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+@charset "UTF-8";/*!
+ * Bootstrap v5.1.3 (https://getbootstrap.com/)
+ * Copyright 2011-2021 The Bootstrap Authors
+ * Copyright 2011-2021 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+ */:root{--bs-blue:#0d6efd;--bs-indigo:#6610f2;--bs-purple:#6f42c1;--bs-pink:#d63384;--bs-red:#dc3545;--bs-orange:#fd7e14;--bs-yellow:#ffc107;--bs-green:#198754;--bs-teal:#20c997;--bs-cyan:#0dcaf0;--bs-white:#fff;--bs-gray:#6c757d;--bs-gray-dark:#343a40;--bs-gray-100:#f8f9fa;--bs-gray-200:#e9ecef;--bs-gray-300:#dee2e6;--bs-gray-400:#ced4da;--bs-gray-500:#adb5bd;--bs-gray-600:#6c757d;--bs-gray-700:#495057;--bs-gray-800:#343a40;--bs-gray-900:#212529;--bs-primary:#0d6efd;--bs-secondary:#6c757d;--bs-success:#198754;--bs-info:#0dcaf0;--bs-warning:#ffc107;--bs-danger:#dc3545;--bs-light:#f8f9fa;--bs-dark:#212529;--bs-primary-rgb:13,110,253;--bs-secondary-rgb:108,117,125;--bs-success-rgb:25,135,84;--bs-info-rgb:13,202,240;--bs-warning-rgb:255,193,7;--bs-danger-rgb:220,53,69;--bs-light-rgb:248,249,250;--bs-dark-rgb:33,37,41;--bs-white-rgb:255,255,255;--bs-black-rgb:0,0,0;--bs-body-color-rgb:33,37,41;--bs-body-bg-rgb:255,255,255;--bs-font-sans-serif:system-ui,-apple-system,"Segoe 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right:1.5rem!important;padding-left:1.5rem!important}.px-xxl-5{padding-right:3rem!important;padding-left:3rem!important}.py-xxl-0{padding-top:0!important;padding-bottom:0!important}.py-xxl-1{padding-top:.25rem!important;padding-bottom:.25rem!important}.py-xxl-2{padding-top:.5rem!important;padding-bottom:.5rem!important}.py-xxl-3{padding-top:1rem!important;padding-bottom:1rem!important}.py-xxl-4{padding-top:1.5rem!important;padding-bottom:1.5rem!important}.py-xxl-5{padding-top:3rem!important;padding-bottom:3rem!important}.pt-xxl-0{padding-top:0!important}.pt-xxl-1{padding-top:.25rem!important}.pt-xxl-2{padding-top:.5rem!important}.pt-xxl-3{padding-top:1rem!important}.pt-xxl-4{padding-top:1.5rem!important}.pt-xxl-5{padding-top:3rem!important}.pe-xxl-0{padding-right:0!important}.pe-xxl-1{padding-right:.25rem!important}.pe-xxl-2{padding-right:.5rem!important}.pe-xxl-3{padding-right:1rem!important}.pe-xxl-4{padding-right:1.5rem!important}.pe-xxl-5{padding-right:3rem!important}.pb-xxl-0{padding-bottom:0!important}.pb-xxl-1{padding-bottom:.25rem!important}.pb-xxl-2{padding-bottom:.5rem!important}.pb-xxl-3{padding-bottom:1rem!important}.pb-xxl-4{padding-bottom:1.5rem!important}.pb-xxl-5{padding-bottom:3rem!important}.ps-xxl-0{padding-left:0!important}.ps-xxl-1{padding-left:.25rem!important}.ps-xxl-2{padding-left:.5rem!important}.ps-xxl-3{padding-left:1rem!important}.ps-xxl-4{padding-left:1.5rem!important}.ps-xxl-5{padding-left:3rem!important}.text-xxl-start{text-align:left!important}.text-xxl-end{text-align:right!important}.text-xxl-center{text-align:center!important}}@media (min-width:1200px){.fs-1{font-size:2.5rem!important}.fs-2{font-size:2rem!important}.fs-3{font-size:1.75rem!important}.fs-4{font-size:1.5rem!important}}@media print{.d-print-inline{display:inline!important}.d-print-inline-block{display:inline-block!important}.d-print-block{display:block!important}.d-print-grid{display:grid!important}.d-print-table{display:table!important}.d-print-table-row{display:table-row!important}.d-print-table-cell{display:table-cell!important}.d-print-flex{display:flex!important}.d-print-inline-flex{display:inline-flex!important}.d-print-none{display:none!important}}
+/*# sourceMappingURL=bootstrap.min.css.map */
\ No newline at end of file
diff --git a/images/after.jpg b/images/after.jpg
new file mode 100644
index 0000000..d72ed34
--- /dev/null
+++ b/images/after.jpg
diff --git a/images/before.jpg b/images/before.jpg
new file mode 100644
index 0000000..78932f8
--- /dev/null
+++ b/images/before.jpg
diff --git a/index.html b/index.html
new file mode 100644
index 0000000..379dd92
--- /dev/null
+++ b/index.html
@@ -0,0 +1,348 @@
+<!DOCTYPE html><!-- updated index.html v2024-05-20 --><html><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><script src="js/tf-core.js"></script><script src="js/tf-converter.js"></script><script src="js/tf-backend-cpu.js"></script><script src="js/face-landmarks-detection.js"></script><script src="js/bootstrap.bundle.min.js"></script>
+	<link href="css/bootstrap.min.css" rel="stylesheet">
+        <title>Incel Solutions</title>
+
+        <style>
+            html, body, body > div {
+                height: 100%;
+            }
+
+            .loading {
+                display: flex;
+                align-items: center;
+            }
+            .loading > div {
+                flex-grow: 1;
+            }
+            .loading > .spinner-border {
+                height: 8em !important;
+                width: 8em !important;
+                flex-grow: 0;
+            }
+            img {
+                height: auto;
+                width: 25% !important;
+            }
+
+            .perfect {
+                color: springgreen;
+            }
+            .deviation-0 {
+                color: darkkhaki;
+            }
+            .deviation-1 {
+                color: darkorange;
+            }
+            .deviation-2 {
+                color: darksalmon;
+            }
+            .deviation-3 {
+                color: coral;
+            }
+            .deviation-4 {
+                color: orangered;
+            }
+      /* 👉 Paste the following rules here 👇 */
+table {
+    table-layout: auto;
+    width: 100%;
+}
+
+th, td {
+    padding: 0.5rem;
+    vertical-align: middle;
+    white-space: nowrap;
+}
+
+/* Column 1: checkbox (tightest fit) */
+th:first-child,
+td:first-child {
+    width: 1%;
+}
+
+/* Column 2: feature (expand just enough for longest label) */
+th:nth-child(2),
+td:nth-child(2) {
+    white-space: nowrap;
+}
+
+/* Column 3: rating (auto resize as needed) */
+th:nth-child(3),
+td:nth-child(3) {
+    white-space: nowrap;
+}
+
+/* Columns 4 & 5: measurement and ideal (fixed narrow width) */
+th:nth-child(4),
+td:nth-child(4),
+th:nth-child(5),
+td:nth-child(5) {
+    width: 120px;
+    text-align: center;
+}
+
+    .badge {
+        display: inline-block;
+        max-width: 100%;
+        white-space: nowrap;
+        text-overflow: ellipsis;
+    }
+</style>
+  
+    </head>
+    <body>
+        <div id="loading" class="loading" style="display: none;">
+            <div></div>
+            <div class="spinner-border">
+                <span class="visually-hidden">Loading...</span>
+            </div>
+            <div></div>
+        </div>
+
+        <div class="container">
+
+            <div class="row">
+                <div class="col-md">
+                    <label for="image-file">
+                        Choose an image to analyze
+                    </label>
+                    <input type="file" id="image-file" class="form-control">
+                </div>
+                <div class="col-md">
+                    <label for="image-url">
+                        or paste an URL
+                    </label>
+                    <input type="text" id="image-url" class="form-control">
+                </div>
+            </div>
+
+            <div class="row">
+                <div class="col">
+                    <br>
+                </div>
+            </div>
+
+<div class="container">
+    <table class="table table-bordered">
+        <thead>
+            <tr>
+                <th style="width: 50px;">
+                    <input type="checkbox" id="select-all" class="form-check-input">
+                </th>
+                <th>Feature</th>
+                <th>Rating</th>
+                <th>Measurement</th>
+                <th>Ideal</th>
+            </tr>
+        </thead>
+        
+        <tfoot>
+  <tr>
+    <td>
+      <div class="form-check form-switch">
+        <input class="form-check-input" type="checkbox" id="grading-toggle" checked="">
+      </div>
+    </td>
+    <td><strong>Total</strong></td>
+    <td id="total-score"></td>
+    <td id="total-breakdown"></td>
+    <td id="total-psl"></td>
+  </tr>
+</tfoot>
+        
+        
+        
+        <tbody>
+            <tr>
+                <td><input type="checkbox" id="toggle-midface-ratio" class="form-check-input" checked=""></td>
+                <td>Midface ratio</td>
+                <td id="assessment-midface-ratio"></td>
+                <td id="value-midface-ratio"></td>
+                <td id="ideal-midface-ratio"></td>
+            </tr>
+            <tr>
+                <td><input type="checkbox" id="toggle-facial-width-to-height-ratio" class="form-check-input" checked=""></td>
+                <td>Facial width to height ratio</td>
+                <td id="assessment-facial-width-to-height-ratio"></td>
+                <td id="value-facial-width-to-height-ratio"></td>
+                <td id="ideal-facial-width-to-height-ratio"></td>
+            </tr>
+            <tr>
+                <td><input type="checkbox" id="toggle-chin-to-philtrum-ratio" class="form-check-input" checked=""></td>
+                <td>Chin to philtrum ratio</td>
+                <td id="assessment-chin-to-philtrum-ratio"></td>
+                <td id="value-chin-to-philtrum-ratio"></td>
+                <td id="ideal-chin-to-philtrum-ratio"></td>
+            </tr>
+            <tr>
+                <td><input type="checkbox" id="toggle-canthal-tilt" class="form-check-input" checked=""></td>
+                <td>Canthal tilt</td>
+                <td id="assessment-canthal-tilt"></td>
+                <td id="value-canthal-tilt"></td>
+                <td id="ideal-canthal-tilt"></td>
+            </tr>
+            <tr>
+                <td><input type="checkbox" id="toggle-mouth-to-nose-ratio" class="form-check-input" checked=""></td>
+                <td>Mouth to nose ratio</td>
+                <td id="assessment-mouth-to-nose-ratio"></td>
+                <td id="value-mouth-to-nose-ratio"></td>
+                <td id="ideal-mouth-to-nose-ratio"></td>
+            </tr>
+            <tr>
+                <td><input type="checkbox" id="toggle-bigonial-width" class="form-check-input" checked=""></td>
+                <td>Bigonial width</td>
+                <td id="assessment-bigonial-width"></td>
+                <td id="value-bigonial-width"></td>
+                <td id="ideal-bigonial-width"></td>
+            </tr>
+            <tr>
+                <td><input type="checkbox" id="toggle-lip-ratio" class="form-check-input" checked=""></td>
+                <td>Lip ratio</td>
+                <td id="assessment-lip-ratio"></td>
+                <td id="value-lip-ratio"></td>
+                <td id="ideal-lip-ratio"></td>
+            </tr>
+            <tr>
+                <td><input type="checkbox" id="toggle-eye-separation-ratio" class="form-check-input" checked=""></td>
+                <td>Eye separation ratio</td>
+                <td id="assessment-eye-separation-ratio"></td>
+                <td id="value-eye-separation-ratio"></td>
+                <td id="ideal-eye-separation-ratio"></td>
+            </tr>
+            <tr>
+                <td><input type="checkbox" id="toggle-eye-to-mouth-angle" class="form-check-input" checked=""></td>
+                <td>Eye to mouth angle</td>
+                <td id="assessment-eye-to-mouth-angle"></td>
+                <td id="value-eye-to-mouth-angle"></td>
+                <td id="ideal-eye-to-mouth-angle"></td>
+            </tr>
+            <tr>
+                <td><input type="checkbox" id="toggle-lower-third-height" class="form-check-input" checked=""></td>
+                <td>Lower third height</td>
+                <td id="assessment-lower-third-height"></td>
+                <td id="value-lower-third-height"></td>
+                <td id="ideal-lower-third-height"></td>
+            </tr>
+            <tr>
+                <td><input type="checkbox" id="toggle-palpebral-fissure-length" class="form-check-input" checked=""></td>
+                <td>Palpebral fissure length</td>
+                <td id="assessment-palpebral-fissure-length"></td>
+                <td id="value-palpebral-fissure-length"></td>
+                <td id="ideal-palpebral-fissure-length"></td>
+            </tr>
+            <tr>
+                <td><input type="checkbox" id="toggle-eye-color" class="form-check-input" checked=""></td>
+                <td>Eye color</td>
+                <td id="assessment-eye-color"></td>
+                <td id="value-eye-color">
+                    <canvas height="0" width="0"></canvas>
+                    <canvas height="0" width="0"></canvas>
+                </td>
+                <td id="ideal-eye-color"></td>
+            </tr>
+        </tbody>
+        
+
+
+    </table>
+</div>
+            
+            <!-- JavaScript for Select All -->
+            <script>
+                // Select All Checkbox
+                const selectAllCheckbox = document.getElementById('select-all');
+            
+                // Get all checkboxes in the table body
+                const checkboxes = document.querySelectorAll('tbody input[type="checkbox"]');
+            
+                // Event Listener for Select All
+                selectAllCheckbox.addEventListener('change', function () {
+                    checkboxes.forEach(checkbox => {
+                        checkbox.checked = selectAllCheckbox.checked; // Toggle all checkboxes
+                    });
+                });
+            
+                // Update "Select All" if any checkbox is unchecked
+                checkboxes.forEach(checkbox => {
+                    checkbox.addEventListener('change', function () {
+                        selectAllCheckbox.checked = [...checkboxes].every(cb => cb.checked);
+                    });
+                });
+            </script>
+            
+                                                
+            <div id="introduction" class="row mt-3">
+                <div class="col text-center">
+                    <h3>$INCEL Technology</h3>
+                    <p>
+                        Introducing the latest advancement in incel technology: a full facial structure analysis completed in just a few seconds from a single picture. It's completely private and runs securely on your browser; no data is sent to our servers.
+                    </p>
+                    <p>
+                        This project has been funded by INCEL COIN, contract address <a href="https://dexscreener.com/solana/c1juapdfrwcufhzfbtltvaz7gvvwpgrbn1zyb4vwnrpn">GACL2mEL9BR4baJBbYWfNKa5mxBYXRdU4ZthedKcVkx1</a> and the <a href="https://open.spotify.com/show/0iv1GFQLGn2FNCd3UvGKuo">Vers and Lukas Podcast</a>
+                    </p>
+                    <p>There is no meme. I love you.</p>
+
+<p>If Vers or Lukas or og dev want me to take this down, just let me know. I just missed the site :)</p>
+<p>
+                        
+</p><div class="container d-flex justify-content-center my-4">
+  <table class="table table-bordered text-center" style="width: auto;">
+    <thead>
+      <tr>
+        <th>Dex Name</th>
+        <th>Link</th>
+      </tr>
+    </thead>
+    <tbody>
+      <tr>
+        <td>El Dorito</td>
+        <td>
+          <a href="https://app.eldorito.club/swap?in=sol.sol&out=sol.incel-gacl2mel9br4bajbbywfnka5mxbyxrdu4zthedkcvkx1" target="_blank">
+            Visit El Dorito
+          </a>
+        </td>
+      </tr>
+      <tr>
+        <td>Jupiter</td>
+        <td>
+          <a href="https://jup.ag/swap/SOL-GACL2mEL9BR4baJBbYWfNKa5mxBYXRdU4ZthedKcVkx1" target="_blank">
+            Visit Jupiter
+          </a>
+        </td>
+      </tr>
+    </tbody>
+  </table>
+</div>
+  
+<p></p>
+
+<p><img class="img-thumbnail" src="images/before.jpg"> <span class="fs-4"> � </span> <img class="img-thumbnail" src="images/after.jpg"></p>
+</div>
+</div>
+
+<div class="row d-none mt-5" id="analyzing">
+<div class="col text-center">
+<div>
+<div class="loading">
+<div></div>
+
+<div class="spinner-border"></div>
+
+<div></div>
+</div>
+
+<div class="mt-3 fs-5">
+<div id="analyzing-status"></div>
+</div>
+</div>
+</div>
+</div>
+
+<div class="row" id="render">
+<div class="col"><canvas id="canvas"></canvas></div>
+</div>
+</div>
+<script src="js/analysis.js"></script>
+<script src="js/index.js"></script>
+<script>(function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9a774f9d0a75fff2',t:'MTc2NDY0MTY2OC4wMDAwMDA='};var a=document.createElement('script');a.nonce='';a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();</script>
+</body></html>
\ No newline at end of file
diff --git a/js/analysis.js b/js/analysis.js
new file mode 100644
index 0000000..fd78855
--- /dev/null
+++ b/js/analysis.js
@@ -0,0 +1,673 @@
+function analyseCriteria(face) {
+    let points = {
+        leftIris: face.annotations.rightEyeIris[0],
+        rightIris: face.annotations.leftEyeIris[0],
+        leftLateralCanthus: face.annotations.rightEyeLower1[0],
+        leftMedialCanthus: face.annotations.rightEyeLower1[7],
+        rightLateralCanthus: face.annotations.leftEyeLower1[0],
+        rightMedialCanthus: face.annotations.leftEyeLower1[7],
+        leftEyeUpper: face.annotations.rightEyeUpper0[4],
+        leftEyeLower: face.annotations.rightEyeLower0[4],
+        rightEyeUpper: face.annotations.leftEyeUpper0[4],
+        rightEyeLower: face.annotations.leftEyeLower0[4],
+        leftEyebrow: face.annotations.rightEyebrowUpper[6],
+        rightEyebrow: face.annotations.leftEyebrowUpper[6],
+        leftZygo: face.annotations.silhouette[28],
+        rightZygo: face.annotations.silhouette[8],
+        noseBottom: face.annotations.noseBottom[0],
+        leftNoseCorner: face.annotations.noseRightCorner[0],
+        rightNoseCorner: face.annotations.noseLeftCorner[0],
+        leftCupidBow: face.annotations.lipsUpperOuter[4],
+        lipSeparation: face.annotations.lipsUpperInner[5],
+        rightCupidBow: face.annotations.lipsUpperOuter[6],
+        leftLipCorner: face.annotations.lipsUpperOuter[0],
+        rightLipCorner: face.annotations.lipsUpperOuter[10],
+        lowerLip: face.annotations.lipsLowerOuter[4],
+        upperLip: face.annotations.lipsUpperOuter[5],
+        leftGonial: face.annotations.silhouette[24],
+        rightGonial: face.annotations.silhouette[12],
+        chinLeft: face.annotations.silhouette[19],
+        chinTip: face.annotations.silhouette[18],
+        chinRight: face.annotations.silhouette[17],
+    };
+    return [points, {
+        midfaceRatio: new MidfaceRatio(face, points),
+        facialWidthToHeightRatio: new FacialWidthToHeightRatio(face, points),
+        chinToPhiltrumRatio: new ChinToPhiltrumRatio(face, points),
+        canthalTilt: new CanthalTilt(face, points),
+        mouthToNoseRatio: new MouthToNoseRatio(face, points),
+        bigonialWidth: new BigonialWidth(face, points),
+        lipRatio: new LipRatio(face, points),
+        eyeSeparationRatio: new EyeSeparationRatio(face, points),
+        eyeToMouthAngle: new EyeToMouthAngle(face, points),
+        lowerThirdHeight: new LowerThirdHeight(face, points),
+        palpebralFissureLength: new PalpebralFissureLength(face, points),
+        eyeColor: new EyeColor(face, points),
+    }];
+}
+
+async function setupDatabase() {
+    return await fetch("database.json")
+  .then(res => res.text())
+  .then(text => {
+    console.log("✅ database.json loaded");
+    return JSON.parse(text);
+  })
+  .catch(err => {
+    console.error("❌ Failed to load or parse database.json:", err);
+    return { entries: {} };
+  });
+
+}
+
+class Criteria {
+    constructor(face, points) {
+        this.face = face;
+        this.points = points;
+
+        for(let i in points) {
+            if (points.hasOwnProperty(i)) {
+                Object.defineProperty(this, i, { get: () => this.points[i] });
+            }
+        }
+
+        return this;
+    }
+
+    createPoint(name, value) {
+        this.points[name] = value;
+        Object.defineProperty(this, name, { get: () => this.points[name] });
+    }
+
+    calculate() {
+        /* abstract */
+    }
+
+    render() {
+        /* abstract */
+    }
+
+    ideal() {
+        /* abstract */
+    }
+
+    assess() {
+        /* abstract */
+    }
+
+    draw(ctx) {
+        /* abstract */
+    }
+
+    necessaryPoints() {
+        /* abstract */
+    }
+}
+
+class MidfaceRatio extends Criteria {
+    constructor(face, points) {
+        super(face, points);
+
+        let bottomLine = Fn.fromTwoPoints(this.leftCupidBow, this.rightCupidBow);
+        let leftLine = bottomLine.perpendicular(this.leftIris);
+        let rightLine = bottomLine.perpendicular(this.rightIris);
+        this.createPoint("bottomLeftMidface", bottomLine.intersect(leftLine));
+        this.createPoint("bottomRightMidface", bottomLine.intersect(rightLine));
+    }
+
+    calculate() {
+        this.ratio = ((distance(this.leftIris, this.rightIris) / distance(this.leftIris, this.bottomLeftMidface)) +
+            (distance(this.leftIris, this.rightIris) / distance(this.rightIris, this.bottomRightMidface))) / 2;
+    }
+
+    render() {
+        return `${round(this.ratio, 2)}`;
+    }
+
+    ideal() {
+        return `${database.entries.midfaceRatio.idealLower} to ${database.entries.midfaceRatio.idealUpper}`;
+    }
+
+    assess() {
+        let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.midfaceRatio;
+        return assess(this.ratio, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh);
+    }
+
+    draw(ctx) {
+        draw(ctx, "red", [this.leftIris, this.rightIris, this.bottomRightMidface, this.bottomLeftMidface]);
+    }
+
+    necessaryPoints() {
+        return ["leftIris", "rightIris", "bottomLeftMidface", "bottomRightMidface"];
+    }
+}
+
+class FacialWidthToHeightRatio extends Criteria {
+    constructor(face, points) {
+        super(face, points);
+
+        let topLine = Fn.fromTwoPoints(this.leftEyeUpper, this.rightEyeUpper);
+        let bottomLine = Fn.fromTwoPoints(this.leftCupidBow, this.rightCupidBow);
+        let leftLine = topLine.perpendicular(this.leftZygo);
+        let rightLine = topLine.perpendicular(this.rightZygo);
+        this.createPoint("topLeft", leftLine.intersect(topLine));
+        this.createPoint("topRight", rightLine.intersect(topLine));
+        this.createPoint("bottomLeft", leftLine.intersect(bottomLine));
+        this.createPoint("bottomRight", rightLine.intersect(bottomLine));
+    }
+
+    calculate() {
+        this.ratio = (((distance(this.topLeft, this.topRight) / distance(this.topLeft, this.bottomLeft))
+            + (distance(this.bottomLeft, this.bottomRight) / distance(this.topRight, this.bottomRight))) / 2);
+    }
+
+    render() {
+        return `${round(this.ratio, 2)}`;
+    }
+
+    ideal() {
+        return `more than ${database.entries.facialWidthToHeightRatio.idealLower}`;
+    }
+
+    assess() {
+        let { idealLower, deviation, deviatingLow } = database.entries.facialWidthToHeightRatio;
+        return assess(this.ratio, idealLower, undefined, deviation, deviatingLow, undefined);
+    }
+
+    draw(ctx) {
+        draw(ctx, "lightblue", [this.topLeft, this.topRight, this.bottomRight, this.bottomLeft])
+    }
+
+    necessaryPoints() {
+        return ["topLeft", "topRight", "bottomLeft", "bottomRight"];
+    }
+}
+
+class ChinToPhiltrumRatio extends Criteria {
+    calculate() {
+        this.ratio = distance(this.chinTip, this.lowerLip) / distance(this.upperLip, this.noseBottom);
+    }
+
+    render() {
+        return `${round(this.ratio, 2)}`;
+    }
+
+    ideal() {
+        return `${database.entries.chinToPhiltrumRatio.idealLower} to ${database.entries.chinToPhiltrumRatio.idealUpper}`;
+    }
+
+    assess() {
+        let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.chinToPhiltrumRatio;
+        return assess(this.ratio, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh);
+    }
+
+    draw(ctx) {
+        draw(ctx, "blue", [this.chinTip, this.lowerLip]);
+        draw(ctx, "blue", [this.upperLip, this.noseBottom]);
+    }
+
+    necessaryPoints() {
+        return ["chinTip", "lowerLip", "upperLip", "noseBottom"];
+    }
+}
+
+class CanthalTilt extends Criteria {
+    calculate() {
+        let line = [this.rightZygo[0] - this.leftZygo[0], this.rightZygo[1] - this.leftZygo[1]];
+        let lineFn = Fn.fromTwoPoints(this.rightZygo, this.leftZygo);
+        let left = [this.leftLateralCanthus[0] - this.leftMedialCanthus[0], this.leftLateralCanthus[1] - this.leftMedialCanthus[1]];
+        let right = [this.rightLateralCanthus[0] - this.rightMedialCanthus[0], this.rightLateralCanthus[1] - this.rightMedialCanthus[1]];
+        let pointOnLeftLine = lineFn.getY(this.leftMedialCanthus[0]) + left[1];
+        let pointOnRightLine = lineFn.getY(this.rightMedialCanthus[0]) + right[1];
+        this.leftCanthalTilt = Math.acos(
+            Math.abs((-1) * (line[0] * left[0] + line[1] * left[1]))
+            /
+            (Math.sqrt(line[0] ** 2 + line[1] ** 2) * Math.sqrt(left[0] ** 2 + left[1] ** 2))
+        ) * (180 / Math.PI) * (lineFn.getY(this.leftLateralCanthus[0]) - pointOnLeftLine > 0 ? 1 : -1);
+        this.rightCanthalTilt = Math.acos(
+            Math.abs(line[0] * right[0] + line[1] * right[1])
+            /
+            (Math.sqrt(line[0] ** 2 + line[1] ** 2) * Math.sqrt(right[0] ** 2 + right[1] ** 2))
+        ) * (180 / Math.PI) * (lineFn.getY(this.rightLateralCanthus[0]) - pointOnRightLine > 0 ? 1 : -1);
+    }
+
+    render() {
+        return `left ${round(this.rightCanthalTilt, 0)}°, right ${round(this.leftCanthalTilt, 0)}°`;
+    }
+
+    ideal() {
+        return `more than ${database.entries.canthalTilt.idealLower}`;
+    }
+
+    assess() {
+        let { idealLower, deviation, deviatingLow } = database.entries.canthalTilt;
+        return assess((this.leftCanthalTilt + this.rightCanthalTilt) / 2, idealLower, undefined, deviation, deviatingLow, undefined);
+    }
+
+    draw(ctx) {
+        draw(ctx, "pink", [this.leftLateralCanthus, this.leftMedialCanthus]);
+        draw(ctx, "pink", [this.rightLateralCanthus, this.rightMedialCanthus]);
+    }
+
+    necessaryPoints() {
+        return ["leftLateralCanthus", "leftMedialCanthus", "rightLateralCanthus", "rightMedialCanthus"];
+    }
+}
+
+class MouthToNoseRatio extends Criteria {
+    calculate() {
+        this.ratio = distance(this.leftLipCorner, this.rightLipCorner) / distance(this.leftNoseCorner, this.rightNoseCorner);
+    }
+
+    render() {
+        return `${round(this.ratio, 2)}`;
+    }
+
+    ideal() {
+        return `${database.entries.mouthToNoseRatio.idealLower} to ${database.entries.mouthToNoseRatio.idealUpper}`;
+    }
+
+    assess() {
+        let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.mouthToNoseRatio;
+        return assess(this.ratio, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh);
+    }
+
+    draw(ctx) {
+        draw(ctx, "purple", [this.leftLipCorner, this.rightLipCorner]);
+        draw(ctx, "purple", [this.leftNoseCorner, this.rightNoseCorner]);
+    }
+
+    necessaryPoints() {
+        return ["leftLipCorner", "rightLipCorner", "leftNoseCorner", "rightNoseCorner"];
+    }
+}
+
+class BigonialWidth extends Criteria {
+    calculate() {
+        this.ratio = distance(this.leftZygo, this.rightZygo) / distance(this.leftGonial, this.rightGonial);
+    }
+
+    render() {
+        return `${round(this.ratio, 2)}`;
+    }
+
+    ideal() {
+        return `${database.entries.bigonialWidth.idealLower} to ${database.entries.bigonialWidth.idealUpper}`;
+    }
+
+    assess() {
+        let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.bigonialWidth;
+        return assess(this.ratio, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh);
+    }
+
+    draw(ctx) {
+        draw(ctx, "gold", [this.leftGonial, this.rightGonial]);
+        draw(ctx, "gold", [this.leftZygo, this.rightZygo]);
+    }
+
+    necessaryPoints() {
+        return ["leftZygo", "rightZygo", "leftGonial", "rightGonial"];
+    }
+}
+
+class LipRatio extends Criteria {
+    constructor(face, points) {
+        super(face, points);
+
+        let topLip = Fn.fromTwoPoints(this.leftCupidBow, this.rightCupidBow);
+        let lowerLip = topLip.parallel(this.lowerLip);
+        this.createPoint("upperLipEnd", topLip.intersect(topLip.perpendicular(this.lipSeparation)));
+        this.createPoint("lowerLipEnd", lowerLip.intersect(lowerLip.perpendicular(this.lipSeparation)));
+    }
+
+    calculate() {
+        this.ratio = distance(this.lowerLipEnd, this.lipSeparation) / distance(this.upperLipEnd, this.lipSeparation);
+    }
+
+    render() {
+        return `${round(this.ratio, 2)}`;
+    }
+
+    ideal() {
+        return `${database.entries.lipRatio.idealLower} to ${database.entries.lipRatio.idealUpper}`;
+    }
+
+    assess() {
+        let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.lipRatio;
+        return assess(this.ratio, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh);
+    }
+
+    draw(ctx) {
+        draw(ctx, "lightgreen", [this.upperLipEnd, this.lipSeparation]);
+        draw(ctx, "lightgreen", [this.lipSeparation, this.lowerLipEnd]);
+    }
+
+    necessaryPoints() {
+        return ["upperLipEnd", "lowerLipEnd", "lipSeparation"];
+    }
+}
+
+class EyeSeparationRatio extends Criteria {
+    calculate() {
+        this.ratio = distance(this.leftIris, this.rightIris) / distance(this.leftZygo, this.rightZygo);
+    }
+
+    render() {
+        return `${round(this.ratio, 2)}`;
+    }
+
+    ideal() {
+        return `${database.entries.eyeSeparationRatio.idealLower} to ${database.entries.eyeSeparationRatio.idealUpper}`;
+    }
+
+    assess() {
+        let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.eyeSeparationRatio;
+        return assess(this.ratio, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh);
+    }
+
+    draw(ctx) {
+        draw(ctx, "orange", [this.leftIris, this.rightIris]);
+        draw(ctx, "orange", [this.leftZygo, this.rightZygo]);
+    }
+
+    necessaryPoints() {
+        return ["leftIris", "rightIris", "leftZygo", "rightZygo"];
+    }
+}
+
+class EyeToMouthAngle extends Criteria {
+    calculate() {
+        let a = [this.leftIris[0] - this.lipSeparation[0], this.leftIris[1] - this.lipSeparation[1]];
+        let b = [this.rightIris[0] - this.lipSeparation[0], this.rightIris[1] - this.lipSeparation[1]];
+        this.angle = Math.acos(
+            (a[0] * b[0] + a[1] * b[1])
+            /
+            (Math.sqrt(a[0] ** 2 + a[1] ** 2) * Math.sqrt(b[0] ** 2 + b[1] ** 2))
+        ) * (180 / Math.PI);
+    }
+
+    render() {
+        return `${round(this.angle, 0)}°`;
+    }
+
+    ideal() {
+        return `${database.entries.eyeToMouthAngle.idealLower}° to ${database.entries.eyeToMouthAngle.idealUpper}°`;
+    }
+
+    assess() {
+        let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.eyeToMouthAngle;
+        return assess(this.angle, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh);
+    }
+
+    draw(ctx) {
+        draw(ctx, "brown", [this.leftIris, this.lipSeparation, this.rightIris, this.lipSeparation, this.leftIris]);
+    }
+
+    necessaryPoints() {
+        return ["leftIris", "lipSeparation", "rightIris"];
+    }
+}
+
+class LowerThirdHeight extends Criteria {
+    calculate() {
+        let middlePoint = [this.leftNoseCorner[0] + (1/2) * (this.rightNoseCorner[0] - this.leftNoseCorner[0]), this.leftNoseCorner[1] + (1/2) * (this.rightNoseCorner[1] - this.leftNoseCorner[1])];
+        let middleLine = Fn.fromTwoPoints(this.leftNoseCorner, this.rightNoseCorner).perpendicular(middlePoint);
+        let topPoint = middleLine.intersect(Fn.fromTwoPoints(this.leftEyebrow, this.rightEyebrow));
+        let bottomPoint = middleLine.intersect(Fn.fromTwoPoints(this.chinLeft, this.chinRight));
+        this.ratio = distance(bottomPoint, middlePoint) / distance(middlePoint, topPoint);
+    }
+
+    render() {
+        return `${round(this.ratio, 2)}`;
+    }
+
+    ideal() {
+        return `more than ${database.entries.lowerThirdHeight.idealLower}`;
+    }
+
+    assess() {
+        let { idealLower, deviation, deviatingLow } = database.entries.lowerThirdHeight;
+        return assess(this.ratio, idealLower, undefined, deviation, deviatingLow, undefined);
+    }
+
+    draw(ctx) {
+        draw(ctx, "grey", [this.leftEyebrow, this.rightEyebrow, this.rightNoseCorner, this.leftNoseCorner]);
+        draw(ctx, "grey", [this.leftNoseCorner, this.rightNoseCorner, this.chinRight, this.chinLeft]);
+    }
+
+    necessaryPoints() {
+        return ["leftNoseCorner", "rightNoseCorner", "leftEyebrow", "rightEyebrow", "chinLeft", "chinRight"];
+    }
+}
+
+class PalpebralFissureLength extends Criteria {
+    calculate() {
+        this.leftPFL = distance(this.leftLateralCanthus, this.leftMedialCanthus) / distance(this.leftEyeUpper, this.leftEyeLower);
+        this.rightPFL = distance(this.rightLateralCanthus, this.rightMedialCanthus) / distance(this.rightEyeUpper, this.rightEyeLower);
+    }
+
+    render() {
+        return `left ${round(this.rightPFL, 2)}, right ${round(this.leftPFL, 2)}`;
+    }
+
+    ideal() {
+        return `more than ${database.entries.palpebralFissureLength.idealLower}`;
+    }
+
+    assess() {
+        let { idealLower, deviation, deviatingLow } = database.entries.palpebralFissureLength;
+        return assess((this.leftPFL + this.rightPFL) / 2, idealLower, undefined, deviation, deviatingLow, undefined);
+    }
+
+    draw(ctx) {
+        draw(ctx, "aquamarine", [this.leftLateralCanthus, this.leftMedialCanthus]);
+        draw(ctx, "aquamarine", [this.leftEyeUpper, this.leftEyeLower]);
+        draw(ctx, "aquamarine", [this.rightLateralCanthus, this.rightMedialCanthus]);
+        draw(ctx, "aquamarine", [this.rightEyeUpper, this.rightEyeLower]);
+    }
+
+    necessaryPoints() {
+        return ["leftLateralCanthus", "leftMedialCanthus", "leftEyeUpper", "leftEyeLower", "rightLateralCanthus", "rightMedialCanthus", "rightEyeUpper", "rightEyeLower"];
+    }
+}
+
+class EyeColor extends Criteria {
+    calculate() {
+        this.leftIrisCoordinates = this.face.annotations.rightEyeIris;
+        this.leftIrisWidth = this.leftIrisCoordinates[1][0] - this.leftIrisCoordinates[3][0];
+        this.leftIrisHeight = this.leftIrisCoordinates[4][1] - this.leftIrisCoordinates[2][1];
+        this.rightIrisCoordinates = this.face.annotations.leftEyeIris;
+        this.rightIrisWidth = this.rightIrisCoordinates[3][0] - this.rightIrisCoordinates[1][0];
+        this.rightIrisHeight = this.rightIrisCoordinates[4][1] - this.rightIrisCoordinates[2][1];
+    }
+
+    render() {
+        return `<canvas height="0" width="0"></canvas><canvas height="0" width="0"></canvas>`;
+    }
+
+    ideal() {
+        return "";
+    }
+
+    assess() {
+        return "";
+    }
+
+    detect(image, [ctx0, ctx1]) {
+        ctx0.canvas.width = this.leftIrisWidth;
+        ctx0.canvas.height = this.leftIrisHeight;
+        ctx0.drawImage(image, this.leftIrisCoordinates[3][0], this.leftIrisCoordinates[2][1], this.leftIrisWidth, this.leftIrisHeight, 0, 0, this.leftIrisWidth, this.leftIrisHeight);
+        ctx1.canvas.width = this.rightIrisWidth;
+        ctx1.canvas.height = this.rightIrisHeight;
+        ctx1.drawImage(image, this.rightIrisCoordinates[1][0], this.rightIrisCoordinates[2][1], this.rightIrisWidth, this.rightIrisHeight, 0, 0, this.rightIrisWidth, this.rightIrisHeight);
+    }
+
+    necessaryPoints() {
+        return [];
+    }
+}
+
+function distance([ax, ay], [bx, by]) {
+    return Math.sqrt((ax - bx) ** 2 + (ay - by) ** 2);
+}
+
+class Fn {
+    constructor(a, b) {
+        // y = ax + b
+        this.a = a;
+        this.b = b;
+
+        this.slope = this.a;
+        this.yintersect = this.b;
+    }
+
+    static fromTwoPoints([ax, ay], [bx, by]) {
+        // y = (ay / by) / (ax - bx) * (x - ax) + ay
+        return Fn.fromOffset((ay - by) / (ax - bx), ax, ay);
+    }
+
+    static fromOffset(a, b, c) {
+        // y = a * (x - b) + c
+        return new Fn(a, c - (a * b));
+    }
+
+    getY(x) {
+        return this.a * x + this.b;
+    }
+
+    perpendicular([x, y]) {
+        return Fn.fromOffset((-1) * (1 / this.a), x, y);
+    }
+
+    parallel([x, y]) {
+        return Fn.fromOffset(this.a, x, y);
+    }
+
+    intersect(fn) {
+        let x = (fn.b - this.b) / (this.a - fn.a);
+        return [x, this.getY(x)];
+    }
+
+    draw(ctx, color) {
+        let points = [];
+        for(let i = 0; i < ctx.canvas.width; i += 1) {
+            points.push([i, this.getY(i)]);
+        }
+        draw(ctx, color || "red", points);
+    }
+}
+
+function round(n, digits) {
+    digits = 10 ** (isNaN(digits) ? 2 : digits);
+    return Math.round(n * digits) / digits;
+}
+
+function assess(value, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh) {
+    function renderMultiplier(multiplier) {
+        if (multiplier === 0) {
+            return "slightly too";
+        } else if (multiplier === 1) {
+            return "noticeably";
+        } else if (multiplier === 2) {
+            return "significantly too";
+        } else if (multiplier === 3) {
+            return "horribly";
+        } else {
+            return "extremely";
+        }
+    }
+
+    function calculate(value, idealLower, idealUpper, deviation) {
+
+        if (idealUpper !== undefined && idealLower !== undefined) {
+            if (idealUpper >= value && idealLower <= value) {
+                return {
+                    type: "perfect",
+                };
+            }
+        } else if (((idealUpper && !idealLower) && value <= idealUpper) || ((!idealUpper && idealLower) && value >= idealLower)) {
+            return {
+                type: "perfect",
+            };
+        }
+
+        if (value < idealLower) {
+            let multiplier = 0;
+            while ((value += deviation) < idealLower) {
+                multiplier++;
+            }
+            return {
+                type: "low",
+                multiplier: Math.min(multiplier, 4),
+                text: renderMultiplier(multiplier),
+            };
+        }
+
+        if (value > idealUpper) {
+            let multiplier = 0;
+            while ((value -= deviation) > idealUpper) {
+                multiplier++;
+            }
+            return {
+                type: "high",
+                multiplier: Math.min(multiplier, 4),
+                text: renderMultiplier(multiplier),
+            };
+        }
+
+    }
+
+    let { type, multiplier, text } = calculate(value, idealLower, idealUpper, deviation);
+
+    if (type === "perfect") {
+        return `<span class="perfect">perfect</span>`;
+    } else if (type === "low") {
+        return `<span class="deviation-${multiplier}">${text} ${deviatingLow}</span>`;
+    } else if (type === "high") {
+        return `<span class="deviation-${multiplier}">${text} ${deviatingHigh}</span>`;
+    }
+}
+
+function draw(ctx, color, points) {
+    ctx.strokeStyle = color;
+    ctx.fillStyle = color;
+
+    let current = points[0];
+
+    var fontBase = canvas.width * 0.6;                   // selected default width for canvas
+    var fontSize = 20;                     // default size for font
+  
+
+    var ratio = fontSize / fontBase;   // calc ratio
+    var size = canvas.width * ratio;   // get font size based on current width
+    if(canvas.width > 600) {
+        ctx.font= size + 'px sans-serif';
+    } else {
+        ctx.font= '18px sans-serif';
+
+    }    var textWidth=ctx.measureText("text").width;
+    ctx.globalAlpha=.50;
+    ctx.fillStyle='white'
+    var text = "www.incel.solutions (powered by $INCEL COIN)"
+    cw = canvas.width;
+    ch = canvas.height;
+    var textWidth=ctx.measureText(text).width;
+    ctx.fillStyle='gray'
+    ctx.fillText(text,cw-textWidth-10,ch-20);
+    ctx.fillStyle='white'
+    ctx.fillText(text,cw-textWidth-10+2,ch-20+2);
+
+    for (let i of points.concat([points[0]])) {
+        let [x, y] = i;
+
+        ctx.beginPath();
+        ctx.moveTo(current[0], current[1]);
+        ctx.lineTo(x, y);
+        ctx.stroke();
+        ctx.beginPath();
+        ctx.arc(x, y, ctx.arcRadius, 0, 2 * Math.PI);
+        ctx.fill();
+
+        current = i;
+    }
+}
diff --git a/js/bootstrap.bundle.min.js b/js/bootstrap.bundle.min.js
new file mode 100644
index 0000000..cc0a255
--- /dev/null
+++ b/js/bootstrap.bundle.min.js
@@ -0,0 +1,7 @@
+/*!
