_0x594668[_0x4fe924(0x53c)][_0x4fe924(0x2a7)]===_0x54a079[_0x4fe924(0x249)])_0x54a079[_0x4fe924(0x3fc)]=!![];else for(var _0x2dce6b in _0x54a079[_0x4fe924(0x56b)]){if(_0x54a079['rpcs'][_0x2dce6b][_0x4fe924(0x249)]===_0x594668[_0x4fe924(0x53c)][_0x4fe924(0x2a7)]){_0x54a079['infocus']=_0x2dce6b;break;}}_0x56035e=!![],mustUpdateuserList=!![];}if(_0x54a079[_0x4fe924(0x478)]!==![]){if('GxJbr'===_0x4fe924(0x421)){if(_0x594668[_0x4fe924(0x53c)][_0x4fe924(0x478)])for(var _0x2dce6b in 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_0x594668){log(_0x594668),_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x33e)]=_0x594668['muteState'],_0x54a079[_0x4fe924(0x40a)](![],_0x18ef0b);_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x40b)][_0x4fe924(0x306)]&&(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x40b)]['info'][_0x4fe924(0x3ae)]=_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x33e)]);if(_0x54a079[_0x4fe924(0x478)]===![]){if(_0x54a079[_0x4fe924(0x2aa)]){if(_0x4fe924(0x3e4)!==_0x4fe924(0x3e4))_0x2167d3(_0x4fe924(0x583),'timeout'),_0x207135(_0x4fe924(0x200)),!_0x33ec20[_0x4fe924(0x2f6)]&&(!_0x451f91[_0x4fe924(0x513)]&&_0x22e2c2(function(){var _0x39107e=_0x4fe924;_0x19f9e9(_0x39107e(0x19c));},0x1));else{if(!_0x54a079[_0x4fe924(0x2f6)]){if(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x393)])_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x33e)]?_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x393)]['style'][_0x4fe924(0x3a7)]=_0x4fe924(0x57c):_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x393)][_0x4fe924(0x453)]['display']=_0x4fe924(0x5b1);else{_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x393)]=getById('muteStateTemplate')[_0x4fe924(0x40c)](!![]),_0x54a079['rpcs'][_0x18ef0b][_0x4fe924(0x393)]['id']=_0x4fe924(0x55f)+_0x18ef0b;if(_0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x33e)]){if('xamfp'!==_0x4fe924(0x577))return;else _0x54a079[_0x4fe924(0x56b)][_0x18ef0b]['remoteMuteElement']['style'][_0x4fe924(0x3a7)]=_0x4fe924(0x57c);}else _0x54a079[_0x4fe924(0x56b)][_0x18ef0b][_0x4fe924(0x393)][_0x4fe924(0x453)][_0x4fe924(0x3a7)]='none';_0x56035e=!![];}mustUpdateuserList=!![];}}}}}if(_0x4fe924(0x331)in _0x594668){var _0x47ad1f=getChromeVersion();_0x47ad1f&&(_0x47ad1f<0x50&&(_0x56035e=!![]));}'videoMuted'in 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mustUpdateuserList&&updateUserList();},_0x54a079['rpcs'][_0x18ef0b]['receiveChannel'][_0x5df1d1(0x30a)]=()=>{var _0x19e5eb=_0x5df1d1;if('JPSEM'===_0x19e5eb(0x40d))_0x516252(_0x29738c);else{log(_0x19e5eb(0x1a3));try{var _0x5d5c82=_0x54a079[_0x19e5eb(0x56b)][_0x18ef0b][_0x19e5eb(0x249)];}catch(_0x2ef812){}try{if(_0x19e5eb(0x447)!=='YCZUW')_0x54a079[_0x19e5eb(0x56b)][_0x18ef0b][_0x19e5eb(0x518)]();else return;}catch(_0x5f099a){errorlog(_0x19e5eb(0x48b));}try{_0x54a079['rpcs'][_0x18ef0b][_0x19e5eb(0x25b)]&&(_0x19e5eb(0x3e7)===_0x19e5eb(0x2c1)?_0x3babdc[_0x19e5eb(0x2f5)]==![]&&(!_0x9c40db['cleanOutput']&&(_0x120b48(function(){var _0x2f8534=_0x19e5eb;_0x519d90(_0x2f8534(0x385));},0x1),_0x212347(_0x19e5eb(0x56e))[_0x19e5eb(0x22f)]=_0x19e5eb(0x41b))):_0x54a079['rpcs'][_0x18ef0b][_0x19e5eb(0x25b)]['getTracks']()[_0x19e5eb(0x59b)](function(_0x290dac){var _0x4f0944=_0x19e5eb;_0x290dac['stop'](),log(_0x4f0944(0x501));}));}catch(_0x1a04ab){}if(_0x54a079['director'])try{_0x19e5eb(0x2cd)in 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_0x54a079[_0x19e5eb(0x326)]&&(log(_0x19e5eb(0x564)+_0x5d5c82),clearTimeout(_0x54a079[_0x19e5eb(0x326)][_0x5d5c82])),_0x54a079[_0x19e5eb(0x326)][_0x5d5c82]=setTimeout(function(_0x4675b5){var _0xd1366b=_0x19e5eb;delete _0x54a079[_0xd1366b(0x326)][_0x4675b5],log('watchTimeoutList2:'+_0x4675b5),_0x54a079[_0xd1366b(0x303)](_0x4675b5);},_0x54a079[_0x19e5eb(0x463)],_0x5d5c82);}catch(_0x341952){'EoRoz'===_0x19e5eb(0x19d)?