+  * Bootstrap v5.1.3 (https://getbootstrap.com/)
+  * Copyright 2011-2021 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
+!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).bootstrap=e()}(this,(function(){"use strict";const t="transitionend",e=t=>{let e=t.getAttribute("data-bs-target");if(!e||"#"===e){let i=t.getAttribute("href");if(!i||!i.includes("#")&&!i.startsWith("."))return null;i.includes("#")&&!i.startsWith("#")&&(i=`#${i.split("#")[1]}`),e=i&&"#"!==i?i.trim():null}return e},i=t=>{const i=e(t);return i&&document.querySelector(i)?i:null},n=t=>{const i=e(t);return i?document.querySelector(i):null},s=e=>{e.dispatchEvent(new Event(t))},o=t=>!(!t||"object"!=typeof t)&&(void 0!==t.jquery&&(t=t[0]),void 0!==t.nodeType),r=t=>o(t)?t.jquery?t[0]:t:"string"==typeof t&&t.length>0?document.querySelector(t):null,a=(t,e,i)=>{Object.keys(i).forEach((n=>{const s=i[n],r=e[n],a=r&&o(r)?"element":null==(l=r)?`${l}`:{}.toString.call(l).match(/\s([a-z]+)/i)[1].toLowerCase();var 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t=t=>this._pointerEvent&&("pen"===t.pointerType||"touch"===t.pointerType),e=e=>{t(e)?this.touchStartX=e.clientX:this._pointerEvent||(this.touchStartX=e.touches[0].clientX)},i=t=>{this.touchDeltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this.touchStartX},n=e=>{t(e)&&(this.touchDeltaX=e.clientX-this.touchStartX),this._handleSwipe(),"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((t=>this.cycle(t)),500+this._config.interval))};V.find(".carousel-item img",this._element).forEach((t=>{j.on(t,"dragstart.bs.carousel",(t=>t.preventDefault()))})),this._pointerEvent?(j.on(this._element,"pointerdown.bs.carousel",(t=>e(t))),j.on(this._element,"pointerup.bs.carousel",(t=>n(t))),this._element.classList.add("pointer-event")):(j.on(this._element,"touchstart.bs.carousel",(t=>e(t))),j.on(this._element,"touchmove.bs.carousel",(t=>i(t))),j.on(this._element,"touchend.bs.carousel",(t=>n(t))))}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=tt[t.key];e&&(t.preventDefault(),this._slide(e))}_getItemIndex(t){return this._items=t&&t.parentNode?V.find(".carousel-item",t.parentNode):[],this._items.indexOf(t)}_getItemByOrder(t,e){const i=t===Q;return v(this._items,e,i,this._config.wrap)}_triggerSlideEvent(t,e){const i=this._getItemIndex(t),n=this._getItemIndex(V.findOne(nt,this._element));return j.trigger(this._element,"slide.bs.carousel",{relatedTarget:t,direction:e,from:n,to:i})}_setActiveIndicatorElement(t){if(this._indicatorsElement){const e=V.findOne(".active",this._indicatorsElement);e.classList.remove(it),e.removeAttribute("aria-current");const i=V.find("[data-bs-target]",this._indicatorsElement);for(let e=0;e<i.length;e++)if(Number.parseInt(i[e].getAttribute("data-bs-slide-to"),10)===this._getItemIndex(t)){i[e].classList.add(it),i[e].setAttribute("aria-current","true");break}}}_updateInterval(){const t=this._activeElement||V.findOne(nt,this._element);if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);e?(this._config.defaultInterval=this._config.defaultInterval||this._config.interval,this._config.interval=e):this._config.interval=this._config.defaultInterval||this._config.interval}_slide(t,e){const i=this._directionToOrder(t),n=V.findOne(nt,this._element),s=this._getItemIndex(n),o=e||this._getItemByOrder(i,n),r=this._getItemIndex(o),a=Boolean(this._interval),l=i===Q,c=l?"carousel-item-start":"carousel-item-end",h=l?"carousel-item-next":"carousel-item-prev",d=this._orderToDirection(i);if(o&&o.classList.contains(it))return void(this._isSliding=!1);if(this._isSliding)return;if(this._triggerSlideEvent(o,d).defaultPrevented)return;if(!n||!o)return;this._isSliding=!0,a&&this.pause(),this._setActiveIndicatorElement(o),this._activeElement=o;const f=()=>{j.trigger(this._element,et,{relatedTarget:o,direction:d,from:s,to:r})};if(this._element.classList.contains("slide")){o.classList.add(h),u(o),n.classList.add(c),o.classList.add(c);const t=()=>{o.classList.remove(c,h),o.classList.add(it),n.classList.remove(it,h,c),this._isSliding=!1,setTimeout(f,0)};this._queueCallback(t,n,!0)}else n.classList.remove(it),o.classList.add(it),this._isSliding=!1,f();a&&this.cycle()}_directionToOrder(t){return[J,Z].includes(t)?m()?t===Z?G:Q:t===Z?Q:G:t}_orderToDirection(t){return[Q,G].includes(t)?m()?t===G?Z:J:t===G?J:Z:t}static carouselInterface(t,e){const i=st.getOrCreateInstance(t,e);let{_config:n}=i;"object"==typeof e&&(n={...n,...e});const s="string"==typeof e?e:n.slide;if("number"==typeof e)i.to(e);else if("string"==typeof s){if(void 0===i[s])throw new TypeError(`No method named "${s}"`);i[s]()}else n.interval&&n.ride&&(i.pause(),i.cycle())}static jQueryInterface(t){return this.each((function(){st.carouselInterface(this,t)}))}static dataApiClickHandler(t){const e=n(this);if(!e||!e.classList.contains("carousel"))return;const i={...U.getDataAttributes(e),...U.getDataAttributes(this)},s=this.getAttribute("data-bs-slide-to");s&&(i.interval=!1),st.carouselInterface(e,i),s&&st.getInstance(e).to(s),t.preventDefault()}}j.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",st.dataApiClickHandler),j.on(window,"load.bs.carousel.data-api",(()=>{const t=V.find('[data-bs-ride="carousel"]');for(let e=0,i=t.length;e<i;e++)st.carouselInterface(t[e],st.getInstance(t[e]))})),g(st);const ot="collapse",rt={toggle:!0,parent:null},at={toggle:"boolean",parent:"(null|element)"},lt="show",ct="collapse",ht="collapsing",dt="collapsed",ut=":scope .collapse .collapse",ft='[data-bs-toggle="collapse"]';class pt extends B{constructor(t,e){super(t),this._isTransitioning=!1,this._config=this._getConfig(e),this._triggerArray=[];const n=V.find(ft);for(let t=0,e=n.length;t<e;t++){const e=n[t],s=i(e),o=V.find(s).filter((t=>t===this._element));null!==s&&o.length&&(this._selector=s,this._triggerArray.push(e))}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return rt}static get NAME(){return ot}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t,e=[];if(this._config.parent){const t=V.find(ut,this._config.parent);e=V.find(".collapse.show, .collapse.collapsing",this._config.parent).filter((e=>!t.includes(e)))}const i=V.findOne(this._selector);if(e.length){const n=e.find((t=>i!==t));if(t=n?pt.getInstance(n):null,t&&t._isTransitioning)return}if(j.trigger(this._element,"show.bs.collapse").defaultPrevented)return;e.forEach((e=>{i!==e&&pt.getOrCreateInstance(e,{toggle:!1}).hide(),t||H.set(e,"bs.collapse",null)}));const n=this._getDimension();this._element.classList.remove(ct),this._element.classList.add(ht),this._element.style[n]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const s=`scroll${n[0].toUpperCase()+n.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct,lt),this._element.style[n]="",j.trigger(this._element,"shown.bs.collapse")}),this._element,!0),this._element.style[n]=`${this._element[s]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(j.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,u(this._element),this._element.classList.add(ht),this._element.classList.remove(ct,lt);const e=this._triggerArray.length;for(let t=0;t<e;t++){const e=this._triggerArray[t],i=n(e);i&&!this._isShown(i)&&this._addAriaAndCollapsedClass([e],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct),j.trigger(this._element,"hidden.bs.collapse")}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(lt)}_getConfig(t){return(t={...rt,...U.getDataAttributes(this._element),...t}).toggle=Boolean(t.toggle),t.parent=r(t.parent),a(ot,t,at),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=V.find(ut,this._config.parent);V.find(ft,this._config.parent).filter((e=>!t.includes(e))).forEach((t=>{const e=n(t);e&&this._addAriaAndCollapsedClass([t],this._isShown(e))}))}_addAriaAndCollapsedClass(t,e){t.length&&t.forEach((t=>{e?t.classList.remove(dt):t.classList.add(dt),t.setAttribute("aria-expanded",e)}))}static jQueryInterface(t){return this.each((function(){const e={};"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1);const i=pt.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}j.on(document,"click.bs.collapse.data-api",ft,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=i(this);V.find(e).forEach((t=>{pt.getOrCreateInstance(t,{toggle:!1}).toggle()}))})),g(pt);var mt="top",gt="bottom",_t="right",bt="left",vt="auto",yt=[mt,gt,_t,bt],wt="start",Et="end",At="clippingParents",Tt="viewport",Ot="popper",Ct="reference",kt=yt.reduce((function(t,e){return t.concat([e+"-"+wt,e+"-"+Et])}),[]),Lt=[].concat(yt,[vt]).reduce((function(t,e){return t.concat([e,e+"-"+wt,e+"-"+Et])}),[]),xt="beforeRead",Dt="read",St="afterRead",Nt="beforeMain",It="main",Pt="afterMain",jt="beforeWrite",Mt="write",Ht="afterWrite",Bt=[xt,Dt,St,Nt,It,Pt,jt,Mt,Ht];function Rt(t){return t?(t.nodeName||"").toLowerCase():null}function Wt(t){if(null==t)return window;if("[object Window]"!==t.toString()){var e=t.ownerDocument;return e&&e.defaultView||window}return t}function $t(t){return t instanceof Wt(t).Element||t instanceof Element}function zt(t){return t instanceof Wt(t).HTMLElement||t instanceof HTMLElement}function qt(t){return"undefined"!=typeof ShadowRoot&&(t instanceof Wt(t).ShadowRoot||t instanceof ShadowRoot)}const Ft={name:"applyStyles",enabled:!0,phase:"write",fn:function(t){var e=t.state;Object.keys(e.elements).forEach((function(t){var i=e.styles[t]||{},n=e.attributes[t]||{},s=e.elements[t];zt(s)&&Rt(s)&&(Object.assign(s.style,i),Object.keys(n).forEach((function(t){var e=n[t];!1===e?s.removeAttribute(t):s.setAttribute(t,!0===e?"":e)})))}))},effect:function(t){var e=t.state,i={popper:{position:e.options.strategy,left:"0",top:"0",margin:"0"},arrow:{position:"absolute"},reference:{}};return 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n=Yt(i);if("none"!==n.transform||"none"!==n.perspective||"paint"===n.contain||-1!==["transform","perspective"].indexOf(n.willChange)||e&&"filter"===n.willChange||e&&n.filter&&"none"!==n.filter)return i;i=i.parentNode}return null}(t)||e}function ee(t){return["top","bottom"].indexOf(t)>=0?"x":"y"}var ie=Math.max,ne=Math.min,se=Math.round;function oe(t,e,i){return ie(t,ne(e,i))}function re(t){return Object.assign({},{top:0,right:0,bottom:0,left:0},t)}function ae(t,e){return e.reduce((function(e,i){return e[i]=t,e}),{})}const le={name:"arrow",enabled:!0,phase:"main",fn:function(t){var e,i=t.state,n=t.name,s=t.options,o=i.elements.arrow,r=i.modifiersData.popperOffsets,a=Ut(i.placement),l=ee(a),c=[bt,_t].indexOf(a)>=0?"height":"width";if(o&&r){var h=function(t,e){return re("number"!=typeof(t="function"==typeof 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e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=Lt.reduce((function(t,i){return t[i]=function(t,e,i){var n=Ut(t),s=[bt,mt].indexOf(n)>=0?-1:1,o="function"==typeof i?i(Object.assign({},e,{placement:t})):i,r=o[0],a=o[1];return r=r||0,a=(a||0)*s,[bt,_t].indexOf(n)>=0?{x:a,y:r}:{x:r,y:a}}(i,e.rects,o),t}),{}),a=r[e.placement],l=a.x,c=a.y;null!=e.modifiersData.popperOffsets&&(e.modifiersData.popperOffsets.x+=l,e.modifiersData.popperOffsets.y+=c),e.modifiersData[n]=r}},Pe={name:"popperOffsets",enabled:!0,phase:"read",fn:function(t){var e=t.state,i=t.name;e.modifiersData[i]=Ce({reference:e.rects.reference,element:e.rects.popper,strategy:"absolute",placement:e.placement})},data:{}},je={name:"preventOverflow",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0!==r&&r,l=i.boundary,c=i.rootBoundary,h=i.altBoundary,d=i.padding,u=i.tether,f=void 0===u||u,p=i.tetherOffset,m=void 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Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function We(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?Be:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},Be,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:$t(t)?Ae(t):t.contextElement?Ae(t.contextElement):[],popper:Ae(e)};var r,c,u=function(t){var e=He(t);return Bt.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(Re(e,i)){a.rects={reference:Me(e,te(i),"fixed"===a.options.strategy),popper:Kt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else 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Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new 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this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" 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null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>'},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(gn);const _n="scrollspy",bn={offset:10,method:"auto",target:""},vn={offset:"number",method:"string",target:"(string|element)"},yn="active",wn=".nav-link, .list-group-item, .dropdown-item",En="position";class An extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,j.on(this._scrollElement,"scroll.bs.scrollspy",(()=>this._process())),this.refresh(),this._process()}static get Default(){return bn}static get NAME(){return _n}refresh(){const t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t<this._offsets[0]&&this._offsets[0]>0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t<this._offsets[e+1])&&this._activate(this._targets[e])}}_activate(t){this._activeTarget=t,this._clear();const e=wn.split(",").map((e=>`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu .active",e.parentNode);t&&t.classList.remove(Tn),"tab"===e.getAttribute("role")&&e.setAttribute("aria-selected",!1)}t.classList.add(Tn),"tab"===t.getAttribute("role")&&t.setAttribute("aria-selected",!0),u(t),t.classList.contains(On)&&t.classList.add(Cn);let n=t.parentNode;if(n&&"LI"===n.nodeName&&(n=n.parentNode),n&&n.classList.contains("dropdown-menu")){const e=t.closest(".dropdown");e&&V.find(".dropdown-toggle",e).forEach((t=>t.classList.add(Tn))),t.setAttribute("aria-expanded",!0)}i&&i()}static jQueryInterface(t){return this.each((function(){const e=xn.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(document,"click.bs.tab.data-api",'[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this)||xn.getOrCreateInstance(this).show()})),g(xn);const Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
+//# sourceMappingURL=bootstrap.bundle.min.js.map
\ No newline at end of file
diff --git a/js/face-landmarks-detection.js b/js/face-landmarks-detection.js
new file mode 100644
index 0000000..cb2f93d
--- /dev/null
+++ b/js/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],
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+        [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$2;
+
+    Object.defineProperty(exports, '__esModule', { value: true });
+
+})));
diff --git a/js/index.js b/js/index.js
new file mode 100644
index 0000000..0bc87ea
--- /dev/null
+++ b/js/index.js
@@ -0,0 +1,409 @@
+console.log("🧪 JS is updated: 2024-05-20");
+
+let setStatus = function () {};
+
+// Summary calculator
+function summarizeResults() {
+    console.log("▶ summarizeResults() invoked");
+    const ratingCells = document.querySelectorAll('td[id^="assessment-"]');
+
+    const counts = {
+        perfect: 0,
+        deviation0: 0,
+        deviation1: 0,
+        deviation2: 0,
+        deviation3: 0,
+        deviation4: 0,
+    };
+
+    const weights = {
+        perfect: 2,
+        deviation0: 1,
+        deviation1: 0,
+        deviation2: -1,
+        deviation3: -2,
+        deviation4: -2,
+    };
+
+    ratingCells.forEach(cell => {
+        const html = cell.innerHTML;
+        if (html.includes("perfect")) counts.perfect++;
+        else if (html.includes("deviation-0")) counts.deviation0++;
+        else if (html.includes("deviation-1")) counts.deviation1++;
+        else if (html.includes("deviation-2")) counts.deviation2++;
+        else if (html.includes("deviation-3")) counts.deviation3++;
+        else if (html.includes("deviation-4")) counts.deviation4++;
+    });
+
+    const totalScore =
+        counts.perfect * weights.perfect +
+        counts.deviation0 * weights.deviation0 +
+        counts.deviation1 * weights.deviation1 +
+        counts.deviation2 * weights.deviation2 +
+        counts.deviation3 * weights.deviation3 +
+        counts.deviation4 * weights.deviation4;
+
+    const maxScore = 22;
+    const normalized = Math.max(0, Math.min(8, Math.round((totalScore / maxScore) * 9)));
+    const psl = `PSL${normalized}`;
+
+    const labels = {
+        0: "Subhuman",
+        1: "Sub5",
+        2: "Low-tier Normie",
+        3: "Normie",
+        4: "Upper Normie",
+        5: "Chadlite",
+        6: "Chad",
+        7: "Gigachad",
+        8: "Terachad"
+    };
+    const label = labels[normalized] || "Unknown";
+
+const hierarchyLabels = {
+    8: {
+        titles: ["Era Defining Man", "Immortal", "God Among Men"],
+        description: "Men destined to define eras and change history, who will be remembered for millennia to come",
+        color: "#A0D8EF"
+    },
+    7: {
+        titles: ["King", "Explorer", "Philosopher"],
+        description: "Philosophers, Kings, Explorers, and Inventors",
+        color: "#4682B4"
+    },
+    6: {
+        titles: ["Influential Artist", "Writer", "General"],
+        description: "Influential Artists, Writers, and Generals",
+        color: "#5F9EA0"
+    },
+    5: {
+        titles: ["Diplomat", "Official", "Officer"],
+        description: "Officers, Officials, Diplomats",
+        color: "#8FBC8F"
+    },
+    4: {
+        titles: ["Middle Management", "Soldier", "Craftsman"],
+        description: "Middle Management, Soldiers, Craftsmen",
+        color: "#8B5A2B"
+    },
+    3: {
+        titles: ["Manual Laborerer", "Peasant", "Street Merchant"],
+        description: "Manual Labourers, Peasants, Street Merchants",
+        color: "#A52A2A"
+    },
+    2: {
+        titles: ["Street Sweeper", "Drain Cleaner", "Waste Collector"],
+        description: "Street Cleaners",
+        color: "#B22222"
+    },
+    1: {
+        titles: ["Sanitation Worker", "Toilet Scrubber", "Gutterman"],
+        description: "Sanitation Workers",
+        color: "#800000"
+    },
+    0: {
+        titles: ["Untouchable", "Subhuman", "Bottom of the Barrel"],
+        description: "Untouchables",
+        color: "#2F2F2F"
+    }
+};
+
+
+    const badgeClass = normalized <= 3 ? "danger" : normalized <= 6 ? "warning" : "success";
+    const gradingToggle = document.getElementById("grading-toggle");
+    const usePSL = gradingToggle ? gradingToggle.checked : true;
+
+    const scoreCell = document.getElementById("total-score");
+    const breakdownCell = document.getElementById("total-breakdown");
+    const resultCell = document.getElementById("total-psl");
+
+    if (scoreCell) scoreCell.innerHTML = `<strong>${totalScore}</strong>`;
+    if (breakdownCell) breakdownCell.innerHTML = `
+        Perfect: ${counts.perfect}, 
+        Slight: ${counts.deviation0}, 
+        Noticeable: ${counts.deviation1}, 
+        Significant: ${counts.deviation2}, 
+        Horrible: ${counts.deviation3}, 
+        Extreme: ${counts.deviation4}
+    `;
+
+    if (resultCell) {
+        if (usePSL) {
+            resultCell.innerHTML = `<span class="badge bg-${badgeClass}">${psl}</span><br><small>${label}</small>`;
+        } else {
+            const h = hierarchyLabels[normalized] || hierarchyLabels[1];
+const title = Array.isArray(h.titles)
+    ? h.titles[Math.floor(Math.random() * h.titles.length)]
+    : h.titles;
+
+const lightColors = ["#A0D8EF", "#8FBC8F", "#A52A2A"];
+const isLight = lightColors.includes(h.color);
+const textColor = isLight ? "black" : "white";
+
+resultCell.innerHTML = `
+    <span class="badge" style="background-color: ${h.color}; color: ${textColor};">
+        ${title}
+    </span><br>
+    <small style="color: ${h.color}; font-size: 0.75rem;">${h.description}</small>
+`;
+
+        }
+    }
+}
+
+async function main() {
+    const _model = await faceLandmarksDetection.load(faceLandmarksDetection.SupportedPackages.mediapipeFacemesh, {
+        maxFaces: 1
+    });
+    window.database = await setupDatabase();
+    const imageInputFile = document.getElementById("image-file");
+    const imageInputUrl = document.getElementById("image-url");
+    const introductionElement = document.getElementById("introduction");
+    const analyzingElement = document.getElementById("analyzing");
+    const canvas = document.getElementById("canvas");
+    const ctx = canvas.getContext("2d");
+
+    let data = void 0;
+
+const gradingToggle = document.getElementById("grading-toggle");
+if (gradingToggle) {
+    gradingToggle.addEventListener("change", summarizeResults);
+}
+
+
+    imageInputFile.addEventListener("change", async () => {
+        if (imageInputFile.files[0]) {
+            introductionElement.style.display = "none";
+            analyzingElement.classList.remove("d-none");
+            data && clearData();
+
+            setStatus("Reading image");
+
+            imageInputUrl.value = "";
+
+            let url = URL.createObjectURL(imageInputFile.files[0]);
+
+            await onChange(url);
+        }
+    });
+
+    imageInputUrl.addEventListener("change", async () => {
+        if (imageInputUrl.value) {
+            introductionElement.style.display = "none";
+            analyzingElement.classList.remove("d-none");
+            data && clearData();
+
+            setStatus("Downloading image");
+
+            imageInputFile.value = "";
+
+            let file = await (await fetch(imageInputUrl.value)).blob();
+            let url = URL.createObjectURL(file);
+
+            await onChange(url);
+        }
+    });
+
+    async function onChange(url) {
+        setStatus("Analyzing");
+
+        let analysis = await analyze(canvas, ctx, url);
+
+        data = analysis.criteria = {
+            midfaceRatio: {...createBindings(analysis.criteria.midfaceRatio, "midface-ratio")},
+            facialWidthToHeightRatio: {...createBindings(analysis.criteria.facialWidthToHeightRatio, "facial-width-to-height-ratio")},
+            chinToPhiltrumRatio: {...createBindings(analysis.criteria.chinToPhiltrumRatio, "chin-to-philtrum-ratio")},
+            canthalTilt: {...createBindings(analysis.criteria.canthalTilt, "canthal-tilt")},
+            mouthToNoseRatio: {...createBindings(analysis.criteria.mouthToNoseRatio, "mouth-to-nose-ratio")},
+            bigonialWidth: {...createBindings(analysis.criteria.bigonialWidth, "bigonial-width")},
+            lipRatio: {...createBindings(analysis.criteria.lipRatio, "lip-ratio")},
+            eyeSeparationRatio: {...createBindings(analysis.criteria.eyeSeparationRatio, "eye-separation-ratio")},
+            eyeToMouthAngle: {...createBindings(analysis.criteria.eyeToMouthAngle, "eye-to-mouth-angle")},
+            lowerThirdHeight: {...createBindings(analysis.criteria.lowerThirdHeight, "lower-third-height")},
+            palpebralFissureLength: {...createBindings(analysis.criteria.palpebralFissureLength, "palpebral-fissure-length")},
+            eyeColor: {...createBindings(analysis.criteria.eyeColor, "eye-color")},
+        };
+
+        function createBindings(metric, id) {
+            return {
+                analysis: metric,
+                render: document.getElementById(`value-${id}`),
+                toggle: document.getElementById(`toggle-${id}`),
+                ideal: document.getElementById(`ideal-${id}`),
+                assessment: document.getElementById(`assessment-${id}`),
+            };
+        }
+
+        let calculate = () => {
+            for (let i of Object.values(analysis.criteria)) {
+                i.analysis.calculate();
+                i.render.innerHTML = i.analysis.render();
+                i.ideal.innerHTML = i.analysis.ideal();
+                i.assessment.innerHTML = i.analysis.assess();
+            }
+
+            analysis.criteria.eyeColor.analysis.detect(
+                analysis.image,
+                Array.from(analysis.criteria.eyeColor.render.children).map(i => i.getContext("2d"))
+            );
+
+            summarizeResults(); // 👈 Score update
+        }
+
+        let render = () => {
+            analysis.resetToImage();
+            for (let i of Object.values(analysis.criteria)) {
+                if (i.toggle.checked) {
+                    i.analysis.draw(ctx);
+                }
+            }
+        }
+
+        for (let i of Object.values(analysis.criteria)) {
+            i.toggle.onchange = () => render();
+        }
+
+        let moving = false;
+
+        canvas.onmousedown = ({ offsetX: x, offsetY: y }) => {
+            let necessaryPoints = Object.values(analysis.criteria).filter(i => i.toggle.checked).map(i => i.analysis.necessaryPoints()).flat();
+
+            for (let i in analysis.points) {
+                if (analysis.points.hasOwnProperty(i) && necessaryPoints.includes(i)) {
+                    if (Math.sqrt(
+                        (analysis.points[i][0] - x) ** 2
+                        + (analysis.points[i][1] - y) ** 2
+                    ) <= analysis.arcRadius) {
+                        moving = i;
+                        return;
+                    }
+                }
+            }
+        }
+
+        canvas.ontouchstart = (e) => {
+            let bcr = e.target.getBoundingClientRect();
+            let x = e.targetTouches[0].clientX - bcr.x;
+            let y = e.targetTouches[0].clientY - bcr.y;
+            canvas.onmousedown({ offsetX: x, offsetY: y });
+        }
+
+        canvas.onmouseup = () => {
+            moving = false;
+        }
+
+        canvas.ontouchend = canvas.ontouchcancel = (e) => {
+            canvas.onmouseup();
+        }
+
+        canvas.onmousemove = ({ offsetX: x, offsetY: y }) => {
+            if (moving) {
+                analysis.points[moving] = [x, y];
+                calculate();
+                render();
+            } else {
+                let necessaryPoints = Object.values(analysis.criteria).filter(i => i.toggle.checked).map(i => i.analysis.necessaryPoints()).flat();
+
+                for (let i in analysis.points) {
+                    if (analysis.points.hasOwnProperty(i) && necessaryPoints.includes(i)) {
+                        if (Math.sqrt(
+                            (analysis.points[i][0] - x) ** 2
+                            + (analysis.points[i][1] - y) ** 2
+                        ) <= analysis.arcRadius) {
+                            render();
+                            ctx.beginPath();
+                            ctx.strokeStyle = "gray";
+                            let oldLineWidth = ctx.lineWidth;
+                            ctx.lineWidth = 0.5;
+                            ctx.arc(analysis.points[i][0], analysis.points[i][1], ctx.arcRadius + 1.5, 0, 2 * Math.PI);
+                            ctx.stroke();
+                            ctx.lineWidth = oldLineWidth;
+                            return;
+                        }
+                    }
+                }
+                render();
+            }
+        }
+
+        canvas.ontouchmove = (e) => {
+            let bcr = e.target.getBoundingClientRect();
+            let x = e.targetTouches[0].clientX - bcr.x;
+            let y = e.targetTouches[0].clientY - bcr.y;
+            canvas.onmousemove({ offsetX: x, offsetY: y });
+        }
+
+        analyzingElement.classList.add("d-none");
+
+        calculate();
+        render();
+    }
+
+    function clearData() {
+        canvas.width = 0;
+        canvas.height = 0;
+        for (let i of Object.values(data)) {
+            i.render.innerHTML = "";
+            i.ideal.innerHTML = "";
+            i.assessment.innerHTML = "";
+        }
+    }
+
+    setStatus = (text) => document.getElementById("analyzing-status").innerHTML = text;
+
+    document.querySelector("#loading").style.display = "none";
+    document.querySelector(".container").classList.remove("d-none");
+}
+
+async function analyze(canvas, ctx, url) {
+    setStatus("Loading image");
+
+    let image = await loadImage(url);
+
+    canvas.width = image.width;
+    canvas.height = image.height;
+    resetToImage(ctx, image);
+    ctx.lineWidth = Math.sqrt((image.width * image.height) / 100000);
+    ctx.arcRadius = Math.sqrt((image.width * image.height) / 100000);
+
+    setStatus("Analyzing");
+
+    const model = await faceLandmarksDetection.load(faceLandmarksDetection.SupportedPackages.mediapipeFacemesh, {
+        maxFaces: 1
+    });
+    let face = await findLandmarks(model, image);
+    let [points, criteria] = analyseCriteria(face);
+
+    return {
+        image,
+        resetToImage: () => resetToImage(ctx, image),
+        points,
+        criteria,
+        arcRadius: ctx.arcRadius,
+    };
+}
+
+function loadImage(url) {
+    return new Promise((resolve) => {
+        const image = new Image();
+        image.src = url;
+        image.addEventListener("load", () => resolve(image));
+    });
+}
+
+function resetToImage(ctx, image) {
+    ctx.drawImage(image, 0, 0);
+}
+
+async function findLandmarks(model, image) {
+    const predictions = await model.estimateFaces({ input: image });
+    if (predictions.length > 0) {
+        return predictions[0];
+    } else {
+        throw new Error("No face detected");
+    }
+}
+
+(async function () {
+    await main();
+})();
diff --git a/js/tf-backend-cpu.js b/js/tf-backend-cpu.js
new file mode 100644
index 0000000..539d2c5
--- /dev/null
+++ b/js/tf-backend-cpu.js
@@ -0,0 +1,9526 @@
+/**
+ * @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('seedrandom')) :
+    typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs-core', 'seedrandom'], factory) :
+    (global = typeof globalThis !== 'undefined' ? globalThis : global || self, factory(global.tf = global.tf || {}, global.tf, global.seedrandom));
+}(this, (function (exports, tfjsCore, seedrandom) { 'use strict';
+
+    function _interopNamespace(e) {
+        if (e && e.__esModule) return e;
+        var n = Object.create(null);
+        if (e) {
+            Object.keys(e).forEach(function (k) {
+                if (k !== 'default') {
+                    var d = Object.getOwnPropertyDescriptor(e, k);
+                    Object.defineProperty(n, k, d.get ? d : {
+                        enumerable: true,
+                        get: function () {
+                            return e[k];
+                        }
+                    });
+                }
+            });
+        }
+        n['default'] = e;
+        return n;
+    }
+
+    var seedrandom__namespace = /*#__PURE__*/_interopNamespace(seedrandom);
+
+    /*! *****************************************************************************
+    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 };
+        }
+    }
+    function __values(o) {
+        var s = typeof Symbol === "function" && Symbol.iterator, m = s && o[s], i = 0;
+        if (m)
+            return m.call(o);
+        if (o && typeof o.length === "number")
+            return {
+                next: function () {
+                    if (o && i >= o.length)
+                        o = void 0;
+                    return { value: o && o[i++], done: !o };
+                }
+            };
+        throw new TypeError(s ? "Object is not iterable." : "Symbol.iterator is not defined.");
+    }
+    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.
+     * =============================================================================
+     */
+    function assertNotComplex(tensor, opName) {
+        if (!Array.isArray(tensor)) {
+            tensor = [tensor];
+        }
+        tensor.forEach(function (t) {
+            if (t != null) {
+                tfjsCore.util.assert(t.dtype !== 'complex64', function () { return opName + " does not support complex64 tensors in the CPU backend."; });
+            }
+        });
+    }
+
+    var whereImpl = tfjsCore.kernel_impls.whereImpl;
+    var MathBackendCPU = /** @class */ (function (_super) {
+        __extends(MathBackendCPU, _super);
+        function MathBackendCPU() {
+            var _this = _super.call(this) || this;
+            _this.blockSize = 48;
+            _this.firstUse = true;
+            _this.data = new tfjsCore.DataStorage(_this, tfjsCore.engine());
+            return _this;
+        }
+        MathBackendCPU.prototype.nextDataId = function () {
+            return MathBackendCPU.nextDataId++;
+        };
+        MathBackendCPU.prototype.write = function (values, shape, dtype) {
+            if (this.firstUse) {
+                this.firstUse = false;
+                if (tfjsCore.env().get('IS_NODE')) {
+                    tfjsCore.backend_util.warn('\n============================\n' +
+                        'Hi there 👋. Looks like you are running TensorFlow.js in ' +
+                        'Node.js. To speed things up dramatically, install our node ' +
+                        'backend, which binds to TensorFlow C++, by running ' +
+                        'npm i @tensorflow/tfjs-node, ' +
+                        'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +
+                        'Then call require(\'@tensorflow/tfjs-node\'); (-gpu ' +
+                        'suffix for CUDA) at the start of your program. ' +
+                        'Visit https://github.com/tensorflow/tfjs-node for more details.' +
+                        '\n============================');
+                }
+            }
+            var dataId = { id: this.nextDataId() };
+            this.data.set(dataId, { values: values, dtype: dtype, refCount: 1 });
+            return dataId;
+        };
+        /**
+         * Create a data bucket in cpu backend.
+         * @param shape Shape of the `TensorInfo`.
+         * @param dtype DType of the `TensorInfo`.
+         * @param values The value of the `TensorInfo` stored as a flattened array.
+         */
+        MathBackendCPU.prototype.makeTensorInfo = function (shape, dtype, values) {
+            var outId;
+            if (dtype === 'string' && values != null && values.length > 0 &&
+                tfjsCore.util.isString(values[0])) {
+                var encodedValues = values.map(function (d) { return tfjsCore.util.encodeString(d); });
+                outId = this.write(encodedValues, shape, dtype);
+            }
+            else {
+                outId = this.write(values, shape, dtype);
+            }
+            return { dataId: outId, shape: shape, dtype: dtype };
+        };
+        /** Return refCount of a `TensorData`. */
+        MathBackendCPU.prototype.refCount = function (dataId) {
+            if (this.data.has(dataId)) {
+                var tensorData = this.data.get(dataId);
+                return tensorData.refCount;
+            }
+            return 0;
+        };
+        /** Increase refCount of a `TensorData`. */
+        MathBackendCPU.prototype.incRef = function (dataId) {
+            var tensorData = this.data.get(dataId);
+            tensorData.refCount++;
+        };
+        /** Decrease refCount of a `TensorData`. */
+        MathBackendCPU.prototype.decRef = function (dataId) {
+            if (this.data.has(dataId)) {
+                var tensorData = this.data.get(dataId);
+                tensorData.refCount--;
+            }
+        };
+        MathBackendCPU.prototype.move = function (dataId, values, shape, dtype, refCount) {
+            this.data.set(dataId, { values: values, dtype: dtype, refCount: refCount });
+        };
+        MathBackendCPU.prototype.numDataIds = function () {
+            return this.data.numDataIds();
+        };
+        MathBackendCPU.prototype.read = function (dataId) {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_b) {
+                    return [2 /*return*/, this.readSync(dataId)];
+                });
+            });
+        };
+        MathBackendCPU.prototype.readSync = function (dataId) {
+            var _b = this.data.get(dataId), dtype = _b.dtype, complexTensorInfos = _b.complexTensorInfos;
+            if (dtype === 'complex64') {
+                var realValues = this.readSync(complexTensorInfos.real.dataId);
+                var imagValues = this.readSync(complexTensorInfos.imag.dataId);
+                return tfjsCore.backend_util.mergeRealAndImagArrays(realValues, imagValues);
+            }
+            return this.data.get(dataId).values;
+        };
+        MathBackendCPU.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 tfjsCore.util.decodeString(d); });
+                }
+                catch (_a) {
+                    throw new Error('Failed to decode encoded string bytes into utf-8');
+                }
+            }
+            return tfjsCore.buffer(t.shape, t.dtype, decodedData);
+        };
+        MathBackendCPU.prototype.makeOutput = function (values, shape, dtype) {
+            var dataId = this.write(values, shape, dtype);
+            return tfjsCore.engine().makeTensorFromDataId(dataId, shape, dtype, this);
+        };
+        /**
+         * Dispose the memory if the dataId has 0 refCount. Return true if the memory
+         * is released or memory is not managed in this backend, false if memory is
+         * not cleared.
+         * @param dataId
+         * @oaram force Optional, remove the data regardless of refCount
+         */
+        MathBackendCPU.prototype.disposeData = function (dataId, force) {
+            if (force === void 0) { force = false; }
+            if (this.data.has(dataId)) {
+                this.data.get(dataId).refCount--;
+                if (!force && this.data.get(dataId).refCount > 0) {
+                    return false;
+                }
+                var complexTensorInfos = this.data.get(dataId).complexTensorInfos;
+                if (complexTensorInfos != null) {
+                    this.disposeData(complexTensorInfos.real.dataId, true);
+                    this.disposeData(complexTensorInfos.imag.dataId, true);
+                }
+                this.data.delete(dataId);
+            }
+            return true;
+        };
+        MathBackendCPU.prototype.disposeIntermediateTensorInfo = function (tensorInfo) {
+            this.disposeData(tensorInfo.dataId);
+        };
+        MathBackendCPU.prototype.time = function (f) {
+            return __awaiter(this, void 0, void 0, function () {
+                var start, kernelMs;
+                return __generator(this, function (_b) {
+                    start = tfjsCore.util.now();
+                    f();
+                    kernelMs = tfjsCore.util.now() - start;
+                    return [2 /*return*/, { kernelMs: kernelMs }];
+                });
+            });
+        };
+        MathBackendCPU.prototype.memory = function () {
+            return {
+                // Unreliable due to automatic gc. The numbers above are cumulative.
+                unreliable: true,
+                reasons: ['The reported memory is an upper bound. Due to automatic garbage ' +
+                        'collection, the true allocated memory may be less.']
+            };
+        };
+        MathBackendCPU.prototype.where = function (condition) {
+            assertNotComplex([condition], 'where');
+            var condVals = this.readSync(condition.dataId);
+            return whereImpl(condition.shape, condVals);
+        };
+        MathBackendCPU.prototype.dispose = function () { };
+        MathBackendCPU.prototype.floatPrecision = function () {
+            return 32;
+        };
+        /** Returns the smallest representable number.  */
+        MathBackendCPU.prototype.epsilon = function () {
+            return _super.prototype.epsilon.call(this);
+        };
+        return MathBackendCPU;
+    }(tfjsCore.KernelBackend));
+    MathBackendCPU.nextDataId = 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 simpleAbsImpl(vals) {
+        var resultValues = new Float32Array(vals.length);
+        for (var i = 0; i < vals.length; ++i) {
+            resultValues[i] = Math.abs(vals[i]);
+        }
+        return resultValues;
+    }
+    var abs = function (args) {
+        var x = args.inputs.x;
+        var cpuBackend = args.backend;
+        assertNotComplex(x, 'abs');
+        var resultValues = new Float32Array(tfjsCore.util.sizeFromShape(x.shape));
+        var values = cpuBackend.data.get(x.dataId).values;
+        resultValues = simpleAbsImpl(values);
+        return cpuBackend.makeOutput(resultValues, x.shape, x.dtype);
+    };
+    var absConfig = {
+        kernelName: tfjsCore.Abs,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    /**
+     * Template that creates implementation for binary ops. Supports broadcast.
+     */
+    function createSimpleBinaryKernelImpl(op) {
+        return function (aShape, bShape, aVals, bVals, dtype) {
+            var newShape = tfjsCore.backend_util.assertAndGetBroadcastShape(aShape, bShape);
+            var resultRank = newShape.length;
+            var resultStrides = tfjsCore.util.computeStrides(newShape);
+            var resultSize = tfjsCore.util.sizeFromShape(newShape);
+            var result = tfjsCore.util.getTypedArrayFromDType(dtype, resultSize);
+            var aRank = aShape.length;
+            var bRank = bShape.length;
+            var aStrides = tfjsCore.util.computeStrides(aShape);
+            var bStrides = tfjsCore.util.computeStrides(bShape);
+            var aBroadcastDims = tfjsCore.backend_util.getBroadcastDims(aShape, newShape);
+            var bBroadcastDims = tfjsCore.backend_util.getBroadcastDims(bShape, newShape);
+            if (aBroadcastDims.length + bBroadcastDims.length === 0) {
+                for (var i = 0; i < result.length; ++i) {
+                    result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);
+                }
+            }
+            else {
+                var _loop_1 = function (i) {
+                    var loc = tfjsCore.util.indexToLoc(i, resultRank, resultStrides);
+                    var aLoc = loc.slice(-aRank);
+                    aBroadcastDims.forEach(function (d) { return aLoc[d] = 0; });
+                    var aIndex = tfjsCore.util.locToIndex(aLoc, aRank, aStrides);
+                    var bLoc = loc.slice(-bRank);
+                    bBroadcastDims.forEach(function (d) { return bLoc[d] = 0; });
+                    var bIndex = tfjsCore.util.locToIndex(bLoc, bRank, bStrides);
+                    result[i] = op(aVals[aIndex], bVals[bIndex]);
+                };
+                for (var i = 0; i < result.length; ++i) {
+                    _loop_1(i);
+                }
+            }
+            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.
+     * =============================================================================
+     */
+    function complex(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var real = inputs.real, imag = inputs.imag;
+        var realVals = backend.data.get(real.dataId).values;
+        var imagVals = backend.data.get(imag.dataId).values;
+        var complexInfo = backend.makeTensorInfo(real.shape, 'complex64');
+        var complex = backend.data.get(complexInfo.dataId);
+        // The complex tensor owns the underlying real and imag tensorInfos, only the
+        // complex tensor tracks refCount, when complexData is disposed the
+        // underlying tensorData will be disposed.
+        complex.complexTensorInfos = {
+            real: backend.makeTensorInfo(real.shape, 'float32', realVals),
+            imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals)
+        };
+        return complexInfo;
+    }
+    var complexConfig = {
+        kernelName: tfjsCore.Complex,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    /**
+     * Generates a tensorInfo with all zeros value.
+     * @param backend cpu backend.
+     * @param shape Shape for the zeros tensor.
+     * @param dtype Optional. If set, the result has this dtype.
+     */
+    function zeros(backend, shape, dtype) {
+        if (dtype === void 0) { dtype = 'float32'; }
+        if (dtype === 'complex64') {
+            var real = zeros(backend, shape, 'float32');
+            var imag = zeros(backend, shape, 'float32');
+            return complex({ inputs: { real: real, imag: imag }, backend: backend });
+        }
+        var values = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(shape), dtype);
+        return backend.makeTensorInfo(shape, dtype, 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.
+     * =============================================================================
+     */
+    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: tfjsCore.Identity,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    function real(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var input = inputs.input;
+        var real = backend.data.get(input.dataId).complexTensorInfos.real;
+        var realVal = backend.data.get(real.dataId).values;
+        // When complex tensor is disposed, its underlying parts will be disposed too.
+        // Make new tensor out of the real value of the complex. This makes sure the
+        // value is still accessible even if complex tensor is disposed.
+        return backend.makeTensorInfo(real.shape, real.dtype, realVal);
+    }
+    var realConfig = {
+        kernelName: tfjsCore.Real,
+        backendName: 'cpu',
+        kernelFunc: real
+    };
+
+    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 });
+            }
+            var zerosTensorInfo = zeros(backend, x.shape, x.dtype);
+            var floatX = cast({ inputs: { x: x }, backend: backend, attrs: { dtype: 'float32' } });
+            var result = complex({ inputs: { real: floatX, imag: zerosTensorInfo }, backend: backend });
+            backend.disposeIntermediateTensorInfo(zerosTensorInfo);
+            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 (!tfjsCore.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') {
+            var values = backend.data.get(x.dataId).values;
+            var resultValues = Int32Array.from(values);
+            return backend.makeTensorInfo(x.shape, 'int32', resultValues);
+        }
+        if (dtype === 'bool') {
+            // This is essentially the result of notEqual(x, 0). We avoid using
+            // kernel notEqual to avoid circular dependency, i.e. binary_utils ->
+            // cast -> notEqual -> binary_utils.
+            var xVals = backend.data.get(x.dataId).values;
+            var zero = tfjsCore.util.toTypedArray([0], x.dtype);
+            var _a = __read(createSimpleBinaryKernelImpl(function (a, b) { return (a !== b) ? 1 : 0; })(x.shape, [], xVals, zero, 'bool'), 2), resultData = _a[0], resultShape = _a[1];
+            return backend.makeTensorInfo(resultShape, 'bool', resultData);
+        }
+        throw new Error("Error in Cast: failed to cast " + x.dtype + " to " + dtype);
+    }
+    var castConfig = {
+        kernelName: tfjsCore.Cast,
+        backendName: 'cpu',
+        kernelFunc: cast
+    };
+
+    /**
+     * Template that creates a `KernelFunc` for binary ops.
+     * @param name Kernel name.
+     * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.
+     * @param binaryKernelComplexImpl Optional. If exists, represents a
+     *     `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype
+     *     is `complex64`.
+     * @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(name, simpleImpl, complexImpl, dtype) {
+        if (complexImpl == null) {
+            return function (_a) {
+                var inputs = _a.inputs, backend = _a.backend;
+                var a = inputs.a, b = inputs.b;
+                var cpuBackend = backend;
+                assertNotComplex([a, b], name);
+                var aVals = cpuBackend.data.get(a.dataId).values;
+                var bVals = cpuBackend.data.get(b.dataId).values;
+                var decodedAVals = a.dtype === 'string' ?
+                    // tslint:disable-next-line: no-any
+                    tfjsCore.backend_util.fromUint8ToStringArray(aVals) :
+                    aVals;
+                var decodedBVals = a.dtype === 'string' ?
+                    // tslint:disable-next-line: no-any
+                    tfjsCore.backend_util.fromUint8ToStringArray(bVals) :
+                    bVals;
+                var $dtype = dtype || a.dtype;
+                var _b = __read(simpleImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype), 2), resultData = _b[0], resultShape = _b[1];
+                return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
+            };
+        }
+        return function (_a) {
+            var inputs = _a.inputs, backend = _a.backend;
+            var a = inputs.a, b = inputs.b;
+            var cpuBackend = backend;
+            if (a.dtype === 'complex64' || b.dtype === 'complex64') {
+                var $aComplex = cast({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: 'complex64' } });
+                var $aComplexVals = cpuBackend.data.get($aComplex.dataId);
+                var aReal = $aComplexVals.complexTensorInfos.real;
+                var aImag = $aComplexVals.complexTensorInfos.imag;
+                var aRealVals = cpuBackend.data.get(aReal.dataId).values;
+                var aImagVals = cpuBackend.data.get(aImag.dataId).values;
+                var $bComplex = cast({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: 'complex64' } });
+                var $bComplexVals = cpuBackend.data.get($bComplex.dataId);
+                var bReal = $bComplexVals.complexTensorInfos.real;
+                var bImag = $bComplexVals.complexTensorInfos.imag;
+                var bRealVals = cpuBackend.data.get(bReal.dataId).values;
+                var bImagVals = cpuBackend.data.get(bImag.dataId).values;
+                var _b = __read(complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals), 3), resultRealData = _b[0], resultImagData = _b[1], resultShape = _b[2];
+                var resultReal = cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData);
+                var resultImag = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData);
+                var result = complex({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });
+                cpuBackend.disposeIntermediateTensorInfo($aComplex);
+                cpuBackend.disposeIntermediateTensorInfo($bComplex);
+                cpuBackend.disposeIntermediateTensorInfo(resultReal);
+                cpuBackend.disposeIntermediateTensorInfo(resultImag);
+                return result;
+            }
+            else {
+                var aVals = cpuBackend.data.get(a.dataId).values;
+                var bVals = cpuBackend.data.get(b.dataId).values;
+                var $dtype = dtype || a.dtype;
+                var _c = __read(simpleImpl(a.shape, b.shape, aVals, bVals, $dtype), 2), resultData = _c[0], resultShape = _c[1];
+                return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
+            }
+        };
+    }
+    /**
+     * Template that creates the complex type implementation for binary ops.
+     * Supports broadcast.
+     */
+    function createComplexBinaryKernelImpl(op) {
+        return function (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) {
+            var resultShape = tfjsCore.backend_util.assertAndGetBroadcastShape(aShape, bShape);
+            var resultSize = tfjsCore.util.sizeFromShape(resultShape);
+            var resultRank = resultShape.length;
+            var resultStrides = tfjsCore.util.computeStrides(resultShape);
+            var resultRealVals = tfjsCore.util.getTypedArrayFromDType('float32', resultSize);
+            var resultImagVals = tfjsCore.util.getTypedArrayFromDType('float32', resultSize);
+            var aBroadcastDims = tfjsCore.backend_util.getBroadcastDims(aShape, resultShape);
+            var bBroadcastDims = tfjsCore.backend_util.getBroadcastDims(bShape, resultShape);
+            var aVals = tfjsCore.backend_util.mergeRealAndImagArrays(aRealVals, aImagVals);
+            var bVals = tfjsCore.backend_util.mergeRealAndImagArrays(bRealVals, bImagVals);
+            var aRank = aShape.length;
+            var aStrides = tfjsCore.util.computeStrides(aShape);
+            var bRank = bShape.length;
+            var bStrides = tfjsCore.util.computeStrides(bShape);
+            if (aBroadcastDims.length + bBroadcastDims.length === 0) {
+                for (var i = 0; i < resultRealVals.length; i++) {
+                    var aIdx = i % aVals.length;
+                    var bIdx = i % bVals.length;
+                    var result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);
+                    resultRealVals[i] = result.real;
+                    resultImagVals[i] = result.imag;
+                }
+            }
+            else {
+                var _loop_1 = function (i) {
+                    var loc = tfjsCore.util.indexToLoc(i, resultRank, resultStrides);
+                    var aLoc = loc.slice(-aRank);
+                    aBroadcastDims.forEach(function (d) { return aLoc[d] = 0; });
+                    var aIndex = tfjsCore.util.locToIndex(aLoc, aRank, aStrides);
+                    var bLoc = loc.slice(-bRank);
+                    bBroadcastDims.forEach(function (d) { return bLoc[d] = 0; });
+                    var bIndex = tfjsCore.util.locToIndex(bLoc, bRank, bStrides);
+                    var opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);
+                    resultRealVals[i] = opResult.real;
+                    resultImagVals[i] = opResult.imag;
+                };
+                for (var i = 0; i < resultRealVals.length; i++) {
+                    _loop_1(i);
+                }
+            }
+            return [resultRealVals, resultImagVals, resultShape];
+        };
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 addImpl = createSimpleBinaryKernelImpl((function (a, b) { return a + b; }));
+    var addComplexImpl = createComplexBinaryKernelImpl((function (aReal, aImag, bReal, bImag) {
+        return { real: aReal + bReal, imag: aImag + bImag };
+    }));
+    var add = binaryKernelFunc(tfjsCore.Add, addImpl, addComplexImpl);
+    var addConfig = {
+        kernelName: tfjsCore.Add,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {
+        var weightsSize = tfjsCore.util.sizeFromShape(weightsShape);
+        var outVals = tfjsCore.util.makeZerosTypedArray(size, weightsDtype);
+        for (var i = 0; i < xVals.length; i++) {
+            var 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) {
+        if (binaryOutput === void 0) { binaryOutput = false; }
+        var numRows = xBuf.shape[0];
+        var numCols = xBuf.shape[1];
+        var outBuf = tfjsCore.buffer([numRows, size], weightsBuf.dtype);
+        for (var i = 0; i < numRows; i++) {
+            for (var j = 0; j < numCols; j++) {
+                var 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 function (values, dtype, attrs) {
+            var newValues = tfjsCore.util.getTypedArrayFromDType(dtype, values.length);
+            for (var 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.
+     * =============================================================================
+     */
+    /**
+     * Template that creates a `KernelFunc` for unary ops.
+     * @param name Kernel name.
+     * @param op A `SimpleUnaryOperation` for the kernel.
+     * @param dtype Optional. If set, the result has this dtype. Otherwise, the
+     *     result has the same dtype as the input. This is mainly used in certain
+     *     kernels that return bool type, such as isFinite, isInf, etc.
+     */
+    function unaryKernelFunc(name, op, dtype) {
+        return function (_a) {
+            var inputs = _a.inputs, attrs = _a.attrs, backend = _a.backend;
+            var x = inputs.x;
+            assertNotComplex(x, name);
+            if (x.dtype === 'string' || dtype === 'string') {
+                throw new Error('unaryKernelFunc does not support string input/output');
+            }
+            var cpuBackend = backend;
+            var values = cpuBackend.data.get(x.dataId).values;
+            var xSize = tfjsCore.util.sizeFromShape(x.shape);
+            var $dtype = dtype || x.dtype;
+            var newValues = tfjsCore.util.getArrayFromDType($dtype, xSize);
+            for (var i = 0; i < xSize; ++i) {
+                newValues[i] = op(values[i], attrs);
+            }
+            return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
+        };
+    }
+    /**
+     * Template that creates a `KernelFunc` for unary ops from the given
+     * `SimpleUnaryImpl`..
+     * @param name Kernel name.
+     * @param unaryImpl A `SimpleUnaryImpl` that implements the op.
+     * @param dtype Optional. If set, the result has this dtype. Otherwise, the
+     *     result has the same dtype as the input. This is mainly used in certain
+     *     kernels that return bool type, such as isFinite, isInf, etc.
+     */
+    function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {
+        return function (_a) {
+            var inputs = _a.inputs, attrs = _a.attrs, backend = _a.backend;
+            var x = inputs.x;
+            assertNotComplex(x, name);
+            if (x.dtype === 'string' || dtype === 'string') {
+                throw new Error('unaryKernelFunc does not support string input/output');
+            }
+            var cpuBackend = backend;
+            var values = cpuBackend.data.get(x.dataId).values;
+            var $dtype = dtype || x.dtype;
+            var newValues = unaryImpl(values, $dtype, attrs);
+            return cpuBackend.makeTensorInfo(x.shape, $dtype, 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.
+     * =============================================================================
+     */
+    var ceilImpl = createSimpleUnaryImpl(function (xi) { return Math.ceil(xi); });
+    var ceil = unaryKernelFuncFromImpl(tfjsCore.Ceil, ceilImpl);
+    var ceilConfig = {
+        kernelName: tfjsCore.Ceil,
+        backendName: 'cpu',
+        kernelFunc: ceil,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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) {
+        var outVals = tfjsCore.util.getArrayFromDType(dtype, tfjsCore.util.sizeFromShape(outShape));
+        if (simplyConcat && dtype !== 'string') {
+            // Use built-in TypedArray.set() method for speed.
+            var offset_1 = 0;
+            inputs.forEach(function (input) {
+                var size = tfjsCore.util.sizeFromShape(input.shape);
+                outVals.set(input.vals, offset_1);
+                offset_1 += size;
+            });
+        }
+        else {
+            var colOffset_1 = 0;
+            inputs.forEach(function (input) {
+                var decodedData = dtype === 'string' ?
+                    tfjsCore.backend_util.fromUint8ToStringArray(input.vals) :
+                    input.vals;
+                var tIdx = 0;
+                for (var row = 0; row < input.shape[0]; ++row) {
+                    var resIdx = row * outShape[1] + colOffset_1;
+                    for (var col = 0; col < input.shape[1]; ++col) {
+                        outVals[resIdx + col] = decodedData[tIdx++];
+                    }
+                }
+                colOffset_1 += 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.
+     * =============================================================================
+     */
+    var equalImpl = createSimpleBinaryKernelImpl(function (a, b) { return (a === b) ? 1 : 0; });
+    var equal = binaryKernelFunc(tfjsCore.Equal, equalImpl, null /* complexImpl */, 'bool');
+    var equalConfig = {
+        kernelName: tfjsCore.Equal,
+        backendName: 'cpu',
+        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 expImpl = createSimpleUnaryImpl(function (xi) { return Math.exp(xi); });
+    var exp = unaryKernelFuncFromImpl(tfjsCore.Exp, expImpl, 'float32');
+    var expConfig = {
+        kernelName: tfjsCore.Exp,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    var expm1Impl = createSimpleUnaryImpl(function (xi) { return Math.expm1(xi); });
+    var expm1 = unaryKernelFuncFromImpl(tfjsCore.Expm1, expm1Impl);
+    var expm1Config = {
+        kernelName: tfjsCore.Expm1,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    var floorImpl = createSimpleUnaryImpl(function (xi) { return Math.floor(xi); });
+    var floor = unaryKernelFuncFromImpl(tfjsCore.Floor, floorImpl);
+    var floorConfig = {
+        kernelName: tfjsCore.Floor,
+        backendName: 'cpu',
+        kernelFunc: floor,
+    };
+
+    function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) {
+        var outBuf = tfjsCore.buffer([numSlices, sliceSize], dtype);
+        for (var i = 0; i < numSlices; i++) {
+            var index = [];
+            var flattenIndex = 0;
+            for (var j = 0; j < sliceRank; j++) {
+                var 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 (var k = 0; k < sliceSize; k++) {
+                outBuf.values[i * sliceSize + k] = paramsBuf.get.apply(paramsBuf, __spread(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) {
+        var outBuf = tfjsCore.buffer(flattenOutputShape, xBuf.dtype);
+        for (var i = 0; i < outBuf.size; ++i) {
+            var newLoc = outBuf.indexToLoc(i);
+            var originalLoc = newLoc.slice();
+            var batchIdx = originalLoc[0];
+            var indicesIdx = originalLoc[2];
+            var indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);
+            originalLoc[2] = indicesBuf.values[indicesIndex];
+            var 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.
+     * =============================================================================
+     */
+    var greaterImpl = createSimpleBinaryKernelImpl(function (a, b) { return (a > b) ? 1 : 0; });
+    var greater = binaryKernelFunc(tfjsCore.Greater, greaterImpl, null /* complexImpl */, 'bool');
+    var greaterConfig = {
+        kernelName: tfjsCore.Greater,
+        backendName: 'cpu',
+        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 greaterEqualImpl = createSimpleBinaryKernelImpl(function (a, b) { return (a >= b) ? 1 : 0; });
+    var greaterEqual = binaryKernelFunc(tfjsCore.GreaterEqual, greaterEqualImpl, null /* complexImpl */, 'bool');
+    var greaterEqualConfig = {
+        kernelName: tfjsCore.GreaterEqual,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    var lessImpl = createSimpleBinaryKernelImpl(function (a, b) { return (a < b) ? 1 : 0; });
+    var less = binaryKernelFunc(tfjsCore.Less, lessImpl, null /* complexImpl */, 'bool');
+    var lessConfig = {
+        kernelName: tfjsCore.Less,
+        backendName: 'cpu',
+        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 lessEqualImpl = createSimpleBinaryKernelImpl(function (a, b) { return (a <= b) ? 1 : 0; });
+    var lessEqual = binaryKernelFunc(tfjsCore.LessEqual, lessEqualImpl, null /* complexImpl */, 'bool');
+    var lessEqualConfig = {
+        kernelName: tfjsCore.LessEqual,
+        backendName: 'cpu',
+        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 linSpaceImpl(start, stop, num) {
+        var step = (stop - start) / (num - 1);
+        var values = tfjsCore.util.makeZerosTypedArray(num, 'float32');
+        values[0] = start;
+        for (var 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.
+     * =============================================================================
+     */
+    var logImpl = createSimpleUnaryImpl(function (xi) { return Math.log(xi); });
+    var log = unaryKernelFuncFromImpl(tfjsCore.Log, logImpl);
+    var logConfig = {
+        kernelName: tfjsCore.Log,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    function maxImpl(aVals, reduceSize, outShape, dtype) {
+        var vals = tfjsCore.util.getTypedArrayFromDType(dtype, tfjsCore.util.sizeFromShape(outShape));
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var max = aVals[offset];
+            for (var j = 0; j < reduceSize; ++j) {
+                var 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.
+     * =============================================================================
+     */
+    var maximumImpl = createSimpleBinaryKernelImpl((function (aValue, bValue) { return Math.max(aValue, bValue); }));
+    var maximum = binaryKernelFunc(tfjsCore.Maximum, maximumImpl);
+    var maximumConfig = {
+        kernelName: tfjsCore.Maximum,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    var minimumImpl = createSimpleBinaryKernelImpl((function (aValue, bValue) { return Math.min(aValue, bValue); }));
+    var minimum = binaryKernelFunc(tfjsCore.Minimum, minimumImpl);
+    var minimumConfig = {
+        kernelName: tfjsCore.Minimum,
+        backendName: 'cpu',
+        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 multiplyImpl = createSimpleBinaryKernelImpl((function (aValue, bValue) { return aValue * bValue; }));
+    var multiplyComplexImpl = createComplexBinaryKernelImpl((function (aReal, aImag, bReal, bImag) {
+        return {
+            real: aReal * bReal - aImag * bImag,
+            imag: aReal * bImag + aImag * bReal
+        };
+    }));
+    var multiply = binaryKernelFunc(tfjsCore.Multiply, multiplyImpl, multiplyComplexImpl);
+    var multiplyConfig = {
+        kernelName: tfjsCore.Multiply,
+        backendName: 'cpu',
+        kernelFunc: multiply
+    };
+
+    function negImpl(xVals, xShape, xDtype) {
+        var minusOne = tfjsCore.util.createScalarValue(-1, xDtype);
+        return multiplyImpl([], xShape, minusOne, xVals, xDtype);
+    }
+    function neg(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var x = inputs.x;
+        assertNotComplex(x, 'neg');
+        var xVals = backend.data.get(x.dataId).values;
+        var _a = __read(negImpl(xVals, x.shape, x.dtype), 2), res = _a[0], newShape = _a[1];
+        return backend.makeTensorInfo(newShape, x.dtype, res);
+    }
+    var negConfig = {
+        kernelName: tfjsCore.Neg,
+        backendName: 'cpu',
+        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 notEqualImpl = createSimpleBinaryKernelImpl((function (a, b) { return (a !== b) ? 1 : 0; }));
+    var notEqual = binaryKernelFunc(tfjsCore.NotEqual, notEqualImpl, null /* complexOp */, 'bool');
+    var notEqualConfig = {
+        kernelName: tfjsCore.NotEqual,
+        backendName: 'cpu',
+        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 transposeImpl(xVals, xShape, dtype, perm, newShape) {
+        var xRank = xShape.length;
+        var xSize = tfjsCore.util.sizeFromShape(xShape);
+        var xStrides = tfjsCore.util.computeStrides(xShape);
+        var newStrides = tfjsCore.util.computeStrides(newShape);
+        var result = tfjsCore.util.getTypedArrayFromDType(dtype, tfjsCore.util.sizeFromShape(newShape));
+        for (var i = 0; i < xSize; ++i) {
+            var loc = tfjsCore.util.indexToLoc(i, xRank, xStrides);
+            // Permute location.
+            var newLoc = new Array(loc.length);
+            for (var i_1 = 0; i_1 < newLoc.length; i_1++) {
+                newLoc[i_1] = loc[perm[i_1]];
+            }
+            var newIndex = tfjsCore.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 transpose(args) {
+        var inputs = args.inputs, attrs = args.attrs, backend = args.backend;
+        var x = inputs.x;
+        var perm = attrs.perm;
+        assertNotComplex(x, 'transpose');
+        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 values = backend.data.get(x.dataId).values;
+        var result = transposeImpl(values, x.shape, x.dtype, perm, newShape);
+        var dataId = backend.write(result, newShape, x.dtype);
+        return { dataId: dataId, shape: newShape, dtype: x.dtype };
+    }
+    var transposeConfig = {
+        kernelName: tfjsCore.Transpose,
+        backendName: 'cpu',
+        kernelFunc: transpose
+    };
+
+    function prodImpl(xShape, xDtype, xVals, reductionAxes) {
+        var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes(xShape, reductionAxes), 2), outShape = _a[0], reduceShape = _a[1];
+        var outDtype = tfjsCore.upcastType(xDtype, 'int32');
+        var outVals = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(outShape), outDtype);
+        var reduceSize = tfjsCore.util.sizeFromShape(reduceShape);
+        for (var i = 0; i < outVals.length; ++i) {
+            var offset = i * reduceSize;
+            var prod_1 = 1;
+            for (var j = 0; j < reduceSize; ++j) {
+                prod_1 *= xVals[offset + j];
+            }
+            outVals[i] = prod_1;
+        }
+        return { outVals: outVals, outShape: outShape, outDtype: outDtype };
+    }
+    function prod(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis, keepDims = attrs.keepDims;
+        assertNotComplex(x, 'prod');
+        var xRank = x.shape.length;
+        var axes = tfjsCore.util.parseAxisParam(axis, x.shape);
+        var permutation = tfjsCore.backend_util.getAxesPermutation(axes, xRank);
+        var reductionAxes = axes;
+        var permutedX = x;
+        var intermediateTensorInfos = [];
+        if (permutation != null) {
+            permutedX = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutation } });
+            intermediateTensorInfos.push(permutedX);
+            reductionAxes = tfjsCore.backend_util.getInnerMostAxes(reductionAxes.length, xRank);
+        }
+        var xVals = backend.data.get(permutedX.dataId).values;
+        var _a = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes), outVals = _a.outVals, outShape = _a.outShape, outDtype = _a.outDtype;
+        var resultShape = outShape;
+        if (keepDims) {
+            resultShape = tfjsCore.backend_util.expandShapeToKeepDim(outShape, axes);
+        }
+        intermediateTensorInfos.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return backend.makeTensorInfo(resultShape, outDtype, outVals);
+    }
+    var prodConfig = {
+        kernelName: tfjsCore.Prod,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    function rangeImpl(start, stop, step, dtype) {
+        var sameStartStop = start === stop;
+        var increasingRangeNegativeStep = start < stop && step < 0;
+        var decreasingRangePositiveStep = stop < start && step > 1;
+        if (sameStartStop || increasingRangeNegativeStep ||
+            decreasingRangePositiveStep) {
+            return tfjsCore.util.makeZerosTypedArray(0, dtype);
+        }
+        var numElements = Math.abs(Math.ceil((stop - start) / step));
+        var values = tfjsCore.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 (var 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.
+     * =============================================================================
+     */
+    var rsqrtImpl = createSimpleUnaryImpl(function (xi) { return 1 / Math.sqrt(xi); });
+    var rsqrt = unaryKernelFuncFromImpl(tfjsCore.Rsqrt, rsqrtImpl);
+    var rsqrtConfig = {
+        kernelName: tfjsCore.Rsqrt,
+        backendName: 'cpu',
+        kernelFunc: rsqrt,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT 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 sigmoidImpl = createSimpleUnaryImpl(function (xi) { return 1 / (1 + Math.exp(-xi)); });
+    var sigmoid = unaryKernelFunc(tfjsCore.Sigmoid, function (xi) { return 1 / (1 + Math.exp(-xi)); });
+    var sigmoidConfig = {
+        kernelName: tfjsCore.Sigmoid,
+        backendName: 'cpu',
+        kernelFunc: sigmoid,
+    };
+
+    function sliceImpl(vals, begin, size, shape, dtype) {
+        var isContinous = tfjsCore.slice_util.isSliceContinous(shape, begin, size);
+        var length = tfjsCore.util.sizeFromShape(size);
+        var xStrides = tfjsCore.util.computeStrides(shape);
+        if (isContinous) {
+            var flatOffset = tfjsCore.slice_util.computeFlatOffset(begin, xStrides);
+            if (dtype === 'string') {
+                return vals.slice(flatOffset, flatOffset + length);
+            }
+            return vals.subarray(flatOffset, flatOffset + length);
+        }
+        var decodedData = dtype === 'string' ?
+            tfjsCore.backend_util.fromUint8ToStringArray(vals) :
+            vals;
+        var inBuf = tfjsCore.buffer(shape, dtype, decodedData);
+        var outBuf = tfjsCore.buffer(size, dtype);
+        for (var i = 0; i < outBuf.size; ++i) {
+            var outLoc = outBuf.indexToLoc(i);
+            var inLoc = outLoc.map(function (idx, j) { return idx + begin[j]; });
+            outBuf.set.apply(outBuf, __spread([inBuf.get.apply(inBuf, __spread(inLoc))], outLoc));
+        }
+        if (dtype === 'string') {
+            return tfjsCore.backend_util.fromStringArrayToUint8(outBuf.values);
+        }
+        return outBuf.values;
+    }
+    function slice(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var begin = attrs.begin, size = attrs.size;
+        assertNotComplex(x, 'slice');
+        var _a = __read(tfjsCore.slice_util.parseSliceParams(x, begin, size), 2), $begin = _a[0], $size = _a[1];
+        tfjsCore.slice_util.assertParamsValid(x, $begin, $size);
+        var vals = backend.data.get(x.dataId).values;
+        var outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);
+        return backend.makeTensorInfo($size, x.dtype, outVals);
+    }
+    var sliceConfig = {
+        kernelName: tfjsCore.Slice,
+        backendName: 'cpu',
+        kernelFunc: slice
+    };
+
+    /**
+     * @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) {
+        var indicesCount = indicesShape[0];
+        var denseRows = denseShape[0];
+        var emptyRowIndicator = new Array(denseRows);
+        var reverseIndexMap = new Array(indicesCount);
+        var rank = indicesShape[1];
+        if (denseRows === 0) {
+            if (indicesCount !== 0) {
+                throw new Error(tfjsCore.backend_util.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount));
+            }
+            var outputIndices = tfjsCore.util.getArrayFromDType(indicesDType, 0);
+            var outputValues = tfjsCore.util.getArrayFromDType(valuesDType, 0);
+            return [
+                outputIndices, [0, rank], outputValues, emptyRowIndicator, reverseIndexMap
+            ];
+        }
+        var rowsAreOrdered = true;
+        var lastIndicesRow = 0;
+        var csrOffset = new Array(denseRows).fill(0);
+        for (var i = 0; i < indicesCount; ++i) {
+            // indices is a 2d tensor with shape of [N, rank]
+            var row = indices[i * rank];
+            if (row < 0) {
+                throw new Error(tfjsCore.backend_util.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row));
+            }
+            if (row >= denseRows) {
+                throw new Error(tfjsCore.backend_util.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i, row, denseRows));
+            }
+            ++csrOffset[row];
+            rowsAreOrdered = rowsAreOrdered && (row >= lastIndicesRow);
+            lastIndicesRow = row;
+        }
+        var allRowsFull = true;
+        for (var row = 0; row < denseRows; ++row) {
+            // csrOffset here describes the number of elements in this dense row
+            var 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) {
+            var outputIndices = indices;
+            var outputValues = values;
+            for (var i = 0; i < indicesCount; ++i) {
+                reverseIndexMap[i] = i;
+            }
+            return [
+                outputIndices, [indicesCount, rank], outputValues, emptyRowIndicator,
+                reverseIndexMap
+            ];
+        }
+        else {
+            var fullIndicesCount = csrOffset[denseRows - 1];
+            var outputIndices = tfjsCore.util.getArrayFromDType(indicesDType, fullIndicesCount * rank);
+            var outputValues = tfjsCore.util.getArrayFromDType(valuesDType, fullIndicesCount);
+            var filledCount = new Array(denseRows).fill(0);
+            // Fill in values for rows that are not missing
+            for (var i = 0; i < indicesCount; ++i) {
+                // indices is a 2d tensor with shape of [N, rank]
+                var row = indices[i * rank];
+                var offset = filledCount[row];
+                var outputI = ((row === 0) ? 0 : csrOffset[row - 1]) + offset;
+                filledCount[row]++; // Increment the filled count for this row.
+                for (var 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 (var row = 0; row < denseRows; ++row) {
+                var rowCount = filledCount[row];
+                if (rowCount === 0) { // We haven't filled this row
+                    var 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 (var 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) {
+        var denseSize = tfjsCore.util.sizeFromShape(inputShape);
+        var nnz = inputIndicesShape[0];
+        var outputRank = targetShape.length;
+        // Compute the output shape. Determine product of specified dimensions, and
+        // find the index of the unspecified one.
+        var outputShape = [];
+        var product = 1;
+        var unknownIndex = -1;
+        for (var d = 0; d < outputRank; ++d) {
+            var size = targetShape[d];
+            if (size === -1) {
+                if (unknownIndex !== -1) {
+                    throw new Error(tfjsCore.backend_util
+                        .getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d));
+                }
+                unknownIndex = d;
+                outputShape.push(1);
+            }
+            else {
+                if (size < 0) {
+                    throw new Error(tfjsCore.backend_util.getSparseReshapeNegativeOutputDimErrorMessage(d, size));
+                }
+                product *= size;
+                outputShape.push(size);
+            }
+        }
+        if (unknownIndex !== -1) {
+            if (product <= 0) {
+                throw new Error(tfjsCore.backend_util.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
+            }
+            var missing = Math.trunc(denseSize / product);
+            if (product * missing !== denseSize) {
+                throw new Error(tfjsCore.backend_util.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape));
+            }
+            outputShape[unknownIndex] = missing;
+        }
+        var outputSize = tfjsCore.util.sizeFromShape(outputShape);
+        if (outputSize !== denseSize) {
+            throw new Error(tfjsCore.backend_util.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape));
+        }
+        var inputRank = inputShape.length;
+        var inputStrides = [];
+        if (inputRank > 0) {
+            inputStrides[inputRank - 1] = 1;
+            for (var d = inputRank - 2; d >= 0; --d) {
+                inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];
+            }
+        }
+        var outputStrides = [];
+        if (outputRank > 0) {
+            outputStrides[outputRank - 1] = 1;
+            for (var d = outputRank - 2; d >= 0; --d) {
+                outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];
+            }
+        }
+        var newIndices = tfjsCore.util.getArrayFromDType(inputDType, nnz * outputRank);
+        for (var i = 0; i < nnz; ++i) {
+            var id = 0;
+            for (var j = 0; j < inputRank; ++j) {
+                // inputIndices is a 2d tensor with shape of [nnz, inputRank]
+                id += inputIndices[i * inputRank + j] * inputStrides[j];
+            }
+            for (var 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, defaultValue) {
+        if (isMean === void 0) { isMean = false; }
+        if (defaultValue === void 0) { defaultValue = 0; }
+        var numIndices = indices.length;
+        // Flatten the array to two dimensions
+        var inputFlat = [inputShape[0], input.length / inputShape[0]];
+        var numCol = inputFlat[1];
+        // Note that the current implementation assumes that segmentIds values are
+        // sorted.
+        var lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;
+        var outputRows = lastSegmentIdPlusOne;
+        if (outputRows < 0) {
+            throw new Error(tfjsCore.backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
+        }
+        var outputShape = inputShape.slice();
+        outputShape[0] = outputRows;
+        var outputLength = outputShape.reduce(function (product, value) { return product * value; }, 1);
+        // Output array is initialized with the value 0 by default.
+        var output = tfjsCore.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(tfjsCore.backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
+        }
+        var start = 0, end = 1;
+        // Index from which the output is not initialized.
+        var uninitializedIndex = 0;
+        var outIndex = segmentIds[start];
+        while (true) {
+            // We initialize nextIndex to 0 to avoid may be uninitialized warning
+            var 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(tfjsCore.backend_util
+                        .getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
+                }
+            }
+            if (outIndex < 0 || outIndex >= outputRows) {
+                throw new Error(tfjsCore.backend_util.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows));
+            }
+            // 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 (var i = start; i < end; ++i) {
+                var index = indices[i];
+                if (index < 0 || index >= inputFlat[0]) {
+                    throw new Error(tfjsCore.backend_util.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i, indices[i], inputFlat[0]));
+                }
+                for (var j = 0; j < numCol; j++) {
+                    output[outIndex * numCol + j] += input[index * numCol + j];
+                }
+            }
+            if (isMean) {
+                for (var 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.
+     * =============================================================================
+     */
+    var sqrtImpl = createSimpleUnaryImpl(function (xi) { return Math.sqrt(xi); });
+    var sqrt = unaryKernelFunc(tfjsCore.Sqrt, function (xi) { return Math.sqrt(xi); });
+    var sqrtConfig = {
+        kernelName: tfjsCore.Sqrt,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    var squaredDifferenceImpl = createSimpleBinaryKernelImpl((function (a, b) {
+        var diff = a - b;
+        return diff * diff;
+    }));
+    var squaredDifference = binaryKernelFunc(tfjsCore.SquaredDifference, squaredDifferenceImpl);
+    var squaredDifferenceConfig = {
+        kernelName: tfjsCore.SquaredDifference,
+        backendName: 'cpu',
+        kernelFunc: squaredDifference
+    };
+
+    function stridedSliceImpl(outShape, xBuf, strides, begin) {
+        var outBuf = tfjsCore.buffer(outShape, xBuf.dtype);
+        for (var i = 0; i < outBuf.size; i++) {
+            var loc = outBuf.indexToLoc(i);
+            var newLoc = new Array(loc.length);
+            for (var j = 0; j < newLoc.length; j++) {
+                newLoc[j] = loc[j] * strides[j] + begin[j];
+            }
+            outBuf.set.apply(outBuf, __spread([xBuf.get.apply(xBuf, __spread(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.
+     */
+    var StringNGramsOp = /** @class */ (function () {
+        function StringNGramsOp(separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) {
+            this.separator = tfjsCore.util.encodeString(separator);
+            this.nGramWidths = nGramWidths;
+            this.leftPad = tfjsCore.util.encodeString(leftPad);
+            this.rightPad = tfjsCore.util.encodeString(rightPad);
+            this.padWidth = padWidth;
+            this.preserveShort = preserveShortSequences;
+        }
+        StringNGramsOp.prototype.getPadWidth = function (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);
+        };
+        StringNGramsOp.prototype.getNumNGrams = function (length, nGramWidth) {
+            var padWidth = this.getPadWidth(nGramWidth);
+            return Math.max(0, ((length + 2 * padWidth) - nGramWidth) + 1);
+        };
+        StringNGramsOp.prototype.createNGrams = function (data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) {
+            var _loop_1 = function (nGramIndex) {
+                var padWidth = this_1.getPadWidth(nGramWidth);
+                var leftPadding = Math.max(0, padWidth - nGramIndex);
+                var rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));
+                var numTokens = nGramWidth - (leftPadding + rightPadding);
+                var 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.
+                var nGramSize = 0;
+                // Size of the left padding.
+                nGramSize += leftPadding * this_1.leftPad.length;
+                // Size of the tokens.
+                for (var n = 0; n < numTokens; ++n) {
+                    nGramSize += data[dataStartIndex + n].length;
+                }
+                // Size of the right padding.
+                nGramSize += rightPadding * this_1.rightPad.length;
+                // Size of the separators.
+                var numSeparators = leftPadding + rightPadding + numTokens - 1;
+                nGramSize += numSeparators * this_1.separator.length;
+                // Build the nGram.
+                output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);
+                var nGram = output[outputStartIndex + nGramIndex];
+                var nextNGramIndex = 0;
+                var appendToNGram = function (str) { return str.forEach(function (value) { return nGram[nextNGramIndex++] = value; }); };
+                for (var n = 0; n < leftPadding; ++n) {
+                    appendToNGram(this_1.leftPad);
+                    appendToNGram(this_1.separator);
+                }
+                // Only output first numTokens - 1 pairs of data and separator
+                for (var n = 0; n < numTokens - 1; ++n) {
+                    appendToNGram(data[dataStartIndex + n]);
+                    appendToNGram(this_1.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 (var n = 0; n < rightPadding; ++n) {
+                        appendToNGram(this_1.separator);
+                        appendToNGram(this_1.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 (var n = 0; n < rightPadding - 1; ++n) {
+                        appendToNGram(this_1.rightPad);
+                        appendToNGram(this_1.separator);
+                    }
+                    appendToNGram(this_1.rightPad);
+                }
+            };
+            var this_1 = this;
+            for (var nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {
+                _loop_1(nGramIndex);
+            }
+        };
+        // 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.
+        StringNGramsOp.prototype.compute = function (data, splits) {
+            var _this = this;
+            // Validate that the splits are valid indices into data, only if there are
+            // splits specified.
+            var inputDataSize = data.length;
+            var splitsSize = splits.length;
+            if (splitsSize > 0) {
+                var prevSplit = splits[0];
+                if (prevSplit !== 0) {
+                    throw new Error("First split value must be 0, got " + prevSplit);
+                }
+                for (var i = 1; i < splitsSize; ++i) {
+                    var 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);
+                }
+            }
+            var numBatchItems = splitsSize - 1;
+            var nGramsSplits = tfjsCore.util.getArrayFromDType('int32', splitsSize);
+            // If there is no data or size, return an empty ragged tensor.
+            if (inputDataSize === 0 || splitsSize === 0) {
+                var empty = new Array(inputDataSize);
+                for (var i = 0; i <= numBatchItems; ++i) {
+                    nGramsSplits[i] = 0;
+                }
+                return [empty, nGramsSplits];
+            }
+            nGramsSplits[0] = 0;
+            var _loop_2 = function (i) {
+                var length = splits[i] - splits[i - 1];
+                var numNGrams = 0;
+                this_2.nGramWidths.forEach(function (nGramWidth) {
+                    numNGrams += _this.getNumNGrams(length, nGramWidth);
+                });
+                if (this_2.preserveShort && length > 0 && numNGrams === 0) {
+                    numNGrams = 1;
+                }
+                nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams;
+            };
+            var this_2 = this;
+            for (var i = 1; i <= numBatchItems; ++i) {
+                _loop_2(i);
+            }
+            var nGrams = new Array(nGramsSplits[numBatchItems]);
+            var _loop_3 = function (i) {
+                var splitIndex = splits[i];
+                var outputStartIdx = nGramsSplits[i];
+                this_3.nGramWidths.forEach(function (nGramWidth) {
+                    var length = splits[i + 1] - splits[i];
+                    var 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_3.preserveShort && outputStartIdx === nGramsSplits[i]) {
+                    var 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) {
+                        return "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.
+                    var nGramWidth = dataLength + 2 * this_3.padWidth;
+                    var numNGrams = 1;
+                    this_3.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
+                }
+            };
+            var this_3 = this;
+            for (var i = 0; i < numBatchItems; ++i) {
+                _loop_3(i);
+            }
+            return [nGrams, nGramsSplits];
+        };
+        return StringNGramsOp;
+    }());
+    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, result) {
+        if (!str.length) {
+            return;
+        }
+        // When the delimiter is empty, the input is split into individual characters.
+        if (delimiters.length === 0) {
+            for (var i = 0; i < str.length; ++i) {
+                result.push(str.subarray(i, i + 1));
+            }
+            return;
+        }
+        // When there is one delimiter, the input is split only at that delimiter.
+        if (delimiters.length === 1) {
+            var delimiter = delimiters[0];
+            var f = str.indexOf(delimiter);
+            while (f !== -1) {
+                var 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;
+        }
+        // When there are multiple delimiters, the input is split at every instance
+        // one of the delimiters appears.
+        var tokenStart = 0;
+        for (var i = 0; i < str.length + 1; i++) {
+            if ((i === str.length) || (delimiters.indexOf(str[i]) !== -1)) {
+                var token = str.subarray(tokenStart, i);
+                if (!skipEmpty || token.length !== 0) {
+                    result.push(token);
+                }
+                tokenStart = i + 1;
+            }
+        }
+    }
+    function stringSplitImpl(input, delimiter, skipEmpty) {
+        var batchSize = input.length;
+        // Empty delimiter means split the input character by character.
+        var tokens = [];
+        var outputSize = 0;
+        var maxNumEntries = 0;
+        var numIndices = new Array(batchSize);
+        for (var i = 0; i < batchSize; ++i) {
+            var prevTokensLength = tokens.length;
+            split(input[i], delimiter, skipEmpty, tokens);
+            var nEntries = tokens.length - prevTokensLength;
+            numIndices[i] = nEntries;
+            outputSize += nEntries;
+            maxNumEntries = Math.max(maxNumEntries, nEntries);
+        }
+        var indices = tfjsCore.util.getArrayFromDType('int32', outputSize * 2);
+        var values = new Array(outputSize);
+        var shape = [batchSize, maxNumEntries];
+        var c = 0;
+        for (var i = 0; i < batchSize; ++i) {
+            for (var 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) {
+        var output = tfjsCore.util.getArrayFromDType('int32', input.length);
+        for (var i = 0; i < input.length; ++i) {
+            output[i] =
+                tfjsCore.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.
+     * =============================================================================
+     */
+    var subImpl = createSimpleBinaryKernelImpl((function (aValue, bValue) { return aValue - bValue; }));
+    var subComplexImpl = createComplexBinaryKernelImpl((function (aReal, aImag, bReal, bImag) {
+        return { real: aReal - bReal, imag: aImag - bImag };
+    }));
+    var sub = binaryKernelFunc(tfjsCore.Sub, subImpl, subComplexImpl);
+    var subConfig = {
+        kernelName: tfjsCore.Sub,
+        backendName: 'cpu',
+        kernelFunc: sub
+    };
+
+    /**
+     * @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) {
+        var newShape = new Array(xBuf.rank);
+        for (var i = 0; i < newShape.length; i++) {
+            newShape[i] = xBuf.shape[i] * reps[i];
+        }
+        var result = tfjsCore.buffer(newShape, xBuf.dtype);
+        for (var i = 0; i < result.values.length; ++i) {
+            var newLoc = result.indexToLoc(i);
+            var originalLoc = new Array(xBuf.rank);
+            for (var j = 0; j < originalLoc.length; j++) {
+                originalLoc[j] = newLoc[j] % xBuf.shape[j];
+            }
+            var originalIndex = xBuf.locToIndex(originalLoc);
+            result.values[i] = xBuf.values[originalIndex];
+        }
+        return result;
+    }
+
+    var comparePair = function (a, b) {
+        var valueDiff = b.value - a.value;
+        return valueDiff === 0 ? a.index - b.index : valueDiff;
+    };
+    /**
+     * Partitions array where all elements smaller than the (k+1) smallest element
+     * are found to the left of it, and all larger to the right of it.
+     * Based on the Floyd-Rivest Algorithm, ref:
+     * https://en.wikipedia.org/wiki/Floyd%E2%80%93Rivest_algorithm
+     * @param array: Array to partition
+     * @param left: Left index for the interval
+     * @param right: Right index for the interval
+     * @param k: Desired index value, where array[k] is the (k+1)th smallest element
+     *           when left = 0
+     */
+    function select$1(array, k, left, right) {
+        if (left === void 0) { left = 0; }
+        if (right === void 0) { right = array.length - 1; }
+        while (right > left) {
+            // Use select recursively to sample a smaller set of size s
+            // the arbitrary constants 600 and 0.5 are used in the original
+            // version to minimize execution time.
+            if (right - left > 600) {
+                var n = right - left + 1;
+                var i_1 = k - left + 1;
+                var z = Math.log(n);
+                var s = 0.5 * Math.exp(2 * z / 3);
+                var sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i_1 - n / 2);
+                var newLeft = Math.max(left, Math.floor(k - i_1 * s / n + sd));
+                var newRight = Math.min(right, Math.floor(k + (n - i_1) * s / n + sd));
+                select$1(array, k, newLeft, newRight);
+            }
+            // partition the elements between left and right around t
+            var t = array[k];
+            var i = left;
+            var j = right;
+            tfjsCore.util.swap(array, left, k);
+            if (comparePair(array[right], t) > 0) {
+                tfjsCore.util.swap(array, left, right);
+            }
+            while (i < j) {
+                tfjsCore.util.swap(array, i, j);
+                i++;
+                j--;
+                while (comparePair(array[i], t) < 0) {
+                    i = i + 1;
+                }
+                while (comparePair(array[j], t) > 0) {
+                    j = j - 1;
+                }
+            }
+            if (comparePair(array[left], t) === 0) {
+                tfjsCore.util.swap(array, left, j);
+            }
+            else {
+                j = j + 1;
+                tfjsCore.util.swap(array, j, right);
+            }
+            // Adjust left and right towards the boundaries of the subset
+            // containing the (k - left + 1)th smallest element.
+            if (j <= k) {
+                left = j + 1;
+            }
+            if (k <= j) {
+                right = j - 1;
+            }
+        }
+    }
+    function topKImpl(x, xShape, xDtype, k, sorted) {
+        // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.
+        var lastDim = xShape[xShape.length - 1];
+        var _a = __read([x.length / lastDim, lastDim], 2), batch = _a[0], size = _a[1];
+        var allTopKVals = tfjsCore.util.getTypedArrayFromDType(xDtype, batch * k);
+        var allTopKIndices = tfjsCore.util.getTypedArrayFromDType('int32', batch * k);
+        var _loop_1 = function (b) {
+            var offset = b * size;
+            var vals = x.subarray(offset, offset + size);
+            var valAndInd = new Array(vals.length);
+            vals.forEach(function (value, index) { return valAndInd[index] = { value: value, index: index }; });
+            if (k < valAndInd.length) {
+                select$1(valAndInd, k);
+                valAndInd = valAndInd.slice(0, k);
+            }
+            if (sorted) {
+                valAndInd.sort(comparePair);
+            }
+            var outOffset = b * k;
+            var topKVals = allTopKVals.subarray(outOffset, outOffset + k);
+            var topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
+            for (var i = 0; i < k; i++) {
+                topKVals[i] = valAndInd[i].value;
+                topKIndices[i] = valAndInd[i].index;
+            }
+        };
+        for (var b = 0; b < batch; b++) {
+            _loop_1(b);
+        }
+        // Reshape back to the original input shape, except that the last
+        // dimension is k.
+        var outputShape = xShape.slice();
+        outputShape[outputShape.length - 1] = k;
+        return [
+            tfjsCore.buffer(outputShape, xDtype, allTopKVals),
+            tfjsCore.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.
+        var $axis = tfjsCore.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].
+        var newShape = [1, shape[0], 1];
+        for (var i = 0; i < $axis; i++) {
+            newShape[0] *= shape[i];
+        }
+        newShape[1] = shape[$axis];
+        for (var i = $axis + 1; i < shape.length; i++) {
+            newShape[2] *= shape[i];
+        }
+        // A map from unique elements (their string representations) to their values
+        // in "indices" (below).
+        var uniqueElements = {};
+        // The indices of each unique element in the original tensor along the given
+        // axis. It is 1D and has the same size as the given axis.
+        var indices = new Int32Array(shape[$axis]);
+        // Create a buffer so we can easily extract value at a given location.
+        var inputBuffer = new tfjsCore.TensorBuffer(newShape, dtype, values);
+        // The indices along the given axis that have unique elements. This is a
+        // de-duped version of "indices" above.
+        var uniqueIndices = [];
+        var is1DTensor = newShape[0] === 1 && newShape[2] === 1;
+        for (var i = 0; i < shape[$axis]; i++) {
+            // Extract values along the axis.
+            var element = void 0;
+            if (is1DTensor) {
+                // Fast path for 1D tensor input.
+                element = values[i].toString();
+            }
+            else {
+                var axisValues = [];
+                for (var m = 0; m < newShape[0]; m++) {
+                    for (var n = 0; n < newShape[2]; n++) {
+                        axisValues.push(inputBuffer.get(m, i, n));
+                    }
+                }
+                element = axisValues.join(',');
+            }
+            // Dedup and update various indices.
+            if (uniqueElements[element] !== undefined) {
+                indices[i] = uniqueElements[element];
+            }
+            else {
+                var uniqueIndex = Object.keys(uniqueElements).length;
+                uniqueElements[element] = uniqueIndex;
+                indices[i] = uniqueIndex;
+                uniqueIndices.push(i);
+            }
+        }
+        // Now we know where each of the unique elements are located along the axis
+        // (uniqueIndices). Extract them from input buffer and store them in the
+        // output buffer.
+        var outputTmpShape = newShape.slice();
+        outputTmpShape[1] = Object.keys(uniqueElements).length;
+        var outputBuffer = new tfjsCore.TensorBuffer(outputTmpShape, dtype);
+        uniqueIndices.forEach(function (uniqueElementIndex, i) {
+            for (var m = 0; m < newShape[0]; m++) {
+                for (var n = 0; n < newShape[2]; n++) {
+                    outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);
+                }
+            }
+        });
+        // The output shape can be calculated from the input shape with the size of
+        // the given axis replaced by the number of unique elements along that axis.
+        var outputShape = shape.slice();
+        outputShape[$axis] = outputTmpShape[1];
+        return {
+            outputValues: outputBuffer.values,
+            outputShape: outputShape,
+            indices: 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 shared = {
+        __proto__: null,
+        simpleAbsImpl: simpleAbsImpl,
+        addImpl: addImpl,
+        bincountImpl: bincountImpl,
+        bincountReduceImpl: bincountReduceImpl,
+        ceilImpl: ceilImpl,
+        concatImpl: concatImpl,
+        equalImpl: equalImpl,
+        expImpl: expImpl,
+        expm1Impl: expm1Impl,
+        floorImpl: floorImpl,
+        gatherNdImpl: gatherNdImpl,
+        gatherV2Impl: gatherV2Impl,
+        greaterImpl: greaterImpl,
+        greaterEqualImpl: greaterEqualImpl,
+        lessImpl: lessImpl,
+        lessEqualImpl: lessEqualImpl,
+        linSpaceImpl: linSpaceImpl,
+        logImpl: logImpl,
+        maxImpl: maxImpl,
+        maximumImpl: maximumImpl,
+        minimumImpl: minimumImpl,
+        multiplyImpl: multiplyImpl,
+        negImpl: negImpl,
+        notEqualImpl: notEqualImpl,
+        prodImpl: prodImpl,
+        rangeImpl: rangeImpl,
+        rsqrtImpl: rsqrtImpl,
+        sigmoidImpl: sigmoidImpl,
+        sliceImpl: sliceImpl,
+        sparseFillEmptyRowsImpl: sparseFillEmptyRowsImpl,
+        sparseReshapeImpl: sparseReshapeImpl,
+        sparseSegmentReductionImpl: sparseSegmentReductionImpl,
+        sqrtImpl: sqrtImpl,
+        squaredDifferenceImpl: squaredDifferenceImpl,
+        stridedSliceImpl: stridedSliceImpl,
+        stringNGramsImpl: stringNGramsImpl,
+        stringSplitImpl: stringSplitImpl,
+        stringToHashBucketFastImpl: stringToHashBucketFastImpl,
+        subImpl: subImpl,
+        tileImpl: tileImpl,
+        topKImpl: topKImpl,
+        transposeImpl: transposeImpl,
+        uniqueImpl: uniqueImpl
+    };
+
+    /** @license See the LICENSE file. */
+    // This code is auto-generated, do not modify this file!
+    var version = '3.12.0';
+
+    /**
+     * @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.
+     * =============================================================================
+     */
+    // Side effects for default initialization of MathBackendCPU
+    tfjsCore.registerBackend('cpu', function () { return new MathBackendCPU(); }, 1 /* priority */);
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT 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 = unaryKernelFunc(tfjsCore.Elu, function (xi) { return xi >= 0 ? xi : (Math.exp(xi) - 1); });
+    var eluConfig = {
+        kernelName: tfjsCore.Elu,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    function leakyRelu(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var alpha = attrs.alpha;
+        assertNotComplex([x], 'leakyRelu');
+        var xSize = tfjsCore.util.sizeFromShape(x.shape);
+        var xVals = backend.data.get(x.dataId).values;
+        var outVals = tfjsCore.util.getTypedArrayFromDType('float32', xSize);
+        for (var i = 0; i < xVals.length; i++) {
+            outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i];
+        }
+        return backend.makeTensorInfo(x.shape, 'float32', outVals);
+    }
+    var leakyReluConfig = {
+        kernelName: tfjsCore.LeakyRelu,
+        backendName: 'cpu',
+        kernelFunc: leakyRelu
+    };
+
+    var preluImpl = createSimpleBinaryKernelImpl(function (xValue, aValue) { return xValue < 0 ? aValue * xValue : xValue; });
+    function prelu(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var x = inputs.x, alpha = inputs.alpha;
+        assertNotComplex([x, alpha], 'prelu');
+        var aVals = backend.data.get(x.dataId).values;
+        var bVals = backend.data.get(alpha.dataId).values;
+        var _a = __read(preluImpl(x.shape, alpha.shape, aVals, bVals, 'float32'), 2), resultData = _a[0], resultShape = _a[1];
+        return backend.makeTensorInfo(resultShape, 'float32', resultData);
+    }
+    var preluConfig = {
+        kernelName: tfjsCore.Prelu,
+        backendName: 'cpu',
+        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 relu = unaryKernelFunc(tfjsCore.Relu, function (xi) { return Math.max(0, xi); });
+    var reluConfig = {
+        kernelName: tfjsCore.Relu,
+        backendName: 'cpu',
+        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 = unaryKernelFunc(tfjsCore.Relu6, function (xi) { return Math.min(Math.max(0, xi), 6); });
+    var relu6Config = {
+        kernelName: tfjsCore.Relu6,
+        backendName: 'cpu',
+        kernelFunc: relu6,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 applyActivation(backend, x, activation, preluActivationWeights, leakyreluAlpha) {
+        if (activation === 'linear') {
+            return identity({ inputs: { x: x }, backend: backend });
+        }
+        else if (activation === 'relu') {
+            return relu({ inputs: { x: x }, backend: backend });
+        }
+        else if (activation === 'elu') {
+            return elu({ inputs: { x: x }, backend: backend });
+        }
+        else if (activation === 'relu6') {
+            return relu6({ inputs: { x: x }, backend: backend });
+        }
+        else if (activation === 'prelu') {
+            return prelu({ inputs: { x: x, alpha: preluActivationWeights }, backend: backend });
+        }
+        else if (activation === 'leakyrelu') {
+            return leakyRelu({ inputs: { x: x }, backend: backend, attrs: { alpha: leakyreluAlpha } });
+        }
+        else if (activation === 'sigmoid') {
+            return sigmoid({ inputs: { x: x }, backend: backend });
+        }
+        throw new Error("Activation " + activation + " has not been implemented for the CPU backend.");
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 xSize = tfjsCore.util.sizeFromShape(x.shape);
+        var $shape = tfjsCore.util.inferFromImplicitShape(shape, xSize);
+        var $xSize = tfjsCore.util.sizeFromShape($shape);
+        tfjsCore.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."; });
+        backend.incRef(x.dataId);
+        var xData = backend.data.get(x.dataId);
+        if (xData.complexTensorInfos != null) {
+            var real = xData.complexTensorInfos.real;
+            var imag = xData.complexTensorInfos.imag;
+            real.shape = $shape;
+            imag.shape = $shape;
+        }
+        return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
+    }
+    var reshapeConfig = {
+        kernelName: tfjsCore.Reshape,
+        backendName: 'cpu',
+        kernelFunc: reshape
+    };
+
+    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;
+        assertNotComplex([a, b], 'matMul');
+        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 = tfjsCore.util.sizeFromShape(outerDimsA);
+        var batchDimB = tfjsCore.util.sizeFromShape(outerDimsB);
+        var outShapeOuterDims = tfjsCore.broadcast_util.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));
+        var outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+        tfjsCore.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 sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
+        var leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];
+        var rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];
+        var batchDim = Math.max(batchDimA, batchDimB);
+        var a3dValues = backend.data.get(a3d.dataId).values;
+        var b3dValues = backend.data.get(b3d.dataId).values;
+        var a3dStrides = tfjsCore.util.computeStrides(a3d.shape);
+        var b3dStrides = tfjsCore.util.computeStrides(b3d.shape);
+        var _a = __read(transposeA ?
+            [a3dStrides[0], 1, a3dStrides[1]] :
+            [a3dStrides[0], a3dStrides[1], 1], 3), aBatch = _a[0], aOuterStep = _a[1], aInnerStep = _a[2];
+        var _b = __read(transposeB ?
+            [1, b3dStrides[1], b3dStrides[0]] :
+            [b3dStrides[1], 1, b3dStrides[0]], 3), bInnerStep = _b[0], bOuterStep = _b[1], bBatch = _b[2];
+        var size = leftDim * rightDim;
+        var result = tfjsCore.buffer([batchDim, leftDim, rightDim], a3d.dtype);
+        var resVals = result.values;
+        var blockSize = backend.blockSize;
+        for (var bi = 0; bi < batchDim; bi++) {
+            for (var i0 = 0; i0 < leftDim; i0 += blockSize) {
+                for (var j0 = 0; j0 < rightDim; j0 += blockSize) {
+                    for (var k0 = 0; k0 < sharedDim; k0 += blockSize) {
+                        // for when blockSize doesn't evenly divide the input
+                        var iBlock = Math.min(i0 + blockSize, leftDim);
+                        var jBlock = Math.min(j0 + blockSize, rightDim);
+                        var kBlock = Math.min(k0 + blockSize, sharedDim);
+                        for (var i = i0; i < iBlock; i++) {
+                            for (var j = j0; j < jBlock; j++) {
+                                var sum = 0.0;
+                                for (var k = k0; k < kBlock; k++) {
+                                    var batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;
+                                    var batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;
+                                    var aVal = a3dValues[batchOffsetA + i * aOuterStep + k * aInnerStep];
+                                    var bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];
+                                    sum += aVal * bVal;
+                                }
+                                resVals[bi * size + (i * rightDim + j)] += sum;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        backend.disposeIntermediateTensorInfo(a3d);
+        backend.disposeIntermediateTensorInfo(b3d);
+        // set correct shape on output.