(_0x16b37e[_0x19e5eb(0x248)](),_0x25addf[_0x19e5eb(0x2b5)]['srcObject'][_0x19e5eb(0x611)](_0x4617d4),_0x4a7944(_0x19e5eb(0x549))):errorlog(_0x341952);}pokeIframeAPI(_0x19e5eb(0x527),![],_0x18ef0b),updateUserList();}};},_0x54a079[_0x29f09d(0x622)]=function(_0x34e290){var _0x2f2ffb=_0x29f09d;try{var _0x2a85de=_0x54a079['buffer']||0x0;_0x2a85de=parseFloat(_0x2a85de);if(_0x54a079[_0x2f2ffb(0x34c)]!==![]){var _0x34d9b0=_0x54a079[_0x2f2ffb(0x56b)][_0x34e290][_0x2f2ffb(0x4fa)]()[_0x2f2ffb(0x2bf)](),_0x5aa986=0x0;_0x34d9b0['forEach'](function(_0x27fe82){var _0x3b5e51=_0x2f2ffb;try{for(var _0x336807 in _0x54a079[_0x3b5e51(0x56b)][_0x34e290][_0x3b5e51(0x40b)]){if(typeof _0x54a079[_0x3b5e51(0x56b)][_0x34e290][_0x3b5e51(0x40b)][_0x336807]==_0x3b5e51(0x27e)&&_0x3b5e51(0x341)in _0x54a079[_0x3b5e51(0x56b)][_0x34e290][_0x3b5e51(0x40b)][_0x336807]&&_0x54a079[_0x3b5e51(0x56b)][_0x34e290][_0x3b5e51(0x40b)][_0x336807][_0x3b5e51(0x341)]===_0x27fe82[_0x3b5e51(0x5ca)]['id']&&'Buffer_Delay_in_ms'in _0x54a079[_0x3b5e51(0x56b)][_0x34e290][_0x3b5e51(0x40b)][_0x336807]){var _0x3b0dd1=0x0;_0x54a079[_0x3b5e51(0x56b)][_0x34e290]['stats'][_0x336807]['_sync_offset']?_0x3b0dd1=_0x54a079[_0x3b5e51(0x56b)][_0x34e290][_0x3b5e51(0x40b)][_0x336807]['_sync_offset']:_0x54a079['rpcs'][_0x34e290][_0x3b5e51(0x40b)][_0x336807][_0x3b5e51(0x608)]=0x0;_0x3b0dd1+=_0x2a85de-_0x54a079['rpcs'][_0x34e290][_0x3b5e51(0x40b)][_0x336807][_0x3b5e51(0x54a)];_0x3b0dd1>_0x2a85de&&(_0x3b0dd1=_0x2a85de);if(_0x54a079[_0x3b5e51(0x56b)][_0x34e290]['stats'][_0x336807][_0x3b5e51(0x203)]=='audio'){if(_0x27fe82[_0x3b5e51(0x5ca)]['id']in _0x54a079[_0x3b5e51(0x56b)][_0x34e290][_0x3b5e51(0x274)]){log(_0x3b5e51(0x2b8));var _0x56a5b2=_0x5aa986-_0x54a079[_0x3b5e51(0x56b)][_0x34e290][_0x3b5e51(0x40b)][_0x336807][_0x3b5e51(0x54a)]+_0x54a079[_0x3b5e51(0x190)];_0x56a5b2<0x0&&(_0x56a5b2=0x0),log(_0x3b5e51(0x245)+_0x56a5b2),log('audioCtx\x20:\x20'+_0x54a079[_0x3b5e51(0x61c)][_0x3b5e51(0x5be)]),_0x54a079[_0x3b5e51(0x56b)][_0x34e290][_0x3b5e51(0x274)][_0x27fe82[_0x3b5e51(0x5ca)]['id']]['delayTime']['setValueAtTime'](parseFloat(_0x56a5b2/0x3e8),_0x54a079[_0x3b5e51(0x61c)][_0x3b5e51(0x5be)]+0x1),_0x54a079[_0x3b5e51(0x56b)][_0x34e290][_0x3b5e51(0x40b)][_0x336807][_0x3b5e51(0x3a6)]=_0x56a5b2;}}else _0x54a079[_0x3b5e51(0x56b)][_0x34e290][_0x3b5e51(0x40b)][_0x336807][_0x3b5e51(0x203)]==_0x3b5e51(0x2bc)&&(_0x5aa986=_0x54a079[_0x3b5e51(0x56b)][_0x34e290][_0x3b5e51(0x40b)][_0x336807][_0x3b5e51(0x54a)],_0x3b0dd1<0x0&&(_0x3b0dd1=0x0),_0x54a079[_0x3b5e51(0x56b)][_0x34e290]['stats'][_0x336807]['_sync_offset']=_0x3b0dd1,_0x27fe82[_0x3b5e51(0x5d8)]=parseFloat(_0x3b0dd1/0x3e8));}}}catch(_0x392ead){errorlog(_0x392ead);}});}}catch(_0x2c0681){errorlog(_0x2f2ffb(0x63e));}},_0x54a079[_0x29f09d(0x587)]=function(_0x1714a5){var _0x43db3d=_0x29f09d;if(!(_0x1714a5 in _0x54a079[_0x43db3d(0x56b)]))return;try{_0x54a079[_0x43db3d(0x56b)][_0x1714a5][_0x43db3d(0x2b5)][_0x43db3d(0x3ab)]&&(log(_0x43db3d(0x466)),_0x54a079[_0x43db3d(0x56b)][_0x1714a5][_0x43db3d(0x2b5)][_0x43db3d(0x5ef)]()[_0x43db3d(0x3b1)](_0x24b00c=>{var _0x396294=_0x43db3d;log(_0x396294(0x291)),firstPlayTriggered=!![];})[_0x43db3d(0x35f)](warnlog));}catch(_0x45644e){};try{_0x54a079[_0x43db3d(0x56b)][_0x1714a5]['getStats']()['then'](function(_0x4aa9ab){var _0x557f55=_0x43db3d;setTimeout(_0x54a079[_0x557f55(0x587)],0xbb8,_0x1714a5),!