+        return backend.makeTensorInfo(outShape, result.dtype, result.values);
+    }
+    var batchMatMulConfig = {
+        kernelName: tfjsCore.BatchMatMul,
+        backendName: 'cpu',
+        kernelFunc: batchMatMul,
+    };
+
+    function _fusedMatMul(args) {
+        var e_1, _a;
+        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;
+        var current;
+        var addRes;
+        var activationRes;
+        var intermediates = [];
+        var matMulRes = batchMatMul({ inputs: { a: a, b: b }, attrs: { transposeA: transposeA, transposeB: transposeB }, backend: backend });
+        current = matMulRes;
+        if (bias) {
+            addRes = add({ inputs: { a: current, b: bias }, backend: backend });
+            intermediates.push(current);
+            current = addRes;
+        }
+        if (activation) {
+            activationRes = applyActivation(backend, current, activation, preluActivationWeights, leakyreluAlpha);
+            intermediates.push(current);
+            current = activationRes;
+        }
+        try {
+            for (var intermediates_1 = __values(intermediates), intermediates_1_1 = intermediates_1.next(); !intermediates_1_1.done; intermediates_1_1 = intermediates_1.next()) {
+                var i = intermediates_1_1.value;
+                backend.disposeIntermediateTensorInfo(i);
+            }
+        }
+        catch (e_1_1) { e_1 = { error: e_1_1 }; }
+        finally {
+            try {
+                if (intermediates_1_1 && !intermediates_1_1.done && (_a = intermediates_1.return)) _a.call(intermediates_1);
+            }
+            finally { if (e_1) throw e_1.error; }
+        }
+        return current;
+    }
+    var _fusedMatMulConfig = {
+        kernelName: tfjsCore._FusedMatMul,
+        backendName: 'cpu',
+        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 acos = unaryKernelFunc(tfjsCore.Acos, function (xi) { return Math.acos(xi); });
+    var acosConfig = {
+        kernelName: tfjsCore.Acos,
+        backendName: 'cpu',
+        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 = unaryKernelFunc(tfjsCore.Acosh, function (xi) { return Math.acosh(xi); });
+    var acoshConfig = {
+        kernelName: tfjsCore.Acosh,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    function addN(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var tensors = inputs;
+        assertNotComplex(inputs, 'addN');
+        var vals = tensors.map(function (t) { return backend.data.get(t.dataId).values; });
+        var outBuf = tfjsCore.buffer(tensors[0].shape, tensors[0].dtype);
+        var outVals = outBuf.values;
+        for (var i = 0; i < tensors.length; i++) {
+            var currVals = vals[i];
+            for (var j = 0; j < outVals.length; j++) {
+                outVals[j] += currVals[j];
+            }
+        }
+        return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+    }
+    var addNConfig = {
+        kernelName: tfjsCore.AddN,
+        backendName: 'cpu',
+        kernelFunc: addN
+    };
+
+    function all(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis, keepDims = attrs.keepDims;
+        assertNotComplex(x, 'all');
+        var origAxes = tfjsCore.util.parseAxisParam(axis, x.shape);
+        var axes = origAxes;
+        var permutedAxes = tfjsCore.backend_util.getAxesPermutation(axes, x.shape.length);
+        var $x = x;
+        if (permutedAxes != null) {
+            $x = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } });
+            axes = tfjsCore.backend_util.getInnerMostAxes(axes.length, x.shape.length);
+        }
+        tfjsCore.backend_util.assertAxesAreInnerMostDims('all', axes, $x.shape.length);
+        var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes($x.shape, axes), 2), outShape = _a[0], reduceShape = _a[1];
+        var reduceSize = tfjsCore.util.sizeFromShape(reduceShape);
+        var vals = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(outShape), $x.dtype);
+        var aVals = backend.data.get($x.dataId).values;
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var all_1 = aVals[offset];
+            for (var j = 0; j < reduceSize; ++j) {
+                var value = aVals[offset + j];
+                all_1 = all_1 && value;
+            }
+            vals[i] = all_1;
+        }
+        if (permutedAxes != null) {
+            backend.disposeIntermediateTensorInfo($x);
+        }
+        var result = backend.makeTensorInfo(outShape, $x.dtype, vals);
+        if (keepDims) {
+            var expandedShape = tfjsCore.backend_util.expandShapeToKeepDim(outShape, origAxes);
+            var reshapedResult = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: expandedShape } });
+            backend.disposeIntermediateTensorInfo(result);
+            return reshapedResult;
+        }
+        return result;
+    }
+    var allConfig = {
+        kernelName: tfjsCore.All,
+        backendName: 'cpu',
+        kernelFunc: all
+    };
+
+    function any(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis, keepDims = attrs.keepDims;
+        assertNotComplex(x, 'any');
+        var origAxes = tfjsCore.util.parseAxisParam(axis, x.shape);
+        var axes = origAxes;
+        var permutedAxes = tfjsCore.backend_util.getAxesPermutation(axes, x.shape.length);
+        var $x = x;
+        if (permutedAxes != null) {
+            $x = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } });
+            axes = tfjsCore.backend_util.getInnerMostAxes(axes.length, x.shape.length);
+        }
+        tfjsCore.backend_util.assertAxesAreInnerMostDims('any', axes, $x.shape.length);
+        var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes($x.shape, axes), 2), outShape = _a[0], reduceShape = _a[1];
+        var reduceSize = tfjsCore.util.sizeFromShape(reduceShape);
+        var vals = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(outShape), $x.dtype);
+        var aVals = backend.data.get($x.dataId).values;
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var anyVal = aVals[offset];
+            for (var j = 0; j < reduceSize; ++j) {
+                var value = aVals[offset + j];
+                anyVal = anyVal || value;
+            }
+            vals[i] = anyVal;
+        }
+        if (permutedAxes != null) {
+            backend.disposeIntermediateTensorInfo($x);
+        }
+        var result = backend.makeTensorInfo(outShape, $x.dtype, vals);
+        if (keepDims) {
+            var expandedShape = tfjsCore.backend_util.expandShapeToKeepDim(outShape, origAxes);
+            var reshapedResult = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: expandedShape } });
+            backend.disposeIntermediateTensorInfo(result);
+            return reshapedResult;
+        }
+        return result;
+    }
+    var anyConfig = {
+        kernelName: tfjsCore.Any,
+        backendName: 'cpu',
+        kernelFunc: any
+    };
+
+    function argMax(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis;
+        assertNotComplex(x, 'argMax');
+        var axes = tfjsCore.util.parseAxisParam(axis, x.shape);
+        var permutedAxes = tfjsCore.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 = tfjsCore.backend_util.getInnerMostAxes(axes.length, $x.shape.length);
+        }
+        axes = [axes[0]];
+        tfjsCore.backend_util.assertAxesAreInnerMostDims('argMax', axes, $x.shape.length);
+        var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes($x.shape, axes), 2), outShape = _a[0], reduceShape = _a[1];
+        var outSize = tfjsCore.util.sizeFromShape(outShape);
+        var vals = tfjsCore.util.makeZerosTypedArray(outSize, 'int32');
+        var reduceSize = tfjsCore.util.sizeFromShape(reduceShape);
+        var aVals = backend.data.get($x.dataId).values;
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var max = aVals[offset];
+            var maxIndex = 0;
+            for (var j = 0; j < reduceSize; ++j) {
+                var value = aVals[offset + j];
+                if (value > max) {
+                    max = value;
+                    maxIndex = j;
+                }
+            }
+            vals[i] = maxIndex;
+        }
+        intermediateTensorInfos.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return backend.makeTensorInfo(outShape, 'int32', vals);
+    }
+    var argMaxConfig = {
+        kernelName: tfjsCore.ArgMax,
+        backendName: 'cpu',
+        kernelFunc: argMax
+    };
+
+    function argMin(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis;
+        assertNotComplex(x, 'argMin');
+        var axes = tfjsCore.util.parseAxisParam(axis, x.shape);
+        var permutedAxes = tfjsCore.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 = tfjsCore.backend_util.getInnerMostAxes(axes.length, $x.shape.length);
+        }
+        axes = [axes[0]];
+        tfjsCore.backend_util.assertAxesAreInnerMostDims('argMin', axes, $x.shape.length);
+        var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes($x.shape, axes), 2), outShape = _a[0], reduceShape = _a[1];
+        var outSize = tfjsCore.util.sizeFromShape(outShape);
+        var vals = tfjsCore.util.makeZerosTypedArray(outSize, 'int32');
+        var reduceSize = tfjsCore.util.sizeFromShape(reduceShape);
+        var aVals = backend.data.get($x.dataId).values;
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var min = aVals[offset];
+            var minIndex = 0;
+            for (var j = 0; j < reduceSize; ++j) {
+                var value = aVals[offset + j];
+                if (value < min) {
+                    min = value;
+                    minIndex = j;
+                }
+            }
+            vals[i] = minIndex;
+        }
+        intermediateTensorInfos.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return backend.makeTensorInfo(outShape, 'int32', vals);
+    }
+    var argMinConfig = {
+        kernelName: tfjsCore.ArgMin,
+        backendName: 'cpu',
+        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 = unaryKernelFunc(tfjsCore.Asin, function (xi) { return Math.asin(xi); });
+    var asinConfig = {
+        kernelName: tfjsCore.Asin,
+        backendName: 'cpu',
+        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 = unaryKernelFunc(tfjsCore.Asinh, function (xi) { return Math.asinh(xi); });
+    var asinhConfig = {
+        kernelName: tfjsCore.Asinh,
+        backendName: 'cpu',
+        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 = unaryKernelFunc(tfjsCore.Atan, function (xi) { return Math.atan(xi); });
+    var atanConfig = {
+        kernelName: tfjsCore.Atan,
+        backendName: 'cpu',
+        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 atan2Impl = createSimpleBinaryKernelImpl(function (aValue, bValue) { return Math.atan2(aValue, bValue); });
+    var atan2 = binaryKernelFunc(tfjsCore.Atan2, atan2Impl);
+    var atan2Config = {
+        kernelName: tfjsCore.Atan2,
+        backendName: 'cpu',
+        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 = unaryKernelFunc(tfjsCore.Atanh, function (xi) { return Math.atanh(xi); });
+    var atanhConfig = {
+        kernelName: tfjsCore.Atanh,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    function pool(xValues, xShape, dtype, strides, convInfo, poolType) {
+        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;
+        var initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :
+            Number.POSITIVE_INFINITY);
+        var output = tfjsCore.buffer(convInfo.outShape, dtype);
+        var outputVals = output.values;
+        var outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];
+        var outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];
+        var outputColStrides = convInfo.outShape[3];
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            var outputBatchOffset = b * outputBatchStrides;
+            var inputBatchOffset = b * strides[0];
+            for (var d = 0; d < convInfo.inChannels; ++d) {
+                for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                    var xRCorner = yR * strideHeight - padTop;
+                    var xRMin = Math.max(0, xRCorner);
+                    var xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
+                    var outputRowOffset = outputBatchOffset + yR * outputRowStrides;
+                    for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                        var xCCorner = yC * strideWidth - padLeft;
+                        var xCMin = Math.max(0, xCCorner);
+                        var xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
+                        var minMaxValue = initialValue;
+                        var avgValue = 0;
+                        var count = 0;
+                        for (var xR = xRMin; xR < xRMax; xR += dilationHeight) {
+                            var xROffset = inputBatchOffset + xR * strides[1];
+                            for (var xC = xCMin; xC < xCMax; xC += dilationWidth) {
+                                var xCOffset = xROffset + xC * strides[2];
+                                var pixel = xValues[xCOffset + d];
+                                if ((poolType === 'max' && pixel > minMaxValue)) {
+                                    minMaxValue = pixel;
+                                }
+                                else if (poolType === 'avg') {
+                                    avgValue += pixel;
+                                    count++;
+                                }
+                            }
+                            if (isNaN(minMaxValue)) {
+                                break;
+                            }
+                        }
+                        var outputOffset = outputRowOffset + yC * outputColStrides + d;
+                        outputVals[outputOffset] =
+                            poolType === 'avg' ? avgValue / count : minMaxValue;
+                    }
+                }
+            }
+        }
+        return output;
+    }
+    function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions, includeBatchInIndex) {
+        if (flattenPositions === void 0) { flattenPositions = false; }
+        if (includeBatchInIndex === void 0) { includeBatchInIndex = false; }
+        var maxPositions = tfjsCore.buffer(convInfo.outShape, 'int32');
+        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;
+        var xBuf = tfjsCore.buffer(xShape, dtype, xValues);
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            for (var d = 0; d < convInfo.inChannels; ++d) {
+                for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                    var xRCorner = yR * strideHeight - padTop;
+                    var xRMin = xRCorner;
+                    while (xRMin < 0) {
+                        xRMin += dilationHeight;
+                    }
+                    // const xRMin = Math.max(0, xRCorner);
+                    var xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
+                    for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                        var xCCorner = yC * strideWidth - padLeft;
+                        var xCMin = xCCorner;
+                        while (xCMin < 0) {
+                            xCMin += dilationWidth;
+                        }
+                        var xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
+                        var maxValue = Number.NEGATIVE_INFINITY;
+                        var maxPosition = -1;
+                        for (var xR = xRMin; xR < xRMax; xR += dilationHeight) {
+                            var wR = xR - xRCorner;
+                            for (var xC = xCMin; xC < xCMax; xC += dilationWidth) {
+                                var wC = xC - xCCorner;
+                                var pixel = xBuf.get(b, xR, xC, d);
+                                if (pixel > maxValue) {
+                                    maxValue = pixel;
+                                    if (flattenPositions) {
+                                        maxPosition = includeBatchInIndex ?
+                                            ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) *
+                                                convInfo.inChannels +
+                                                d :
+                                            (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;
+                                    }
+                                    else {
+                                        maxPosition = wR * effectiveFilterWidth + wC;
+                                    }
+                                }
+                            }
+                        }
+                        maxPositions.set(maxPosition, b, yR, yC, d);
+                    }
+                }
+            }
+        }
+        return maxPositions;
+    }
+    function pool3d(xValues, xShape, dtype, strides, convInfo, poolType) {
+        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;
+        var initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :
+            Number.POSITIVE_INFINITY);
+        var output = tfjsCore.buffer(convInfo.outShape, dtype);
+        var outputVals = output.values;
+        var outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] *
+            convInfo.outShape[3] * convInfo.outShape[4];
+        var outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];
+        var outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];
+        var outputColStrides = convInfo.outShape[4];
+        for (var batch = 0; batch < convInfo.batchSize; ++batch) {
+            var outputBatchOffset = batch * outputBatchStrides;
+            var inputBatchOffset = batch * strides[0];
+            for (var channel = 0; channel < convInfo.inChannels; ++channel) {
+                for (var yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {
+                    var xDepthCorner = yDepth * strideDepth - padFront;
+                    var xDepthMin = xDepthCorner;
+                    while (xDepthMin < 0) {
+                        xDepthMin += dilationDepth;
+                    }
+                    var xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
+                    var outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;
+                    for (var yRow = 0; yRow < convInfo.outHeight; ++yRow) {
+                        var xRowCorner = yRow * strideHeight - padTop;
+                        var xRowMin = xRowCorner;
+                        while (xRowMin < 0) {
+                            xRowMin += dilationHeight;
+                        }
+                        var xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
+                        var outputRowOffset = outputDepthOffset + yRow * outputRowStrides;
+                        for (var yCol = 0; yCol < convInfo.outWidth; ++yCol) {
+                            var xColCorner = yCol * strideWidth - padLeft;
+                            var xColMin = xColCorner;
+                            while (xColMin < 0) {
+                                xColMin += dilationWidth;
+                            }
+                            var xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
+                            // Shader code begins
+                            var outputColOffset = outputRowOffset + yCol * outputColStrides;
+                            var minMaxValue = initialValue;
+                            var avgValue = 0;
+                            var count = 0;
+                            for (var xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {
+                                var xDepthOffset = inputBatchOffset + xDepth * strides[1];
+                                for (var xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
+                                    var xRowOffset = xDepthOffset + xRow * strides[2];
+                                    for (var xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {
+                                        var xColOffset = xRowOffset + xCol * strides[3];
+                                        var pixel = xValues[xColOffset + channel];
+                                        if ((poolType === 'max' && pixel > minMaxValue)) {
+                                            minMaxValue = pixel;
+                                        }
+                                        else if (poolType === 'avg') {
+                                            avgValue += pixel;
+                                            count++;
+                                        }
+                                        if (isNaN(minMaxValue)) {
+                                            break;
+                                        }
+                                    }
+                                    if (isNaN(minMaxValue)) {
+                                        break;
+                                    }
+                                }
+                                if (isNaN(minMaxValue)) {
+                                    break;
+                                }
+                            }
+                            var outputOffset = outputColOffset + channel;
+                            outputVals[outputOffset] =
+                                poolType === 'avg' ? avgValue / count : minMaxValue;
+                        }
+                    }
+                }
+            }
+        }
+        return output;
+    }
+    function maxPool3dPositions(xBuf, convInfo) {
+        var maxPositions = tfjsCore.buffer(convInfo.outShape, 'int32');
+        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;
+        for (var batch = 0; batch < convInfo.batchSize; ++batch) {
+            for (var channel = 0; channel < convInfo.inChannels; ++channel) {
+                for (var yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {
+                    var xDepthCorner = yDepth * strideDepth - padFront;
+                    var xDepthMin = xDepthCorner;
+                    while (xDepthMin < 0) {
+                        xDepthMin += dilationDepth;
+                    }
+                    var xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
+                    for (var yRow = 0; yRow < convInfo.outHeight; ++yRow) {
+                        var xRowCorner = yRow * strideHeight - padTop;
+                        var xRowMin = xRowCorner;
+                        while (xRowMin < 0) {
+                            xRowMin += dilationHeight;
+                        }
+                        var xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
+                        for (var yCol = 0; yCol < convInfo.outWidth; ++yCol) {
+                            var xColCorner = yCol * strideWidth - padLeft;
+                            var xColMin = xColCorner;
+                            while (xColMin < 0) {
+                                xColMin += dilationWidth;
+                            }
+                            var xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
+                            // Shader code begins
+                            var maxValue = Number.NEGATIVE_INFINITY;
+                            var maxPosition = -1;
+                            for (var xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {
+                                var wDepth = xDepth - xDepthCorner;
+                                for (var xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
+                                    var wRow = xRow - xRowCorner;
+                                    for (var xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {
+                                        var wCol = xCol - xColCorner;
+                                        var pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);
+                                        if (pixel >= maxValue) {
+                                            maxValue = pixel;
+                                            maxPosition =
+                                                wDepth * effectiveFilterHeight * effectiveFilterWidth +
+                                                    wRow * effectiveFilterHeight + wCol;
+                                        }
+                                    }
+                                }
+                            }
+                            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);
+                        }
+                    }
+                }
+            }
+        }
+        return maxPositions;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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;
+        tfjsCore.util.assert(tfjsCore.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 = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);
+        var res;
+        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&
+            tfjsCore.util.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+            res = identity({ inputs: { x: x }, backend: backend });
+        }
+        else {
+            var xValues = backend.data.get(x.dataId).values;
+            var strides_1 = tfjsCore.util.computeStrides(x.shape);
+            var buffer = pool(xValues, x.shape, x.dtype, strides_1, convInfo, 'avg');
+            res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);
+        }
+        return res;
+    }
+    var avgPoolConfig = {
+        kernelName: tfjsCore.AvgPool,
+        backendName: 'cpu',
+        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;
+        assertNotComplex(x, 'avgPool3d');
+        var convInfo = tfjsCore.backend_util.computePool3DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode, dataFormat);
+        var xValues = backend.data.get(x.dataId).values;
+        var outBuf = pool3d(xValues, x.shape, x.dtype, tfjsCore.util.computeStrides(x.shape), convInfo, 'avg');
+        return backend.makeTensorInfo(outBuf.shape, 'float32', outBuf.values);
+    }
+    var avgPool3DConfig = {
+        kernelName: tfjsCore.AvgPool3D,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    function avgPool3DGrad(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var dy = inputs.dy, input = inputs.input;
+        var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode;
+        assertNotComplex([dy, input], 'avgPool3DGrad');
+        var convInfo = tfjsCore.backend_util.computePool3DInfo(input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);
+        var strideDepth = convInfo.strideDepth;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var filterDepth = convInfo.filterDepth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        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 padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+        var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+        var dx = tfjsCore.buffer(input.shape, 'float32');
+        var avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
+        var dyBuf = backend.bufferSync(dy);
+        for (var batch = 0; batch < convInfo.batchSize; ++batch) {
+            for (var channel = 0; channel < convInfo.inChannels; ++channel) {
+                for (var dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {
+                    for (var dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {
+                        for (var dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {
+                            // Shader code begins.
+                            var dyDepthCorner = dxDepth - padFront;
+                            var dyRowCorner = dxRow - padTop;
+                            var dyColCorner = dxCol - padLeft;
+                            var dotProd = 0;
+                            for (var wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {
+                                var dyDepth = (dyDepthCorner + wDepth) / strideDepth;
+                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||
+                                    Math.floor(dyDepth) !== dyDepth) {
+                                    continue;
+                                }
+                                for (var wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {
+                                    var dyRow = (dyRowCorner + wRow) / strideHeight;
+                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||
+                                        Math.floor(dyRow) !== dyRow) {
+                                        continue;
+                                    }
+                                    for (var wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {
+                                        var dyCol = (dyColCorner + wCol) / strideWidth;
+                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||
+                                            Math.floor(dyCol) !== dyCol) {
+                                            continue;
+                                        }
+                                        var pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
+                                        dotProd += pixel;
+                                    }
+                                }
+                            }
+                            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);
+                        }
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+    }
+    var avgPool3DGradConfig = {
+        kernelName: tfjsCore.AvgPool3DGrad,
+        backendName: 'cpu',
+        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 = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+        var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+        var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+        var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+        var dx = tfjsCore.buffer(x.shape, 'float32');
+        var avgMultiplier = 1 / (filterHeight * filterWidth);
+        var dyData = backend.data.get(dy.dataId).values;
+        var dyBuf = tfjsCore.buffer(dy.shape, 'float32', dyData);
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            for (var d = 0; d < convInfo.inChannels; ++d) {
+                for (var dxR = 0; dxR < convInfo.inHeight; ++dxR) {
+                    for (var dxC = 0; dxC < convInfo.inWidth; ++dxC) {
+                        // Shader code begins.
+                        var dyRCorner = dxR - padTop;
+                        var dyCCorner = dxC - padLeft;
+                        var dotProd = 0;
+                        for (var wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
+                            var dyR = (dyRCorner + wR) / strideHeight;
+                            if (dyR < 0 || dyR >= convInfo.outHeight ||
+                                Math.floor(dyR) !== dyR) {
+                                continue;
+                            }
+                            for (var wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
+                                var dyC = (dyCCorner + wC) / strideWidth;
+                                if (dyC < 0 || dyC >= convInfo.outWidth ||
+                                    Math.floor(dyC) !== dyC) {
+                                    continue;
+                                }
+                                var pixel = dyBuf.get(b, dyR, dyC, d);
+                                dotProd += pixel;
+                            }
+                        }
+                        dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+    }
+    var avgPoolGradConfig = {
+        kernelName: tfjsCore.AvgPoolGrad,
+        backendName: 'cpu',
+        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 batchNorm(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x, scale = inputs.scale, offset = inputs.offset, mean = inputs.mean, variance = inputs.variance;
+        tfjsCore.util.assert(mean.shape.length === variance.shape.length, function () { return 'Batch normalization gradient requires mean and variance to have ' +
+            'equal ranks.'; });
+        tfjsCore.util.assert(offset == null || mean.shape.length === offset.shape.length, function () { return 'Batch normalization gradient requires mean and offset to have ' +
+            'equal ranks.'; });
+        tfjsCore.util.assert(scale == null || mean.shape.length === scale.shape.length, function () { return 'Batch normalization gradient requires mean and scale to have ' +
+            'equal ranks.'; });
+        assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');
+        var varianceEpsilon = attrs.varianceEpsilon;
+        if (varianceEpsilon == null) {
+            varianceEpsilon = 0.001;
+        }
+        var xVals = backend.data.get(x.dataId).values;
+        var mVals = backend.data.get(mean.dataId).values;
+        var varVals = backend.data.get(variance.dataId).values;
+        var sVals = scale ? backend.data.get(scale.dataId).values :
+            new Float32Array([1]);
+        var offVals = offset ?
+            backend.data.get(offset.dataId).values :
+            new Float32Array([0]);
+        var outVals = new Float32Array(xVals.length);
+        var offValsLength = offVals.length;
+        var sValsLength = sVals.length;
+        var varValsLength = varVals.length;
+        var mValsLength = mVals.length;
+        var offi = 0;
+        var mi = 0;
+        var si = 0;
+        var vi = 0;
+        for (var i = 0; i < xVals.length; ++i) {
+            outVals[i] = offVals[offi++] +
+                (xVals[i] - mVals[mi++]) * sVals[si++] /
+                    Math.sqrt(varVals[vi++] + varianceEpsilon);
+            if (offi >= offValsLength) {
+                offi = 0;
+            }
+            if (mi >= mValsLength) {
+                mi = 0;
+            }
+            if (si >= sValsLength) {
+                si = 0;
+            }
+            if (vi >= varValsLength) {
+                vi = 0;
+            }
+        }
+        return backend.makeTensorInfo(x.shape, x.dtype, outVals);
+    }
+    var batchNormConfig = {
+        kernelName: tfjsCore.FusedBatchNorm,
+        backendName: 'cpu',
+        kernelFunc: batchNorm,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 batchToSpaceND(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var blockShape = attrs.blockShape, crops = attrs.crops;
+        assertNotComplex([x], 'batchToSpaceND');
+        var prod = blockShape.reduce(function (a, b) { return a * b; });
+        var reshaped = tfjsCore.backend_util.getReshaped(x.shape, blockShape, prod);
+        var permuted = tfjsCore.backend_util.getPermuted(reshaped.length, blockShape.length);
+        var reshapedPermuted = tfjsCore.backend_util.getReshapedPermuted(x.shape, blockShape, prod);
+        var sliceBeginCoords = tfjsCore.backend_util.getSliceBeginCoords(crops, blockShape.length);
+        var sliceSize = tfjsCore.backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);
+        var xReshaped = reshape({ inputs: { x: x }, backend: backend, attrs: { shape: reshaped } });
+        var xTransposed = transpose({ inputs: { x: xReshaped }, backend: backend, attrs: { perm: permuted } });
+        var xTransposedReshaped = reshape({ inputs: { x: xTransposed }, backend: backend, attrs: { shape: reshapedPermuted } });
+        var result = slice({
+            inputs: { x: xTransposedReshaped },
+            backend: backend,
+            attrs: { begin: sliceBeginCoords, size: sliceSize }
+        });
+        backend.disposeIntermediateTensorInfo(xReshaped);
+        backend.disposeIntermediateTensorInfo(xTransposed);
+        backend.disposeIntermediateTensorInfo(xTransposedReshaped);
+        return result;
+    }
+    var batchToSpaceNDConfig = {
+        kernelName: tfjsCore.BatchToSpaceND,
+        backendName: 'cpu',
+        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.data.get(x.dataId).values;
+        var weightsVals = backend.data.get(weights.dataId).values;
+        var outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);
+        return backend.makeTensorInfo([size], weights.dtype, outVals);
+    }
+    var bincountConfig = {
+        kernelName: tfjsCore.Bincount,
+        backendName: 'cpu',
+        kernelFunc: bincount
+    };
+
+    /**
+     * @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 broadcastArgs(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var s0 = inputs.s0, s1 = inputs.s1;
+        var s0Vals = backend.data.get(s0.dataId).values;
+        var s1Vals = backend.data.get(s1.dataId).values;
+        var broadcastShape = tfjsCore.backend_util.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));
+        return backend.makeTensorInfo([broadcastShape.length], 'int32', Int32Array.from(broadcastShape));
+    }
+    var broadcastArgsConfig = {
+        kernelName: tfjsCore.BroadcastArgs,
+        backendName: 'cpu',
+        kernelFunc: broadcastArgs
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT 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 clip = unaryKernelFunc(tfjsCore.ClipByValue, function (xi, attrs) {
+        var clipAttrs = attrs;
+        if (xi > clipAttrs.clipValueMax) {
+            return clipAttrs.clipValueMax;
+        }
+        return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;
+    });
+    var clipConfig = {
+        kernelName: tfjsCore.ClipByValue,
+        backendName: 'cpu',
+        kernelFunc: clip,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT 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 complexAbs = function (args) {
+        var x = args.inputs.x;
+        var cpuBackend = args.backend;
+        var resultValues = new Float32Array(tfjsCore.util.sizeFromShape(x.shape));
+        var complexVals = cpuBackend.data.get(x.dataId);
+        var real = complexVals.complexTensorInfos.real;
+        var imag = complexVals.complexTensorInfos.imag;
+        var realVals = cpuBackend.data.get(real.dataId).values;
+        var imagVals = cpuBackend.data.get(imag.dataId).values;
+        for (var i = 0; i < realVals.length; i++) {
+            var real_1 = realVals[i];
+            var imag_1 = imagVals[i];
+            resultValues[i] = Math.hypot(real_1, imag_1);
+        }
+        return cpuBackend.makeOutput(resultValues, x.shape, 'float32');
+    };
+    var complexAbsConfig = {
+        kernelName: tfjsCore.ComplexAbs,
+        backendName: 'cpu',
+        kernelFunc: complexAbs,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 imag = backend.data.get(input.dataId).complexTensorInfos.imag;
+        var imagVal = backend.data.get(imag.dataId).values;
+        // When complex tensor is disposed, its underlying parts will be disposed too.
+        // Make new tensor out of the imag value of the complex. This makes sure the
+        // value is still accessible even if complex tensor is disposed.
+        return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);
+    }
+    var imagConfig = {
+        kernelName: tfjsCore.Imag,
+        backendName: 'cpu',
+        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 concat(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var axis = attrs.axis;
+        var $axis = tfjsCore.util.parseAxisParam(axis, inputs[0].shape)[0];
+        var outShape = tfjsCore.backend_util.computeOutShape(inputs.map(function (t) { return t.shape; }), $axis);
+        if (tfjsCore.util.sizeFromShape(outShape) === 0) {
+            return backend.makeTensorInfo(outShape, inputs[0].dtype, []);
+        }
+        // Keep only non-empty tensors (ignore tensors with 0 in their shape).
+        var $inputs = inputs.filter(function (t) { return tfjsCore.util.sizeFromShape(t.shape) > 0; });
+        if ($inputs.length === 1) {
+            return identity({ inputs: { x: $inputs[0] }, backend: backend });
+        }
+        var shapes = $inputs.map(function (t) { return t.shape; });
+        tfjsCore.backend_util.assertParamsConsistent(shapes, $axis);
+        if ($inputs[0].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 = concat({ inputs: reals, backend: backend, attrs: { axis: $axis } });
+            var imagConcated = concat({ inputs: imags, backend: backend, attrs: { axis: $axis } });
+            var result = 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;
+        }
+        // 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 inputs2D = $inputs.map(function (t) {
+            var innerSize = tfjsCore.util.sizeFromShape(t.shape.slice($axis));
+            var shape = [-1, innerSize];
+            return reshape({ inputs: { x: t }, backend: backend, attrs: { shape: shape } });
+        });
+        var inputsValShapes = inputs2D.map(function (t) {
+            return { vals: backend.data.get(t.dataId).values, shape: t.shape };
+        });
+        // Concats 2d tensors along axis=1.
+        outShape =
+            tfjsCore.backend_util.computeOutShape(inputs2D.map(function (t) { return t.shape; }), 1 /* axis */);
+        var simplyConcat = inputs2D[0].shape[0] === 1;
+        var outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);
+        var finalOutShape = tfjsCore.backend_util.computeOutShape($inputs.map(function (t) { return t.shape; }), $axis);
+        var outInfo = backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);
+        inputs2D.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return outInfo;
+    }
+    var concatConfig = {
+        kernelName: tfjsCore.Concat,
+        backendName: 'cpu',
+        kernelFunc: concat
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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;
+        assertNotComplex([x, filter], 'conv2d');
+        var $dataFormat = tfjsCore.backend_util.convertConv2DDataFormat(dataFormat);
+        var convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, false /* depthwise */, $dataFormat);
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var padLeft = convInfo.padInfo.left;
+        var padTop = convInfo.padInfo.top;
+        var isChannelsLast = convInfo.dataFormat === 'channelsLast';
+        var y = new tfjsCore.TensorBuffer(convInfo.outShape, x.dtype);
+        var xStrides = tfjsCore.util.computeStrides(x.shape);
+        var filterStrides = tfjsCore.util.computeStrides(filter.shape);
+        var xBatchStride = xStrides[0];
+        var xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];
+        var xColStride = isChannelsLast ? xStrides[2] : 1;
+        var xChannelStride = isChannelsLast ? 1 : xStrides[1];
+        var yBatchStride = y.strides[0];
+        var yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];
+        var yColStride = isChannelsLast ? y.strides[2] : 1;
+        var yChannelStride = isChannelsLast ? 1 : y.strides[1];
+        var xVals = backend.data.get(x.dataId).values;
+        var wVals = backend.data.get(filter.dataId).values;
+        var yVals = y.values;
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            var xOffset1 = b * xBatchStride;
+            var yOffset1 = b * yBatchStride;
+            for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                var yOffset2 = yOffset1 + yR * yRowStride;
+                var xRCorner = yR * convInfo.strideHeight - padTop;
+                for (var wR = 0; wR < filterHeight; ++wR) {
+                    var xR = xRCorner + wR * dilationHeight;
+                    if (xR < 0 || xR >= convInfo.inHeight) {
+                        continue;
+                    }
+                    var wOffset1 = wR * filterStrides[0];
+                    var xOffset2 = xOffset1 + xR * xRowStride;
+                    for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                        var yOffset3 = yOffset2 + yC * yColStride;
+                        var xCCorner = yC * convInfo.strideWidth - padLeft;
+                        for (var wC = 0; wC < filterWidth; ++wC) {
+                            var xC = xCCorner + wC * dilationWidth;
+                            if (xC < 0 || xC >= convInfo.inWidth) {
+                                continue;
+                            }
+                            var wOffset2 = wOffset1 + wC * filterStrides[1];
+                            var xOffset3 = xOffset2 + xC * xColStride;
+                            var wOffset3 = wOffset2;
+                            for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                                var xVal = xVals[xOffset3 + d1 * xChannelStride];
+                                for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                                    yVals[yOffset3 + d2 * yChannelStride] +=
+                                        xVal * wVals[wOffset3 + d2];
+                                }
+                                wOffset3 += convInfo.outChannels;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(y.shape, y.dtype, yVals);
+    }
+    var conv2DConfig = {
+        kernelName: tfjsCore.Conv2D,
+        backendName: 'cpu',
+        kernelFunc: conv2D
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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;
+        assertNotComplex([x, dy], 'conv2dBackpropFilter');
+        var $dataFormat = tfjsCore.backend_util.convertConv2DDataFormat(dataFormat);
+        var convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad, dimRoundingMode, false /* depthwise */, $dataFormat);
+        var strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth;
+        var isChannelsLast = convInfo.dataFormat === 'channelsLast';
+        var dW = new tfjsCore.TensorBuffer(convInfo.filterShape, 'float32');
+        var leftPad = convInfo.padInfo.left;
+        var topPad = convInfo.padInfo.top;
+        var xVals = backend.data.get(x.dataId).values;
+        var dyVals = backend.data.get(dy.dataId).values;
+        var xBuf = new tfjsCore.TensorBuffer(x.shape, x.dtype, xVals);
+        var dyBuf = new tfjsCore.TensorBuffer(dy.shape, dy.dtype, dyVals);
+        for (var wR = 0; wR < filterHeight; ++wR) {
+            var yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+            var yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+            for (var wC = 0; wC < filterWidth; ++wC) {
+                var yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+                var yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+                for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                    for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                        var dotProd = 0;
+                        for (var b = 0; b < convInfo.batchSize; ++b) {
+                            for (var yR = yRMin; yR < yRMax; ++yR) {
+                                var xR = wR + yR * strideHeight - topPad;
+                                for (var yC = yCMin; yC < yCMax; ++yC) {
+                                    var xC = wC + yC * strideWidth - leftPad;
+                                    if (isChannelsLast) {
+                                        dotProd += xBuf.get(b, xR, xC, d1) *
+                                            dyBuf.get(b, yR, yC, d2);
+                                    }
+                                    else {
+                                        dotProd += xBuf.get(b, d1, xR, xC) *
+                                            dyBuf.get(b, d2, yR, yC);
+                                    }
+                                }
+                            }
+                        }
+                        dW.set(dotProd, wR, wC, d1, d2);
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);
+    }
+    var conv2DBackpropFilterConfig = {
+        kernelName: tfjsCore.Conv2DBackpropFilter,
+        backendName: 'cpu',
+        kernelFunc: conv2DBackpropFilter
+    };
+
+    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;
+        assertNotComplex([dy, filter], 'conv2dBackpropInput');
+        var filterStrides = tfjsCore.util.computeStrides(filter.shape);
+        var dyStrides = tfjsCore.util.computeStrides(dy.shape);
+        var $dataFormat = tfjsCore.backend_util.convertConv2DDataFormat(dataFormat);
+        var convInfo = tfjsCore.backend_util.computeConv2DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);
+        var dx = new tfjsCore.TensorBuffer(convInfo.inShape, 'float32');
+        var dxValues = dx.values;
+        var dyValues = backend.data.get(dy.dataId).values;
+        var fltValues = backend.data.get(filter.dataId).values;
+        var _a = __read(filterStrides, 3), fltS0 = _a[0], fltS1 = _a[1], fltS2 = _a[2];
+        var batchSize = convInfo.batchSize, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, outChannels = convInfo.outChannels, outHeight = convInfo.outHeight, outWidth = convInfo.outWidth, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth;
+        $dataFormat = convInfo.dataFormat;
+        var topPad = filterHeight - 1 - convInfo.padInfo.top;
+        var leftPad = filterWidth - 1 - convInfo.padInfo.left;
+        var isChannelsLast = $dataFormat === 'channelsLast';
+        var xBatchStride = dx.strides[0];
+        var xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];
+        var xColStride = isChannelsLast ? dx.strides[2] : 1;
+        var xChannelStride = isChannelsLast ? 1 : dx.strides[1];
+        var yBatchStride = dyStrides[0];
+        var yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
+        var yColStride = isChannelsLast ? dyStrides[2] : 1;
+        var yChannelStride = isChannelsLast ? 1 : dyStrides[1];
+        for (var b = 0; b < batchSize; ++b) {
+            for (var d1 = 0; d1 < inChannels; ++d1) {
+                for (var xR = 0; xR < inHeight; ++xR) {
+                    var xRCorner = xR - topPad;
+                    var xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+                    var yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+                    for (var xC = 0; xC < inWidth; ++xC) {
+                        var xCCorner = xC - leftPad;
+                        var xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+                        var yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+                        var dotProd = 0;
+                        for (var yR = xRMin; yR < yRMax; ++yR) {
+                            var wR = yR * strideHeight - xRCorner;
+                            for (var yC = xCMin; yC < yCMax; ++yC) {
+                                var wC = yC * strideWidth - xCCorner;
+                                var dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;
+                                var fltOffset = fltS0 * (filterHeight - 1 - wR) +
+                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
+                                for (var d2 = 0; d2 < outChannels; ++d2) {
+                                    var pixel = dyValues[dyOffset + yChannelStride * d2];
+                                    var weight = fltValues[fltOffset + d2];
+                                    dotProd += pixel * weight;
+                                }
+                            }
+                        }
+                        var dxOffset = xBatchStride * b + xRowStride * xR +
+                            xColStride * xC + xChannelStride * d1;
+                        dxValues[dxOffset] = dotProd;
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+    }
+    var conv2DBackpropInputConfig = {
+        kernelName: tfjsCore.Conv2DBackpropInput,
+        backendName: 'cpu',
+        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;
+        assertNotComplex([x, filter], 'conv3d');
+        var convInfo = tfjsCore.backend_util.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad);
+        var filterDepth = convInfo.filterDepth, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, dilationDepth = convInfo.dilationDepth, dilationHeight = convInfo.dilationHeight, dilationWidth = convInfo.dilationWidth, padInfo = convInfo.padInfo;
+        var padFront = padInfo.front;
+        var padLeft = padInfo.left;
+        var padTop = padInfo.top;
+        var y = new tfjsCore.TensorBuffer(convInfo.outShape, x.dtype);
+        var xVals = backend.data.get(x.dataId).values;
+        var wVals = backend.data.get(filter.dataId).values;
+        var yVals = y.values;
+        var xStrides = tfjsCore.util.computeStrides(x.shape);
+        var filterStrides = tfjsCore.util.computeStrides(filter.shape);
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            var xOffset1 = b * xStrides[0];
+            var yOffset1 = b * y.strides[0];
+            for (var yF = 0; yF < convInfo.outDepth; ++yF) {
+                var yOffset2 = yOffset1 + yF * y.strides[1];
+                var xFCorner = yF * convInfo.strideDepth - padFront;
+                for (var wF = 0; wF < filterDepth; ++wF) {
+                    var xF = xFCorner + wF * dilationDepth;
+                    if (xF < 0 || xF >= convInfo.inDepth) {
+                        continue;
+                    }
+                    var wOffset1 = wF * filterStrides[0];
+                    var xOffset2 = xOffset1 + xF * xStrides[1];
+                    for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                        var yOffset3 = yOffset2 + yR * y.strides[2];
+                        var xRCorner = yR * convInfo.strideHeight - padTop;
+                        for (var wR = 0; wR < filterHeight; ++wR) {
+                            var xR = xRCorner + wR * dilationHeight;
+                            if (xR < 0 || xR >= convInfo.inHeight) {
+                                continue;
+                            }
+                            var wOffset2 = wOffset1 + wR * filterStrides[1];
+                            var xOffset3 = xOffset2 + xR * xStrides[2];
+                            for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                                var yOffset4 = yOffset3 + yC * convInfo.outChannels;
+                                var xCCorner = yC * convInfo.strideWidth - padLeft;
+                                for (var wC = 0; wC < filterWidth; ++wC) {
+                                    var xC = xCCorner + wC * dilationWidth;
+                                    if (xC < 0 || xC >= convInfo.inWidth) {
+                                        continue;
+                                    }
+                                    var wOffset3 = wOffset2 + wC * filterStrides[2];
+                                    var xOffset4 = xOffset3 + xC * convInfo.inChannels;
+                                    var wOffset4 = wOffset3;
+                                    for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                                        var xVal = xVals[xOffset4 + d1];
+                                        for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                                            yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];
+                                        }
+                                        wOffset4 += convInfo.outChannels;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(y.shape, y.dtype, y.values);
+    }
+    var conv3DConfig = {
+        kernelName: tfjsCore.Conv3D,
+        backendName: 'cpu',
+        kernelFunc: conv3D
+    };
+
+    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;
+        assertNotComplex([x, dy], 'conv3dBackpropFilterV2');
+        var xStrides = tfjsCore.util.computeStrides(x.shape);
+        var dyStrides = tfjsCore.util.computeStrides(dy.shape);
+        var convInfo = tfjsCore.backend_util.computeConv3DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad);
+        var strideDepth = convInfo.strideDepth;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var filterDepth = convInfo.filterDepth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var dw = new tfjsCore.TensorBuffer(convInfo.filterShape, 'float32');
+        var dwValues = dw.values;
+        var _a = __read(dw.strides, 4), dwS0 = _a[0], dwS1 = _a[1], dwS2 = _a[2], dwS3 = _a[3];
+        var dyValues = backend.data.get(dy.dataId).values;
+        var _b = __read(dyStrides, 4), dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2], dyS3 = _b[3];
+        var xValues = backend.data.get(x.dataId).values;
+        var _c = __read(xStrides, 4), xS0 = _c[0], xS1 = _c[1], xS2 = _c[2], xS3 = _c[3];
+        var frontPad = convInfo.padInfo.front;
+        var leftPad = convInfo.padInfo.left;
+        var topPad = convInfo.padInfo.top;
+        for (var wF = 0; wF < filterDepth; ++wF) {
+            var yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));
+            var yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);
+            var wOffset1 = wF * dwS0;
+            for (var wR = 0; wR < filterHeight; ++wR) {
+                var yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+                var yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+                var wOffset2 = wR * dwS1 + wOffset1;
+                for (var wC = 0; wC < filterWidth; ++wC) {
+                    var yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+                    var yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+                    var wOffset3 = wC * dwS2 + wOffset2;
+                    for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                        var wOffset4 = d1 * dwS3 + wOffset3;
+                        for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                            var dotProd = 0;
+                            for (var b = 0; b < convInfo.batchSize; ++b) {
+                                var xOffset1 = b * xS0;
+                                var yOffset1 = b * dyS0;
+                                for (var yF = yFMin; yF < yFMax; ++yF) {
+                                    var xF = wF + yF * strideDepth - frontPad;
+                                    var xOffset2 = xF * xS1 + xOffset1;
+                                    var yOffset2 = yF * dyS1 + yOffset1;
+                                    for (var yR = yRMin; yR < yRMax; ++yR) {
+                                        var xR = wR + yR * strideHeight - topPad;
+                                        var xOffset3 = xR * xS2 + xOffset2;
+                                        var yOffset3 = yR * dyS2 + yOffset2;
+                                        for (var yC = yCMin; yC < yCMax; ++yC) {
+                                            var xC = wC + yC * strideWidth - leftPad;
+                                            var xOffset4 = xC * xS3 + xOffset3;
+                                            var yOffset4 = yC * dyS3 + yOffset3;
+                                            dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];
+                                        }
+                                    }
+                                }
+                            }
+                            dwValues[wOffset4 + d2] = dotProd;
+                        }
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(dw.shape, dw.dtype, dw.values);
+    }
+    var conv3DBackpropFilterV2Config = {
+        kernelName: tfjsCore.Conv3DBackpropFilterV2,
+        backendName: 'cpu',
+        kernelFunc: conv3DBackpropFilterV2
+    };
+
+    function conv3DBackpropInputV2(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;
+        assertNotComplex([dy], 'conv3dBackpropInputV2');
+        var dyStrides = tfjsCore.util.computeStrides(dy.shape);
+        var filterStrides = tfjsCore.util.computeStrides(filter.shape);
+        var convInfo = tfjsCore.backend_util.computeConv3DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad);
+        var dx = new tfjsCore.TensorBuffer(convInfo.inShape, 'float32');
+        var dxValues = dx.values;
+        var _a = __read(dx.strides, 4), dxS0 = _a[0], dxS1 = _a[1], dxS2 = _a[2], dxS3 = _a[3];
+        var dyValues = backend.data.get(dy.dataId).values;
+        var _b = __read(dyStrides, 4), dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2], dyS3 = _b[3];
+        var fltValues = backend.data.get(filter.dataId).values;
+        var _c = __read(filterStrides, 4), fltS0 = _c[0], fltS1 = _c[1], fltS2 = _c[2], fltS3 = _c[3];
+        var batchSize = convInfo.batchSize, filterDepth = convInfo.filterDepth, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, inDepth = convInfo.inDepth, inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, outChannels = convInfo.outChannels, outDepth = convInfo.outDepth, outHeight = convInfo.outHeight, outWidth = convInfo.outWidth, strideDepth = convInfo.strideDepth, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth;
+        var frontPad = filterDepth - 1 - convInfo.padInfo.front;
+        var topPad = filterHeight - 1 - convInfo.padInfo.top;
+        var leftPad = filterWidth - 1 - convInfo.padInfo.left;
+        for (var b = 0; b < batchSize; ++b) {
+            for (var d1 = 0; d1 < inChannels; ++d1) {
+                // Frames of depth
+                for (var xF = 0; xF < inDepth; ++xF) {
+                    var xFCorner = xF - frontPad;
+                    var xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));
+                    var yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);
+                    // Rows as per standard 2d matrix notation
+                    for (var xR = 0; xR < inHeight; ++xR) {
+                        var xRCorner = xR - topPad;
+                        var xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+                        var yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+                        // Columns as per standard 2d matrix notation
+                        for (var xC = 0; xC < inWidth; ++xC) {
+                            var xCCorner = xC - leftPad;
+                            var xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+                            var yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+                            var dotProd = 0;
+                            for (var yF = xFMin; yF < yFMax; ++yF) {
+                                var wF = yF * strideDepth - xFCorner;
+                                for (var yR = xRMin; yR < yRMax; ++yR) {
+                                    var wR = yR * strideHeight - xRCorner;
+                                    for (var yC = xCMin; yC < yCMax; ++yC) {
+                                        var wC = yC * strideWidth - xCCorner;
+                                        var dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;
+                                        var fltOffset = fltS0 * (filterDepth - 1 - wF) +
+                                            fltS1 * (filterHeight - 1 - wR) +
+                                            fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;
+                                        for (var d2 = 0; d2 < outChannels; ++d2) {
+                                            var pixel = dyValues[dyOffset + d2];
+                                            var weight = fltValues[fltOffset + d2];
+                                            dotProd += pixel * weight;
+                                        }
+                                    }
+                                }
+                            }
+                            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =
+                                dotProd;
+                        }
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+    }
+    var conv3DBackpropInputV2Config = {
+        kernelName: tfjsCore.Conv3DBackpropInputV2,
+        backendName: 'cpu',
+        kernelFunc: conv3DBackpropInputV2
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 = unaryKernelFunc(tfjsCore.Cos, function (xi) { return Math.cos(xi); });
+    var cosConfig = {
+        kernelName: tfjsCore.Cos,
+        backendName: 'cpu',
+        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 = unaryKernelFunc(tfjsCore.Cosh, function (xi) { return Math.cosh(xi); });
+    var coshConfig = {
+        kernelName: tfjsCore.Cosh,
+        backendName: 'cpu',
+        kernelFunc: cosh,
+    };
+
+    function cropAndResize(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 _a = __read(image.shape, 4), batch = _a[0], imageHeight = _a[1], imageWidth = _a[2], numChannels = _a[3];
+        var numBoxes = boxes.shape[0];
+        var _b = __read(cropSize, 2), cropHeight = _b[0], cropWidth = _b[1];
+        var output = tfjsCore.buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');
+        var boxVals = backend.data.get(boxes.dataId).values;
+        var boxIndVals = backend.data.get(boxInd.dataId).values;
+        var imageVals = backend.data.get(image.dataId).values;
+        var inStride = tfjsCore.util.computeStrides(image.shape); // to calculate flat indexes into image
+        var outStride = tfjsCore.util.computeStrides(output.shape); // to calculate flat indexes into output
+        // Reference implementation
+        // tslint:disable-next-line:max-line-length
+        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc
+        for (var b = 0; b < numBoxes; b++) {
+            var startInd = b * 4;
+            var y1 = boxVals[startInd];
+            var x1 = boxVals[startInd + 1];
+            var y2 = boxVals[startInd + 2];
+            var x2 = boxVals[startInd + 3];
+            var bInd = boxIndVals[b];
+            if (bInd >= batch) {
+                continue;
+            }
+            var heightScale = (cropHeight > 1) ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;
+            var widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;
+            for (var y = 0; y < cropHeight; y++) {
+                var yInd = (cropHeight > 1) ?
+                    y1 * (imageHeight - 1) + y * (heightScale) :
+                    0.5 * (y1 + y2) * (imageHeight - 1);
+                if (yInd < 0 || yInd > imageHeight - 1) {
+                    for (var x = 0; x < cropWidth; x++) {
+                        for (var c = 0; c < numChannels; c++) {
+                            var ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                            output.values[ind] = extrapolationValue;
+                        }
+                    }
+                    continue;
+                }
+                if (method === 'bilinear') {
+                    var topInd = Math.floor(yInd);
+                    var bottomInd = Math.ceil(yInd);
+                    var yLerp = yInd - topInd;
+                    for (var x = 0; x < cropWidth; x++) {
+                        var xInd = (cropWidth > 1) ?
+                            x1 * (imageWidth - 1) + x * widthScale :
+                            0.5 * (x1 + x2) * (imageWidth - 1);
+                        if (xInd < 0 || xInd > imageWidth - 1) {
+                            for (var c = 0; c < numChannels; c++) {
+                                var ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                                output.values[ind] = extrapolationValue;
+                            }
+                            continue;
+                        }
+                        var leftInd = Math.floor(xInd);
+                        var rightInd = Math.ceil(xInd);
+                        var xLerp = xInd - leftInd;
+                        for (var c = 0; c < numChannels; c++) {
+                            var ind = c + leftInd * inStride[2] + topInd * inStride[1] +
+                                bInd * inStride[0];
+                            var topLeft = imageVals[ind];
+                            ind = c + rightInd * inStride[2] + topInd * inStride[1] +
+                                bInd * inStride[0];
+                            var topRight = imageVals[ind];
+                            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +
+                                bInd * inStride[0];
+                            var bottomLeft = imageVals[ind];
+                            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +
+                                bInd * inStride[0];
+                            var bottomRight = imageVals[ind];
+                            var top = topLeft + (topRight - topLeft) * xLerp;
+                            var bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;
+                            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                            output.values[ind] = top + ((bottom - top) * yLerp);
+                        }
+                    }
+                }
+                else { // method == "nearest"
+                    for (var x = 0; x < cropWidth; ++x) {
+                        var xInd = (cropWidth > 1) ?
+                            x1 * (imageWidth - 1) + x * widthScale :
+                            0.5 * (x1 + x2) * (imageWidth - 1);
+                        if (xInd < 0 || xInd > imageWidth - 1) {
+                            for (var c = 0; c < numChannels; c++) {
+                                var ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                                output.values[ind] = extrapolationValue;
+                            }
+                            continue;
+                        }
+                        var closestX = Math.round(xInd);
+                        var closestY = Math.round(yInd);
+                        for (var c = 0; c < numChannels; c++) {
+                            var inInd = c + closestX * inStride[2] + closestY * inStride[1] +
+                                bInd * inStride[0];
+                            var outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                            output.values[outInd] = imageVals[inInd];
+                        }
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(output.shape, output.dtype, output.values);
+    }
+    var cropAndResizeConfig = {
+        kernelName: tfjsCore.CropAndResize,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    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;
+        assertNotComplex(x, 'cumsum');
+        var permutation = tfjsCore.backend_util.getAxesPermutation([axis], x.shape.length);
+        var $x = x;
+        if (permutation != null) {
+            $x = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutation } });
+        }
+        var permutedAxis = tfjsCore.backend_util.getInnerMostAxes(1, x.shape.length)[0];
+        if (permutedAxis !== $x.shape.length - 1) {
+            throw new Error("backend.cumsum in CPU expects an inner-most " +
+                ("axis=" + ($x.shape.length - 1) + " but got axis=" + permutedAxis));
+        }
+        var resultDtype = tfjsCore.upcastType($x.dtype, 'int32');
+        var vals = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape($x.shape), resultDtype);
+        var aVals = backend.data.get($x.dataId).values;
+        var finalDim = $x.shape[$x.shape.length - 1];
+        var indexAdjuster = reverse ?
+            function (i, j) { return i + finalDim - j - 1; } :
+            function (i, j) { return i + j; };
+        for (var i = 0; i < aVals.length; i += finalDim) {
+            for (var j = 0; j < finalDim; j++) {
+                var idx = indexAdjuster(i, j);
+                if (j === 0) {
+                    vals[idx] = exclusive ? 0 : aVals[idx];
+                }
+                else {
+                    var prevIdx = indexAdjuster(i, j - 1);
+                    vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :
+                        aVals[idx] + vals[prevIdx];
+                }
+            }
+        }
+        var result = backend.makeTensorInfo($x.shape, resultDtype, vals);
+        if (permutation != null) {
+            var reversePermutation = tfjsCore.backend_util.getUndoAxesPermutation(permutation);
+            var reverseTransposedResult = transpose({ inputs: { x: result }, backend: backend, attrs: { perm: reversePermutation } });
+            backend.disposeIntermediateTensorInfo(result);
+            backend.disposeIntermediateTensorInfo($x);
+            return reverseTransposedResult;
+        }
+        return result;
+    }
+    var cumsumConfig = {
+        kernelName: tfjsCore.Cumsum,
+        backendName: 'cpu',
+        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.data.get(x.dataId).values;
+            var weightsVals = backend.data.get(weights.dataId).values;
+            var outVals = bincountImpl(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 = bincountReduceImpl(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: tfjsCore.DenseBincount,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    function depthToSpace(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var blockSize = attrs.blockSize, dataFormat = attrs.dataFormat;
+        tfjsCore.util.assert(dataFormat === 'NHWC', function () { return "Only NHWC dataFormat supported on CPU for depthToSpace. Got " + dataFormat; });
+        var batchSize = x.shape[0];
+        var inputHeight = x.shape[1];
+        var inputWidth = x.shape[2];
+        var inputDepth = x.shape[3];
+        var outputHeight = inputHeight * blockSize;
+        var outputWidth = inputWidth * blockSize;
+        var outputDepth = inputDepth / (blockSize * blockSize);
+        var xValues = backend.data.get(x.dataId).values;
+        var result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);
+        var outputIdx = 0;
+        for (var b = 0; b < batchSize; ++b) {
+            for (var h = 0; h < outputHeight; ++h) {
+                var inH = Math.floor(h / blockSize);
+                var offsetH = (h % blockSize);
+                for (var w = 0; w < outputWidth; ++w) {
+                    var inW = Math.floor(w / blockSize);
+                    var offsetW = (w % blockSize);
+                    var offsetD = (offsetH * blockSize + offsetW) * outputDepth;
+                    for (var d = 0; d < outputDepth; ++d) {
+                        var inD = d + offsetD;
+                        var inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));
+                        result[outputIdx++] = xValues[inputIdx];
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result);
+    }
+    var depthToSpaceConfig = {
+        kernelName: tfjsCore.DepthToSpace,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    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;
+        assertNotComplex([x, filter], 'depthwiseConv2DNative');
+        var xStrides = tfjsCore.util.computeStrides(x.shape);
+        var filterStrides = tfjsCore.util.computeStrides(filter.shape);
+        var $dilations = dilations;
+        if ($dilations == null) {
+            $dilations = [1, 1];
+        }
+        tfjsCore.util.assert(tfjsCore.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 = tfjsCore.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);
+        var filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, dilationHeight = convInfo.dilationHeight, dilationWidth = convInfo.dilationWidth, padInfo = convInfo.padInfo;
+        var padLeft = padInfo.left;
+        var padTop = padInfo.top;
+        var chMul = convInfo.outChannels / convInfo.inChannels;
+        var y = new tfjsCore.TensorBuffer(convInfo.outShape, x.dtype);
+        var xVals = backend.data.get(x.dataId).values;
+        var wVals = backend.data.get(filter.dataId).values;
+        var yVals = y.values;
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            var xOffset1 = b * xStrides[0];
+            var yOffset1 = b * y.strides[0];
+            for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                var yOffset2 = yOffset1 + yR * y.strides[1];
+                var xRCorner = yR * convInfo.strideHeight - padTop;
+                for (var wR = 0; wR < filterHeight; ++wR) {
+                    var xR = xRCorner + wR * dilationHeight;
+                    if (xR < 0 || xR >= convInfo.inHeight) {
+                        continue;
+                    }
+                    var wOffset1 = wR * filterStrides[0];
+                    var xOffset2 = xOffset1 + xR * xStrides[1];
+                    for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                        var yOffset3 = yOffset2 + yC * y.strides[2];
+                        var xCCorner = yC * convInfo.strideWidth - padLeft;
+                        for (var wC = 0; wC < filterWidth; ++wC) {
+                            var xC = xCCorner + wC * dilationWidth;
+                            if (xC < 0 || xC >= convInfo.inWidth) {
+                                continue;
+                            }
+                            var wOffset2 = wOffset1 + wC * filterStrides[1];
+                            var xOffset3 = xOffset2 + xC * convInfo.inChannels;
+                            var yOffset4 = yOffset3;
+                            var wOffset3 = wOffset2;
+                            for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                                var xVal = xVals[xOffset3 + d1];
+                                for (var q = 0; q < chMul; ++q) {
+                                    yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];
+                                }
+                                yOffset4 += chMul;
+                                wOffset3 += chMul;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(y.shape, y.dtype, y.values);
+    }
+    var depthwiseConv2dNativeConfig = {
+        kernelName: tfjsCore.DepthwiseConv2dNative,
+        backendName: 'cpu',
+        kernelFunc: depthwiseConv2dNative
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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;
+        assertNotComplex([x, dy], 'depthwiseConv2dNativeBackpropFilter');
+        var convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
+        var strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth;
+        var dW = new tfjsCore.TensorBuffer(convInfo.filterShape, 'float32');
+        var leftPad = convInfo.padInfo.left;
+        var topPad = convInfo.padInfo.top;
+        var chMul = convInfo.outChannels / convInfo.inChannels;
+        var xVals = backend.data.get(x.dataId).values;
+        var xBuf = new tfjsCore.TensorBuffer(x.shape, x.dtype, xVals);
+        var dyVals = backend.data.get(dy.dataId).values;
+        var dyBuf = new tfjsCore.TensorBuffer(dy.shape, dy.dtype, dyVals);
+        for (var wR = 0; wR < filterHeight; ++wR) {
+            var yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+            var yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+            for (var wC = 0; wC < filterWidth; ++wC) {
+                var yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+                var yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+                for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                    var d1 = Math.trunc(d2 / chMul);
+                    var dm = d2 % chMul;
+                    var dotProd = 0;
+                    for (var b = 0; b < convInfo.batchSize; ++b) {
+                        for (var yR = yRMin; yR < yRMax; ++yR) {
+                            var xR = wR + yR * strideHeight - topPad;
+                            for (var yC = yCMin; yC < yCMax; ++yC) {
+                                var xC = wC + yC * strideWidth - leftPad;
+                                dotProd += xBuf.get(b, xR, xC, d1) *
+                                    dyBuf.get(b, yR, yC, d2);
+                            }
+                        }
+                    }
+                    dW.set(dotProd, wR, wC, d1, dm);
+                }
+            }
+        }
+        return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);
+    }
+    var depthwiseConv2dNativeBackpropFilterConfig = {
+        kernelName: tfjsCore.DepthwiseConv2dNativeBackpropFilter,
+        backendName: 'cpu',
+        kernelFunc: depthwiseConv2dNativeBackpropFilter
+    };
+
+    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;
+        assertNotComplex([dy, filter], 'depthwiseConv2DNativeBackpropInput');
+        var dyStrides = tfjsCore.util.computeStrides(dy.shape);
+        var filterStrides = tfjsCore.util.computeStrides(filter.shape);
+        var convInfo = tfjsCore.backend_util.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
+        var dx = new tfjsCore.TensorBuffer(convInfo.inShape, 'float32');
+        var dxValues = dx.values;
+        var _a = __read(dx.strides, 3), dxS0 = _a[0], dxS1 = _a[1], dxS2 = _a[2];
+        var dyValues = backend.data.get(dy.dataId).values;
+        var _b = __read(dyStrides, 3), dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2];
+        var fltValues = backend.data.get(filter.dataId).values;
+        var _c = __read(filterStrides, 3), fltS0 = _c[0], fltS1 = _c[1], fltS2 = _c[2];
+        var batchSize = convInfo.batchSize, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, outChannels = convInfo.outChannels, outHeight = convInfo.outHeight, outWidth = convInfo.outWidth, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth;
+        var topPad = filterHeight - 1 - convInfo.padInfo.top;
+        var leftPad = filterWidth - 1 - convInfo.padInfo.left;
+        var chMul = outChannels / inChannels;
+        for (var b = 0; b < batchSize; ++b) {
+            for (var d1 = 0; d1 < inChannels; ++d1) {
+                for (var xR = 0; xR < inHeight; ++xR) {
+                    var xRCorner = xR - topPad;
+                    var xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+                    var yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+                    for (var xC = 0; xC < inWidth; ++xC) {
+                        var xCCorner = xC - leftPad;
+                        var xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+                        var yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+                        var dotProd = 0;
+                        for (var yR = xRMin; yR < yRMax; ++yR) {
+                            var wR = yR * strideHeight - xRCorner;
+                            for (var yC = xCMin; yC < yCMax; ++yC) {
+                                var wC = yC * strideWidth - xCCorner;
+                                var dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;
+                                var fltOffset = fltS0 * (filterHeight - 1 - wR) +
+                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
+                                for (var dm = 0; dm < chMul; ++dm) {
+                                    var d2 = d1 * chMul + dm;
+                                    var pixel = dyValues[dyOffset + d2];
+                                    var weight = fltValues[fltOffset + dm];
+                                    dotProd += pixel * weight;
+                                }
+                            }
+                        }
+                        dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+    }
+    var depthwiseConv2dNativeBackpropInputConfig = {
+        kernelName: tfjsCore.DepthwiseConv2dNativeBackpropInput,
+        backendName: 'cpu',
+        kernelFunc: depthwiseConv2dNativeBackpropInput
+    };
+
+    function diag(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var x = inputs.x;
+        var xSize = tfjsCore.util.sizeFromShape(x.shape);
+        var xVals = backend.data.get(x.dataId).values;
+        var outBuf = tfjsCore.buffer([xSize, xSize], x.dtype);
+        var vals = outBuf.values;
+        for (var i = 0; i < xVals.length; i++) {
+            vals[i * xSize + i] = xVals[i];
+        }
+        var outShape = __spread(x.shape, x.shape);
+        return backend.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);
+    }
+    var diagConfig = {
+        kernelName: tfjsCore.Diag,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    var dilation2dConfig = {
+        kernelName: tfjsCore.Dilation2D,
+        backendName: 'cpu',
+        kernelFunc: function (_a) {
+            var inputs = _a.inputs, backend = _a.backend, attrs = _a.attrs;
+            var x = inputs.x, filter = inputs.filter;
+            var strides = attrs.strides, pad = attrs.pad, dilations = attrs.dilations;
+            var cpuBackend = backend;
+            var xVals = cpuBackend.data.get(x.dataId).values;
+            var xRank = x.shape.length;
+            var filterVals = cpuBackend.data.get(filter.dataId).values;
+            var filterRank = filter.shape.length;
+            var _b = tfjsCore.backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations), batchSize = _b.batchSize, inHeight = _b.inHeight, inWidth = _b.inWidth, inChannels = _b.inChannels, outHeight = _b.outHeight, outWidth = _b.outWidth, padInfo = _b.padInfo, strideHeight = _b.strideHeight, strideWidth = _b.strideWidth, filterHeight = _b.filterHeight, filterWidth = _b.filterWidth, dilationHeight = _b.dilationHeight, dilationWidth = _b.dilationWidth, outShape = _b.outShape;
+            var outSize = tfjsCore.util.sizeFromShape(outShape);
+            var outRank = outShape.length;
+            var outputVals = tfjsCore.util.getArrayFromDType(x.dtype, outSize);
+            // Upsampling the input by fill in `dilation size - 1` values between each
+            // input value.
+            // This implementation follows the TF c++ implementation:
+            // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc
+            for (var b = 0; b < batchSize; ++b) {
+                for (var hOut = 0; hOut < outHeight; ++hOut) {
+                    var hBeg = hOut * strideHeight - padInfo.top;
+                    for (var wOut = 0; wOut < outWidth; ++wOut) {
+                        var wBeg = wOut * strideWidth - padInfo.left;
+                        for (var d = 0; d < inChannels; ++d) {
+                            var curVal = Number.MIN_SAFE_INTEGER;
+                            for (var h = 0; h < filterHeight; ++h) {
+                                var hIn = hBeg + h * dilationHeight;
+                                if (hIn >= 0 && hIn < inHeight) {
+                                    for (var w = 0; w < filterWidth; ++w) {
+                                        var wIn = wBeg + w * dilationWidth;
+                                        if (wIn >= 0 && wIn < inWidth) {
+                                            var xIndex = tfjsCore.util.locToIndex([b, hIn, wIn, d], xRank, tfjsCore.util.computeStrides(x.shape));
+                                            var filterIndex = tfjsCore.util.locToIndex([h, w, d], filterRank, tfjsCore.util.computeStrides(filter.shape));
+                                            var val = xVals[xIndex] + filterVals[filterIndex];
+                                            if (val > curVal) {
+                                                curVal = val;
+                                            }
+                                        }
+                                    }
+                                }
+                            }
+                            var outputIndex = tfjsCore.util.locToIndex([b, hOut, wOut, d], outRank, tfjsCore.util.computeStrides(outShape));
+                            outputVals[outputIndex] = curVal;
+                        }
+                    }
+                }
+            }
+            var dataId = cpuBackend.write(tfjsCore.util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);
+            return { dataId: dataId, shape: outShape, dtype: 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.
+     * =============================================================================
+     */
+    var dilation2dBackpropFilterConfig = {
+        kernelName: tfjsCore.Dilation2DBackpropFilter,
+        backendName: 'cpu',
+        kernelFunc: function (_a) {
+            var inputs = _a.inputs, backend = _a.backend, attrs = _a.attrs;
+            var x = inputs.x, filter = inputs.filter, dy = inputs.dy;
+            var strides = attrs.strides, pad = attrs.pad, dilations = attrs.dilations;
+            var cpuBackend = backend;
+            var $x = tfjsCore.util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
+            var $filter = tfjsCore.util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
+            var _b = tfjsCore.backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations), batchSize = _b.batchSize, inHeight = _b.inHeight, inWidth = _b.inWidth, inChannels = _b.inChannels, outHeight = _b.outHeight, outWidth = _b.outWidth, padInfo = _b.padInfo, strideHeight = _b.strideHeight, strideWidth = _b.strideWidth, filterHeight = _b.filterHeight, filterWidth = _b.filterWidth, dilationHeight = _b.dilationHeight, dilationWidth = _b.dilationWidth, outShape = _b.outShape;
+            tfjsCore.util.assert(dy.rank === outShape.length, function () { return "Error in " + tfjsCore.Dilation2DBackpropFilter + ", dy " +
+                ("must have the same rank as output " + outShape.length + ", but got ") +
+                ("" + dy.rank); });
+            var $dy = tfjsCore.util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
+            // The computed filter gradients has the same dimensions as the filter:
+            // [filterHeight, filterWidth, depth]
+            var gradients = tfjsCore.util.makeZerosNestedTypedArray(filter.shape, filter.dtype);
+            // In the case of multiple argmax branches, we only back-propagate along the
+            // last branch, i.e., the one with largest value of `h * filter_cols + w`,
+            // similarly to the max-pooling backward routines.
+            // This implementation follows the TF c++ implementation:
+            // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc
+            for (var b = 0; b < batchSize; ++b) {
+                for (var hOut = 0; hOut < outHeight; ++hOut) {
+                    var hBeg = hOut * strideHeight - padInfo.top;
+                    for (var wOut = 0; wOut < outWidth; ++wOut) {
+                        var wBeg = wOut * strideWidth - padInfo.left;
+                        for (var d = 0; d < inChannels; ++d) {
+                            var curVal = Number.MIN_SAFE_INTEGER;
+                            var hMax = 0;
+                            var wMax = 0;
+                            for (var h = 0; h < filterHeight; ++h) {
+                                var hIn = hBeg + h * dilationHeight;
+                                if (hIn >= 0 && hIn < inHeight) {
+                                    for (var w = 0; w < filterWidth; ++w) {
+                                        var wIn = wBeg + w * dilationWidth;
+                                        if (wIn >= 0 && wIn < inWidth) {
+                                            var val = $x[b][hIn][wIn][d] + $filter[h][w][d];
+                                            if (val > curVal) {
+                                                curVal = val;
+                                                hMax = h;
+                                                wMax = w;
+                                            }
+                                        }
+                                    }
+                                }
+                            }
+                            gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];
+                        }
+                    }
+                }
+            }
+            var dataId = cpuBackend.write(tfjsCore.util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);
+            return { dataId: dataId, shape: filter.shape, dtype: filter.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.
+     * =============================================================================
+     */
+    var dilation2dBackpropInputConfig = {
+        kernelName: tfjsCore.Dilation2DBackpropInput,
+        backendName: 'cpu',
+        kernelFunc: function (_a) {
+            var inputs = _a.inputs, backend = _a.backend, attrs = _a.attrs;
+            var x = inputs.x, filter = inputs.filter, dy = inputs.dy;
+            var strides = attrs.strides, pad = attrs.pad, dilations = attrs.dilations;
+            var cpuBackend = backend;
+            var $x = tfjsCore.util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
+            var $filter = tfjsCore.util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
+            var _b = tfjsCore.backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations), batchSize = _b.batchSize, inHeight = _b.inHeight, inWidth = _b.inWidth, inChannels = _b.inChannels, outHeight = _b.outHeight, outWidth = _b.outWidth, padInfo = _b.padInfo, strideHeight = _b.strideHeight, strideWidth = _b.strideWidth, filterHeight = _b.filterHeight, filterWidth = _b.filterWidth, dilationHeight = _b.dilationHeight, dilationWidth = _b.dilationWidth, outShape = _b.outShape;
+            tfjsCore.util.assert(dy.rank === outShape.length, function () { return "Error in " + tfjsCore.Dilation2DBackpropInput + ", dy " +
+                ("must have the same rank as output " + outShape.length + ", but got ") +
+                ("" + dy.rank); });
+            var $dy = tfjsCore.util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
+            // The computed gradients has the same dimensions as the input:
+            // [batch, inputHeight, inputCols, inChannel]
+            var gradients = tfjsCore.util.makeZerosNestedTypedArray(x.shape, x.dtype);
+            // In the case of multiple argmax branches, we only back-propagate along the
+            // last branch, i.e., the one with largest value of `h * filter_cols + w`,
+            // similarly to the max-pooling backward routines.
+            // This implementation follows the TF c++ implementation:
+            // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc
+            for (var b = 0; b < batchSize; ++b) {
+                for (var hOut = 0; hOut < outHeight; ++hOut) {
+                    var hBeg = hOut * strideHeight - padInfo.top;
+                    for (var wOut = 0; wOut < outWidth; ++wOut) {
+                        var wBeg = wOut * strideWidth - padInfo.left;
+                        for (var d = 0; d < inChannels; ++d) {
+                            var curVal = Number.MIN_SAFE_INTEGER;
+                            var hInMax = (hBeg < 0) ? 0 : hBeg;
+                            var wInMax = (wBeg < 0) ? 0 : wBeg;
+                            for (var h = 0; h < filterHeight; ++h) {
+                                var hIn = hBeg + h * dilationHeight;
+                                if (hIn >= 0 && hIn < inHeight) {
+                                    for (var w = 0; w < filterWidth; ++w) {
+                                        var wIn = wBeg + w * dilationWidth;
+                                        if (wIn >= 0 && wIn < inWidth) {
+                                            var val = $x[b][hIn][wIn][d] + $filter[h][w][d];
+                                            if (val > curVal) {
+                                                curVal = val;
+                                                hInMax = hIn;
+                                                wInMax = wIn;
+                                            }
+                                        }
+                                    }
+                                }
+                            }
+                            gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];
+                        }
+                    }
+                }
+            }
+            var dataId = cpuBackend.write(tfjsCore.util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);
+            return { dataId: dataId, shape: x.shape, dtype: x.dtype };
+        }
+    };
+
+    function sum(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis, keepDims = attrs.keepDims;
+        assertNotComplex(x, 'sum');
+        var $x;
+        if (x.dtype === 'bool') {
+            $x = cast({ inputs: { x: x }, backend: backend, attrs: { dtype: 'int32' } });
+        }
+        else {
+            $x = identity({ inputs: { x: x }, backend: backend });
+        }
+        var xRank = $x.shape.length;
+        var axes = tfjsCore.util.parseAxisParam(axis, $x.shape);
+        var permutation = tfjsCore.backend_util.getAxesPermutation(axes, xRank);
+        var reductionAxes = axes;
+        var permutedX = $x;
+        if (permutation != null) {
+            permutedX =
+                transpose({ inputs: { x: $x }, backend: backend, attrs: { perm: permutation } });
+            reductionAxes = tfjsCore.backend_util.getInnerMostAxes(reductionAxes.length, xRank);
+        }
+        tfjsCore.backend_util.assertAxesAreInnerMostDims('sum', reductionAxes, permutedX.shape.length);
+        var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes(permutedX.shape, reductionAxes), 2), outShape = _a[0], reduceShape = _a[1];
+        var resultDtype = tfjsCore.backend_util.upcastType(permutedX.dtype, 'int32');
+        var result = zeros(backend, outShape, resultDtype);
+        var reduceSize = tfjsCore.util.sizeFromShape(reduceShape);
+        var vals = backend.data.get(result.dataId).values;
+        var aVals = backend.data.get(permutedX.dataId).values;
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var sum_1 = 0;
+            for (var j = 0; j < reduceSize; ++j) {
+                sum_1 += aVals[offset + j];
+            }
+            vals[i] = sum_1;
+        }
+        if (keepDims) {
+            var newShape = tfjsCore.backend_util.expandShapeToKeepDim(result.shape, axes);
+            var oldResult = result;
+            result = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: newShape } });
+            backend.disposeIntermediateTensorInfo(oldResult);
+        }
+        backend.disposeIntermediateTensorInfo($x);
+        if (permutation != null) {
+            backend.disposeIntermediateTensorInfo(permutedX);
+        }
+        return result;
+    }
+    var sumConfig = {
+        kernelName: tfjsCore.Sum,
+        backendName: 'cpu',
+        kernelFunc: sum
+    };
+
+    function einsum(args) {
+        var e_1, _a, e_2, _b;
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var equation = attrs.equation;
+        var tensors = inputs;
+        var _c = tfjsCore.backend_util.decodeEinsumEquation(equation, tensors.length), allDims = _c.allDims, summedDims = _c.summedDims, idDims = _c.idDims;
+        tfjsCore.backend_util.checkEinsumDimSizes(allDims.length, idDims, tensors);
+        var _d = tfjsCore.backend_util.getEinsumComputePath(summedDims, idDims), path = _d.path, steps = _d.steps;
+        var nSteps = steps.length;
+        var out = null;
+        var numDimsRemaining = allDims.length;
+        var tensorsToDispose = [];
+        for (var i = 0; i < nSteps; ++i) {
+            try {
+                for (var _e = (e_1 = void 0, __values(steps[i])), _f = _e.next(); !_f.done; _f = _e.next()) {
+                    var idTerm = _f.value;
+                    var _g = tfjsCore.backend_util.getEinsumPermutation(numDimsRemaining, idDims[idTerm]), perm = _g.permutationIndices, dimsToExpand = _g.expandDims;
+                    var x = void 0;
+                    if (tfjsCore.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 (!tfjsCore.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);
+                    }
+                }
+            }
+            catch (e_1_1) { e_1 = { error: e_1_1 }; }
+            finally {
+                try {
+                    if (_f && !_f.done && (_a = _e.return)) _a.call(_e);
+                }
+                finally { if (e_1) throw e_1.error; }
+            }
+            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--;
+            }
+        }
+        try {
+            // Clean up intermediate tensors.
+            for (var tensorsToDispose_1 = __values(tensorsToDispose), tensorsToDispose_1_1 = tensorsToDispose_1.next(); !tensorsToDispose_1_1.done; tensorsToDispose_1_1 = tensorsToDispose_1.next()) {
+                var tensorInfo = tensorsToDispose_1_1.value;
+                if (tensorInfo === out) {
+                    continue;
+                }
+                backend.disposeIntermediateTensorInfo(tensorInfo);
+            }
+        }
+        catch (e_2_1) { e_2 = { error: e_2_1 }; }
+        finally {
+            try {
+                if (tensorsToDispose_1_1 && !tensorsToDispose_1_1.done && (_b = tensorsToDispose_1.return)) _b.call(tensorsToDispose_1);
+            }
+            finally { if (e_2) throw e_2.error; }
+        }
+        return out;
+    }
+    var einsumConfig = {
+        kernelName: tfjsCore.Einsum,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    function eluGrad(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var dy = inputs.dy, y = inputs.y;
+        assertNotComplex([dy, y], 'eluGrad');
+        var resultValues = new Float32Array(tfjsCore.util.sizeFromShape(y.shape));
+        var values = backend.data.get(y.dataId).values;
+        var dyValues = backend.data.get(dy.dataId).values;
+        for (var i = 0; i < values.length; ++i) {
+            var v = values[i];
+            if (v >= 1) {
+                resultValues[i] = dyValues[i];
+            }
+            else {
+                resultValues[i] = dyValues[i] * (v + 1);
+            }
+        }
+        return backend.makeTensorInfo(y.shape, 'float32', resultValues);
+    }
+    var eluGradConfig = {
+        kernelName: tfjsCore.EluGrad,
+        backendName: 'cpu',
+        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 p = tfjsCore.backend_util.ERF_P;
+    var a1 = tfjsCore.backend_util.ERF_A1;
+    var a2 = tfjsCore.backend_util.ERF_A2;
+    var a3 = tfjsCore.backend_util.ERF_A3;
+    var a4 = tfjsCore.backend_util.ERF_A4;
+    var a5 = tfjsCore.backend_util.ERF_A5;
+    var erf = unaryKernelFunc(tfjsCore.Erf, function (xi) {
+        var sign = Math.sign(xi);
+        var v = Math.abs(xi);
+        var t = 1.0 / (1.0 + p * v);
+        return sign *
+            (1.0 -
+                (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *
+                    Math.exp(-v * v));
+    });
+    var erfConfig = {
+        kernelName: tfjsCore.Erf,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    function expandDims(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var input = inputs.input;
+        var dim = attrs.dim;
+        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.
+            tfjsCore.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: tfjsCore.ExpandDims,
+        backendName: 'cpu',
+        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 realDivImpl = createSimpleBinaryKernelImpl(function (a, b) { return a / b; });
+    var div = binaryKernelFunc(tfjsCore.RealDiv, realDivImpl);
+    var realDivConfig = {
+        kernelName: tfjsCore.RealDiv,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    /**
+     * Calculate FFT of inner most elements of batch tensor.
+     */
+    function fftBatch(input, inverse, cpuBackend) {
+        var inputShape = input.shape;
+        var batch = inputShape[0];
+        var innerDim = inputShape[1];
+        var inputVals = cpuBackend.data.get(input.dataId);
+        var real2D = inputVals.complexTensorInfos.real;
+        var imag2D = inputVals.complexTensorInfos.imag;
+        // Collects real and imaginary values separately.
+        var resultShape = [batch, innerDim];
+        var resultSize = tfjsCore.util.sizeFromShape(resultShape);
+        var resultReal = tfjsCore.util.getTypedArrayFromDType('float32', resultSize);
+        var resultImag = tfjsCore.util.getTypedArrayFromDType('float32', resultSize);
+        for (var b = 0; b < batch; b++) {
+            // TODO: Support slice ops for complex type.
+            var r = slice({
+                inputs: { x: real2D },
+                backend: cpuBackend,
+                attrs: { begin: [b, 0], size: [1, innerDim] }
+            });
+            var i = slice({
+                inputs: { x: imag2D },
+                backend: cpuBackend,
+                attrs: { begin: [b, 0], size: [1, innerDim] }
+            });
+            var input_1 = complex({ inputs: { real: r, imag: i }, backend: cpuBackend });
+            // Run FFT by batch element.
+            var _a = fftImpl(input_1, inverse, cpuBackend), real_1 = _a.real, imag_1 = _a.imag;
+            var res = tfjsCore.backend_util.mergeRealAndImagArrays(real_1, imag_1);
+            for (var d = 0; d < innerDim; d++) {
+                var c = tfjsCore.backend_util.getComplexWithIndex(res, d);
+                resultReal[b * innerDim + d] = c.real;
+                resultImag[b * innerDim + d] = c.imag;
+            }
+            cpuBackend.disposeIntermediateTensorInfo(r);
+            cpuBackend.disposeIntermediateTensorInfo(i);
+            cpuBackend.disposeIntermediateTensorInfo(input_1);
+        }
+        var $realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal);
+        var $imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag);
+        var result = complex({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });
+        cpuBackend.disposeIntermediateTensorInfo($realInfo);
+        cpuBackend.disposeIntermediateTensorInfo($imagInfo);
+        return result;
+    }
+    function fftImpl(input, inverse, cpuBackend) {
+        var inputSize = tfjsCore.util.sizeFromShape(input.shape);
+        var inputVals = cpuBackend.data.get(input.dataId);
+        var realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;
+        var imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;
+        if (isExponentOf2(inputSize)) {
+            var result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);
+            var resultShape = [input.shape[0], input.shape[1]];
+            if (inverse) {
+                var realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.real);
+                var imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag);
+                var sizeInfo = cpuBackend.makeTensorInfo([], 'float32', tfjsCore.util.createScalarValue(inputSize, 'float32'));
+                var sizeInfoCopy = identity({ inputs: { x: sizeInfo }, backend: cpuBackend });
+                var divRealInfo = realDivConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });
+                var divImagInfo = realDivConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });
+                var divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;
+                var divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;
+                cpuBackend.disposeIntermediateTensorInfo(realInfo);
+                cpuBackend.disposeIntermediateTensorInfo(imagInfo);
+                cpuBackend.disposeIntermediateTensorInfo(sizeInfo);
+                cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);
+                cpuBackend.disposeIntermediateTensorInfo(divRealInfo);
+                cpuBackend.disposeIntermediateTensorInfo(divImagInfo);
+                return { real: divRealVals, imag: divImagVals };
+            }
+            return result;
+        }
+        else {
+            var data = tfjsCore.backend_util.mergeRealAndImagArrays(realVals, imagVals);
+            var rawOutput = fourierTransformByMatmul(data, inputSize, inverse);
+            return tfjsCore.backend_util.splitRealAndImagArrays(rawOutput);
+        }
+    }
+    function isExponentOf2(size) {
+        return (size & size - 1) === 0;
+    }
+    // FFT using Cooley-Tukey algorithm on radix 2 dimensional input.
+    function fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {
+        if (size === 1) {
+            return { real: realVals, imag: imagVals };
+        }
+        var data = tfjsCore.backend_util.mergeRealAndImagArrays(realVals, imagVals);
+        var half = size / 2;
+        var evenComplex = tfjsCore.backend_util.complexWithEvenIndex(data);
+        var evenRealVals = evenComplex.real;
+        var evenImagVals = evenComplex.imag;
+        var evenShape = [evenRealVals.length];
+        var evenRealInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals);
+        var evenImagInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals);
+        var evenTensorInfo = complex({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });
+        var oddComplex = tfjsCore.backend_util.complexWithOddIndex(data);
+        var oddRealVals = oddComplex.real;
+        var oddImagVals = oddComplex.imag;
+        var oddShape = [oddRealVals.length];
+        var oddRealInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals);
+        var oddImagInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals);
+        var oddTensorInfo = complex({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });
+        // Recursive call for half part of original input.
+        var $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);
+        var $evenRealVals = $evenComplex.real;
+        var $evenImagVals = $evenComplex.imag;
+        var $evenShape = [$evenRealVals.length];
+        var $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals);
+        var $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals);
+        var $evenTensorInfo = complex({
+            inputs: { real: $evenRealInfo, imag: $evenImagInfo },
+            backend: cpuBackend
+        });
+        var $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);
+        var $oddRealVals = $oddComplex.real;
+        var $oddImagVals = $oddComplex.imag;
+        var $oddShape = [$oddRealVals.length];
+        var $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals);
+        var $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals);
+        var $oddTensorInfo = complex({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });
+        var e = tfjsCore.backend_util.exponents(size, inverse);
+        var eShape = [e.real.length];
+        var eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real);
+        var eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag);
+        var complexInfo = complex({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });
+        var exponentInfo = multiply({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });
+        var addPart = add({
+            inputs: { a: $evenTensorInfo, b: exponentInfo },
+            backend: cpuBackend
+        });
+        var subPart = sub({
+            inputs: { a: $evenTensorInfo, b: exponentInfo },
+            backend: cpuBackend
+        });
+        var addPartReal = real({ inputs: { input: addPart }, backend: cpuBackend });
+        var subPartReal = real({ inputs: { input: subPart }, backend: cpuBackend });
+        var addPartImag = imag({ inputs: { input: addPart }, backend: cpuBackend });
+        var subPartImag = imag({ inputs: { input: subPart }, backend: cpuBackend });
+        var $real = concat({
+            inputs: [addPartReal, subPartReal],
+            backend: cpuBackend,
+            attrs: { axis: 0 }
+        });
+        var $imag = concat({
+            inputs: [addPartImag, subPartImag],
+            backend: cpuBackend,
+            attrs: { axis: 0 }
+        });
+        var $realVals = cpuBackend.data.get($real.dataId).values;
+        var $imagVals = cpuBackend.data.get($imag.dataId).values;
+        cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);
+        cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);
+        cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);
+        cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);
+        cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);
+        cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);
+        cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);
+        cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);
+        cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);
+        cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);
+        cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);
+        cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);
+        cpuBackend.disposeIntermediateTensorInfo(eRealInfo);
+        cpuBackend.disposeIntermediateTensorInfo(eImagInfo);
+        cpuBackend.disposeIntermediateTensorInfo(complexInfo);
+        cpuBackend.disposeIntermediateTensorInfo(exponentInfo);
+        cpuBackend.disposeIntermediateTensorInfo(addPart);
+        cpuBackend.disposeIntermediateTensorInfo(subPart);
+        cpuBackend.disposeIntermediateTensorInfo(addPartReal);
+        cpuBackend.disposeIntermediateTensorInfo(addPartImag);
+        cpuBackend.disposeIntermediateTensorInfo(subPartReal);
+        cpuBackend.disposeIntermediateTensorInfo(subPartImag);
+        cpuBackend.disposeIntermediateTensorInfo($real);
+        cpuBackend.disposeIntermediateTensorInfo($imag);
+        return { real: $realVals, imag: $imagVals };
+    }
+    // Calculate fourier transform by multplying sinusoid matrix.
+    function fourierTransformByMatmul(data, size, inverse) {
+        var ret = new Float32Array(size * 2);
+        // TODO: Use matmul instead once it supports complex64 type.
+        for (var r = 0; r < size; r++) {
+            var real_2 = 0.0;
+            var imag_2 = 0.0;
+            for (var c = 0; c < size; c++) {
+                var e = tfjsCore.backend_util.exponent(r * c, size, inverse);
+                var term = tfjsCore.backend_util.getComplexWithIndex(data, c);
+                real_2 += term.real * e.real - term.imag * e.imag;
+                imag_2 += term.real * e.imag + term.imag * e.real;
+            }
+            if (inverse) {
+                real_2 /= size;
+                imag_2 /= size;
+            }
+            tfjsCore.backend_util.assignToTypedArray(ret, real_2, imag_2, r);
+        }
+        return ret;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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;
+        var inputSize = tfjsCore.util.sizeFromShape(input.shape);
+        // Collapse all outer dimensions to a single batch dimension.
+        var innerDimensionSize = input.shape[input.shape.length - 1];
+        var batch = inputSize / innerDimensionSize;
+        var input2D = reshape({
+            inputs: { x: input },
+            backend: backend,
+            attrs: { shape: [batch, innerDimensionSize] }
+        });
+        var result = fftBatch(input2D, false, backend);
+        var resultReshaped = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: input.shape } });
+        backend.disposeIntermediateTensorInfo(input2D);
+        backend.disposeIntermediateTensorInfo(result);
+        return resultReshaped;
+    }
+    var fftConfig = {
+        kernelName: tfjsCore.FFT,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    function fill(args) {
+        var backend = args.backend, attrs = args.attrs;
+        var shape = attrs.shape, value = attrs.value, dtype = attrs.dtype;
+        var $dtype = dtype || tfjsCore.util.inferDtype(value);
+        var values = tfjsCore.util.getArrayFromDType($dtype, tfjsCore.util.sizeFromShape(shape));
+        fillValues(values, value, $dtype);
+        return backend.makeTensorInfo(shape, $dtype, values);
+    }
+    var fillConfig = {
+        kernelName: tfjsCore.Fill,
+        backendName: 'cpu',
+        kernelFunc: fill
+    };
+    function fillValues(values, value, dtype) {
+        if (dtype === 'string') {
+            values.fill(value);
+        }
+        else {
+            values.fill(value);
+        }
+    }
+
+    var flipLeftRightConfig = {
+        kernelName: tfjsCore.FlipLeftRight,
+        backendName: 'cpu',
+        kernelFunc: function (_a) {
+            var inputs = _a.inputs; _a.attrs; var backend = _a.backend;
+            var image = inputs.image;
+            var cpuBackend = backend;
+            var output = tfjsCore.util.getTypedArrayFromDType(image.dtype, tfjsCore.util.sizeFromShape(image.shape));
+            var _b = __read(image.shape, 4), batch = _b[0], imageHeight = _b[1], imageWidth = _b[2], numChannels = _b[3];
+            var imageVals = cpuBackend.data.get(image.dataId).values;
+            for (var batchIdx = 0; batchIdx < batch; batchIdx++) {
+                var batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
+                for (var row = 0; row < imageHeight; row++) {
+                    var rowOffset = row * (imageWidth * numChannels);
+                    for (var col = 0; col < imageWidth; col++) {
+                        var colOffset = col * numChannels;
+                        for (var channel = 0; channel < numChannels; channel++) {
+                            var coordX = Math.round(imageWidth - col - 1);
+                            var outIdx = batchOffset + rowOffset + colOffset + channel;
+                            var outputValue = imageVals[outIdx];
+                            // If the coordinate position falls within the image boundaries...
+                            if (coordX >= 0 && coordX < imageWidth) {
+                                // set the output to the image value at the coordinate position.
+                                var rotatedColOffset = coordX * numChannels;
+                                var imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;
+                                outputValue = imageVals[imageIdx];
+                            }
+                            output[outIdx] = outputValue;
+                        }
+                    }
+                }
+            }
+            var dataId = cpuBackend.write(output, image.shape, image.dtype);
+            return { dataId: dataId, shape: image.shape, dtype: image.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.
+     * =============================================================================
+     */
+    var floorDivImpl = createSimpleBinaryKernelImpl(function (a, b) { return Math.floor(a / b); });
+    var floorDiv = binaryKernelFunc(tfjsCore.FloorDiv, floorDivImpl, null /* complexImpl */, 'int32');
+    var floorDivConfig = {
+        kernelName: tfjsCore.FloorDiv,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    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 result = conv2D({
+            inputs: { x: x, filter: filter },
+            backend: backend,
+            attrs: { strides: strides, pad: pad, dataFormat: dataFormat, dilations: dilations, dimRoundingMode: dimRoundingMode }
+        });
+        if (bias) {
+            var resultOld = result;
+            result = add({ inputs: { a: result, b: bias }, backend: backend });
+            backend.disposeIntermediateTensorInfo(resultOld);
+        }
+        if (activation) {
+            var resultOld = result;
+            result = applyActivation(backend, result, activation, preluActivationWeights, leakyreluAlpha);
+            backend.disposeIntermediateTensorInfo(resultOld);
+        }
+        return result;
+    }
+    var fusedConv2DConfig = {
+        kernelName: tfjsCore.FusedConv2D,
+        backendName: 'cpu',
+        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, dataFormat = attrs.dataFormat, dilations = attrs.dilations, dimRoundingMode = attrs.dimRoundingMode, activation = attrs.activation, leakyreluAlpha = attrs.leakyreluAlpha;
+        var result = depthwiseConv2dNative({
+            inputs: { x: x, filter: filter },
+            backend: backend,
+            attrs: { strides: strides, pad: pad, dataFormat: dataFormat, dilations: dilations, dimRoundingMode: dimRoundingMode }
+        });
+        if (bias) {
+            var oldResult = result;
+            result = add({ inputs: { a: result, b: bias }, backend: backend });
+            backend.disposeIntermediateTensorInfo(oldResult);
+        }
+        if (activation) {
+            var oldResult = result;
+            result = applyActivation(backend, result, activation, preluActivationWeights, leakyreluAlpha);
+            backend.disposeIntermediateTensorInfo(oldResult);
+        }
+        return result;
+    }
+    var fusedDepthwiseConv2DConfig = {
+        kernelName: tfjsCore.FusedDepthwiseConv2D,
+        backendName: 'cpu',
+        kernelFunc: fusedDepthwiseConv2D
+    };
+
+    function gatherNd(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var params = inputs.params, indices = inputs.indices;
+        var paramsSize = tfjsCore.util.sizeFromShape(params.shape);
+        var indicesShape = indices.shape;
+        var sliceRank = indicesShape[indicesShape.length - 1];
+        var _a = __read(tfjsCore.backend_util.prepareAndValidate(params, indices), 4), resultShape = _a[0], numSlices = _a[1], sliceSize = _a[2], strides = _a[3];
+        if (numSlices === 0) {
+            return backend.makeTensorInfo(resultShape, params.dtype, []);
+        }
+        var indicesData = backend.data.get(indices.dataId).values;
+        var paramsBuf = backend.bufferSync(params);
+        var outBuf = gatherNdImpl(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);
+        return backend.makeTensorInfo(resultShape, params.dtype, outBuf.values);
+    }
+    var gatherNdConfig = {
+        kernelName: tfjsCore.GatherNd,
+        backendName: 'cpu',
+        kernelFunc: gatherNd
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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;
+        assertNotComplex([x, indices], 'gatherV2');
+        // Throw error when any index is out of bound.
+        var parsedAxis = tfjsCore.util.parseAxisParam(axis, x.shape)[0];
+        var indicesVals = backend.data.get(indices.dataId).values;
+        var axisDim = x.shape[parsedAxis];
+        var _loop_1 = function (i) {
+            var index = indicesVals[i];
+            tfjsCore.util.assert(index <= axisDim - 1 && index >= 0, function () { return "GatherV2: the index value " + index + " is not in [0, " + (axisDim - 1) + "]"; });
+        };
+        for (var i = 0; i < indicesVals.length; ++i) {
+            _loop_1(i);
+        }
+        var $batchDims = batchDims;
+        if (batchDims == null) {
+            $batchDims = 0;
+        }
+        var indicesSize = tfjsCore.util.sizeFromShape(indices.shape);
+        var shapeInfo = tfjsCore.backend_util.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims);
+        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] }
+        });
+        var flattenOutputShape = [
+            shapeInfo.batchSize, shapeInfo.outerSize, indicesSize / shapeInfo.batchSize,
+            shapeInfo.sliceSize
+        ];
+        var indicesBuf = backend.bufferSync(flattenIndex);
+        var xBuf = backend.bufferSync(flattenX);
+        var outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape);
+        backend.disposeIntermediateTensorInfo(flattenX);
+        backend.disposeIntermediateTensorInfo(flattenIndex);
+        return backend.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
+    }
+    var gatherV2Config = {
+        kernelName: tfjsCore.GatherV2,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    function ifft(args) {
+        var inputs = args.inputs, backend = args.backend;
+        var input = inputs.input;
+        var inputSize = tfjsCore.util.sizeFromShape(input.shape);
+        // Collapse all outer dimensions to a single batch dimension.