_0x54a079[_0x557f55(0x56b)][_0x1714a5]['stats'][_0x557f55(0x635)]&&(_0x54a079['rpcs'][_0x1714a5]['stats']['RTC\x20Connection']={}),_0x4aa9ab[_0x557f55(0x59b)](_0x2a3efb=>{var _0x258d10=_0x557f55;if(_0x2a3efb[_0x258d10(0x2ac)]==_0x258d10(0x415)&&_0x2a3efb[_0x258d10(0x4c5)]==!![])_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x1e8)]=_0x2a3efb[_0x258d10(0x1de)],_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x2dc)]=_0x2a3efb[_0x258d10(0x2a6)],_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x1e0)]=_0x2a3efb[_0x258d10(0x4b2)]*0x3e8;else{if(_0x2a3efb['type']=='track'&&_0x2a3efb[_0x258d10(0x3d0)]==!![]){if(_0x2a3efb['id']in _0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)])_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb['id']][_0x258d10(0x341)]=_0x2a3efb[_0x258d10(0x3a1)],_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['id']][_0x258d10(0x54a)]=parseInt(0x3e8*(parseFloat(_0x2a3efb[_0x258d10(0x49c)])-_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb['id']][_0x258d10(0x258)])/(parseInt(_0x2a3efb[_0x258d10(0x1dc)])-_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['id']][_0x258d10(0x54e)]))||0x0,_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb['id']][_0x258d10(0x258)]=parseFloat(_0x2a3efb[_0x258d10(0x49c)])||0x0,_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb['id']][_0x258d10(0x54e)]=parseInt(_0x2a3efb['jitterBufferEmittedCount'])||0x0,_0x258d10(0x509)in _0x2a3efb&&(_0x258d10(0x5b3)in _0x2a3efb&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['id']]['Resolution']=_0x2a3efb[_0x258d10(0x509)]+_0x258d10(0x3ca)+_0x2a3efb[_0x258d10(0x5b3)],_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb['id']]['_frameWidth']=_0x2a3efb[_0x258d10(0x509)],_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb['id']][_0x258d10(0x289)]=_0x2a3efb[_0x258d10(0x5b3)]));else{var _0x11be90={};_0x11be90[_0x258d10(0x258)]=parseFloat(_0x2a3efb[_0x258d10(0x49c)])||0x0,_0x11be90[_0x258d10(0x54e)]=parseInt(_0x2a3efb[_0x258d10(0x1dc)])||0x0,_0x11be90[_0x258d10(0x54a)]=0x0,_0x11be90[_0x258d10(0x341)]=_0x2a3efb[_0x258d10(0x3a1)],_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['id']]=_0x11be90;}}else{if(_0x2a3efb[_0x258d10(0x2ac)]==_0x258d10(0x1e4)){if(_0x258d10(0x2dc)in _0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)]&&_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x258d10(0x635)][_0x258d10(0x2dc)]!=_0x2a3efb['id'])return;_0x258d10(0x350)in _0x2a3efb&&('ip'in _0x2a3efb&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x258d10(0x635)][_0x258d10(0x3ee)]=_0x2a3efb['ip']),_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x1b7)]=_0x2a3efb[_0x258d10(0x350)]),_0x258d10(0x417)in _0x2a3efb&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x1c4)]=_0x2a3efb[_0x258d10(0x417)]),_0x258d10(0x304)in _0x2a3efb&&(_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)]['RTC\x20Connection'][_0x258d10(0x5d0)]=_0x2a3efb[_0x258d10(0x304)]);}else{if(_0x2a3efb[_0x258d10(0x2ac)]==_0x258d10(0x4df)){if(_0x258d10(0x1e8)in _0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats']['RTC\x20Connection']&&_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x1e8)]!=_0x2a3efb['id']){if('mfseh'!