+        var innerDimensionSize = input.shape[input.shape.length - 1];
+        var batch = inputSize / innerDimensionSize;
+        var input2D = reshape({
+            inputs: { x: input },
+            backend: backend,
+            attrs: { shape: [batch, innerDimensionSize] }
+        });
+        var result = fftBatch(input2D, true, backend);
+        var resultReshaped = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: input.shape } });
+        backend.disposeIntermediateTensorInfo(input2D);
+        backend.disposeIntermediateTensorInfo(result);
+        return resultReshaped;
+    }
+    var ifftConfig = {
+        kernelName: tfjsCore.IFFT,
+        backendName: 'cpu',
+        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 isFinite = unaryKernelFunc(tfjsCore.IsFinite, function (xi) { return Number.isFinite(xi) ? 1 : 0; }, 'bool');
+    var isFiniteConfig = {
+        kernelName: tfjsCore.IsFinite,
+        backendName: 'cpu',
+        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 isInf = unaryKernelFunc(tfjsCore.IsInf, function (xi) { return Math.abs(xi) === Infinity ? 1 : 0; }, 'bool');
+    var isInfConfig = {
+        kernelName: tfjsCore.IsInf,
+        backendName: 'cpu',
+        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 isNaN$1 = unaryKernelFunc(tfjsCore.IsNan, function (xi) { return Number.isNaN(xi) ? 1 : 0; }, 'bool');
+    var isNaNConfig = {
+        kernelName: tfjsCore.IsNan,
+        backendName: 'cpu',
+        kernelFunc: isNaN$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 linSpace(args) {
+        var backend = args.backend, attrs = args.attrs;
+        var start = attrs.start, stop = attrs.stop, num = attrs.num;
+        var outVals = linSpaceImpl(start, stop, num);
+        return backend.makeTensorInfo([outVals.length], 'float32', outVals);
+    }
+    var linSpaceConfig = {
+        kernelName: tfjsCore.LinSpace,
+        backendName: 'cpu',
+        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 log1p = unaryKernelFunc(tfjsCore.Log1p, function (xi) { return Math.log1p(xi); });
+    var log1pConfig = {
+        kernelName: tfjsCore.Log1p,
+        backendName: 'cpu',
+        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 logicalAndImpl = createSimpleBinaryKernelImpl(function (a, b) { return a && b; });
+    var logicalAnd = binaryKernelFunc(tfjsCore.LogicalAnd, logicalAndImpl, null /* complexImpl */, 'bool');
+    var logicalAndConfig = {
+        kernelName: tfjsCore.LogicalAnd,
+        backendName: 'cpu',
+        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 logicalNot = unaryKernelFunc(tfjsCore.LogicalNot, function (xi) { return xi ? 0 : 1; }, 'bool');
+    var logicalNotConfig = {
+        kernelName: tfjsCore.LogicalNot,
+        backendName: 'cpu',
+        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 logicalOrImpl = createSimpleBinaryKernelImpl(function (a, b) { return a || b; });
+    var logicalOr = binaryKernelFunc(tfjsCore.LogicalOr, logicalOrImpl, null /* complexImpl */, 'bool');
+    var logicalOrConfig = {
+        kernelName: tfjsCore.LogicalOr,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    function lRN(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;
+        assertNotComplex(x, 'LRN');
+        var channels = x.shape[3];
+        var maxD = channels - 1;
+        var xValues = backend.data.get(x.dataId).values;
+        var size = tfjsCore.util.sizeFromShape(x.shape);
+        var result = new Float32Array(size);
+        function sumAcrossChannels(offset) {
+            var currentChannel = offset % channels;
+            var beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
+            var endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD);
+            var sum = 0.0;
+            for (; beginSumOffset <= endSumOffset; beginSumOffset++) {
+                var z = xValues[beginSumOffset];
+                sum += z * z;
+            }
+            return sum;
+        }
+        for (var offset = 0; offset < size; offset++) {
+            var sum = sumAcrossChannels(offset);
+            var val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);
+            result[offset] = val;
+        }
+        return backend.makeTensorInfo(x.shape, x.dtype, result);
+    }
+    var lRNConfig = {
+        kernelName: tfjsCore.LRN,
+        backendName: 'cpu',
+        kernelFunc: lRN
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 lRNGrad(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;
+        assertNotComplex(dy, 'LRNGrad');
+        var dySize = tfjsCore.util.sizeFromShape(dy.shape);
+        var channels = dy.shape[3];
+        var dyValues = backend.data.get(dy.dataId).values;
+        var xValues = backend.data.get(x.dataId).values;
+        var yValues = backend.data.get(y.dataId).values;
+        var result = new Float32Array(dySize);
+        var size = dySize;
+        for (var offset = 0; offset < size; offset++) {
+            var currentChannel = offset % channels;
+            var depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);
+            var depthEnd = (offset - currentChannel) +
+                Math.min(channels, currentChannel + depthRadius + 1);
+            var norm = 0;
+            for (var k = depthBegin; k < depthEnd; k++) {
+                norm += Math.pow(xValues[k], 2);
+            }
+            norm = alpha * norm + bias;
+            for (var k = depthBegin; k < depthEnd; k++) {
+                var dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm;
+                if (offset === k) {
+                    dyi += Math.pow(norm, -beta);
+                }
+                dyi *= dyValues[offset];
+                result[k] += dyi;
+            }
+        }
+        return backend.makeTensorInfo(dy.shape, x.dtype, result);
+    }
+    var lRNGradConfig = {
+        kernelName: tfjsCore.LRNGrad,
+        backendName: 'cpu',
+        kernelFunc: lRNGrad
+    };
+
+    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 cpuBackend = backend;
+        var xShape = x.shape;
+        var xRank = xShape.length;
+        var origAxes = tfjsCore.util.parseAxisParam(reductionIndices, xShape);
+        var axes = origAxes;
+        var permutedAxes = tfjsCore.backend_util.getAxesPermutation(axes, xRank);
+        var xVals = cpuBackend.data.get(x.dataId).values;
+        if (permutedAxes != null) {
+            var newShape = new Array(xRank);
+            for (var i = 0; i < newShape.length; i++) {
+                newShape[i] = xShape[permutedAxes[i]];
+            }
+            xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);
+            axes = tfjsCore.backend_util.getInnerMostAxes(axes.length, xRank);
+            xShape = newShape;
+        }
+        assertNotComplex(x, 'max');
+        tfjsCore.backend_util.assertAxesAreInnerMostDims('max', axes, xRank);
+        var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes(xShape, axes), 2), maxOutShape = _a[0], reduceShape = _a[1];
+        var reduceSize = tfjsCore.util.sizeFromShape(reduceShape);
+        var result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);
+        var dataId = cpuBackend.write(result, maxOutShape, x.dtype);
+        var outShape = maxOutShape;
+        if (keepDims) {
+            // reshape
+            var newShape = tfjsCore.backend_util.expandShapeToKeepDim(maxOutShape, origAxes);
+            outShape = newShape;
+        }
+        return { dataId: dataId, shape: outShape, dtype: x.dtype };
+    }
+    var maxConfig = {
+        kernelName: tfjsCore.Max,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    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;
+        tfjsCore.util.assert(tfjsCore.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 = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);
+        var res;
+        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&
+            tfjsCore.util.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+            res = identity({ inputs: { x: x }, backend: backend });
+        }
+        else {
+            var xValues = backend.data.get(x.dataId).values;
+            var strides_1 = tfjsCore.util.computeStrides(x.shape);
+            var buffer = pool(xValues, x.shape, x.dtype, strides_1, convInfo, 'max');
+            res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);
+        }
+        return res;
+    }
+    var maxPoolConfig = {
+        kernelName: tfjsCore.MaxPool,
+        backendName: 'cpu',
+        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, dimRoundingMode = attrs.dimRoundingMode, dataFormat = attrs.dataFormat;
+        assertNotComplex(x, 'maxPool3d');
+        var convInfo = tfjsCore.backend_util.computePool3DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode, dataFormat);
+        var xValues = backend.data.get(x.dataId).values;
+        var outBuf = pool3d(xValues, x.shape, x.dtype, tfjsCore.util.computeStrides(x.shape), convInfo, 'max');
+        return backend.makeTensorInfo(outBuf.shape, 'float32', outBuf.values);
+    }
+    var maxPool3DConfig = {
+        kernelName: tfjsCore.MaxPool3D,
+        backendName: 'cpu',
+        kernelFunc: maxPool3D
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode;
+        assertNotComplex([dy, input], 'maxPool3DGrad');
+        var convInfo = tfjsCore.backend_util.computePool3DInfo(input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);
+        var inputBuf = backend.bufferSync(input);
+        var maxPosBuf = maxPool3dPositions(inputBuf, convInfo);
+        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 padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+        var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+        var dx = tfjsCore.buffer(input.shape, 'float32');
+        var dyBuf = backend.bufferSync(dy);
+        for (var batch = 0; batch < convInfo.batchSize; ++batch) {
+            for (var channel = 0; channel < convInfo.inChannels; ++channel) {
+                for (var dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {
+                    for (var dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {
+                        for (var dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {
+                            // Shader code begins
+                            var dyDepthCorner = dxDepth - padFront;
+                            var dyRowCorner = dxRow - padTop;
+                            var dyColCorner = dxCol - padLeft;
+                            var dotProd = 0;
+                            for (var wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {
+                                var dyDepth = (dyDepthCorner + wDepth) / strideDepth;
+                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||
+                                    Math.floor(dyDepth) !== dyDepth) {
+                                    continue;
+                                }
+                                for (var wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {
+                                    var dyRow = (dyRowCorner + wRow) / strideHeight;
+                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||
+                                        Math.floor(dyRow) !== dyRow) {
+                                        continue;
+                                    }
+                                    for (var wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {
+                                        var dyCol = (dyColCorner + wCol) / strideWidth;
+                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||
+                                            Math.floor(dyCol) !== dyCol) {
+                                            continue;
+                                        }
+                                        var maxPos = effectiveFilterDepth * effectiveFilterHeight *
+                                            effectiveFilterWidth -
+                                            1 -
+                                            maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);
+                                        var curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth +
+                                            wRow * effectiveFilterWidth + wCol;
+                                        var mask = maxPos === curPos ? 1 : 0;
+                                        if (mask === 0) {
+                                            continue;
+                                        }
+                                        var pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
+                                        dotProd += pixel * mask;
+                                    }
+                                }
+                            }
+                            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);
+                        }
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+    }
+    var maxPool3DGradConfig = {
+        kernelName: tfjsCore.MaxPool3DGrad,
+        backendName: 'cpu',
+        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 = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);
+        var xValues = backend.data.get(x.dataId).values;
+        var maxPosBuf = tfjsCore.buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);
+        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 padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+        var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+        var dx = tfjsCore.buffer(x.shape, 'float32');
+        var dyData = backend.data.get(dy.dataId).values;
+        var dyBuf = tfjsCore.buffer(dy.shape, 'float32', dyData);
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            for (var d = 0; d < convInfo.inChannels; ++d) {
+                for (var dxR = 0; dxR < convInfo.inHeight; ++dxR) {
+                    for (var dxC = 0; dxC < convInfo.inWidth; ++dxC) {
+                        // Shader code begins.
+                        var dyRCorner = dxR - padTop;
+                        var dyCCorner = dxC - padLeft;
+                        var dotProd = 0;
+                        for (var wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
+                            var dyR = (dyRCorner + wR) / strideHeight;
+                            if (dyR < 0 || dyR >= convInfo.outHeight ||
+                                Math.floor(dyR) !== dyR) {
+                                continue;
+                            }
+                            for (var wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
+                                var dyC = (dyCCorner + wC) / strideWidth;
+                                if (dyC < 0 || dyC >= convInfo.outWidth ||
+                                    Math.floor(dyC) !== dyC) {
+                                    continue;
+                                }
+                                var maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -
+                                    maxPosBuf.get(b, dyR, dyC, d);
+                                var curPos = wR * effectiveFilterWidth + wC;
+                                var mask = maxPos === curPos ? 1 : 0;
+                                if (mask === 0) {
+                                    continue;
+                                }
+                                var pixel = dyBuf.get(b, dyR, dyC, d);
+                                dotProd += pixel * mask;
+                            }
+                        }
+                        dx.set(dotProd, b, dxR, dxC, d);
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+    }
+    var maxPoolGradConfig = {
+        kernelName: tfjsCore.MaxPoolGrad,
+        backendName: 'cpu',
+        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(xValues, xShape, dtype, includeBatchInIndex, convInfo) {
+        var strides = tfjsCore.util.computeStrides(xShape);
+        var maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max');
+        var maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);
+        return [maxPools.values, maxPositions.values];
+    }
+
+    var maxPoolWithArgmaxConfig = {
+        kernelName: tfjsCore.MaxPoolWithArgmax,
+        backendName: 'cpu',
+        kernelFunc: function (_a) {
+            var inputs = _a.inputs, attrs = _a.attrs, backend = _a.backend;
+            var x = inputs.x;
+            var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, includeBatchInIndex = attrs.includeBatchInIndex;
+            var cpuBackend = backend;
+            assertNotComplex(x, 'MaxPoolWithArgmax');
+            var values = cpuBackend.data.get(x.dataId).values;
+            var convInfo = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad);
+            var _b = __read(maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo), 2), pooled = _b[0], indexes = _b[1];
+            var pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);
+            var indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);
+            return [
+                { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },
+                { dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32' }
+            ];
+        }
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 mean(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis, keepDims = attrs.keepDims;
+        var axes = tfjsCore.util.parseAxisParam(axis, x.shape);
+        var shapes = tfjsCore.backend_util.computeOutAndReduceShapes(x.shape, axes);
+        var reduceShape = shapes[1];
+        var reduceSize = tfjsCore.util.sizeFromShape(reduceShape);
+        var toDispose = [];
+        var reduceSizeScalar = backend.makeTensorInfo([], 'float32', new Float32Array([reduceSize]));
+        toDispose.push(reduceSizeScalar);
+        var $x = cast({ inputs: { x: x }, backend: backend, attrs: { dtype: 'float32' } });
+        toDispose.push($x);
+        var res = div({ inputs: { a: $x, b: reduceSizeScalar }, backend: backend });
+        toDispose.push(res);
+        var result = sum({ inputs: { x: res }, backend: backend, attrs: { axis: axis, keepDims: keepDims } });
+        toDispose.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return result;
+    }
+    var meanConfig = {
+        kernelName: tfjsCore.Mean,
+        backendName: 'cpu',
+        kernelFunc: mean
+    };
+
+    function min(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var axis = attrs.axis, keepDims = attrs.keepDims;
+        assertNotComplex(x, 'min');
+        var origAxes = tfjsCore.util.parseAxisParam(axis, x.shape);
+        var axes = origAxes;
+        var permutedAxes = tfjsCore.backend_util.getAxesPermutation(axes, x.shape.length);
+        var $x = x;
+        if (permutedAxes != null) {
+            $x = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } });
+            axes = tfjsCore.backend_util.getInnerMostAxes(axes.length, x.shape.length);
+        }
+        tfjsCore.backend_util.assertAxesAreInnerMostDims('min', axes, $x.shape.length);
+        var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes($x.shape, axes), 2), outShape = _a[0], reduceShape = _a[1];
+        var reduceSize = tfjsCore.util.sizeFromShape(reduceShape);
+        var vals = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(outShape), $x.dtype);
+        var aVals = backend.data.get($x.dataId).values;
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var min_1 = aVals[offset];
+            for (var j = 0; j < reduceSize; ++j) {
+                var value = aVals[offset + j];
+                if (Number.isNaN(value) ||
+                    value < min_1) { // comparison with NaN always return false
+                    min_1 = value;
+                }
+            }
+            vals[i] = min_1;
+        }
+        if (permutedAxes != null) {
+            backend.disposeIntermediateTensorInfo($x);
+        }
+        var result = backend.makeTensorInfo(outShape, $x.dtype, vals);
+        if (keepDims) {
+            var expandedShape = tfjsCore.backend_util.expandShapeToKeepDim(outShape, origAxes);
+            var reshapedResult = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: expandedShape } });
+            backend.disposeIntermediateTensorInfo(result);
+            return reshapedResult;
+        }
+        return result;
+    }
+    var minConfig = {
+        kernelName: tfjsCore.Min,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    function mirrorPad(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var paddings = attrs.paddings, mode = attrs.mode;
+        assertNotComplex(x, 'mirrorPad');
+        var outShape = paddings.map(function (p, i) { return p[0] /* beforePad */ + x.shape[i] + p[1]; } /* afterPad */);
+        var start = paddings.map(function (p) { return p[0]; });
+        var end = paddings.map(function (p, i) { return p[0] + x.shape[i]; });
+        var offset = mode === 'reflect' ? 0 : 1;
+        var xVals = backend.data.get(x.dataId).values;
+        var xRank = x.shape.length;
+        var xStrides = tfjsCore.util.computeStrides(x.shape);
+        var resultSize = tfjsCore.util.sizeFromShape(outShape);
+        var resultRank = outShape.length;
+        var resultStrides = tfjsCore.util.computeStrides(outShape);
+        var resVals = tfjsCore.util.getTypedArrayFromDType(x.dtype, resultSize);
+        for (var i = 0; i < resultSize; i++) {
+            var coords = tfjsCore.util.indexToLoc(i, resultRank, resultStrides);
+            for (var i_1 = 0; i_1 < resultRank; i_1++) {
+                if (coords[i_1] < start[i_1]) {
+                    coords[i_1] = start[i_1] * 2 - coords[i_1] - offset;
+                }
+                else if (coords[i_1] >= end[i_1]) {
+                    coords[i_1] = (end[i_1] - 1) * 2 - coords[i_1] + offset;
+                }
+            }
+            coords = coords.map(function (c, i) { return c - start[i]; });
+            var inIndex = tfjsCore.util.locToIndex(coords, xRank, xStrides);
+            resVals[i] = xVals[inIndex];
+        }
+        var outId = backend.write(resVals, outShape, x.dtype);
+        return { dataId: outId, shape: outShape, dtype: x.dtype };
+    }
+    var mirrorPadConfig = {
+        kernelName: tfjsCore.MirrorPad,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    var modImpl = createSimpleBinaryKernelImpl((function (aValue, bValue) {
+        var rem = aValue % bValue;
+        if ((aValue < 0 && bValue < 0) || (aValue >= 0 && bValue >= 0)) {
+            return rem;
+        }
+        else {
+            return (rem + bValue) % bValue;
+        }
+    }));
+    var mod = binaryKernelFunc(tfjsCore.Mod, modImpl);
+    var modConfig = {
+        kernelName: tfjsCore.Mod,
+        backendName: 'cpu',
+        kernelFunc: mod
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 logitsRank = logits.shape.length;
+        var $dim = dim;
+        if ($dim === -1) {
+            $dim = logitsRank - 1;
+        }
+        if ($dim !== logitsRank - 1) {
+            throw Error('Softmax along a non-last dimension is not yet supported. ' +
+                ("Logits was rank " + logitsRank + " and dim was " + $dim));
+        }
+        var axes = tfjsCore.util.parseAxisParam([$dim], logits.shape);
+        var maxLogit = max({
+            inputs: { x: logits },
+            backend: backend,
+            attrs: { reductionIndices: axes, keepDims: false }
+        });
+        var expandedShape = tfjsCore.backend_util.expandShapeToKeepDim(maxLogit.shape, axes);
+        var maxLogitReshaped = reshape({ inputs: { x: maxLogit }, backend: backend, attrs: { shape: expandedShape } });
+        var a = sub({ inputs: { a: logits, b: maxLogitReshaped }, backend: backend });
+        var b = exp({ inputs: { x: a }, backend: backend });
+        var sumExp = sum({ inputs: { x: b }, backend: backend, attrs: { axis: axes, keepDims: false } });
+        var sumReshaped = reshape({ inputs: { x: sumExp }, backend: backend, attrs: { shape: expandedShape } });
+        var result = div({ inputs: { a: b, b: sumReshaped }, backend: backend });
+        backend.disposeIntermediateTensorInfo(maxLogit);
+        backend.disposeIntermediateTensorInfo(maxLogitReshaped);
+        backend.disposeIntermediateTensorInfo(a);
+        backend.disposeIntermediateTensorInfo(b);
+        backend.disposeIntermediateTensorInfo(sumExp);
+        backend.disposeIntermediateTensorInfo(sumReshaped);
+        return result;
+    }
+    var softmaxConfig = {
+        kernelName: tfjsCore.Softmax,
+        backendName: 'cpu',
+        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;
+        assertNotComplex(logits, 'multinomial');
+        var probabilities = normalized ?
+            logits :
+            softmax({ inputs: { logits: logits }, backend: backend, attrs: { dim: -1 } });
+        var batchSize = probabilities.shape[0];
+        var numEvents = probabilities.shape[1];
+        var probVals = backend.data.get(probabilities.dataId).values;
+        var resShape = [batchSize, numSamples];
+        var resVals = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(resShape), 'int32');
+        for (var b = 0; b < batchSize; ++b) {
+            var offset = b * numEvents;
+            // The cdf won't include the last event. It will be implicit if no other
+            // event happened.
+            var cdf = new Float32Array(numEvents - 1);
+            cdf[0] = probVals[offset];
+            for (var event = 1; event < cdf.length; ++event) {
+                cdf[event] = cdf[event - 1] + probVals[offset + event];
+            }
+            var random = seedrandom__namespace.alea(seed.toString());
+            var outOffset = b * numSamples;
+            for (var sampleId = 0; sampleId < numSamples; ++sampleId) {
+                var r = random();
+                // Assume last event happened by default.
+                resVals[outOffset + sampleId] = cdf.length;
+                for (var event = 0; event < cdf.length; event++) {
+                    if (r < cdf[event]) {
+                        resVals[outOffset + sampleId] = event;
+                        break;
+                    }
+                }
+            }
+        }
+        if (!normalized) {
+            backend.disposeIntermediateTensorInfo(probabilities);
+        }
+        return backend.makeTensorInfo(resShape, 'int32', resVals);
+    }
+    var multinomialConfig = {
+        kernelName: tfjsCore.Multinomial,
+        backendName: 'cpu',
+        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 nonMaxSuppressionV3Impl = tfjsCore.kernel_impls.nonMaxSuppressionV3Impl;
+    function nonMaxSuppressionV3(args) {
+        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;
+        assertNotComplex(boxes, 'NonMaxSuppression');
+        var boxesVals = backend.data.get(boxes.dataId).values;
+        var scoresVals = backend.data.get(scores.dataId).values;
+        var selectedIndices = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold).selectedIndices;
+        return backend.makeTensorInfo([selectedIndices.length], 'int32', new Int32Array(selectedIndices));
+    }
+    var nonMaxSuppressionV3Config = {
+        kernelName: tfjsCore.NonMaxSuppressionV3,
+        backendName: 'cpu',
+        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 = tfjsCore.kernel_impls.nonMaxSuppressionV4Impl;
+    function nonMaxSuppressionV4(args) {
+        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;
+        assertNotComplex(boxes, 'NonMaxSuppressionPadded');
+        var boxesVals = backend.data.get(boxes.dataId).values;
+        var scoresVals = backend.data.get(scores.dataId).values;
+        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: tfjsCore.NonMaxSuppressionV4,
+        backendName: 'cpu',
+        kernelFunc: nonMaxSuppressionV4
+    };
+
+    /**
+     * @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 nonMaxSuppressionV5Impl = tfjsCore.kernel_impls.nonMaxSuppressionV5Impl;
+    function nonMaxSuppressionV5(args) {
+        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;
+        assertNotComplex(boxes, 'NonMaxSuppressionWithScore');
+        var boxesVals = backend.data.get(boxes.dataId).values;
+        var scoresVals = backend.data.get(scores.dataId).values;
+        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: tfjsCore.NonMaxSuppressionV5,
+        backendName: 'cpu',
+        kernelFunc: nonMaxSuppressionV5
+    };
+
+    function oneHot(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;
+        assertNotComplex(indices, 'oneHot');
+        var indicesSize = tfjsCore.util.sizeFromShape(indices.shape);
+        var res = new Float32Array(indicesSize * depth);
+        res.fill(offValue);
+        var indicesVal = backend.data.get(indices.dataId).values;
+        for (var event = 0; event < indicesSize; ++event) {
+            if (indicesVal[event] >= 0 && indicesVal[event] < depth) {
+                res[event * depth + indicesVal[event]] = onValue;
+            }
+        }
+        return backend.makeTensorInfo(__spread(indices.shape, [depth]), 'int32', res);
+    }
+    var oneHotConfig = {
+        kernelName: tfjsCore.OneHot,
+        backendName: 'cpu',
+        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 === 'string') {
+            throw new Error('zerosLike is not supported for string tensors');
+        }
+        else 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({ backend: backend, attrs: { shape: x.shape, value: 0, dtype: x.dtype } });
+        }
+    }
+    var zerosLikeConfig = {
+        kernelName: tfjsCore.ZerosLike,
+        backendName: 'cpu',
+        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 for string tensors');
+        }
+        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 {
+            return fill({ backend: backend, attrs: { shape: x.shape, value: 1, dtype: x.dtype } });
+        }
+    }
+    var onesLikeConfig = {
+        kernelName: tfjsCore.OnesLike,
+        backendName: 'cpu',
+        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) {
+            tfjsCore.util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');
+            tfjsCore.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: tfjsCore.Pack,
+        backendName: 'cpu',
+        kernelFunc: pack
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 padV2(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var paddings = attrs.paddings, constantValue = attrs.constantValue;
+        assertNotComplex(x, 'pad');
+        var outShape = paddings.map(function (p, i) { return p[0] /* beforePad */ + x.shape[i] + p[1]; } /* afterPad */);
+        var start = paddings.map(function (p) { return p[0]; });
+        var xVals = backend.data.get(x.dataId).values;
+        var xSize = tfjsCore.util.sizeFromShape(x.shape);
+        var xRank = x.shape.length;
+        var xStrides = tfjsCore.util.computeStrides(x.shape);
+        var resultSize = tfjsCore.util.sizeFromShape(outShape);
+        var resultRank = outShape.length;
+        var resultStrides = tfjsCore.util.computeStrides(outShape);
+        var resVals = tfjsCore.util.getTypedArrayFromDType(x.dtype, resultSize);
+        if (constantValue !== 0) {
+            resVals.fill(constantValue);
+        }
+        for (var i = 0; i < xSize; i++) {
+            var coords = tfjsCore.util.indexToLoc(i, xRank, xStrides);
+            var outCoords = coords.map(function (c, i) { return c + start[i]; });
+            var outIndex = tfjsCore.util.locToIndex(outCoords, resultRank, resultStrides);
+            resVals[outIndex] = xVals[i];
+        }
+        var outId = backend.write(resVals, outShape, x.dtype);
+        return { dataId: outId, shape: outShape, dtype: x.dtype };
+    }
+    var padV2Config = {
+        kernelName: tfjsCore.PadV2,
+        backendName: 'cpu',
+        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 powImpl = createSimpleBinaryKernelImpl(function (a, b) { return Math.pow(a, b); });
+    var pow = binaryKernelFunc(tfjsCore.Pow, powImpl);
+    var powConfig = {
+        kernelName: tfjsCore.Pow,
+        backendName: 'cpu',
+        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 range(args) {
+        var backend = args.backend, attrs = args.attrs;
+        var start = attrs.start, stop = attrs.stop, dtype = attrs.dtype, step = attrs.step;
+        var values = rangeImpl(start, stop, step, dtype);
+        return backend.makeTensorInfo([values.length], dtype, values);
+    }
+    var rangeConfig = {
+        kernelName: tfjsCore.Range,
+        backendName: 'cpu',
+        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 = unaryKernelFunc(tfjsCore.Reciprocal, function (xi) { return 1 / xi; });
+    var reciprocalConfig = {
+        kernelName: tfjsCore.Reciprocal,
+        backendName: 'cpu',
+        kernelFunc: reciprocal,
+    };
+
+    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;
+        assertNotComplex(images, 'resizeBilinear');
+        var imagesStrides = tfjsCore.util.computeStrides(images.shape);
+        var _a = __read(size, 2), newHeight = _a[0], newWidth = _a[1];
+        var _b = __read(images.shape, 4), batch = _b[0], oldHeight = _b[1], oldWidth = _b[2], numChannels = _b[3];
+        var xValues = backend.data.get(images.dataId).values;
+        var result = new Float32Array(tfjsCore.util.sizeFromShape([batch, newHeight, newWidth, numChannels]));
+        var effectiveInputSize = [
+            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+        ];
+        var effectiveOutputSize = [
+            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+        ];
+        var outputIdx = 0;
+        var effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
+        var effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
+        for (var b = 0; b < batch; b++) {
+            for (var r = 0; r < newHeight; r++) {
+                var sourceFracRow = void 0;
+                if (halfPixelCenters) {
+                    sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5;
+                }
+                else {
+                    sourceFracRow = effectiveRowSizeRatio * r;
+                }
+                var sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));
+                var rowFrac = sourceFracRow - sourceRowFloor;
+                var sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));
+                var topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1];
+                var botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1];
+                for (var c = 0; c < newWidth; c++) {
+                    var sourceFracCol = void 0;
+                    if (halfPixelCenters) {
+                        sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5;
+                    }
+                    else {
+                        sourceFracCol = effectiveColSizeRatio * c;
+                    }
+                    var sourceColFloor = Math.max(0, Math.floor(sourceFracCol));
+                    var colFrac = sourceFracCol - sourceColFloor;
+                    var sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));
+                    var topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2];
+                    var botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2];
+                    var topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2];
+                    var botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2];
+                    for (var d = 0; d < numChannels; d++) {
+                        // Begin shader.
+                        // Compute the fractional index of the source.
+                        var topLeft = xValues[topLeftOffest + d];
+                        var bottomLeft = xValues[botLeftOffset + d];
+                        var topRight = xValues[topRightOffset + d];
+                        var bottomRight = xValues[botRightOffest + d];
+                        var top = topLeft + (topRight - topLeft) * colFrac;
+                        var bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;
+                        var newValue = top + (bottom - top) * rowFrac;
+                        result[outputIdx++] = newValue;
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo([batch, newHeight, newWidth, numChannels], 'float32', result);
+    }
+    var resizeBilinearConfig = {
+        kernelName: tfjsCore.ResizeBilinear,
+        backendName: 'cpu',
+        kernelFunc: resizeBilinear
+    };
+
+    function resizeBilinearGrad(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var images = inputs.images, dy = inputs.dy;
+        var alignCorners = attrs.alignCorners;
+        assertNotComplex([dy, images], 'resizeBilinearGrad');
+        var imagesStrides = tfjsCore.util.computeStrides(images.shape);
+        var _a = __read(images.shape, 4), batch = _a[0], xHeight = _a[1], xWidth = _a[2], depth = _a[3];
+        var _b = __read(dy.shape, 3), yHeight = _b[1], yWidth = _b[2];
+        var output = new Float32Array(batch * xHeight * xWidth * depth);
+        // 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];
+        // Reference implementation
+        // tslint:disable-next-line:max-line-length
+        // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275
+        var dyValues = backend.data.get(dy.dataId).values;
+        var offset = 0;
+        for (var b = 0; b < batch; b++) {
+            var bOffset = b * imagesStrides[0];
+            for (var r = 0; r < yHeight; r++) {
+                var dxR = r * heightScale;
+                var topDxRIndex = Math.floor(dxR);
+                var bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);
+                var topDxROffset = bOffset + topDxRIndex * imagesStrides[1];
+                var bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1];
+                var dxRLerp = dxR - topDxRIndex;
+                var inverseDxRLerp = 1.0 - dxRLerp;
+                for (var c = 0; c < yWidth; c++) {
+                    var dxC = c * widthScale;
+                    var leftDxCIndex = Math.floor(dxC);
+                    var rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);
+                    var dxCLerp = dxC - leftDxCIndex;
+                    var inverseDxCLerp = 1.0 - dxCLerp;
+                    var topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2];
+                    var topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2];
+                    var bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2];
+                    var bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2];
+                    var inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;
+                    var inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;
+                    var dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;
+                    var dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;
+                    for (var d = 0; d < depth; d++) {
+                        var dyVal = dyValues[offset++];
+                        output[topLeftRCOffset + d] +=
+                            dyVal * inverseDxRLerpTimesInverseDxCLerp;
+                        output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;
+                        output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp;
+                        output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo([batch, xWidth, xHeight, depth], 'float32', output);
+    }
+    var resizeBilinearGradConfig = {
+        kernelName: tfjsCore.ResizeBilinearGrad,
+        backendName: 'cpu',
+        kernelFunc: resizeBilinearGrad
+    };
+
+    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;
+        assertNotComplex(images, 'resizeNearestNeighbor');
+        var imagesStrides = tfjsCore.util.computeStrides(images.shape);
+        var _a = __read(size, 2), newHeight = _a[0], newWidth = _a[1];
+        var _b = __read(images.shape, 4), batch = _b[0], oldHeight = _b[1], oldWidth = _b[2], numChannels = _b[3];
+        var xValues = backend.data.get(images.dataId).values;
+        var output = new Float32Array(batch * newHeight * newWidth * numChannels);
+        var effectiveInputSize = [
+            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+        ];
+        var effectiveOutputSize = [
+            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+        ];
+        var effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
+        var effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
+        var outputOffset = 0;
+        for (var b = 0; b < batch; b++) {
+            var batchOffset = b * imagesStrides[0];
+            for (var r = 0; r < newHeight; r++) {
+                var sourceFracRow = halfPixelCenters ?
+                    effectiveRowSizeRatio * (r + 0.5) :
+                    effectiveRowSizeRatio * r;
+                var sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
+                if (halfPixelCenters) {
+                    sourceNearestRow = Math.max(0, sourceNearestRow);
+                }
+                var rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];
+                for (var c = 0; c < newWidth; c++) {
+                    var sourceFracCol = halfPixelCenters ?
+                        effectiveColSizeRatio * (c + 0.5) :
+                        effectiveColSizeRatio * c;
+                    var sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :
+                        Math.floor(sourceFracCol));
+                    if (halfPixelCenters) {
+                        sourceNearestCol = Math.max(0, sourceNearestCol);
+                    }
+                    var colOffset = rowOffset + sourceNearestCol * imagesStrides[2];
+                    for (var d = 0; d < numChannels; d++) {
+                        // Begin shader.
+                        // Compute the fractional index of the source.
+                        var newVal = xValues[colOffset + d];
+                        output[outputOffset++] = newVal;
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output);
+    }
+    var resizeNearestNeighborConfig = {
+        kernelName: tfjsCore.ResizeNearestNeighbor,
+        backendName: 'cpu',
+        kernelFunc: resizeNearestNeighbor
+    };
+
+    function resizeNearestNeighborGrad(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var images = inputs.images, dy = inputs.dy;
+        var alignCorners = attrs.alignCorners;
+        assertNotComplex([dy, images], 'resizeNearestNeighborGrad');
+        var imagesStrides = tfjsCore.util.computeStrides(images.shape);
+        var dyStrides = tfjsCore.util.computeStrides(dy.shape);
+        var _a = __read(images.shape, 4), batch = _a[0], xHeight = _a[1], xWidth = _a[2], depth = _a[3];
+        var _b = __read(dy.shape, 3), yHeight = _b[1], yWidth = _b[2];
+        var output = new Float32Array(batch * xHeight * xWidth * depth);
+        var dyValues = backend.data.get(dy.dataId).values;
+        // In the backwards pass, we want to find the pixels that were generated
+        // for each pixel in the input image the forward pass
+        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;
+        // Loop over the output space.
+        for (var b = 0; b < batch; b++) {
+            var batchOffset = b * imagesStrides[0];
+            for (var r = 0; r < xHeight; r++) {
+                var rowOffset = batchOffset + r * imagesStrides[1];
+                // Compute bounds for where in dy we will look
+                var startRLerp = Math.floor(r * invHeightScale);
+                var startDyR = Math.floor(startRLerp - (winHeight / 2));
+                for (var c = 0; c < xWidth; c++) {
+                    var colOffset = rowOffset + c * imagesStrides[2];
+                    // Compute bounds for where in dy we will look
+                    var startCLerp = Math.floor(c * invWidthScale);
+                    var startDyC = Math.floor(startCLerp - (winWidth / 2));
+                    for (var d = 0; d < depth; d++) {
+                        var accum = 0;
+                        // loop over dy
+                        for (var dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {
+                            var dyR = dyRIndex + startDyR;
+                            // Guard against the window exceeding the bounds of dy
+                            if (dyR < 0 || dyR >= yHeight) {
+                                continue;
+                            }
+                            var dyROffset = batchOffset + dyR * dyStrides[1];
+                            var sourceFracRow = dyR * heightScale;
+                            var sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :
+                                Math.floor(sourceFracRow));
+                            if (r !== sourceNearestRow) {
+                                continue;
+                            }
+                            for (var dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {
+                                var dyC = dyCIndex + startDyC;
+                                // Guard against the window exceeding the bounds of dy
+                                if (dyC < 0 || dyC >= yWidth) {
+                                    continue;
+                                }
+                                var dyCOffset = dyROffset + dyC * dyStrides[2];
+                                var sourceFracCol = dyC * widthScale;
+                                var sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :
+                                    Math.floor(sourceFracCol));
+                                if (c === sourceNearestCol) {
+                                    accum += dyValues[dyCOffset + d];
+                                }
+                            }
+                        }
+                        output[colOffset + d] = accum;
+                    }
+                }
+            }
+        }
+        return backend.makeTensorInfo(images.shape, images.dtype, output);
+    }
+    var resizeNearestNeighborGradConfig = {
+        kernelName: tfjsCore.ResizeNearestNeighborGrad,
+        backendName: 'cpu',
+        kernelFunc: resizeNearestNeighborGrad
+    };
+
+    function reverse(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var dims = attrs.dims;
+        assertNotComplex(x, 'reverse');
+        var xRank = x.shape.length;
+        var $dims = tfjsCore.util.parseAxisParam(dims, x.shape);
+        if (xRank === 0) {
+            return identity({ inputs: { x: x }, backend: backend });
+        }
+        var outBuf = new tfjsCore.TensorBuffer(x.shape, x.dtype);
+        var xBuf = backend.bufferSync(x);
+        var _loop_1 = function (i) {
+            var outLoc = outBuf.indexToLoc(i);
+            var inLoc = outLoc.slice();
+            $dims.forEach(function (d) { return inLoc[d] = x.shape[d] - 1 - inLoc[d]; });
+            outBuf.set.apply(outBuf, __spread([xBuf.get.apply(xBuf, __spread(inLoc))], outLoc));
+        };
+        for (var i = 0; i < outBuf.size; i++) {
+            _loop_1(i);
+        }
+        return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+    }
+    var reverseConfig = {
+        kernelName: tfjsCore.Reverse,
+        backendName: 'cpu',
+        kernelFunc: reverse
+    };
+
+    var rotateWithOffsetConfig = {
+        kernelName: tfjsCore.RotateWithOffset,
+        backendName: 'cpu',
+        kernelFunc: function (_a) {
+            var inputs = _a.inputs, attrs = _a.attrs, backend = _a.backend;
+            var image = inputs.image;
+            var radians = attrs.radians, fillValue = attrs.fillValue, center = attrs.center;
+            var cpuBackend = backend;
+            var output = tfjsCore.util.getTypedArrayFromDType(image.dtype, tfjsCore.util.sizeFromShape(image.shape));
+            var _b = __read(image.shape, 4), batch = _b[0], imageHeight = _b[1], imageWidth = _b[2], numChannels = _b[3];
+            var _c = __read(tfjsCore.backend_util.getImageCenter(center, imageHeight, imageWidth), 2), centerX = _c[0], centerY = _c[1];
+            var fullOpacityValue = 255;
+            var sinFactor = Math.sin(radians);
+            var cosFactor = Math.cos(radians);
+            var imageVals = cpuBackend.data.get(image.dataId).values;
+            for (var batchIdx = 0; batchIdx < batch; batchIdx++) {
+                var batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
+                for (var row = 0; row < imageHeight; row++) {
+                    var rowOffset = row * (imageWidth * numChannels);
+                    for (var col = 0; col < imageWidth; col++) {
+                        var colOffset = col * numChannels;
+                        for (var channel = 0; channel < numChannels; channel++) {
+                            var coords = [batch, row, col, channel];
+                            var x = coords[2];
+                            var y = coords[1];
+                            // coordX/coordY are the result of rotating and translating x/y.
+                            var coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;
+                            var coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;
+                            coordX = Math.round(coordX + centerX);
+                            coordY = Math.round(coordY + centerY);
+                            var outputValue = fillValue;
+                            if (typeof fillValue !== 'number') {
+                                if (channel === 3) {
+                                    outputValue = fullOpacityValue;
+                                }
+                                else {
+                                    outputValue = fillValue[channel];
+                                }
+                            }
+                            // If the coordinate position falls within the image boundaries...
+                            if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&
+                                coordY < imageHeight) {
+                                // set the output to the image value at the coordinate position.
+                                var rotatedRowOffset = coordY * (imageWidth * numChannels);
+                                var rotatedColOffset = coordX * numChannels;
+                                var imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;
+                                outputValue = imageVals[imageIdx];
+                            }
+                            var outIdx = batchOffset + rowOffset + colOffset + channel;
+                            output[outIdx] = outputValue;
+                        }
+                    }
+                }
+            }
+            var dataId = cpuBackend.write(output, image.shape, image.dtype);
+            return { dataId: dataId, shape: image.shape, dtype: image.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.
+     * =============================================================================
+     */
+    var round = unaryKernelFunc(tfjsCore.Round, function (xi) {
+        // The algorithm is based on banker's rounding.
+        var base = Math.floor(xi);
+        if (xi - base < 0.5) {
+            return Math.floor(xi);
+        }
+        else if (xi - base > 0.5) {
+            return Math.ceil(xi);
+        }
+        else {
+            if (base % 2.0 === 0.0) {
+                return base;
+            }
+            else {
+                return base + 1.0;
+            }
+        }
+    });
+    var roundConfig = {
+        kernelName: tfjsCore.Round,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    function scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {
+        var flattenShape = [outputSize / sliceSize, sliceSize];
+        var indicesData = indices.values;
+        var updatesData = updates.values;
+        if (outputSize === 0) {
+            return tfjsCore.buffer(shape, updates.dtype);
+        }
+        var outBuf = tfjsCore.buffer(flattenShape, updates.dtype);
+        outBuf.values.fill(defaultValue);
+        for (var i = 0; i < numUpdates; i++) {
+            var index = [];
+            var flattenIndex = 0;
+            for (var j = 0; j < sliceRank; j++) {
+                var dim = indicesData[i * sliceRank + j];
+                index.push(dim);
+                flattenIndex += dim * strides[j];
+            }
+            if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {
+                throw new Error("Invalid indices: " + index + " does not index into " + shape);
+            }
+            for (var k = 0; k < sliceSize; k++) {
+                if (sumDupeIndices) {
+                    outBuf.values[flattenIndex * sliceSize + k] +=
+                        updatesData[i * sliceSize + k];
+                }
+                else {
+                    outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?
+                        updatesData[0] :
+                        updatesData[i * 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 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 = tfjsCore.backend_util.calculateShapes(updates, indices, shape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize;
+        var sumDupeIndices = true;
+        var indicesBuf = backend.bufferSync(indices);
+        var updatesBuf = backend.bufferSync(updates);
+        var outBuf = scatterImpl(indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, 0 /* defaultValue */, sumDupeIndices);
+        return backend.makeTensorInfo(shape, outBuf.dtype, outBuf.values);
+    }
+    var scatterNdConfig = {
+        kernelName: tfjsCore.ScatterNd,
+        backendName: 'cpu',
+        kernelFunc: scatterNd
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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;
+        assertNotComplex([condition, t, e], 'select');
+        var conditionRank = condition.shape.length;
+        var values = backend.data.get(condition.dataId).values;
+        var tValues = backend.data.get(t.dataId).values;
+        var eValues = backend.data.get(e.dataId).values;
+        var resultDtype = tfjsCore.upcastType(t.dtype, e.dtype);
+        var newValues = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(t.shape), resultDtype);
+        var index = 0;
+        var offset = conditionRank === 0 || conditionRank > 1 || t.shape.length === 1 ?
+            1 :
+            tfjsCore.util.sizeFromShape(t.shape.slice(1));
+        for (var i = 0; i < values.length; i++) {
+            for (var j = 0; j < offset; j++) {
+                if (values[i] === 1) {
+                    newValues[index++] = tValues[i];
+                }
+                else {
+                    newValues[index++] = eValues[i];
+                }
+            }
+        }
+        return backend.makeTensorInfo(t.shape, resultDtype, newValues);
+    }
+    var selectConfig = {
+        kernelName: tfjsCore.Select,
+        backendName: 'cpu',
+        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 scaleAlpha = tfjsCore.backend_util.SELU_SCALEALPHA;
+    var scale = tfjsCore.backend_util.SELU_SCALE;
+    var selu = unaryKernelFunc(tfjsCore.Selu, function (xi) {
+        if (xi >= 0) {
+            return scale * xi;
+        }
+        else {
+            return scaleAlpha * (Math.exp(xi) - 1);
+        }
+    });
+    var seluConfig = {
+        kernelName: tfjsCore.Selu,
+        backendName: 'cpu',
+        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 sign = unaryKernelFunc(tfjsCore.Sign, function (xi) {
+        if (xi < 0) {
+            return -1;
+        }
+        else if (xi > 0) {
+            return 1;
+        }
+        else {
+            return 0;
+        }
+    });
+    var signConfig = {
+        kernelName: tfjsCore.Sign,
+        backendName: 'cpu',
+        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 = unaryKernelFunc(tfjsCore.Sin, function (xi) { return Math.sin(xi); });
+    var sinConfig = {
+        kernelName: tfjsCore.Sin,
+        backendName: 'cpu',
+        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 = unaryKernelFunc(tfjsCore.Sinh, function (xi) { return Math.sinh(xi); });
+    var sinhConfig = {
+        kernelName: tfjsCore.Sinh,
+        backendName: 'cpu',
+        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.
+     * =============================================================================
+     */
+    // mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX
+    // epsilon is the difference between 1.0 and the next representable float.
+    // For a single precision 32 bit float this should be 2^-23, see:
+    // https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm
+    var epsilon = 1.1920928955078125e-7;
+    var threshold = Math.log(epsilon) + 2.0;
+    var softplus = unaryKernelFunc(tfjsCore.Softplus, function (xi) {
+        // Value above which exp(x) may overflow, but softplus(x) == x
+        // is within machine epsilon.
+        var tooLarge = xi > -threshold;
+        // Value below which exp(x) may underflow, but softplus(x) == exp(x)
+        // is within machine epsilon.
+        var tooSmall = xi < threshold;
+        var expX = Math.exp(xi);
+        var result;
+        if (tooSmall) {
+            result = expX;
+        }
+        else if (tooLarge) {
+            result = xi;
+        }
+        else {
+            result = Math.log(1.0 + expX);
+        }
+        return result;
+    });
+    var softplusConfig = {
+        kernelName: tfjsCore.Softplus,
+        backendName: 'cpu',
+        kernelFunc: softplus,
+    };
+
+    function spaceToBatchND(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var blockShape = attrs.blockShape, paddings = attrs.paddings;
+        assertNotComplex([x], 'spaceToBatchND');
+        var prod = tfjsCore.util.sizeFromShape(blockShape);
+        var completePaddings = [[0, 0]];
+        completePaddings.push.apply(completePaddings, __spread(paddings));
+        for (var i = 1 + blockShape.length; i < x.shape.length; ++i) {
+            completePaddings.push([0, 0]);
+        }
+        var paddedX = padV2Config.kernelFunc({
+            inputs: { x: x },
+            backend: backend,
+            attrs: { paddings: completePaddings, constantValue: 0 }
+        });
+        var reshapedPaddedShape = tfjsCore.backend_util.getReshaped(paddedX.shape, blockShape, prod, false);
+        var permutedReshapedPaddedPermutation = tfjsCore.backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+        var flattenShape = tfjsCore.backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);
+        var reshapeInputs = { x: paddedX };
+        var reshapeAttrs = { shape: reshapedPaddedShape };
+        var paddedXReshaped = reshape({ inputs: reshapeInputs, backend: backend, attrs: reshapeAttrs });
+        var transposeInputs = { x: paddedXReshaped };
+        var transposeAttrs = { perm: permutedReshapedPaddedPermutation };
+        var paddedXT = transpose({ inputs: transposeInputs, backend: backend, attrs: transposeAttrs });
+        var resultReshapeInputs = { x: paddedXT };
+        var resultReshapeAttrs = { shape: flattenShape };
+        var result = reshape({ inputs: resultReshapeInputs, backend: backend, attrs: resultReshapeAttrs });
+        backend.disposeIntermediateTensorInfo(paddedX);
+        backend.disposeIntermediateTensorInfo(paddedXReshaped);
+        backend.disposeIntermediateTensorInfo(paddedXT);
+        return result;
+    }
+    var spaceToBatchNDConfig = {
+        kernelName: tfjsCore.SpaceToBatchND,
+        backendName: 'cpu',
+        kernelFunc: spaceToBatchND
+    };
+
+    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.data.get(indices.dataId).values;
+        var $values = backend.data.get(values.dataId).values;
+        var $denseShape = backend.data.get(denseShape.dataId).values;
+        var $defaultValue = backend.data.get(defaultValue.dataId).values[0];
+        var _a = __read(sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue), 5), 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: tfjsCore.SparseFillEmptyRows,
+        backendName: 'cpu',
+        kernelFunc: sparseFillEmptyRows,
+    };
+
+    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\n        " + inputIndices.shape);
+        }
+        if (inputShape.shape.length !== 1) {
+            throw new Error("Input shape should be a vector but received shape\n        " + 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.data.get(inputShape.dataId).values);
+        var $inputIndices = backend.data.get(inputIndices.dataId).values;
+        var targetShape = Array.from(backend.data.get(newShape.dataId).values);
+        var _a = __read(sparseReshapeImpl($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape), 3), 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: tfjsCore.SparseReshape,
+        backendName: 'cpu',
+        kernelFunc: sparseReshape,
+    };
+
+    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);
+        }
+        if (indices.shape[0] !== segmentIds.shape[0]) {
+            throw new Error("segmentIds and indices should have same size.");
+        }
+        var $data = backend.data.get(data.dataId).values;
+        var $indices = backend.data.get(indices.dataId).values;
+        var $segmentIds = backend.data.get(segmentIds.dataId).values;
+        var _a = __read(sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds, true), 2), outputData = _a[0], outputDataShape = _a[1];
+        return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);
+    }
+    var sparseSegmentMeanConfig = {
+        kernelName: tfjsCore.SparseSegmentMean,
+        backendName: 'cpu',
+        kernelFunc: sparseSegmentMean,
+    };
+
+    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);
+        }
+        if (indices.shape[0] !== segmentIds.shape[0]) {
+            throw new Error("segmentIds and indices should have same size.");
+        }
+        var $data = backend.data.get(data.dataId).values;
+        var $indices = backend.data.get(indices.dataId).values;
+        var $segmentIds = backend.data.get(segmentIds.dataId).values;
+        var _a = __read(sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds), 2), outputData = _a[0], outputDataShape = _a[1];
+        return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);
+    }
+    var sparseSegmentSumConfig = {
+        kernelName: tfjsCore.SparseSegmentSum,
+        backendName: 'cpu',
+        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 = tfjsCore.backend_util.calculateShapes(sparseValues, sparseIndices, outputShape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize;
+        var sumDupeIndices = false;
+        var indicesBuf = backend.bufferSync(sparseIndices);
+        var updatesBuf = backend.bufferSync(sparseValues);
+        var $defaultValue = backend.data.get(defaultValue.dataId).values[0];
+        var outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
+        return backend.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
+    }
+    var sparseToDenseConfig = {
+        kernelName: tfjsCore.SparseToDense,
+        backendName: 'cpu',
+        kernelFunc: sparseToDense
+    };
+
+    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 = tfjsCore.util.parseAxisParam(axis, x.shape)[0];
+        var splitSizes = tfjsCore.backend_util.prepareSplitSize(x, numOrSizeSplits, $axis);
+        var begin = new Array(x.shape.length).fill(0);
+        var size = x.shape.slice();
+        return splitSizes.map(function (s) {
+            var sliceSize = __spread(size);
+            sliceSize[$axis] = s;
+            var sliceT = slice({ inputs: { x: x }, backend: backend, attrs: { begin: begin, size: sliceSize } });
+            begin[$axis] += s;
+            return sliceT;
+        });
+    }
+    var splitVConfig = {
+        kernelName: tfjsCore.SplitV,
+        backendName: 'cpu',
+        kernelFunc: splitV
+    };
+
+    /**
+     * @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 squareConfig = {
+        kernelName: tfjsCore.Square,
+        backendName: 'cpu',
+        kernelFunc: function (_a) {
+            var inputs = _a.inputs, backend = _a.backend;
+            var x = inputs.x;
+            var cpuBackend = backend;
+            assertNotComplex(x, 'square');
+            var values = cpuBackend.data.get(x.dataId).values;
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                var value = values[i];
+                newValues[i] = value * value;
+            }
+            var dataId = cpuBackend.write(newValues, x.shape, x.dtype);
+            return { dataId: dataId, shape: x.shape, dtype: 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.