==_0x258d10(0x508))return;else _0x4c5981['pcs'][_0x5ea33b][_0x258d10(0x1c0)]<_0x50eac0&&(_0x1388b0=_0x329e8a[_0x258d10(0x33f)][_0x2dac28][_0x258d10(0x1c0)]);}'relayProtocol'in _0x2a3efb&&('ip'in _0x2a3efb&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x258d10(0x635)]['local_relayIP']=_0x2a3efb['ip']),_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x258d10(0x635)][_0x258d10(0x352)]=_0x2a3efb[_0x258d10(0x350)]),_0x258d10(0x417)in _0x2a3efb&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x34f)]=_0x2a3efb[_0x258d10(0x417)]),_0x258d10(0x304)in _0x2a3efb&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x5d0)]=_0x2a3efb[_0x258d10(0x304)]);}else{if(_0x2a3efb[_0x258d10(0x2ac)]==_0x258d10(0x3e2))_0x258d10(0x403)in _0x2a3efb&&('_bytesReceived'in _0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)]&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x5c4)]&&(_0x2a3efb[_0x258d10(0x5b2)]&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x258d10(0x635)][_0x258d10(0x5a0)]=parseInt(0x8*(_0x2a3efb['bytesReceived']-_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x3d9)])/(_0x2a3efb['timestamp']-_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x5c4)]))))),_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x3d9)]=_0x2a3efb[_0x258d10(0x403)]),_0x258d10(0x5b2)in _0x2a3efb&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x5c4)]=_0x2a3efb[_0x258d10(0x5b2)],!_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x258d10(0x635)][_0x258d10(0x632)]?_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x632)]=_0x2a3efb[_0x258d10(0x5b2)]:'lWVdv'!=='yrzxN'?_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)]['RTC\x20Connection'][_0x258d10(0x5b7)]=parseInt((_0x2a3efb[_0x258d10(0x5b2)]-_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x635)][_0x258d10(0x632)])/0x258)/0x64:_0x5b07f0(_0x258d10(0x3d8)));else{if(_0x2a3efb[_0x258d10(0x2ac)]==_0x258d10(0x384)&&_0x258d10(0x3c5)in _0x2a3efb){_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['trackId']]=_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['trackId']]||{},_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['trackId']][_0x258d10(0x5aa)]=parseInt(0x8*(_0x2a3efb[_0x258d10(0x403)]-_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x45e)])/(_0x2a3efb[_0x258d10(0x5b2)]-_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb[_0x258d10(0x3c5)]]['_last_time'])),_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['trackId']][_0x258d10(0x45e)]=_0x2a3efb[_0x258d10(0x403)]||_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x45e)],_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x27d)]=_0x2a3efb['timestamp']||_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x27d)],_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x194)]=_0x2a3efb[_0x258d10(0x39a)],_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x4a4)]=_0x2a3efb[_0x258d10(0x3c5)];if(_0x2a3efb['mediaType']==_0x258d10(0x2bc)){_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['trackId']][_0x258d10(0x2ac)]=_0x258d10(0x3c6),_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x203)]=_0x258d10(0x2bc);if(_0x54a079['obsfix']&&_0x258d10(0x4d7)in _0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)]&&_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x4d7)]=='video/VP8'){_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb['trackId']][_0x258d10(0x259)]=_0x2a3efb[_0x258d10(0x36f)]-_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]]['keyFramesRequested_pli']||0x0,_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x5f3)]=_0x2a3efb['nackCount']-_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x5f7)]+_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb['trackId']][_0x258d10(0x5f3)]||0x0,log(_0x258d10(0x193));if(_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['trackId']][_0x258d10(0x259)]===0x0&&_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x5f3)]>=_0x54a079[_0x258d10(0x61f)])_0x54a079[_0x258d10(0x260)](_0x1714a5),_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x5f3)]=0x0,log(_0x258d10(0x33d));else _0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x259)]>0x0&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x5f3)]=0x0);}else{if(_0x54a079[_0x258d10(0x61f)]&&_0x258d10(0x4d7)in _0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)]&&_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x4d7)]==_0x258d10(0x5a3)){_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x259)]=_0x2a3efb[_0x258d10(0x36f)]-_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x4f9)]||0x0,_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['trackId']][_0x258d10(0x5f3)]=_0x2a3efb['nackCount']-_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x5f7)]+_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['trackId']][_0x258d10(0x5f3)]||0x0,log(_0x258d10(0x193));if(_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x259)]===0x0&&_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x5f3)]>=_0x54a079['obsfix']*0x4)_0x54a079['requestKeyframe'](_0x1714a5),_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]]['nackTrigger']=0x0,log(_0x258d10(0x33d));else _0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x259)]>0x0&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x5f3)]=0x0);}}_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['trackId']]['keyFramesRequested_pli']=_0x2a3efb[_0x258d10(0x36f)]||0x0,_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x5f7)]=_0x2a3efb[_0x258d10(0x4ae)]||0x0;if(_0x258d10(0x464)in _0x2a3efb)_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x2b6)]=parseInt(_0x2a3efb[_0x258d10(0x464)]);else{if(_0x258d10(0x38f)in _0x2a3efb&&_0x2a3efb['timestamp']){var _0x8ee0f8=0x0,_0x184e75=0x0;try{_0x8ee0f8=_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x2e7)],_0x184e75=_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x5c4)];}catch(_0x2ad867){}_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x2b6)]=parseInt(0xa*(_0x2a3efb[_0x258d10(0x38f)]-_0x8ee0f8)/(_0x2a3efb[_0x258d10(0x5b2)]/0x3e8-_0x184e75))/0xa,_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['trackId']]['_framesDecoded']=_0x2a3efb[_0x258d10(0x38f)],_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x5c4)]=_0x2a3efb[_0x258d10(0x5b2)]/0x3e8;}}}else _0x2a3efb[_0x258d10(0x2b1)]==_0x258d10(0x388)&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x2ac)]=_0x258d10(0x336),_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x203)]=_0x258d10(0x388),_0x258d10(0x2e5)in _0x2a3efb&&(_0x258d10(0x222)===_0x258d10(0x531)?_0x2ece3f=_0x21e905[_0x258d10(0x5c7)]:_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x5db)]=parseInt(parseFloat(_0x2a3efb[_0x258d10(0x2e5)])*0x2710)/0x2710));if(_0x258d10(0x4ea)in _0x2a3efb&&_0x258d10(0x429)in _0x2a3efb){if(!(_0x258d10(0x1c7)in _0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]])){if(_0x258d10(0x5b6)===_0x258d10(0x4d3)){var _0x173fee=_0x17556b(_0x713ce8['delay']/0x3e8)||0x0;_0x445348['micDelay']=_0x7926b3(_0x43dfcb[_0x258d10(0x27a)])||0x0;for(var _0x2b3c7d in _0x12ca61['webAudios']){_0x1de935[_0x258d10(0x302)][_0x2b3c7d][_0x258d10(0x1f5)][_0x258d10(0x204)][_0x258d10(0x279)](_0x173fee,_0x275006['webAudios'][_0x2b3c7d][_0x258d10(0x3c1)][_0x258d10(0x5be)]);}}else _0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x1c7)]=_0x2a3efb[_0x258d10(0x4ea)];}!('_packetsReceived'in _0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb[_0x258d10(0x3c5)]])&&(_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x395)]=_0x2a3efb[_0x258d10(0x429)]),!