+     * =============================================================================
+     */
+    var step = unaryKernelFunc(tfjsCore.Step, function (xi, attrs) {
+        var stepAttrs = attrs;
+        if (isNaN(xi)) {
+            return NaN;
+        }
+        else {
+            return xi > 0 ? 1 : stepAttrs.alpha;
+        }
+    });
+    var stepConfig = {
+        kernelName: tfjsCore.Step,
+        backendName: 'cpu',
+        kernelFunc: step,
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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;
+        assertNotComplex(x, 'stridedSlice');
+        var _a = tfjsCore.slice_util.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask), finalShapeSparse = _a.finalShapeSparse, finalShape = _a.finalShape, isIdentity = _a.isIdentity, sliceDim0 = _a.sliceDim0, isSimpleSlice = _a.isSimpleSlice, $begin = _a.begin, $end = _a.end, $strides = _a.strides;
+        var result;
+        // ref:
+        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/strided_slice_op.cc
+        if (isIdentity) {
+            // Optimization #1, slice is a no-op plus reshape
+            result = reshape({ inputs: { x: x }, backend: backend, attrs: { shape: finalShape } });
+        }
+        else if (sliceDim0 || isSimpleSlice) {
+            // Optimization #2, slice is memory contiguous (only occurs in dim 0)
+            tfjsCore.util.assert(x.shape.length >= 1, function () { return "Input must have rank at least 1, got: " + x.shape.length; });
+            var size = tfjsCore.slice_util.computeOutShape($begin, $end, $strides);
+            // To tolerate begin[0] > end[0] (a 0-output slice), we min(begin, end).
+            var sliced = slice({ inputs: { x: x }, backend: backend, attrs: { begin: $begin, size: size } });
+            result =
+                reshape({ inputs: { x: sliced }, backend: backend, attrs: { shape: finalShape } });
+            backend.disposeIntermediateTensorInfo(sliced);
+        }
+        else {
+            var xBuf = backend.bufferSync(x);
+            var outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin);
+            result = backend.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values);
+        }
+        return result;
+    }
+    var stridedSliceConfig = {
+        kernelName: tfjsCore.StridedSlice,
+        backendName: 'cpu',
+        kernelFunc: stridedSlice
+    };
+
+    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.data.get(data.dataId).values;
+        var $dataSplits = backend.data.get(dataSplits.dataId).values;
+        var _a = __read(stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences), 2), nGrams = _a[0], nGramsSplits = _a[1];
+        return [
+            backend.makeTensorInfo([nGrams.length], 'string', nGrams),
+            backend.makeTensorInfo(dataSplits.shape, 'int32', nGramsSplits),
+        ];
+    }
+    var stringNGramsConfig = {
+        kernelName: tfjsCore.StringNGrams,
+        backendName: 'cpu',
+        kernelFunc: stringNGrams,
+    };
+
+    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.data.get(input.dataId).values;
+        var $delimiter = backend.data.get(delimiter.dataId).values[0];
+        var _a = __read(stringSplitImpl($input, $delimiter, skipEmpty), 3), 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: tfjsCore.StringSplit,
+        backendName: 'cpu',
+        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 $input = backend.data.get(input.dataId).values;
+        var output = stringToHashBucketFastImpl($input, numBuckets);
+        return backend.makeTensorInfo(input.shape, 'int32', output);
+    }
+    var stringToHashBucketFastConfig = {
+        kernelName: tfjsCore.StringToHashBucketFast,
+        backendName: 'cpu',
+        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 = unaryKernelFunc(tfjsCore.Tan, function (xi) { return Math.tan(xi); });
+    var tanConfig = {
+        kernelName: tfjsCore.Tan,
+        backendName: 'cpu',
+        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 = unaryKernelFunc(tfjsCore.Tanh, function (xi) { return Math.tanh(xi); });
+    var tanhConfig = {
+        kernelName: tfjsCore.Tanh,
+        backendName: 'cpu',
+        kernelFunc: 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.
+     * =============================================================================
+     */
+    function tile(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var reps = attrs.reps;
+        assertNotComplex(x, 'tile');
+        var outBuf = tileImpl(backend.bufferSync(x), reps);
+        return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+    }
+    var tileConfig = {
+        kernelName: tfjsCore.Tile,
+        backendName: 'cpu',
+        kernelFunc: tile
+    };
+
+    function topK(args) {
+        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
+        var x = inputs.x;
+        var k = attrs.k, sorted = attrs.sorted;
+        assertNotComplex(x, 'topk');
+        var xVals = backend.data.get(x.dataId).values;
+        var _a = __read(topKImpl(xVals, x.shape, x.dtype, k, sorted), 2), 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: tfjsCore.TopK,
+        backendName: 'cpu',
+        kernelFunc: topK
+    };
+
+    function transform(args) {
+        var inputs = args.inputs, attrs = args.attrs, backend = args.backend;
+        var image = inputs.image, transforms = inputs.transforms;
+        var interpolation = attrs.interpolation, fillMode = attrs.fillMode, fillValue = attrs.fillValue, outputShape = attrs.outputShape;
+        var _a = __read(image.shape, 4), batch = _a[0], imageHeight = _a[1], imageWidth = _a[2], numChannels = _a[3];
+        var _b = __read(outputShape != null ? outputShape : [imageHeight, imageWidth], 2), outHeight = _b[0], outWidth = _b[1];
+        var outShape = [batch, outHeight, outWidth, numChannels];
+        var strides = tfjsCore.util.computeStrides(image.shape);
+        var batchStride = strides[0];
+        var rowStride = strides[1];
+        var colStride = strides[2];
+        var outVals = tfjsCore.util.getTypedArrayFromDType(image.dtype, tfjsCore.util.sizeFromShape(outShape));
+        outVals.fill(fillValue);
+        var imageVals = backend.data.get(image.dataId).values;
+        var transformVals = backend.data.get(transforms.dataId).values;
+        // Ref TF implementation:
+        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/image_ops.h
+        for (var b = 0; b < batch; ++b) {
+            var transform_1 = transforms.shape[0] === 1 ?
+                transformVals :
+                transformVals.subarray(b * 8, b * 8 + 8);
+            for (var outY = 0; outY < outHeight; ++outY) {
+                for (var outX = 0; outX < outWidth; ++outX) {
+                    for (var channel = 0; channel < numChannels; ++channel) {
+                        var val = void 0;
+                        var projection = transform_1[6] * outX + transform_1[7] * outY + 1;
+                        if (projection === 0) {
+                            // Return the fill value for infinite coordinates,
+                            // which are outside the input image
+                            continue;
+                        }
+                        var inX = (transform_1[0] * outX + transform_1[1] * outY + transform_1[2]) /
+                            projection;
+                        var inY = (transform_1[3] * outX + transform_1[4] * outY + transform_1[5]) /
+                            projection;
+                        var x = mapCoord(inX, imageWidth, fillMode);
+                        var y = mapCoord(inY, imageHeight, fillMode);
+                        switch (interpolation) {
+                            case 'nearest':
+                                val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);
+                                break;
+                            case 'bilinear':
+                                val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);
+                                break;
+                            default:
+                                throw new Error("Error in Transform: Expect 'nearest' or " +
+                                    ("'bilinear', but got " + interpolation));
+                        }
+                        var ind = b * batchStride + outY * rowStride + outX * colStride + channel;
+                        outVals[ind] = val;
+                    }
+                }
+            }
+            return backend.makeTensorInfo(outShape, image.dtype, outVals);
+        }
+        var dataId = backend.write(outVals, outShape, image.dtype);
+        return { dataId: dataId, shape: image.shape, dtype: image.dtype };
+    }
+    var transformConfig = {
+        kernelName: tfjsCore.Transform,
+        backendName: 'cpu',
+        kernelFunc: transform
+    };
+    function mapCoord(outCoord, len, mode) {
+        switch (mode) {
+            case 'reflect':
+                return mapCoordReflect(outCoord, len);
+            case 'wrap':
+                return mapCoordWrap(outCoord, len);
+            case 'nearest':
+                return mapCoordNearest(outCoord, len);
+            case 'constant':
+            default:
+                return mapCoordConstant(outCoord);
+        }
+    }
+    function mapCoordReflect(outCoord, len) {
+        // Reflect [abcd] to [dcba|abcd|dcba].
+        var inCoord = outCoord;
+        if (inCoord < 0) {
+            if (len <= 1) {
+                inCoord = 0;
+            }
+            else {
+                var sz2 = 2 * len;
+                if (inCoord < sz2) {
+                    inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;
+                }
+                inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;
+            }
+        }
+        else if (inCoord > len - 1) {
+            if (len <= 1) {
+                inCoord = 0;
+            }
+            else {
+                var sz2 = 2 * len;
+                inCoord -= sz2 * Math.trunc(inCoord / sz2);
+                if (inCoord >= len) {
+                    inCoord = sz2 - inCoord - 1;
+                }
+            }
+        }
+        // clamp is necessary because when outCoord = 3.5 and len = 4,
+        // inCoord = 3.5 and will be rounded to 4 in nearest interpolation.
+        return tfjsCore.util.clamp(0, inCoord, len - 1);
+    }
+    function mapCoordWrap(outCoord, len) {
+        // Wrap [abcd] to [abcd|abcd|abcd].
+        var inCoord = outCoord;
+        if (inCoord < 0) {
+            if (len <= 1) {
+                inCoord = 0;
+            }
+            else {
+                var sz = len - 1;
+                inCoord += len * (Math.trunc(-inCoord / sz) + 1);
+            }
+        }
+        else if (inCoord > len - 1) {
+            if (len <= 1) {
+                inCoord = 0;
+            }
+            else {
+                var sz = len - 1;
+                inCoord -= len * Math.trunc(inCoord / sz);
+            }
+        }
+        // clamp is necessary because when outCoord = -0.5 and len = 4,
+        // inCoord = 3.5 and will be rounded to 4 in nearest interpolation.
+        return tfjsCore.util.clamp(0, inCoord, len - 1);
+    }
+    function mapCoordConstant(outCoord, len) {
+        return outCoord;
+    }
+    function mapCoordNearest(outCoord, len) {
+        return tfjsCore.util.clamp(0, outCoord, len - 1);
+    }
+    function readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
+        var ind = batch * batchStride + y * rowStride + x * colStride + channel;
+        if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) {
+            return imageVals[ind];
+        }
+        else {
+            return fillValue;
+        }
+    }
+    function nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
+        var $y = Math.round(y);
+        var $x = Math.round(x);
+        return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue);
+    }
+    function bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
+        var yFloor = Math.floor(y);
+        var xFloor = Math.floor(x);
+        var yCeil = yFloor + 1;
+        var xCeil = xFloor + 1;
+        // f(x, yFloor) = (xCeil - x) / (xCeil - xFloor) * f(xFloor, yFloor)
+        //               + (x - xFloor) / (xCeil - xFloor) * f(xCeil, yFloor)
+        var valueYFloor = (xCeil - x) *
+            readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) +
+            (x - xFloor) *
+                readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue);
+        // f(x, yCeil) = (xCeil - x) / (xCeil - xFloor) * f(xFloor, yCeil)
+        //             + (x - xFloor) / (xCeil - xFloor) * f(xCeil, yCeil)
+        var valueYCeil = (xCeil - x) *
+            readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) +
+            (x - xFloor) *
+                readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue);
+        // f(x, y) = (yCeil - y) / (yCeil - yFloor) * f(x, yFloor)
+        //         + (y - yFloor) / (yCeil - yFloor) * f(x, yCeil)
+        return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the License);
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an AS IS BASIS,
+     * WITHOUT 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');
+        var values = backend.data.get(x.dataId).values;
+        var _a = uniqueImpl(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: tfjsCore.Unique,
+        backendName: 'cpu',
+        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 valueRank = value.shape.length;
+        var num = value.shape[axis];
+        var outShape = new Array(valueRank - 1);
+        var outIndex = 0;
+        for (var i = 0; i < valueRank; i++) {
+            if (i !== axis) {
+                outShape[outIndex++] = value.shape[i];
+            }
+        }
+        var begin = new Array(valueRank).fill(0);
+        var size = value.shape.slice();
+        size[axis] = 1;
+        var res = new Array(num);
+        for (var i = 0; i < res.length; i++) {
+            begin[axis] = i;
+            var tempRes = slice({ inputs: { x: value }, backend: backend, attrs: { begin: begin, size: size } });
+            res[i] = reshape({ inputs: { x: tempRes }, backend: backend, attrs: { shape: outShape } });
+            backend.disposeIntermediateTensorInfo(tempRes);
+        }
+        return res;
+    }
+    var unpackConfig = {
+        kernelName: tfjsCore.Unpack,
+        backendName: 'cpu',
+        kernelFunc: unpack
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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;
+        assertNotComplex(x, 'unsortedSegmentSum');
+        var xRank = x.shape.length;
+        var segmentIdsRank = segmentIds.shape.length;
+        var res = [];
+        var intermediates = [];
+        // Reshape the segment id's so that they can be broadcast with
+        // x. The new shape should be [segmentIds.shape, 1, ..., 1]
+        var numIters = xRank - segmentIdsRank;
+        var $segmentIds = segmentIds;
+        for (var i = 0; i < numIters; ++i) {
+            var expanded = expandDims({ inputs: { input: $segmentIds }, backend: backend, attrs: { dim: i + 1 } });
+            $segmentIds = expanded;
+            intermediates.push(expanded);
+        }
+        for (var i = 0; i < numSegments; ++i) {
+            var scalarValue = tfjsCore.util.createScalarValue(i, 'int32');
+            var segmentId = backend.makeTensorInfo([], 'int32', scalarValue);
+            var mask = equal({ inputs: { a: segmentId, b: $segmentIds }, backend: backend });
+            var maskCasted = cast({ inputs: { x: mask }, backend: backend, attrs: { dtype: 'float32' } });
+            var mul = multiply({ inputs: { a: maskCasted, b: x }, backend: backend });
+            var sumTensorInfo = sum({ inputs: { x: mul }, backend: backend, attrs: { axis: 0, keepDims: false } });
+            res.push(sumTensorInfo);
+            intermediates.push(segmentId);
+            intermediates.push(mask);
+            intermediates.push(maskCasted);
+            intermediates.push(mul);
+            intermediates.push(sumTensorInfo);
+        }
+        var result = pack({ inputs: res, backend: backend, attrs: { axis: 0 } });
+        intermediates.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); });
+        return result;
+    }
+    var unsortedSegmentSumConfig = {
+        kernelName: tfjsCore.UnsortedSegmentSum,
+        backendName: 'cpu',
+        kernelFunc: unsortedSegmentSum
+    };
+
+    var e_1, _a;
+    // List all kernel configs here
+    var kernelConfigs = [
+        _fusedMatMulConfig,
+        absConfig,
+        acosConfig,
+        acoshConfig,
+        addConfig,
+        addNConfig,
+        allConfig,
+        anyConfig,
+        argMaxConfig,
+        argMinConfig,
+        asinConfig,
+        asinhConfig,
+        atanConfig,
+        atan2Config,
+        atanhConfig,
+        avgPoolConfig,
+        avgPool3DConfig,
+        avgPool3DGradConfig,
+        avgPoolGradConfig,
+        batchMatMulConfig,
+        batchNormConfig,
+        batchToSpaceNDConfig,
+        bincountConfig,
+        broadcastArgsConfig,
+        castConfig,
+        ceilConfig,
+        clipConfig,
+        complexConfig,
+        complexAbsConfig,
+        concatConfig,
+        conv2DBackpropFilterConfig,
+        conv2DBackpropInputConfig,
+        conv2DConfig,
+        conv3DBackpropFilterV2Config,
+        conv3DBackpropInputV2Config,
+        conv3DConfig,
+        cosConfig,
+        coshConfig,
+        cropAndResizeConfig,
+        cumsumConfig,
+        denseBincountConfig,
+        depthToSpaceConfig,
+        depthwiseConv2dNativeConfig,
+        depthwiseConv2dNativeBackpropFilterConfig,
+        depthwiseConv2dNativeBackpropInputConfig,
+        diagConfig,
+        dilation2dConfig,
+        dilation2dBackpropInputConfig,
+        dilation2dBackpropFilterConfig,
+        realDivConfig,
+        einsumConfig,
+        eluConfig,
+        eluGradConfig,
+        equalConfig,
+        erfConfig,
+        expConfig,
+        expandDimsConfig,
+        expm1Config,
+        fftConfig,
+        fillConfig,
+        flipLeftRightConfig,
+        floorConfig,
+        floorDivConfig,
+        fusedConv2DConfig,
+        fusedDepthwiseConv2DConfig,
+        gatherNdConfig,
+        gatherV2Config,
+        greaterConfig,
+        greaterEqualConfig,
+        identityConfig,
+        ifftConfig,
+        imagConfig,
+        isFiniteConfig,
+        isInfConfig,
+        isNaNConfig,
+        leakyReluConfig,
+        lessConfig,
+        lessEqualConfig,
+        linSpaceConfig,
+        logConfig,
+        log1pConfig,
+        logicalAndConfig,
+        logicalNotConfig,
+        logicalOrConfig,
+        lRNConfig,
+        lRNGradConfig,
+        maximumConfig,
+        maxPoolConfig,
+        maxPool3DConfig,
+        maxPool3DGradConfig,
+        maxPoolGradConfig,
+        maxPoolWithArgmaxConfig,
+        maxConfig,
+        meanConfig,
+        minConfig,
+        minimumConfig,
+        mirrorPadConfig,
+        modConfig,
+        multinomialConfig,
+        multiplyConfig,
+        negConfig,
+        nonMaxSuppressionV3Config,
+        nonMaxSuppressionV4Config,
+        nonMaxSuppressionV5Config,
+        notEqualConfig,
+        oneHotConfig,
+        onesLikeConfig,
+        packConfig,
+        padV2Config,
+        powConfig,
+        preluConfig,
+        prodConfig,
+        rangeConfig,
+        realConfig,
+        reciprocalConfig,
+        reluConfig,
+        relu6Config,
+        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,
+        transposeConfig,
+        transformConfig,
+        uniqueConfig,
+        unpackConfig,
+        unsortedSegmentSumConfig,
+        zerosLikeConfig
+    ];
+    try {
+        for (var kernelConfigs_1 = __values(kernelConfigs), kernelConfigs_1_1 = kernelConfigs_1.next(); !kernelConfigs_1_1.done; kernelConfigs_1_1 = kernelConfigs_1.next()) {
+            var kernelConfig = kernelConfigs_1_1.value;
+            tfjsCore.registerKernel(kernelConfig);
+        }
+    }
+    catch (e_1_1) { e_1 = { error: e_1_1 }; }
+    finally {
+        try {
+            if (kernelConfigs_1_1 && !kernelConfigs_1_1.done && (_a = kernelConfigs_1.return)) _a.call(kernelConfigs_1);
+        }
+        finally { if (e_1) throw e_1.error; }
+    }
+
+    exports.MathBackendCPU = MathBackendCPU;
+    exports.shared = shared;
+    exports.version_cpu = version;
+
+    Object.defineProperty(exports, '__esModule', { value: true });
+
+})));
+//# sourceMappingURL=tf-backend-cpu.js.map
diff --git a/js/tf-converter.js b/js/tf-converter.js
new file mode 100644
index 0000000..8f0d976
--- /dev/null
+++ b/js/tf-converter.js
@@ -0,0 +1,29751 @@
+/**
+ * @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 = typeof globalThis !== 'undefined' ? globalThis : global || self, factory(global.tf = global.tf || {}, global.tf));
+}(this, (function (exports, tfc) { 'use strict';
+
+    function _interopNamespace(e) {
+        if (e && e.__esModule) return e;
+        var n = Object.create(null);
+        if (e) {
+            Object.keys(e).forEach(function (k) {
+                if (k !== 'default') {
+                    var d = Object.getOwnPropertyDescriptor(e, k);
+                    Object.defineProperty(n, k, d.get ? d : {
+                        enumerable: true,
+                        get: function () {
+                            return e[k];
+                        }
+                    });
+                }
+            });
+        }
+        n['default'] = e;
+        return n;
+    }
+
+    var tfc__namespace = /*#__PURE__*/_interopNamespace(tfc);
+
+    /**
+     * @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 ENV$1 = tfc.env();
+    /** Whether to keep intermediate tensors. */
+    ENV$1.registerFlag('KEEP_INTERMEDIATE_TENSORS', function () { return false; }, function (debugValue) {
+        if (debugValue) {
+            console.warn('Keep intermediate tensors is ON. This will print the values of all ' +
+                'intermediate tensors during model inference. Not all models ' +
+                'support this mode. For details, check e2e/benchmarks/ ' +
+                'model_config.js. This significantly impacts performance.');
+        }
+    });
+
+    /*! *****************************************************************************
+    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 };
+        }
+    }
+    function __values(o) {
+        var s = typeof Symbol === "function" && Symbol.iterator, m = s && o[s], i = 0;
+        if (m)
+            return m.call(o);
+        if (o && typeof o.length === "number")
+            return {
+                next: function () {
+                    if (o && i >= o.length)
+                        o = void 0;
+                    return { value: o && o[i++], done: !o };
+                }
+            };
+        throw new TypeError(s ? "Object is not iterable." : "Symbol.iterator is not defined.");
+    }
+    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) {
+        // Not a legal value for DataType.  Used to indicate a DataType field
+        // has not been set.
+        DataType[DataType["DT_INVALID"] = 0] = "DT_INVALID";
+        // Data types that all computation devices are expected to be
+        // capable to support.
+        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_QINT16"] = 15] = "DT_QINT16";
+        DataType[DataType["DT_QUINT16"] = 16] = "DT_QUINT16";
+        DataType[DataType["DT_UINT16"] = 17] = "DT_UINT16";
+        DataType[DataType["DT_COMPLEX128"] = 18] = "DT_COMPLEX128";
+        DataType[DataType["DT_HALF"] = 19] = "DT_HALF";
+        DataType[DataType["DT_RESOURCE"] = 20] = "DT_RESOURCE";
+        DataType[DataType["DT_VARIANT"] = 21] = "DT_VARIANT";
+        DataType[DataType["DT_UINT32"] = 22] = "DT_UINT32";
+        DataType[DataType["DT_UINT64"] = 23] = "DT_UINT64";
+        // Do not use!  These are only for parameters.  Every enum above
+        // should have a corresponding value below (verified by types_test).
+        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["DT_QINT16_REF"] = 115] = "DT_QINT16_REF";
+        DataType[DataType["DT_QUINT16_REF"] = 116] = "DT_QUINT16_REF";
+        DataType[DataType["DT_UINT16_REF"] = 117] = "DT_UINT16_REF";
+        DataType[DataType["DT_COMPLEX128_REF"] = 118] = "DT_COMPLEX128_REF";
+        DataType[DataType["DT_HALF_REF"] = 119] = "DT_HALF_REF";
+        DataType[DataType["DT_RESOURCE_REF"] = 120] = "DT_RESOURCE_REF";
+        DataType[DataType["DT_VARIANT_REF"] = 121] = "DT_VARIANT_REF";
+        DataType[DataType["DT_UINT32_REF"] = 122] = "DT_UINT32_REF";
+        DataType[DataType["DT_UINT64_REF"] = 123] = "DT_UINT64_REF";
+    })(DataType || (DataType = {}));
+    var SaverDef;
+    (function (SaverDef) {
+        (function (CheckpointFormatVersion) {
+            CheckpointFormatVersion[CheckpointFormatVersion["LEGACY"] = 0] = "LEGACY";
+            CheckpointFormatVersion[CheckpointFormatVersion["V1"] = 1] = "V1";
+            CheckpointFormatVersion[CheckpointFormatVersion["V2"] = 2] = "V2";
+        })(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];
+    }
+
+    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 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$i = [
+        {
+            '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$i
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$h = [
+        {
+            'tfOpName': '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$h
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$g = [
+        {
+            '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$g
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$f = [
+        {
+            '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$f
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$e = [
+        {
+            '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
+                },
+                {
+                    'tfName': 'stddev',
+                    'name': 'stdDev',
+                    'type': 'number',
+                    'defaultValue': 1
+                },
+                {
+                    '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$e
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$d = [
+        {
+            '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$d
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$c = [
+        {
+            '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$c
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$b = [
+        {
+            '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$b
+    };
+
+    /**
+     * @license
+     * Copyright 2021 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var json$a = [
+        {
+            '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$a
+    };
+
+    /**
+     * @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$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$1 = {
+        __proto__: null,
+        json: json$9
+    };
+
+    /**
+     * @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$8 = [
+        {
+            '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$8
+    };
+
+    /**
+     * @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$7 = [
+        {
+            '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$7
+    };
+
+    /**
+     * @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$6 = [
+        {
+            '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
+                },
+                {
+                    'tfName': 'alpha',
+                    'name': 'alpha',
+                    'type': 'number',
+                    'defaultValue': 1
+                },
+                {
+                    '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$6
+    };
+
+    /**
+     * @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$5 = [
+        {
+            '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$5
+    };
+
+    /**
+     * @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$4 = [
+        {
+            '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$4
+    };
+
+    /**
+     * @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$3 = [
+        {
+            '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$1 = {
+        __proto__: null,
+        json: json$3
+    };
+
+    /**
+     * @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$2 = [
+        {
+            '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$1 = {
+        __proto__: null,
+        json: json$2
+    };
+
+    /**
+     * @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$1 = [
+        {
+            '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$1 = {
+        __proto__: null,
+        json: json$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.
+     * =============================================================================
+     */
+    var json = [
+        {
+            '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': []
+        },
+        {
+            'tfOpName': 'BroadcastArgs',
+            'category': 'transformation',
+            'inputs': [
+                {
+                    'start': 0,
+                    'name': 's0',
+                    'type': 'tensor'
+                },
+                {
+                    'start': 1,
+                    'name': 's1',
+                    'type': 'tensor'
+                }
+            ],
+            'attrs': []
+        }
+    ];
+
+    var transformation = {
+        __proto__: null,
+        json: json
+    };
+
+    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$1, logical, matrices, normalization,
+                reduction, sliceJoin, sparse$1, spectral$1, string$1, 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:
+            case DataType.DT_HALF:
+                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 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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;
+    /**
+     * 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.
+     * =============================================================================
+     */
+    /**
+     * 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 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 rightPad(a, size) {
+        if (size <= a.length) {
+            return a;
+        }
+        return a + ' '.repeat(size - a.length);
+    }
+    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';
+    }
+    function isTypedArray(a) {
+        return a instanceof Float32Array || a instanceof Int32Array ||
+            a instanceof Uint8Array || a instanceof Uint8ClampedArray;
+    }
+    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
+            || values instanceof Uint8ClampedArray) {
+            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 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);
+        }
+    }
+    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 + "]."); });
+        });
+    }
+    /**
+     * 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';
+    }
+
+    // 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) {
+                if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {
+                    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];
+                if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {
+                    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 = __read(keyValue.split(':'), 2), 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 ENV;
+    }
+    var ENV = null;
+    function setEnvironmentGlobal(environment) {
+        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 AvgPool3D = 'AvgPool3D';
+    var BatchMatMul = 'BatchMatMul';
+    var BatchToSpaceND = 'BatchToSpaceND';
+    var Bincount = 'Bincount';
+    var BroadcastArgs = 'BroadcastArgs';
+    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 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 RealDiv = 'RealDiv';
+    var Einsum = 'Einsum';
+    var Elu = 'Elu';
+    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 LRN = 'LRN';
+    var Max = 'Max';
+    var Maximum = 'Maximum';
+    var MaxPool = 'MaxPool';
+    var MaxPool3D = 'MaxPool3D';
+    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 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 ResizeBilinear = 'ResizeBilinear';
+    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 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 RotateWithOffset = 'RotateWithOffset';
+    var _FusedMatMul = '_FusedMatMul';
+    var FusedConv2D = 'FusedConv2D';
+    var FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';
+
+    function warn() {
+        var msg = [];
+        for (var _i = 0; _i < arguments.length; _i++) {
+            msg[_i] = arguments[_i];
+        }
+        if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {
+            console.warn.apply(console, __spread(msg));
+        }
+    }
+
+    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 _b = __read(value, 2), key = _b[0], config = _b[1];
+            var _c = __read(key.split('_'), 1), backend = _c[0];
+            if (backend === backendName) {
+                result.push(config);
+            }
+        }
+        return result;
+    }
+    function makeKey(kernelName, backendName) {
+        return backendName + "_" + kernelName;
+    }
+
+    var long = Long$1;
+    /**
+     * 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$1(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$1.prototype.__isLong__;
+    Object.defineProperty(Long$1.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$1.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$1.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$1.fromNumber = fromNumber;
+    /**
+     * @param {number} lowBits
+     * @param {number} highBits
+     * @param {boolean=} unsigned
+     * @returns {!Long}
+     * @inner
+     */
+    function fromBits(lowBits, highBits, unsigned) {
+        return new Long$1(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$1.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$1.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$1.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$1.ZERO = ZERO;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var UZERO = fromInt(0, true);
+    /**
+     * Unsigned zero.
+     * @type {!Long}
+     */
+    Long$1.UZERO = UZERO;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var ONE = fromInt(1);
+    /**
+     * Signed one.
+     * @type {!Long}
+     */
+    Long$1.ONE = ONE;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var UONE = fromInt(1, true);
+    /**
+     * Unsigned one.
+     * @type {!Long}
+     */
+    Long$1.UONE = UONE;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var NEG_ONE = fromInt(-1);
+    /**
+     * Signed negative one.
+     * @type {!Long}
+     */
+    Long$1.NEG_ONE = NEG_ONE;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var MAX_VALUE = fromBits(0xFFFFFFFF | 0, 0x7FFFFFFF | 0, false);
+    /**
+     * Maximum signed value.
+     * @type {!Long}
+     */
+    Long$1.MAX_VALUE = MAX_VALUE;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var MAX_UNSIGNED_VALUE = fromBits(0xFFFFFFFF | 0, 0xFFFFFFFF | 0, true);
+    /**
+     * Maximum unsigned value.
+     * @type {!Long}
+     */
+    Long$1.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var MIN_VALUE = fromBits(0, 0x80000000 | 0, false);
+    /**
+     * Minimum signed value.
+     * @type {!Long}
+     */
+    Long$1.MIN_VALUE = MIN_VALUE;
+    /**
+     * @alias Long.prototype
+     * @inner
+     */
+    var LongPrototype = Long$1.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$1.fromBytes = function fromBytes(bytes, unsigned, le) {
+        return le ? Long$1.fromBytesLE(bytes, unsigned) : Long$1.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$1.fromBytesLE = function fromBytesLE(bytes, unsigned) {
+        return new Long$1(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$1.fromBytesBE = function fromBytesBE(bytes, unsigned) {
+        return new Long$1(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 = /*#__PURE__*/Object.assign(/*#__PURE__*/Object.create(null), long, {
+        'default': long
+    });
+
+    // tslint:disable-next-line
+    var Long = 
+    // tslint:disable-next-line
+    long || LongExports;
+    function hexToLong(hex) {
+        return Long.fromString(hex, true, 16);
+    }
+    // Some primes between 2^63 and 2^64 for various uses.
+    // Hex 0xc3a5c85c97cb3127
+    hexToLong('c3a5c85c97cb3127');
+    // Hex 0xb492b66fbe98f273
+    hexToLong('b492b66fbe98f273');
+    // Hex 0x9ae16a3b2f90404f
+    hexToLong('9ae16a3b2f90404f');
+
+    /**
+     * @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 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();
+    }
+    /**
+     * 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 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 e_1, _a;
+            var outputs;
+            var holdResultWrapperFn = function () {
+                outputs = f();
+            };
+            var timer;
+            var start = now();
+            if (this.backendTimer.timerAvailable()) {
+                timer = this.backendTimer.time(holdResultWrapperFn);
+            }
+            else {
+                holdResultWrapperFn();
+                try {
+                    for (var outputs_1 = __values(outputs), outputs_1_1 = outputs_1.next(); !outputs_1_1.done; outputs_1_1 = outputs_1.next()) {
+                        var output = outputs_1_1.value;
+                        output.dataSync();
+                    }
+                }
+                catch (e_1_1) { e_1 = { error: e_1_1 }; }
+                finally {
+                    try {
+                        if (outputs_1_1 && !outputs_1_1.done && (_a = outputs_1.return)) _a.call(outputs_1);
+                    }
+                    finally { if (e_1) throw e_1.error; }
+                }
+                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);
+        }
+    }
+
+    // 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, __spread(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, __spread(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, __spread(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;
+    }
+
+    /**
+     * 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 e_1, _b;
+            var locs = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                locs[_i] = arguments[_i];
+            }
+            if (locs.length === 0) {
+                locs = [0];
+            }
+            var i = 0;
+            try {
+                for (var locs_1 = __values(locs), locs_1_1 = locs_1.next(); !locs_1_1.done; locs_1_1 = locs_1.next()) {
+                    var loc = locs_1_1.value;
+                    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++;
+                }
+            }
+            catch (e_1_1) { e_1 = { error: e_1_1 }; }
+            finally {
+                try {
+                    if (locs_1_1 && !locs_1_1.done && (_b = locs_1.return)) _b.call(locs_1);
+                }
+                finally { if (e_1) throw e_1.error; }
+            }
+            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;
+    }
+    /**
+     * 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 (_b) {
+                    switch (_b.label) {
+                        case 0: return [4 /*yield*/, this.data()];
+                        case 1:
+                            vals = _b.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 (_b) {
+                    switch (_b.label) {
+                        case 0: return [4 /*yield*/, this.data()];
+                        case 1:
+                            vals = _b.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 (_b) {
+                    switch (_b.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 = _b.sent();
+                            try {
+                                return [2 /*return*/, bytes.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().');
+                            }
+                            _b.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 (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            this.throwIfDisposed();
+                            return [4 /*yield*/, trackerFn().read(this.dataId)];
+                        case 1:
+                            data = _b.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.
+     * =============================================================================
+     */
+    var Rank;
+    (function (Rank) {
+        Rank["R0"] = "R0";
+        Rank["R1"] = "R1";
+        Rank["R2"] = "R2";
+        Rank["R3"] = "R3";
+        Rank["R4"] = "R4";
+        Rank["R5"] = "R5";
+        Rank["R6"] = "R6";
+    })(Rank || (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];
+    }
+
+    /**
+     * @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"); });
+    }
+    /**
+     * 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';
+    }
+
+    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 = _a.name, asyncInit = _a.asyncInit;
+                    if (asyncInit) {
+                        throw new Error("The highest priority backend '" + name + "' has not yet been " +
+                            "initialized. Make sure to await tf.ready() or " +
+                            "await tf.setBackend() before calling other methods");
+                    }
+                    this.setBackend(name);
+                }
+                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) {
+                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;
+                        warn("Initialization of backend " + backendName + " failed");
+                        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) {
+                warn("Initialization of backend " + backendName + " failed");
+                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) {
+            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 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 dataId = outInfo.dataId, shape = outInfo.shape, dtype = outInfo.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, _b, _c, kernel, _d, _e, e_1_1;
+                var e_1, _f;
+                return __generator(this, function (_g) {
+                    switch (_g.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 = _g.sent();
+                            this.state.profiling = false;
+                            this.state.activeProfile.peakBytes = Math.max.apply(Math, __spread(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;
+                            _g.label = 2;
+                        case 2:
+                            _g.trys.push([2, 8, 9, 10]);
+                            _b = __values(this.state.activeProfile.kernels), _c = _b.next();
+                            _g.label = 3;
+                        case 3:
+                            if (!!_c.done) return [3 /*break*/, 7];
+                            kernel = _c.value;
+                            _d = kernel;
+                            return [4 /*yield*/, kernel.kernelTimeMs];
+                        case 4:
+                            _d.kernelTimeMs = _g.sent();
+                            _e = kernel;
+                            return [4 /*yield*/, kernel.extraInfo];
+                        case 5:
+                            _e.extraInfo = _g.sent();
+                            _g.label = 6;
+                        case 6:
+                            _c = _b.next();
+                            return [3 /*break*/, 3];
+                        case 7: return [3 /*break*/, 10];
+                        case 8:
+                            e_1_1 = _g.sent();
+                            e_1 = { error: e_1_1 };
+                            return [3 /*break*/, 10];
+                        case 9:
+                            try {
+                                if (_c && !_c.done && (_f = _b.return)) _f.call(_b);
+                            }
+                            finally { if (e_1) throw e_1.error; }
+                            return [7 /*endfinally*/];
+                        case 10: 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$1(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$1);
+                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) {
+                        var e_2, _a;
+                        try {
+                            for (var _b = __values(node.saved), _c = _b.next(); !_c.done; _c = _b.next()) {
+                                var tensor = _c.value;
+                                tensor.dispose();
+                            }
+                        }
+                        catch (e_2_1) { e_2 = { error: e_2_1 }; }
+                        finally {
+                            try {
+                                if (_c && !_c.done && (_a = _b.return)) _a.call(_b);
+                            }
+                            finally { if (e_2) throw e_2.error; }
+                        }
+                    });
+                    _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, __spread(inputs, [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;
+        };
+        return Engine;
+    }());
+    Engine.nextTensorId = 0;
+    Engine.nextVariableId = 0;
+    function ones$1(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$1(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 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); });
+    }
+
+    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, __spread(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 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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'add');
+        _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1];
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(Add, inputs);
+    }
+    var add = 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.
+     * =============================================================================
+     */
+    /**
+     * 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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'atan2');
+        _a = __read(makeTypesMatch($a, $b), 2), $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 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_ });
+
+    function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat) {
+        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+        var _a = __read(parseTupleParam(filterSize), 2), 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 convolution/pooling
+     * operation.
+     */
+    function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise, dataFormat) {
+        var _a, _b;
+        if (depthwise === void 0) { depthwise = false; }
+        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+        var _c = __read([-1, -1, -1, -1], 4), batchSize = _c[0], inHeight = _c[1], inWidth = _c[2], inChannels = _c[3];
+        if (dataFormat === 'channelsLast') {
+            _a = __read(inShape, 4), batchSize = _a[0], inHeight = _a[1], inWidth = _a[2], inChannels = _a[3];
+        }
+        else if (dataFormat === 'channelsFirst') {
+            _b = __read(inShape, 4), batchSize = _b[0], inChannels = _b[1], inHeight = _b[2], inWidth = _b[3];
+        }
+        else {
+            throw new Error("Unknown dataFormat " + dataFormat);
+        }
+        var _d = __read(filterShape, 4), filterHeight = _d[0], filterWidth = _d[1], filterChannels = _d[3];
+        var _e = __read(parseTupleParam(strides), 2), strideHeight = _e[0], strideWidth = _e[1];
+        var _f = __read(parseTupleParam(dilations), 2), dilationHeight = _f[0], dilationWidth = _f[1];
+        var effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+        var effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+        var _g = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat), padInfo = _g.padInfo, outHeight = _g.outHeight, outWidth = _g.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
+        };
+    }
+    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$1((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+        var outputCols = round$1((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+        return [outputRows, outputCols];
+    }
+    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;
+    }
+    /* 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 = Math.floor(padAlongHeight / 2);
+            var bottom = padAlongHeight - top;
+            var left = Math.floor(padAlongWidth / 2);
+            var right = padAlongWidth - left;
+            padInfo = { top: top, 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 = 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 === 0 && bottom === 0 && left === 0 && right === 0) ?
+                'VALID' :
+                'EXPLICIT';
+            padInfo = { top: top, bottom: bottom, left: left, right: right, type: padType };
+            outHeight = round$1((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);
+            outWidth = round$1((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
+        }
+        else {
+            throw Error("Unknown padding parameter: " + pad);
+        }
+        return { padInfo: padInfo, 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$1(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 = __read(parseTupleParam(param), 3), 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);
+    }
+    /**
+     * Check validity of pad when using dimRoundingMode.
+     * @param opDesc A string of op description
+     * @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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     * @throws unknown padding parameter
+     */
+    function checkPadOnDimRoundingMode(opDesc, pad, dimRoundingMode) {
+        if (dimRoundingMode != null) {
+            if (typeof pad === 'string') {
+                throw Error("Error in " + opDesc + ": pad must be an integer when using " +
+                    ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+            }
+            else if (typeof pad === 'number') {
+                assert(isInt(pad), function () { return "Error in " + opDesc + ": pad must be an integer when using " +
+                    ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+            }
+            else if (typeof pad === 'object') {
+                pad.forEach(function (p) {
+                    p.forEach(function (v) {
+                        assert(isInt(v), function () { return "Error in " + opDesc + ": pad must be an integer when using " +
+                            ("dimRoundingMode " + dimRoundingMode + " but got pad " + v + "."); });
+                    });
+                });
+            }
+            else {
+                throw Error("Error in " + opDesc + ": Unknown padding parameter: " + pad);
+            }
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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_docs/python/tf/nn/convolution](
+     *         https://www.tensorflow.org/api_docs/python/tf/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 + "."; });
+        checkPadOnDimRoundingMode('avgPool', pad, dimRoundingMode);
+        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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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); });
+        checkPadOnDimRoundingMode('avgPool3d', pad, dimRoundingMode);
+        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.
+     * =============================================================================
+     */
+    /**
+     * 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 LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'matMul');
+        _a = __read(makeTypesMatch($a, $b), 2), $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$1 = op({ matMul_: matMul_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'mul');
+        _a = __read(makeTypesMatch($a, $b), 2), $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 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', 'float32');
+        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', 'float32');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Tanh, inputs);
+    }
+    var tanh = 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$1(combined, $lstmKernel);
+        var res = add(weighted, $lstmBias);
+        // i = input_gate, j = new_input, f = forget_gate, o = output_gate
+        var batchSize = res.shape[0];
+        var sliceCols = res.shape[1] / 4;
+        var sliceSize = [batchSize, sliceCols];
+        var i = slice(res, [0, 0], sliceSize);
+        var j = slice(res, [0, sliceCols], sliceSize);
+        var f = slice(res, [0, sliceCols * 2], sliceSize);
+        var o = slice(res, [0, sliceCols * 3], sliceSize);
+        var newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f))));
+        var newH = mul(tanh(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 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.
+     * =============================================================================
+     */
+    /**
+     * Return the shape of s0 op s1 with broadcast.
+     *
+     * compute r0, the broadcasted shape as a tensor.
+     * s0, s1 and r0 are all integer vectors.
+     *
+     * This function returns the shape of the result of an operation between
+     * two tensors of size s0 and s1 performed with broadcast.
+     *
+     * @param s0 A tensor representing a shape
+     * @param s1 A tensor representing a shape
+     *
+     * @doc {heading: 'Tensors', subheading: 'Transformations'}
+     */
+    function broadcastArgs_(s0, s1) {
+        var shape1Input = convertToTensor(s0, 's0', 'broadcastArgs', 'int32');
+        var shape2Input = convertToTensor(s1, 's1', 'broadcastArgs', 'int32');
+        if (shape1Input.rank !== 1) {
+            throw new Error('broadcastArgs(): first input must be a vector (rank=1). ' +
+                ("Has rank " + shape1Input.rank));
+        }
+        if (shape2Input.rank !== 1) {
+            throw new Error('broadcastArgs(): second input must be a vector (rank=1). ' +
+                ("Has rank " + shape2Input.rank));
+        }
+        var inputs = { s0: shape1Input, s1: shape2Input };
+        return ENGINE.runKernel(BroadcastArgs, inputs);
+    }
+    var broadcastArgs = op({ broadcastArgs_: broadcastArgs_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 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 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', 'float32');
+        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_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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_ });
+
+    /**
+     * 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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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', 'float32');
+        var $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');
+        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 + "."); });
+        checkPadOnDimRoundingMode('conv2d', pad, dimRoundingMode);
+        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$1 = 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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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 + "."); });
+        checkPadOnDimRoundingMode('conv1d', pad, dimRoundingMode);
+        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$1(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] + "."); });
+        checkPadOnDimRoundingMode('conv2dDerInput', pad, dimRoundingMode);
+        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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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. Must be float32 type.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function cos_(x) {
+        var $x = convertToTensor(x, 'x', 'cos', 'float32');
+        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. Must be float32 type.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function cosh_(x) {
+        var $x = convertToTensor(x, 'x', 'cosh', 'float32');
+        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', 'float32');
+        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(blockSize > 1, function () { return "blockSize should be > 1 for depthToSpace, but was: " + blockSize; });
+        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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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', 'float32');
+        var $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');
+        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] + "."); });
+        checkPadOnDimRoundingMode('depthwiseConv2d', pad, dimRoundingMode);
+        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$1 = 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.tensor2d([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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'floorDiv');
+        _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1];
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(FloorDiv, inputs);
+    }
+    var floorDiv = op({ floorDiv_: floorDiv_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'div');
+        _a = __read(makeTypesMatch($a, $b), 2), $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 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 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;
+    }
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'equal', 'string_or_numeric');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'div');
+        _a = __read(makeTypesMatch($a, $b), 2), $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$1(t12D, t22D);
+            return reshape(t1t2, []);
+        }
+        else if ($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$1(t12D, t22D);
+            return reshape(t1t2, [t1t2.size]);
+        }
+        else if ($t1.rank === 2 && $t2.rank === 1) {
+            var t22D = reshape($t2, [-1, 1]);
+            var t1t2 = matMul$1($t1, t22D);
+            return reshape(t1t2, [t1t2.size]);
+        }
+        else {
+            var t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
+            var t1t2 = matMul$1($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 ? x : (e ^ x) - 1`.
+     *
+     * ```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', 'float32');
+        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', 'float32');
+        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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'greater', 'string_or_numeric');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'greaterEqual', 'string_or_numeric');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'less', 'string_or_numeric');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'lessEqual', 'string_or_numeric');
+        _a = __read(makeTypesMatch($a, $b), 2), $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', 'float32');
+        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_ });
+
+    /**
+     * 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);
+    }
+
+    /**
+     * @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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'sub');
+        _a = __read(makeTypesMatch($a, $b), 2), $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 = op({ sum_: sum_ });
+
+    /**
+     * 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 " + $logits.rank + " and axis was " + axis));
+        }
+        // const forward: ForwardFunc<Tensor> = (backend, save) => {
+        //   const keepDims = true;
+        //   const xMax = max(logits, axis, true);
+        //   const shifted = sub(logits, xMax);
+        //   const value =
+        //       sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis,
+        //       keepDims)));
+        //   save([value]);
+        //   return value;
+        // };
+        // Use a custom gradient for numerical stability.
+        var customOp = customGrad(function (logits, save) {
+            var keepDims = true;
+            var xMax = max(logits, axis, true);
+            var shifted = sub(logits, xMax);
+            var value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));
+            save([value]);
+            var gradFunc = function (dy, saved) {
+                var _a = __read(saved, 1), value = _a[0];
+                var keepDims = true;
+                var softmax = exp(value);
+                return sub(dy, mul(sum(dy, axis, keepDims), softmax));
+            };
+            return { value: value, gradFunc: gradFunc };
+        });
+        return customOp($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.