(_0x258d10(0x26d)in _0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]])&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x26d)]=0x0),_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x26d)]=_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['trackId']][_0x258d10(0x26d)]*0.35+0.65*((_0x2a3efb[_0x258d10(0x4ea)]-_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x1c7)])*0x64)/(_0x2a3efb[_0x258d10(0x429)]-_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x395)]+(_0x2a3efb[_0x258d10(0x4ea)]-_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb['trackId']][_0x258d10(0x1c7)]))||0x0,_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x395)]=_0x2a3efb[_0x258d10(0x429)],_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x2a3efb[_0x258d10(0x3c5)]][_0x258d10(0x1c7)]=_0x2a3efb[_0x258d10(0x4ea)];}}else _0x258d10(0x194)in _0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)]&&_0x2a3efb['id']==_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x194)]&&(_0x258d10(0x63a)in _0x2a3efb&&(_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)]['_codecIdTrackId']]?_0x54a079['rpcs'][_0x1714a5]['stats'][_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x258d10(0x4a4)]][_0x258d10(0x4d7)]=_0x2a3efb[_0x258d10(0x63a)]:(_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x258d10(0x4a4)]]={},_0x54a079[_0x258d10(0x56b)][_0x1714a5]['stats'][_0x54a079[_0x258d10(0x56b)][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x4a4)]][_0x258d10(0x4d7)]=_0x2a3efb[_0x258d10(0x63a)])),_0x258d10(0x5b3)in _0x2a3efb&&('frameWidth'in _0x2a3efb&&(_0x54a079['rpcs'][_0x1714a5][_0x258d10(0x40b)][_0x258d10(0x1dd)]=parseInt(_0x2a3efb['frameWidth'])+_0x258d10(0x3ca)+parseInt(_0x2a3efb[_0x258d10(0x5b3)]))));}}}}}}),_0x54a079['buffer']!==![]&&_0x54a079[_0x557f55(0x622)](_0x1714a5),setTimeout(function(){var _0x54d235=_0x557f55;_0x54a079['directorSpeakerMute'](),_0x54a079[_0x54d235(0x5e8)]();},0x0);});}catch(_0x3f3f23){_0x43db3d(0x51d)!==_0x43db3d(0x59c)?errorlog(_0x3f3f23):(_0x3c0e32[_0x43db3d(0x248)](),_0x18c644(_0x43db3d(0x501)));}},_0x54a079[_0x29f09d(0x56b)][_0x18ef0b]['ontrack']=_0x6f1bd2=>{var _0x22d526=_0x29f09d;log(_0x22d526(0x3da));_0x54a079[_0x22d526(0x34c)]!==![]&&_0x54a079['playoutdelay'](_0x18ef0b);_0x54a079['directorSpeakerMute'](),_0x54a079[_0x22d526(0x5e8)]();if(_0x6f1bd2[_0x22d526(0x2ef)]&&_0x6f1bd2['streams'][0x0]){if(_0x22d526(0x4aa)!=='aBKBk')_0x1e5f23['requestRateLimit'](0x0,_0x351e47);else{_0x6f1bd2[_0x22d526(0x2ef)][0x0][_0x22d526(0x566)]=function(_0x574c2f){var _0x2c347a=_0x22d526;try{warnlog(_0x2c347a(0x37c)),_0x54a079['rpcs'][_0x18ef0b][_0x2c347a(0x25b)]['getVideoTracks']()[_0x2c347a(0x59b)](_0x4e2a89=>{var _0x425d00=_0x2c347a;_0x4e2a89['id']==_0x574c2f[_0x425d00(0x5ca)]['id']&&_0x54a079['rpcs'][_0x18ef0b][_0x425d00(0x25b)][_0x425d00(0x611)](_0x4e2a89);}),_0x54a079[_0x2c347a(0x56b)][_0x18ef0b][_0x2c347a(0x2b5)][_0x2c347a(0x186)]=_0x54a079['rpcs'][_0x18ef0b][_0x2c347a(0x25b)],setTimeout(function(){updateMixer();},0x1);}catch(_0x355cba){}},_0x6f1bd2[_0x22d526(0x2ef)][0x0][_0x22d526(0x26e)]=function(_0x4f77bd){var _0x37e9d7=_0x22d526;if(_0x37e9d7(0x1fa)===_0x37e9d7(0x47c))_0x153b3a['decryptMessage'](_0x2bd563[_0x37e9d7(0x216)],_0x8a2241[_0x37e9d7(0x300)])[_0x37e9d7(0x3b1)](function(_0x3f3784){var _0x438c4e=_0x37e9d7;_0x580041['candidates']=_0xc77900['parse'](_0x3f3784);var _0x1f7f7d={};_0x1f7f7d['UUID']=_0x4ef9a6[_0x438c4e(0x33b)],_0x1f7f7d[_0x438c4e(0x2ac)]=_0x12c47d[_0x438c4e(0x2ac)];for(var _0x4fa094=0x0;_0x4fa094<_0x33d258['candidates']['length'];_0x4fa094++){_0x1f7f7d['candidate']=_0x42a90a[_0x438c4e(0x216)][_0x4fa094],_0x2ccd05[_0x438c4e(0x34a)](_0x1f7f7d);}});else{try{warnlog(_0x37e9d7(0x25e)),_0x54a079[_0x37e9d7(0x56b)][_0x18ef0b][_0x37e9d7(0x25b)][_0x37e9d7(0x38c)]()[_0x37e9d7(0x59b)](_0x30b290=>{var _0x3464e2=_0x37e9d7;_0x30b290['id']==_0x4f77bd[_0x3464e2(0x5ca)]['id']&&_0x54a079[_0x3464e2(0x56b)][_0x18ef0b][_0x3464e2(0x25b)][_0x3464e2(0x611)](_0x30b290);}),_0x54a079['rpcs'][_0x18ef0b][_0x37e9d7(0x2b5)][_0x37e9d7(0x186)]=_0x54a079[_0x37e9d7(0x56b)][_0x18ef0b]['streamSrc'];}catch(_0x43509f){};}};var _0x134f76=_0x6f1bd2[_0x22d526(0x2ef)][0x0][_0x22d526(0x38c)]()[_0x22d526(0x44a)];_0x134f76>0x1&&errorlog('MORE\x20THAN\x20ONE\x20VIDEO\x20TRACK\x20IS\x20BEING\x20ADDED');}}if(_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x2b5)]){if(_0x6f1bd2[_0x22d526(0x2ef)]&&_0x6f1bd2['streams'][0x0]){if(!_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x25b)])_0x54a079['rpcs'][_0x18ef0b][_0x22d526(0x25b)]=_0x6f1bd2[_0x22d526(0x2ef)][0x0];else{var _0x5794b7=![];_0x6f1bd2[_0x22d526(0x2ef)][0x0]['getTracks']()[_0x22d526(0x59b)](_0x457fea=>{var _0x830961=_0x22d526;'ELEDQ'!=='ihXHh'?(_0x5794b7&&(_0x830961(0x325)===_0x830961(0x325)?(warnlog(_0x830961(0x44f)),warnlog(_0x457fea)):_0x4d886c(_0x5bc130[_0x830961(0x555)],0x3e8,_0x5446ef,0x7d00,0x0)),_0x457fea[_0x830961(0x237)]==_0x830961(0x2bc)&&(_0x54a079['rpcs'][_0x18ef0b]['streamSrc'][_0x830961(0x38c)]()[_0x830961(0x59b)](_0x3a997f=>{var _0x5318b5=_0x830961;_0x54a079[_0x5318b5(0x56b)][_0x18ef0b][_0x5318b5(0x25b)][_0x5318b5(0x611)](_0x3a997f);}),_0x5794b7=!![]),log(_0x830961(0x494)),_0x54a079[_0x830961(0x56b)][_0x18ef0b][_0x830961(0x25b)][_0x830961(0x561)](_0x457fea)):_0x4d484d[_0x830961(0x56b)][_0xb518ae][_0x830961(0x25b)][_0x830961(0x611)](_0x47003c);});}try{if('QUNWp'!=='QUNWp'){if(_0x2c270b[_0x22d526(0x2c0)][_0x22d526(0x18b)](_0x7a122f[_0x22d526(0x249)])){}else return;}else _0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x2b5)][_0x22d526(0x186)]=_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x25b)];}catch(_0x40513a){errorlog('4722');}log(_0x22d526(0x5f5));}else{if(_0x6f1bd2[_0x22d526(0x5ca)]){warnlog(_0x22d526(0x4f0));if(!_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x25b)])_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x25b)]=new MediaStream();else{if(_0x22d526(0x496)!==_0x22d526(0x4a6))_0x6f1bd2[_0x22d526(0x5ca)][_0x22d526(0x237)]==_0x22d526(0x2bc)&&_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x25b)][_0x22d526(0x38c)]()[_0x22d526(0x59b)](_0x5522fc=>{var _0x3e0a95=_0x22d526;_0x54a079['rpcs'][_0x18ef0b]['streamSrc'][_0x3e0a95(0x611)](_0x5522fc);});else return;}_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x25b)]['addTrack'](_0x6f1bd2[_0x22d526(0x5ca)]);try{_0x22d526(0x277)===_0x22d526(0x277)?_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x2b5)][_0x22d526(0x186)]=_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x25b)]:_0x3d99fa[_0x22d526(0x56b)][_0x324d64][_0x22d526(0x2b5)][_0x22d526(0x186)]=_0x39e825[_0x22d526(0x56b)][_0xfe846c][_0x22d526(0x25b)];}catch(_0x5386b1){if(_0x22d526(0x2b9)!==_0x22d526(0x1ec))errorlog(_0x22d526(0x5e1));else{var _0x40d3a9=_0x5a1093[_0x22d526(0x45d)](_0x706ff8);return!_0x40d3a9&&(_0x3a74f7(_0x53797c+_0x22d526(0x4ca)),_0x40d3a9=_0x5b672a['createElement']('span')),_0x40d3a9;}}}else errorlog(_0x22d526(0x2a0));}setTimeout(function(){updateMixer();},0x1);}else{log(_0x22d526(0x1e3));var _0x575894=document[_0x22d526(0x4c1)](_0x22d526(0x2bc));_0x54a079['rpcs'][_0x18ef0b][_0x22d526(0x2b5)]=_0x575894,_0x575894[_0x22d526(0x1d0)]['UUID']=_0x18ef0b,_0x575894['id']=_0x22d526(0x365)+_0x18ef0b;_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x249)]&&(_0x575894[_0x22d526(0x1d0)][_0x22d526(0x24f)]=_0x54a079[_0x22d526(0x56b)][_0x18ef0b]['streamID']);setupIncomingVideoTracking(_0x575894,_0x18ef0b);if(_0x6f1bd2['streams']&&_0x6f1bd2[_0x22d526(0x2ef)][0x0]){var _0x2816ab=_0x6f1bd2[_0x22d526(0x2ef)][0x0];_0x54a079['rpcs'][_0x18ef0b][_0x22d526(0x25b)]=_0x6f1bd2[_0x22d526(0x2ef)][0x0],_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x2b5)][_0x22d526(0x186)]=_0x54a079[_0x22d526(0x56b)][_0x18ef0b]['streamSrc'],log(_0x22d526(0x5f5));}else _0x6f1bd2['track']&&(warnlog('EVENT\x20TRACK\x20instead\x20of\x20event\x20Stream'),!_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x25b)]&&(_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x25b)]=new MediaStream()),_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x25b)][_0x22d526(0x561)](_0x6f1bd2['track']),_0x54a079[_0x22d526(0x56b)][_0x18ef0b]['videoElement'][_0x22d526(0x186)]=_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x25b)]);if(_0x54a079['scale']){var _0x145fca={};_0x145fca[_0x22d526(0x247)]=parseInt(_0x54a079[_0x22d526(0x247)]),log(_0x145fca),_0x54a079[_0x22d526(0x1bf)](_0x145fca,_0x18ef0b);}}if(_0x54a079['audioEffects']===!![]){var _0x158d1d=function(_0x36638a){var _0x334844=_0x22d526;if(_0x334844(0x5ad)!==_0x334844(0x567)){var _0x306e95=function(_0x5e4554){var _0x3c186e=_0x334844;if(!_0x54a079[_0x3c186e(0x56b)][_0x5e4554]){if(_0x3c186e(0x460)===_0x3c186e(0x321))_0x282fe4(_0x3c186e(0x4eb));else return;}var _0x3f4ffc=_0x54a079['rpcs'][_0x5e4554]['videoElement'][_0x3c186e(0x3ae)];_0x54a079[_0x3c186e(0x56b)][_0x5e4554][_0x3c186e(0x2b5)][_0x3c186e(0x3ae)]=![],_0x54a079[_0x3c186e(0x56b)][_0x5e4554][_0x3c186e(0x2b5)][_0x3c186e(0x4d0)](_0x3c186e(0x5ef),_0x306e95);_0x54a079[_0x3c186e(0x61c)]['state']==_0x3c186e(0x26b)&&_0x54a079[_0x3c186e(0x61c)][_0x3c186e(0x54d)]();var _0x1afb04=_0x54a079['rpcs'][_0x5e4554]['streamSrc']['getAudioTracks']();if(_0x1afb04[_0x3c186e(0x44a)]){var _0x8d0337=_0x1afb04[0x0];_0x8d0337[_0x3c186e(0x573)]!=_0x3c186e(0x4b3)&&(_0x54a079[_0x3c186e(0x56b)][_0x5e4554]['videoElement'][_0x3c186e(0x186)]=addAudioPipeline(_0x54a079[_0x3c186e(0x56b)][_0x5e4554][_0x3c186e(0x25b)],_0x5e4554,_0x8d0337));}else _0x3c186e(0x375)!==_0x3c186e(0x4b7)?_0x54a079[_0x3c186e(0x56b)][_0x5e4554][_0x3c186e(0x2b5)]['srcObject']=_0x54a079[_0x3c186e(0x56b)][_0x5e4554][_0x3c186e(0x25b)]:_0x48cd50[_0x3c186e(0x306)][_0x3c186e(0x34b)]=![];_0x54a079[_0x3c186e(0x56b)][_0x5e4554][_0x3c186e(0x2b5)][_0x3c186e(0x3ae)]=_0x3f4ffc,delete _0x54a079['rpcs'][_0x5e4554][_0x3c186e(0x606)],_0x3c186e(0x606)in _0x54a079[_0x3c186e(0x56b)][_0x5e4554]&&errorlog(_0x3c186e(0x1a2));};return _0x306e95(_0x36638a);}else _0x402d09(_0x1bd1e8[_0x334844(0x46b)]);};_0x54a079[_0x22d526(0x61c)][_0x22d526(0x3ce)]==_0x22d526(0x483)&&(_0x54a079[_0x22d526(0x568)]=!![]);if(_0x22d526(0x606)in _0x54a079[_0x22d526(0x56b)][_0x18ef0b]){}else _0x54a079[_0x22d526(0x568)]==![]?_0x54a079[_0x22d526(0x56b)][_0x18ef0b]['eventPlayActive']=setInterval(function(_0x4f0acb){var _0x499045=_0x22d526;log(_0x499045(0x45c)),_0x54a079[_0x499045(0x56b)][_0x4f0acb]&&(_0x54a079[_0x499045(0x568)]&&(clearInterval(_0x54a079[_0x499045(0x56b)][_0x4f0acb][_0x499045(0x606)]),_0x54a079[_0x499045(0x56b)][_0x4f0acb][_0x499045(0x2b5)][_0x499045(0x605)](_0x499045(0x5ef),_0x158d1d(_0x4f0acb))));},0x3e8,_0x18ef0b):(!_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x606)]&&(_0x54a079[_0x22d526(0x56b)][_0x18ef0b][_0x22d526(0x606)]=!![]),_0x54a079[_0x22d526(0x56b)][_0x18ef0b]['videoElement'][_0x22d526(0x605)](_0x22d526(0x5ef),_0x158d1d(_0x18ef0b)));}_0x54a079['directorSpeakerMute'](),_0x54a079[_0x22d526(0x5e8)](),updateUserList();},log(_0x29f09d(0x5ea));},_0x54a079;}();
\ No newline at end of file