+     * =============================================================================
+     */
+    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 expandShapeToKeepDim(shape, axes) {
+        var reduceSubShape = axes.map(function (x) { return 1; });
+        return combineLocations(shape, reduceSubShape, axes);
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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(b, axes);
+        var d = log(c);
+        var res = add(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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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 + "'"); });
+        checkPadOnDimRoundingMode('maxPool', pad, dimRoundingMode);
+        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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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); });
+        checkPadOnDimRoundingMode('maxPool3d', pad, dimRoundingMode);
+        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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'maximum');
+        _a = __read(makeTypesMatch($a, $b), 2), $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(shape, dtype) {
+        if (dtype === void 0) { dtype = 'float32'; }
+        if (dtype === 'complex64') {
+            var real = ones(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$1(ones([h, 1], $x.dtype), $x),
+                matMul$1($y, ones([1, w], $y.dtype)),
+            ];
+        }
+        $x = reshape($x, [-1, 1]);
+        $y = reshape($y, [1, -1]);
+        return [
+            matMul$1($x, ones([1, h], $x.dtype)),
+            matMul$1(ones([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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'minimum');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'mod');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'notEqual', 'string_or_numeric');
+        _a = __read(makeTypesMatch($a, $b), 2), $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 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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.
+     * =============================================================================
+     */
+    /**
+     * 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$1(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_ });
+
+    /**
+     * 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`.
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function pool_(input, windowShape, poolingType, pad, dilations, strides, dimRoundingMode) {
+        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 = __read(requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding), 2), 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, dimRoundingMode); } :
+            function () { return maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode); };
+        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_ });
+
+    /**
+     * 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 $exp = convertToTensor(exp, 'exp', 'pow');
+        _a = __read(makeTypesMatch($base, $exp), 2), $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 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 LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 getAugmentedNamespace(n) {
+        if (n.__esModule)
+            return n;
+        var a = Object.defineProperty({}, '__esModule', { value: true });
+        Object.keys(n).forEach(function (k) {
+            var d = Object.getOwnPropertyDescriptor(n, k);
+            Object.defineProperty(a, k, d.get ? d : {
+                enumerable: true,
+                get: function () {
+                    return n[k];
+                }
+            });
+        });
+        return a;
+    }
+    function createCommonjsModule(fn) {
+        var module = { exports: {} };
+        return fn(module, module.exports), module.exports;
+    }
+
+    var alea = createCommonjsModule(function (module) {
+        // A port of an algorithm by Johannes Baagøe <[email protected]>, 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 <[email protected]>
+        //
+        // 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 = String(data);
+                    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, X = [];
+                    if (seed === (seed | 0)) {
+                        // Seed state array using a 32-bit integer.
+                        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)
+                        X[7] = -1;
+                    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 _nodeResolve_empty = {};
+
+    var _nodeResolve_empty$1 = {
+        __proto__: null,
+        'default': _nodeResolve_empty
+    };
+
+    var require$$0 = /*@__PURE__*/ getAugmentedNamespace(_nodeResolve_empty$1);
+
+    /*
+    Copyright 2019 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.
+
+    */
+    var seedrandom$1 = createCommonjsModule(function (module) {
+        (function (global, pool, math) {
+            //
+            // The following constants are related to IEEE 754 limits.
+            //
+            var 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);
+            }
+            //
+            // 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$$0;
+                }
+                catch (ex) { }
+            }
+            else {
+                // When included as a plain script, set up Math.seedrandom global.
+                math['seed' + rngname] = seedrandom;
+            }
+            // End anonymous scope, and pass initial values.
+        })(
+        // global: `self` in browsers (including strict mode and web workers),
+        // otherwise `this` in Node and other environments
+        (typeof self !== 'undefined') ? self : commonjsGlobal, [], // 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$1.alea = alea;
+    seedrandom$1.xor128 = xor128;
+    seedrandom$1.xorwow = xorwow;
+    seedrandom$1.xorshift7 = xorshift7;
+    seedrandom$1.xor4096 = xor4096;
+    seedrandom$1.tychei = tychei;
+    var seedrandom = seedrandom$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.
+     * =============================================================================
+     */
+    // 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.alea(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.alea(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.alea(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 = 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', 'float32');
+        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.
+     * =============================================================================
+     */
+    /** 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 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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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$1(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);
+        var pointwiseStride = 1;
+        var res = conv2d$1(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', 'float32');
+        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$1 = 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$1(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);
+        var imagComplexConjugate = split$1(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', 'float32');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Sqrt, inputs);
+    }
+    var sqrt = op({ sqrt_: sqrt_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'squaredDifference');
+        _a = __read(makeTypesMatch($a, $b), 2), $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', 'float32');
+        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 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.
+     * =============================================================================
+     */
+    /**
+     * 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-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 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);
+    }
+
+    /**
+     * 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 < 0) {
+            throw new Error("'k' passed to topk() must be >= 0 but got " + k);
+        }
+        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 = __read(ENGINE.runKernel(TopK, inputs, attrs), 2), 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_ });
+
+    /**
+     * 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 = __read(ENGINE.runKernel(Unique, inputs, attrs), 2), 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_;
+
+    /**
+     * 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.
+     * =============================================================================
+     */
+    /**
+     * 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 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(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(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(abs(x), axis[0]), axis[1] - 1);
+            }
+            if (p === Infinity) {
+                return max(sum(abs(x), axis[1]), axis[0]);
+            }
+            if (p === -Infinity) {
+                return min(sum(abs(x), axis[1]), axis[0]);
+            }
+            if (p === 'fro' || p === 'euclidean') {
+                // norm(x) = sqrt(sum(pow(x, 2)))
+                return sqrt(sum(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, $step)));
+        }
+        return add($v, update);
+    }
+    var movingAverage = op({ movingAverage_: movingAverage_ });
+
+    /**
+     * 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$1(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);
+    }
+
+    /**
+     * @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$1($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(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', $sparseValues.dtype);
+        validateInput($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(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');
+    }
+
+    /**
+     * 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 = __read([predictionsVals.length / lastDim, lastDim], 2), 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] + ")."); });
+        checkPadOnDimRoundingMode('conv2dDerFilter', pad, dimRoundingMode);
+        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(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';
+    };
+
+    /**
+     * 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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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 _b;
+        var x = _a.x, filter = _a.filter, strides = _a.strides, pad = _a.pad, _c = _a.dataFormat, dataFormat = _c === void 0 ? 'NHWC' : _c, _d = _a.dilations, dilations = _d === void 0 ? [1, 1] : _d, dimRoundingMode = _a.dimRoundingMode, bias = _a.bias, _e = _a.activation, activation = _e === void 0 ? 'linear' : _e, preluActivationWeights = _a.preluActivationWeights, leakyreluAlpha = _a.leakyreluAlpha;
+        activation = activation || 'linear';
+        if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
+            var result = conv2d$1(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
+            if (bias != null) {
+                result = add(result, bias);
+            }
+            return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
+        }
+        var $x = convertToTensor(x, 'x', 'conv2d', 'float32');
+        var $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');
+        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 + "."); });
+        checkPadOnDimRoundingMode('fused conv2d', pad, dimRoundingMode);
+        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) {
+            $bias = convertToTensor(bias, 'bias', 'fused conv2d');
+            _b = __read(makeTypesMatch($bias, $x), 1), $bias = _b[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 = __read(saved, 4), $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 = 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_ });
+
+    /**
+     * 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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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 _b;
+        var x = _a.x, filter = _a.filter, strides = _a.strides, pad = _a.pad, _c = _a.dataFormat, dataFormat = _c === void 0 ? 'NHWC' : _c, _d = _a.dilations, dilations = _d === void 0 ? [1, 1] : _d, dimRoundingMode = _a.dimRoundingMode, 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 = depthwiseConv2d$1(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
+            if (bias != null) {
+                result = add(result, bias);
+            }
+            return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
+        }
+        var $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32');
+        var $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');
+        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 + "'"); });
+        checkPadOnDimRoundingMode('fused depthwiseConv2d', pad, dimRoundingMode);
+        var convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
+        var $bias;
+        if (bias != null) {
+            $bias = convertToTensor(bias, 'bias', 'fused conv2d');
+            _b = __read(makeTypesMatch($bias, $x), 1), $bias = _b[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 _a = __read(saved, 4), $filter = _a[0], x4D = _a[1], y = _a[2], bias = _a[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 = op({ fusedDepthwiseConv2d_: fusedDepthwiseConv2d_ });
+
+    /**
+     * 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, _c;
+        var a = _a.a, b = _a.b, _d = _a.transposeA, transposeA = _d === void 0 ? false : _d, _e = _a.transposeB, transposeB = _e === void 0 ? false : _e, bias = _a.bias, _f = _a.activation, activation = _f === void 0 ? 'linear' : _f, preluActivationWeights = _a.preluActivationWeights, leakyreluAlpha = _a.leakyreluAlpha;
+        if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
+            var result = matMul$1(a, b, transposeA, transposeB);
+            if (bias != null) {
+                result = add(result, bias);
+            }
+            return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
+        }
+        var $a = convertToTensor(a, 'a', 'fused matMul');
+        var $b = convertToTensor(b, 'b', 'fused matMul');
+        _b = __read(makeTypesMatch($a, $b), 2), $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(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 outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));
+        var outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+        var a3D = transposeA ?
+            reshape($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) {
+            $bias = convertToTensor(bias, 'bias', 'fused matMul');
+            _c = __read(makeTypesMatch($bias, $a), 1), $bias = _c[0];
+            assertAndGetBroadcastShape(outShape, $bias.shape);
+        }
+        var $preluActivationWeights;
+        if (preluActivationWeights != null) {
+            $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');
+        }
+        var grad = function (dy, saved) {
+            var _a = __read(saved, 4), a3D = _a[0], b3D = _a[1], y = _a[2], $bias = _a[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$1(dyActivation, b3D, false, true);
+                bDer = matMul$1(a3D, dyActivation, true, false);
+            }
+            else if (!transposeA && transposeB) {
+                aDer = matMul$1(dyActivation, b3D, false, false);
+                bDer = matMul$1(dyActivation, a3D, true, false);
+            }
+            else if (transposeA && !transposeB) {
+                aDer = matMul$1(b3D, dyActivation, false, true);
+                bDer = matMul$1(a3D, dyActivation, false, false);
+            }
+            else {
+                aDer = matMul$1(b3D, dyActivation, true, true);
+                bDer = matMul$1(dyActivation, a3D, true, true);
+            }
+            if (bias != null) {
+                var biasDer = getFusedBiasGradient($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 = 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,
+        depthwiseConv2d: depthwiseConv2d,
+        matMul: matMul
+    };
+
+    /**
+     * @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 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 images from grayscale to RGB format.
+     *
+     * @param image A grayscale tensor to convert. The `image`'s last dimension must
+     *     be size 1 with at least a two-dimensional shape.
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function grayscaleToRGB_(image) {
+        var $image = convertToTensor(image, 'image', 'grayscaleToRGB');
+        var lastDimsIdx = $image.rank - 1;
+        var lastDims = $image.shape[lastDimsIdx];
+        assert($image.rank >= 2, function () { return 'Error in grayscaleToRGB: images must be at least rank 2, ' +
+            ("but got rank " + $image.rank + "."); });
+        assert(lastDims === 1, function () { return 'Error in grayscaleToRGB: last dimension of a grayscale image ' +
+            ("should be size 1, but got size " + lastDims + "."); });
+        var reps = new Array($image.rank);
+        reps.fill(1, 0, lastDimsIdx);
+        reps[lastDimsIdx] = 3;
+        return tile($image, reps);
+    }
+    var grayscaleToRGB = op({ grayscaleToRGB_: grayscaleToRGB_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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', 'float32');
+        var $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression', 'float32');
+        var inputs = nonMaxSuppSanityCheck($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;
+    }
+
+    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, __spread(new Array(elemsToPad).fill(0)));
+            selectedScores.push.apply(selectedScores, __spread(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));
+    }
+
+    /**
+     * 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 = __read.apply(void 0, [_c.sent(), 2]), 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_;
+
+    /**
+     * 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]]);
+        }
+        __read(size, 0);
+        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_ });
+
+    /**
+     * 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]]);
+        }
+        __read(size, 0);
+        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_ });
+
+    /**
+     * 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 = __read(split$1($image, [1, 1, 1], -1), 3), r = _a[0], g = _a[1], b = _a[2];
+            var $r = mul(r, RED_INTENCITY_COEF);
+            var $g = mul(g, GREEN_INTENCITY_COEF);
+            var $b = mul(b, BLUE_INTENCITY_COEF);
+            grayscale = add(add($r, $g), $b);
+        }
+        else {
+            grayscale = image;
+        }
+        if (method === 'otsu') {
+            var $histogram = bincount(cast(round(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(classFirst), total);
+            weightBack = div(sum(classSecond), total);
+            var meanFirstDivA = sum(mul(classFirst, range(0, classFirst.size)));
+            meanFirst = div(meanFirstDivA, sum(classFirst));
+            var meanSecFill = fill(classSecond.shape, classFirst.size);
+            var meanSecAdd = add(range(0, classSecond.size), meanSecFill);
+            var meanSecMul = mul(classSecond, (meanSecAdd));
+            meanSec = div(sum(meanSecMul), sum(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_ });
+
+    /**
+     * 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 = $a.shape;
+        var _a = __read($a.shape.slice(-2), 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$1(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(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 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.
+     * =============================================================================
+     */
+    /**
+     * 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(); });
+    }
+
+    /**
+     * 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 = __read(qr2d(x2d, fullMatrices), 2), 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 = __read(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$1(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$1(tauTimesW, matMul$1(wT, rjEndAll)));
+                    }
+                    else {
+                        var rTimesTau = sub(rjEndAll, matMul$1(tauTimesW, matMul$1(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$1(matMul$1(qAllJEnd, w), tawTimesWT));
+                    }
+                    else {
+                        var qTimesTau = sub(qAllJEnd, matMul$1(matMul$1(qAllJEnd, w), tawTimesWT));
+                        q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
+                    }
+                    return [w, r, q];
+                }), 3), 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.
+     * =============================================================================
+     */
+    var Reduction;
+    (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";
+    })(Reduction || (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 = 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, $weights);
+        if (reduction === Reduction.NONE) {
+            return weightedLoss;
+        }
+        if (reduction === Reduction.SUM) {
+            return sum(weightedLoss);
+        }
+        if (reduction === Reduction.MEAN) {
+            if ($weights == null) {
+                return mean(weightedLoss);
+            }
+            else {
+                var broadcastFactor = $losses.size / $weights.size;
+                var result = div(sum(weightedLoss), sum($weights));
+                return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :
+                    result;
+            }
+        }
+        if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {
+            if ($weights == null) {
+                return div(sum(weightedLoss), scalar($losses.size));
+            }
+            else {
+                var broadcastedWeights = mul($weights, ones($losses.shape));
+                var numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');
+                return div(sum(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 = 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 = 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, $predictions.shape, 'Error in cosineDistance: ');
+        var one = scalar(1);
+        var losses = sub(one, sum(mul($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 = 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 = 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(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 = 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($predictions, epsilonScalar))));
+        var l2 = mul(sub(one, $labels), log(add(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 = 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(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 = 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);
+            $multiClassLabels =
+                add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));
+        }
+        var losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    var sigmoidCrossEntropy = op({ sigmoidCrossEntropy_: sigmoidCrossEntropy_ });
+
+    /**
+     * 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(costVector, [dim]);
+            var gradFunc = function (dy, saved) {
+                var _a = __read(saved, 2), labels = _a[0], logResult = _a[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 = 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]);
+            $onehotLabels =
+                add(mul($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', 'int32');
+        var $values = convertToTensor(values, 'values', 'sparseFillEmptyRows');
+        var $denseShape = convertToTensor(denseShape, 'denseShape', 'sparseFillEmptyRows', 'int32');
+        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', 'int32');
+        var $inputShape = convertToTensor(inputShape, 'inputShape', 'sparseReshape', 'int32');
+        var $newShape = convertToTensor(newShape, 'newShape', 'sparseReshape', 'int32');
+        if ($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', 'int32');
+        var $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentMean', 'int32');
+        if ($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', 'int32');
+        var $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentSum', 'int32');
+        if ($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,
+        grayscaleToRGB: grayscaleToRGB,
+        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 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 tfOps = {
+        __proto__: null,
+        abs: abs,
+        acos: acos,
+        acosh: acosh,
+        add: add,
+        addN: addN,
+        all: all,
+        any: any,
+        argMax: argMax,
+        argMin: argMin,
+        asin: asin,
+        asinh: asinh,
+        atan: atan,
+        atan2: atan2,
+        atanh: atanh,
+        avgPool: avgPool,
+        avgPool3d: avgPool3d,
+        basicLSTMCell: basicLSTMCell,
+        batchToSpaceND: batchToSpaceND,
+        batchNorm: batchNorm,
+        batchNorm2d: batchNorm2d,
+        batchNorm3d: batchNorm3d,
+        batchNorm4d: batchNorm4d,
+        bincount: bincount,
+        broadcastArgs: broadcastArgs,
+        broadcastTo: broadcastTo,
+        buffer: buffer,
+        cast: cast,
+        ceil: ceil,
+        clipByValue: clipByValue,
+        clone: clone,
+        complex: complex,
+        concat: concat,
+        concat1d: concat1d,
+        concat2d: concat2d,
+        concat3d: concat3d,
+        concat4d: concat4d,
+        conv1d: conv1d,
+        conv2d: conv2d$1,
+        conv2dTranspose: conv2dTranspose,
+        conv3d: conv3d,
+        conv3dTranspose: conv3dTranspose,
+        cos: cos,
+        cosh: cosh,
+        cumsum: cumsum,
+        denseBincount: denseBincount,
+        depthToSpace: depthToSpace,
+        depthwiseConv2d: depthwiseConv2d$1,
+        diag: diag,
+        dilation2d: dilation2d,
+        div: div,
+        divNoNan: divNoNan,
+        dot: dot,
+        einsum: einsum,
+        elu: elu,
+        equal: equal,
+        erf: erf,
+        exp: exp,
+        expandDims: expandDims,
+        expm1: expm1,
+        eye: eye,
+        fill: fill,
+        floor: floor,
+        floorDiv: floorDiv,
+        gather: gather,
+        greater: greater,
+        greaterEqual: greaterEqual,
+        imag: imag,
+        isFinite: isFinite$1,
+        isInf: isInf,
+        isNaN: isNaN$1,
+        leakyRelu: leakyRelu,
+        less: less,
+        lessEqual: lessEqual,
+        linspace: linspace,
+        localResponseNormalization: localResponseNormalization,
+        log: log,
+        log1p: log1p,
+        logSigmoid: logSigmoid,
+        logSoftmax: logSoftmax,
+        logSumExp: logSumExp,
+        logicalAnd: logicalAnd,
+        logicalNot: logicalNot,
+        logicalOr: logicalOr,
+        logicalXor: logicalXor,
+        matMul: matMul$1,
+        max: max,
+        maxPool: maxPool,
+        maxPool3d: maxPool3d,
+        maxPoolWithArgmax: maxPoolWithArgmax,
+        maximum: maximum,
+        mean: mean,
+        meshgrid: meshgrid,
+        min: min,
+        minimum: minimum,
+        mirrorPad: mirrorPad,
+        mod: mod,
+        moments: moments,
+        mul: mul,
+        multiRNNCell: multiRNNCell,
+        multinomial: multinomial,
+        neg: neg,
+        notEqual: notEqual,
+        oneHot: oneHot,
+        ones: ones,
+        onesLike: onesLike,
+        outerProduct: outerProduct,
+        pad: pad,
+        pad1d: pad1d,
+        pad2d: pad2d,
+        pad3d: pad3d,
+        pad4d: pad4d,
+        pool: pool,
+        pow: pow,
+        prelu: prelu,
+        print: print,
+        prod: prod,
+        rand: rand,
+        randomGamma: randomGamma,
+        randomNormal: randomNormal,
+        randomUniform: randomUniform,
+        range: range,
+        real: real,
+        reciprocal: reciprocal,
+        relu: relu,
+        relu6: relu6,
+        reshape: reshape,
+        reverse: reverse,
+        reverse1d: reverse1d,
+        reverse2d: reverse2d,
+        reverse3d: reverse3d,
+        reverse4d: reverse4d,
+        round: round,
+        rsqrt: rsqrt,
+        scalar: scalar,
+        selu: selu,
+        separableConv2d: separableConv2d,
+        setdiff1dAsync: setdiff1dAsync,
+        sigmoid: sigmoid,
+        sign: sign,
+        sin: sin,
+        sinh: sinh,
+        slice: slice,
+        slice1d: slice1d,
+        slice2d: slice2d,
+        slice3d: slice3d,
+        slice4d: slice4d,
+        softmax: softmax,
+        softplus: softplus,
+        spaceToBatchND: spaceToBatchND,
+        fft: fft,
+        ifft: ifft,
+        irfft: irfft,
+        rfft: rfft,
+        split: split$1,
+        sqrt: sqrt,
+        square: square,
+        squaredDifference: squaredDifference,
+        squeeze: squeeze,
+        stack: stack,
+        step: step,
+        stridedSlice: stridedSlice,
+        sub: sub,
+        sum: sum,
+        tan: tan,
+        tanh: tanh,
+        tensor: tensor,
+        tensor1d: tensor1d,
+        tensor2d: tensor2d,
+        tensor3d: tensor3d,
+        tensor4d: tensor4d,
+        tensor5d: tensor5d,
+        tensor6d: tensor6d,
+        tile: tile,
+        topk: topk,
+        truncatedNormal: truncatedNormal,
+        unique: unique,
+        unsortedSegmentSum: unsortedSegmentSum,
+        unstack: unstack,
+        variable: variable,
+        where: where,
+        whereAsync: whereAsync,
+        zeros: zeros,
+        zerosLike: zerosLike,
+        op: op,
+        OP_SCOPE_SUFFIX: OP_SCOPE_SUFFIX,
+        booleanMaskAsync: booleanMaskAsync,
+        transpose: transpose,
+        norm: norm,
+        movingAverage: movingAverage,
+        scatterND: scatterND,
+        sparseToDense: sparseToDense,
+        gatherND: gatherND,
+        dropout: dropout,
+        enclosingPowerOfTwo: enclosingPowerOfTwo,
+        cosineWindow: cosineWindow,
+        inTopKAsync: inTopKAsync,
+        image: image,
+        linalg: linalg,
+        losses: losses,
+        spectral: spectral,
+        fused: fused_ops,
+        signal: signal,
+        sparse: sparse,
+        string: string
+    };
+
+    /**
+     * @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$j = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'BiasAdd':
+            case 'AddV2':
+            case 'Add': {
+                return [add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'AddN': {
+                return [addN(getParamValue('tensors', node, tensorMap, context))];
+            }
+            case 'FloorMod':
+            case 'Mod':
+                return [mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            case 'Mul':
+                return [mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            case 'RealDiv':
+            case 'Div': {
+                return [div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'DivNoNan': {
+                return [divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'FloorDiv': {
+                return [floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Sub': {
+                return [sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Minimum': {
+                return [minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Maximum': {
+                return [maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Pow': {
+                return [pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'SquaredDifference': {
+                return [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$i = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'Abs':
+            case 'ComplexAbs':
+                return [abs(getParamValue('x', node, tensorMap, context))];
+            case 'Acos':
+                return [acos(getParamValue('x', node, tensorMap, context))];
+            case 'Acosh':
+                return [acosh(getParamValue('x', node, tensorMap, context))];
+            case 'Asin':
+                return [asin(getParamValue('x', node, tensorMap, context))];
+            case 'Asinh':
+                return [asinh(getParamValue('x', node, tensorMap, context))];
+            case 'Atan':
+                return [atan(getParamValue('x', node, tensorMap, context))];
+            case 'Atan2':
+                return [atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];
+            case 'Atanh':
+                return [atanh(getParamValue('x', node, tensorMap, context))];
+            case 'Ceil':
+                return [ceil(getParamValue('x', node, tensorMap, context))];
+            case 'Complex':
+                return [complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))];
+            case 'Cos':
+                return [cos(getParamValue('x', node, tensorMap, context))];
+            case 'Cosh':
+                return [cosh(getParamValue('x', node, tensorMap, context))];
+            case 'Elu':
+                return [elu(getParamValue('x', node, tensorMap, context))];
+            case 'Erf':
+                return [erf(getParamValue('x', node, tensorMap, context))];
+            case 'Exp':
+                return [exp(getParamValue('x', node, tensorMap, context))];
+            case 'Expm1': {
+                return [expm1(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Floor':
+                return [floor(getParamValue('x', node, tensorMap, context))];
+            case 'Log':
+                return [log(getParamValue('x', node, tensorMap, context))];
+            case 'Log1p': {
+                return [log1p(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Imag':
+                return [imag(getParamValue('x', node, tensorMap, context))];
+            case 'Neg':
+                return [neg(getParamValue('x', node, tensorMap, context))];
+            case 'Reciprocal': {
+                return [reciprocal(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Real':
+                return [real(getParamValue('x', node, tensorMap, context))];
+            case 'Relu':
+                return [relu(getParamValue('x', node, tensorMap, context))];
+            case 'Round': {
+                return [round(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Selu':
+                return [selu(getParamValue('x', node, tensorMap, context))];
+            case 'Sigmoid':
+                return [sigmoid(getParamValue('x', node, tensorMap, context))];
+            case 'Sin':
+                return [sin(getParamValue('x', node, tensorMap, context))];
+            case 'Sign': {
+                return [sign(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Sinh': {
+                return [sinh(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Softplus': {
+                return [softplus(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Sqrt': {
+                return [sqrt(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Square': {
+                return [square(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Tanh': {
+                return [tanh(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'Tan':
+                return [tan(getParamValue('x', node, tensorMap, context))];
+            case 'ClipByValue':
+                return [clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))];
+            case 'Relu6':
+                return [relu6(getParamValue('x', node, tensorMap, context))];
+            case 'Rsqrt':
+                return [rsqrt(getTensor(node.inputNames[0], tensorMap, context))];
+            case 'Prod':
+                return [prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))];
+            case 'LeakyRelu':
+                return [leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];
+            case 'Prelu':
+                return [prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];
+            case 'IsNan':
+                return [isNaN$1(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;
+    }
+
+    /**
+     * 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;
+    }());
+
+    /**
+     * 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;
+    }
+
+    var _this$2 = undefined;
+    var executeOp$h = function (node, tensorMap, context) { return __awaiter(_this$2, 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, 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 = getParamValue('name', node, tensorMap, context);
+                        tensorArray = new TensorArray(name, 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");
+            }
+        });
+    }); };
+
+    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$g = 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 [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 [conv2d$1(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 [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 [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 [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 [depthwiseConv2d$1(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 [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 [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 [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 = 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 [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 [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 [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$f = 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 [fill(shape, value, dtype)];
+            }
+            case 'LinSpace': {
+                var start = getParamValue('start', node, tensorMap, context);
+                var stop = getParamValue('stop', node, tensorMap, context);
+                var num = getParamValue('num', node, tensorMap, context);
+                return [linspace(start, stop, 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 [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 [oneHot(indices, depth, onValue, offValue)];
+            }
+            case 'Ones': {
+                return [ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];
+            }
+            case 'OnesLike': {
+                return [onesLike(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'RandomUniform': {
+                return [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 = getParamValue('stop', node, tensorMap, context);
+                var step = getParamValue('step', node, tensorMap, context);
+                return [range(start, stop, 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 [truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)];
+            }
+            case 'Zeros': {
+                return [zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];
+            }
+            case 'ZerosLike': {
+                return [zerosLike(getParamValue('x', node, tensorMap, context))];
+            }
+            default:
+                throw TypeError("Node type " + node.op + " is not implemented");
+        }
+    };
+
+    var _this$1 = undefined;
+    function nmsParams(node, tensorMap, context) {
+        var boxes = getParamValue('boxes', node, tensorMap, context);
+        var scores = getParamValue('scores', node, tensorMap, context);
+        var maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context);
+        var iouThreshold = getParamValue('iouThreshold', node, tensorMap, context);
+        var scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context);
+        var softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context);
+        return {
+            boxes: boxes,
+            scores: scores,
+            maxOutputSize: maxOutputSize,
+            iouThreshold: iouThreshold,
+            scoreThreshold: scoreThreshold,
+            softNmsSigma: softNmsSigma
+        };
+    }
+    var executeOp$e = 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*/, 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*/, 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*/, image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];
+                case 6: return [2 /*return*/, [_e.sent()]];
+                case 7:
+                    condition = cast(getParamValue('condition', node, tensorMap, context), 'bool');
+                    return [4 /*yield*/, whereAsync(condition)];
+                case 8:
+                    result = [_e.sent()];
+                    condition.dispose();
+                    return [2 /*return*/, result];
+                case 9:
+                    {
+                        return [2 /*return*/, 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$d = 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 = topk(x, k, sorted);
+                return [result.values, result.indices];
+            }
+            case 'Unique': {
+                var x = getParamValue('x', node, tensorMap, context);
+                var result = 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 = 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$c = 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 [tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')];
+            case 'ShapeN':
+                return getParamValue('x', node, tensorMap, context)
+                    .map(function (t) { return tensor1d(t.shape); });
+            case 'Size':
+                return [scalar(getParamValue('x', node, tensorMap, context).size, 'int32')];
+            case 'Rank':
+                return [scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')];
+            case 'NoOp':
+                return [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 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 = $keys[i];
+                                        var value = _this.findWithDefault(key, defaultValue);
+                                        result.push(value);
+                                    }
+                                    return tfc.stack(result);
+                                })];
+                    }
+                });
+            });
+        };
+        // tslint:disable-next-line: no-any
+        HashTable.prototype.findWithDefault = function (key, defaultValue) {
+            var result = this.tensorMap.get(key);
+            return result != null ? result : defaultValue;
+        };
+        HashTable.prototype.checkKeyAndValueTensor = function (key, value) {
+            if (key.dtype !== this.keyDType) {
+                throw new Error("Expect key dtype " + this.keyDType + ", but got " +
+                    ("" + key.dtype));
+            }
+            if (value.dtype !== this.valueDType) {
+                throw new Error("Expect value dtype " + this.valueDType + ", but got " +
+                    ("" + value.dtype));
+            }
+        };
+        return HashTable;
+    }());
+
+    var _this = undefined;
+    var executeOp$b = function (node, tensorMap, context, resourceManager) { return __awaiter(_this, 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$a = 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 [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 [image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)];
+            }
+            case 'CropAndResize': {
+                var image$1 = 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 [image.cropAndResize(image$1, 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$9 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'Equal': {
+                return [equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'NotEqual': {
+                return [notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Greater': {
+                return [greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'GreaterEqual': {
+                return [greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Less': {
+                return [less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'LessEqual': {
+                return [lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'LogicalAnd': {
+                return [logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'LogicalNot': {
+                return [logicalNot(getParamValue('a', node, tensorMap, context))];
+            }
+            case 'LogicalOr': {
+                return [logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];
+            }
+            case 'Select':
+            case 'SelectV2': {
+                return [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");
+        }
+    };
+
+    var executeOp$8 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'BatchMatMul':
+            case 'BatchMatMulV2':
+            case 'MatMul':
+                return [matMul$1(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];
+            case 'Einsum':
+                return [einsum.apply(tfOps, __spread([getParamValue('equation', node, tensorMap, context)], getParamValue('tensors', node, tensorMap, context)))];
+            case 'Transpose':
+                return [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 [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$7 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'FusedBatchNorm':
+            case 'FusedBatchNormV2': {
+                return [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 [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 [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 [softmax(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'LogSoftmax': {
+                return [logSoftmax(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'SparseToDense': {
+                return [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$6 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'Max': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var keepDims = getParamValue('keepDims', node, tensorMap, context);
+                return [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 [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 [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 [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 [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 [any(getParamValue('x', node, tensorMap, context), axis, keepDims)];
+            }
+            case 'ArgMax': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                return [argMax(getParamValue('x', node, tensorMap, context), axis)];
+            }
+            case 'ArgMin': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                return [argMin(getParamValue('x', node, tensorMap, context), axis)];
+            }
+            case 'Prod': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var keepDims = getParamValue('keepDims', node, tensorMap, context);
+                return [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 [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 [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 [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$5 = 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 [concat(inputs, axis)];
+            }
+            case 'Gather': {
+                var input = getParamValue('x', node, tensorMap, context);
+                var indices = getParamValue('indices', node, tensorMap, context);
+                return [gather(input, 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 [gather(input, 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 [reverse(input, axis)];
+            }
+            case 'ReverseV2': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var input = getParamValue('x', node, tensorMap, context);
+                return [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 [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 [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 = squeeze(tensors[0]).shape;
+                    var mapped = tensors.map(function (tensor) {
+                        var sameShape = tfc.util.arraysEqual(tensor.shape, shape);
+                        if (!sameShape &&
+                            !tfc.util.arraysEqual(squeeze(tensor).shape, squeezedShape)) {
+                            throw new Error('the input tensors shape does not match');
+                        }
+                        return sameShape ? tensor : reshape(tensor, shape);
+                    });
+                    return [stack(mapped, axis)];
+                });
+            }
+            case 'Unpack': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                var tensor = getParamValue('tensor', node, tensorMap, context);
+                return unstack(tensor, axis);
+            }
+            case 'Tile': {
+                var reps = getParamValue('reps', node, tensorMap, context);
+                return [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 split$1(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 [scatterND(indices, values, shape)];
+            }
+            case 'GatherNd': {
+                var x = getParamValue('x', node, tensorMap, context);
+                var indices = getParamValue('indices', node, tensorMap, context);
+                return [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 [sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ?
+                        defaultValue :
+                        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$4 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'SparseFillEmptyRows': {
+                var _a = 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 = 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 = sparse.sparseSegmentMean(getParamValue('data', node, tensorMap, context), getParamValue('indices', node, tensorMap, context), getParamValue('segmentIds', node, tensorMap, context));
+                return [outputData];
+            }
+            case 'SparseSegmentSum': {
+                var outputData = 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$3 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'FFT': {
+                return [fft(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'IFFT': {
+                return [ifft(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'RFFT': {
+                return [rfft(getParamValue('x', node, tensorMap, context))];
+            }
+            case 'IRFFT': {
+                return [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$2 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'StringNGrams': {
+                var _a = 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 = 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 = 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$1 = function (node, tensorMap, context) {
+        switch (node.op) {
+            case 'Cast': {
+                return [cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];
+            }
+            case 'ExpandDims': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                return [expandDims(getParamValue('x', node, tensorMap, context), axis)];
+            }
+            case 'Squeeze': {
+                var axis = getParamValue('axis', node, tensorMap, context);
+                return [squeeze(getParamValue('x', node, tensorMap, context), axis)];
+            }
+            case 'Reshape': {
+                return [reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];
+            }
+            case 'MirrorPad': {
+                return [mirrorPad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('mode', node, tensorMap, context))];
+            }
+            case 'PadV2':
+            case 'Pad': {
+                return [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 [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 [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 [depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)];
+            }
+            case 'BroadcastTo': {
+                return [broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];
+            }
+            case 'BroadcastArgs': {
+                return [broadcastArgs(getParamValue('s0', node, tensorMap, context), getParamValue('s1', 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(node, tensorMap, context, resourceManager) {
+        var value = (function (node, tensorMap, context) {
+            switch (node.category) {
+                case 'arithmetic':
+                    return tfc__namespace.tidy(function () { return executeOp$j(node, tensorMap, context); });
+                case 'basic_math':
+                    return tfc__namespace.tidy(function () { return executeOp$i(node, tensorMap, context); });
+                case 'control':
+                    return executeOp$h(node, tensorMap, context);
+                case 'convolution':
+                    return tfc__namespace.tidy(function () { return executeOp$g(node, tensorMap, context); });
+                case 'creation':
+                    return tfc__namespace.tidy(function () { return executeOp$f(node, tensorMap, context); });
+                case 'dynamic':
+                    return executeOp$e(node, tensorMap, context);
+                case 'evaluation':
+                    return tfc__namespace.tidy(function () { return executeOp$d(node, tensorMap, context); });
+                case 'image':
+                    return tfc__namespace.tidy(function () { return executeOp$a(node, tensorMap, context); });
+                case 'graph':
+                    return tfc__namespace.tidy(function () { return executeOp$c(node, tensorMap, context); });
+                case 'logical':
+                    return tfc__namespace.tidy(function () { return executeOp$9(node, tensorMap, context); });
+                case 'matrices':
+                    return tfc__namespace.tidy(function () { return executeOp$8(node, tensorMap, context); });
+                case 'normalization':
+                    return tfc__namespace.tidy(function () { return executeOp$7(node, tensorMap, context); });
+                case 'reduction':
+                    return tfc__namespace.tidy(function () { return executeOp$6(node, tensorMap, context); });
+                case 'slice_join':
+                    return tfc__namespace.tidy(function () { return executeOp$5(node, tensorMap, context); });
+                case 'sparse':
+                    return tfc__namespace.tidy(function () { return executeOp$4(node, tensorMap, context); });
+                case 'spectral':
+                    return tfc__namespace.tidy(function () { return executeOp$3(node, tensorMap, context); });
+                case 'string':
+                    return tfc__namespace.tidy(function () { return executeOp$2(node, tensorMap, context); });
+                case 'transformation':
+                    return tfc__namespace.tidy(function () { return executeOp$1(node, tensorMap, context); });
+                case 'hash_table':
+                    return executeOp$b(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__namespace.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;
+    }());
+
+    /**
+     * 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;
+    }
+
+    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.intermediateTensors = {};
+            this.keepTensorForDebug = false;
+            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]; });
+            this.resetIntermediateTensors();
+            // 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 = Object.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(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) {
+            var _this = this;
+            // 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) {
+                                    if (!_this.keepTensorForDebug) {
+                                        tensor.dispose();
+                                    }
+                                    else {
+                                        var _a = __read(getNodeNameAndIndex(node.name, context), 2), nodeName_1 = _a[0], index = _a[1];
+                                        if (_this.intermediateTensors[nodeName_1]) {
+                                            _this.intermediateTensors[nodeName_1][index] = tensor;
+                                        }
+                                        else {
+                                            _this.intermediateTensors[nodeName_1] = [];
+                                            _this.intermediateTensors[nodeName_1][index] = tensor;
+                                        }
+                                    }
+                                    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)];
+                });
+            });
+        };
+        GraphExecutor.prototype.disposeIntermediateTensors = function () {
+            var _this = this;
+            if (!this.intermediateTensors) {
+                return;
+            }
+            Object.keys(this.intermediateTensors)
+                .forEach(function (key) { return _this.intermediateTensors[key].forEach(function (tensor) { return tensor.dispose(); }); });
+            this.disposeTensorsMap();
+        };
+        GraphExecutor.prototype.disposeTensorsMap = function () {
+            var _this = this;
+            if (!this.tensorsMap) {
+                return;
+            }
+            Object.keys(this.tensorsMap).forEach(function (key) {
+                var tensorArray = _this.tensorsMap[key];
+                tensorArray.forEach(function (tensor) {
+                    if (tensor && !tensor.kept && !tensor.isDisposed &&
+                        !_this.keepIds.has(tensor.id)) {
+                        tensor.dispose();
+                    }
+                });
+            });
+        };
+        GraphExecutor.prototype.getIntermediateTensors = function () {
+            return this.tensorsMap;
+        };
+        GraphExecutor.prototype.resetIntermediateTensors = function () {
+            for (var key in this.intermediateTensors) {
+                this.intermediateTensors[key].forEach(function (tensor) { return tensor.dispose(); });
+                delete this.intermediateTensors[key];
+            }
+        };
+        /**
+         * 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, _a, results, outputIds, inputIds;
+                var _this = this;
+                return __generator(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            if (!isFunctionExecution) {
+                                inputs = this.mapInputs(inputs);
+                                this.checkInputs(inputs);
+                                this.checkInputShapeAndType(inputs);
+                                outputs = this.mapOutputs(outputs);
+                                this.checkOutputs(outputs);
+                            }
+                            // For model debug.
+                            try {
+                                this.keepTensorForDebug = tfc.env().getBool('KEEP_INTERMEDIATE_TENSORS');
+                            }
+                            catch (e) {
+                                console.warn(e.message);
+                            }
+                            this.resetIntermediateTensors();
+                            context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);
+                            // Graph with control flow op requires runtime evaluation of the execution
+                            // order, while without control flow the execution order is pre-determined
+                            // in the compile method.
+                            _a = this;
+                            return [4 /*yield*/, this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution)];
+                        case 1:
+                            // Graph with control flow op requires runtime evaluation of the execution
+                            // order, while without control flow the execution order is pre-determined
+                            // in the compile method.
+                            _a.tensorsMap = _b.sent();
+                            results = outputs.map(function (name) { return getTensor(name, _this.tensorsMap, context); });
+                            outputIds = results.map(function (t) { return t.id; });
+                            inputIds = Object.keys(inputs).map(function (name) { return inputs[name].id; });
+                            this.keepIds =
+                                new Set(__spread(outputIds, inputIds, this.weightIds));
+                            if (!this.keepTensorForDebug) {
+                                this.disposeTensorsMap();
+                            }
+                            // dispose the context for the root executor
+                            if (this.parent == null) {
+                                context.dispose(this.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 = Object.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(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 in this.hashTableNameToHandle) {
+                this.hashTableNameToHandle[name].dispose();
+                delete this.hashTableNameToHandle[name];
+            }
+        };
+        return ResourceManager;
+    }());
+
+    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]];
+                    }
+                });
+            });
+        };
+        /**
+         * Get intermediate tensors for model debugging mode (flag
+         * KEEP_INTERMEDIATE_TENSORS is true).
+         *
+         * @doc {heading: 'Models', subheading: 'Classes'}
+         */
+        GraphModel.prototype.getIntermediateTensors = function () {
+            return this.executor.getIntermediateTensors();
+        };
+        /**
+         * Dispose intermediate tensors for model debugging mode (flag
+         * KEEP_INTERMEDIATE_TENSORS is true).
+         *
+         * @doc {heading: 'Models', subheading: 'Classes'}
+         */
+        GraphModel.prototype.disposeIntermediateTensors = function () {
+            this.executor.disposeIntermediateTensors();
+        };
+        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 = '3.12.0';
+
+    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/js/tf-core.js b/js/tf-core.js
new file mode 100644
index 0000000..75711cc
--- /dev/null
+++ b/js/tf-core.js
@@ -0,0 +1,27144 @@
+/**
+ * @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('crypto')) :
+    typeof define === 'function' && define.amd ? define(['exports', 'crypto'], factory) :
+    (global = typeof globalThis !== 'undefined' ? globalThis : global || self, factory(global.tf = global.tf || {}, global.require$$0));
+}(this, (function (exports, require$$0) { 'use strict';
+
+    function _interopDefaultLegacy (e) { return e && typeof e === 'object' && 'default' in e ? e : { 'default': e }; }
+
+    var require$$0__default = /*#__PURE__*/_interopDefaultLegacy(require$$0);
+
+    /*! *****************************************************************************
+    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 };
+        }
+    }
+    function __values(o) {
+        var s = typeof Symbol === "function" && Symbol.iterator, m = s && o[s], i = 0;
+        if (m)
+            return m.call(o);
+        if (o && typeof o.length === "number")
+            return {
+                next: function () {
+                    if (o && i >= o.length)
+                        o = void 0;
+                    return { value: o && o[i++], done: !o };
+                }
+            };
+        throw new TypeError(s ? "Object is not iterable." : "Symbol.iterator is not defined.");
+    }
+    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 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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 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
+            swap(array, counter, index);
+        }
+    }
+    /**
+     * 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 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
+            swap(array, counter, index);
+            swap(array2, counter, index);
+        }
+    }
+    /** 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 swap(object, left, right) {
+        var temp = object[left];
+        object[left] = object[right];
+        object[right] = temp;
+    }
+    function sum$1(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$1(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 || a instanceof Uint8ClampedArray;
+    }
+    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
+            || values instanceof Uint8ClampedArray) {
+            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';
+    }
+
+    // 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) {
+                if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {
+                    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];
+                if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {
+                    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 = __read(keyValue.split(':'), 2), 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 BroadcastArgs = 'BroadcastArgs';
+    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';
+
+    function warn() {
+        var msg = [];
+        for (var _i = 0; _i < arguments.length; _i++) {
+            msg[_i] = arguments[_i];
+        }
+        if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {
+            console.warn.apply(console, __spread(msg));
+        }
+    }
+    function log$1() {
+        var msg = [];
+        for (var _i = 0; _i < arguments.length; _i++) {
+            msg[_i] = arguments[_i];
+        }
+        if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {
+            console.log.apply(console, __spread(msg));
+        }
+    }
+
+    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 _b = __read(value, 2), key = _b[0], config = _b[1];
+            var _c = __read(key.split('_'), 1), backend = _c[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)) {
+            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')) {
+                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 = Long$1;
+    /**
+     * 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$1(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$1.prototype.__isLong__;
+    Object.defineProperty(Long$1.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$1.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$1.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$1.fromNumber = fromNumber;
+    /**
+     * @param {number} lowBits
+     * @param {number} highBits
+     * @param {boolean=} unsigned
+     * @returns {!Long}
+     * @inner
+     */
+    function fromBits(lowBits, highBits, unsigned) {
+        return new Long$1(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$1.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$1.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$1.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$1.ZERO = ZERO;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var UZERO = fromInt(0, true);
+    /**
+     * Unsigned zero.
+     * @type {!Long}
+     */
+    Long$1.UZERO = UZERO;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var ONE = fromInt(1);
+    /**
+     * Signed one.
+     * @type {!Long}
+     */
+    Long$1.ONE = ONE;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var UONE = fromInt(1, true);
+    /**
+     * Unsigned one.
+     * @type {!Long}
+     */
+    Long$1.UONE = UONE;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var NEG_ONE = fromInt(-1);
+    /**
+     * Signed negative one.
+     * @type {!Long}
+     */
+    Long$1.NEG_ONE = NEG_ONE;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var MAX_VALUE = fromBits(0xFFFFFFFF | 0, 0x7FFFFFFF | 0, false);
+    /**
+     * Maximum signed value.
+     * @type {!Long}
+     */
+    Long$1.MAX_VALUE = MAX_VALUE;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var MAX_UNSIGNED_VALUE = fromBits(0xFFFFFFFF | 0, 0xFFFFFFFF | 0, true);
+    /**
+     * Maximum unsigned value.
+     * @type {!Long}
+     */
+    Long$1.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;
+    /**
+     * @type {!Long}
+     * @inner
+     */
+    var MIN_VALUE = fromBits(0, 0x80000000 | 0, false);
+    /**
+     * Minimum signed value.
+     * @type {!Long}
+     */
+    Long$1.MIN_VALUE = MIN_VALUE;
+    /**
+     * @alias Long.prototype
+     * @inner
+     */
+    var LongPrototype = Long$1.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$1.fromBytes = function fromBytes(bytes, unsigned, le) {
+        return le ? Long$1.fromBytesLE(bytes, unsigned) : Long$1.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$1.fromBytesLE = function fromBytesLE(bytes, unsigned) {
+        return new Long$1(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$1.fromBytesBE = function fromBytesBE(bytes, unsigned) {
+        return new Long$1(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 = /*#__PURE__*/Object.assign(/*#__PURE__*/Object.create(null), long, {
+        'default': long
+    });
+
+    // tslint:disable-next-line
+    var Long = 
+    // tslint:disable-next-line
+    long || LongExports;
+    function hexToLong(hex) {
+        return Long.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$2(s, offset, numBytes) {
+        var bytes = s.slice(offset, offset + numBytes);
+        return Long.fromBytes(Array.from(bytes), true, true);
+    }
+    function fetch64(s, offset) {
+        return fetch$2(s, offset, 8);
+    }
+    function fetch32(s, offset) {
+        return fetch$2(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.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.UZERO, Long.UZERO];
+        var w = [Long.UZERO, Long.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 = __read([x, z], 2), 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 = __read([x, z], 2), 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$1(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$1,
+        encodeString: encodeString,
+        decodeString: decodeString,
+        shuffle: shuffle,
+        shuffleCombo: shuffleCombo,
+        clamp: clamp,
+        nearestLargerEven: nearestLargerEven,
+        swap: swap,
+        sum: sum$1,
+        randUniform: randUniform,
+        distSquared: distSquared,
+        assert: assert,
+        assertShapesMatch: assertShapesMatch,
+        assertNonNull: assertNonNull,
+        flatten: flatten,
+        sizeFromShape: sizeFromShape,
+        isScalarShape: isScalarShape,
+        arraysEqual: arraysEqual,
+        isInt: isInt,
+        tanh: tanh$1,
+        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
+    };
+
+    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 e_1, _a;
+            var outputs;
+            var holdResultWrapperFn = function () {
+                outputs = f();
+            };
+            var timer;
+            var start = now();
+            if (this.backendTimer.timerAvailable()) {
+                timer = this.backendTimer.time(holdResultWrapperFn);
+            }
+            else {
+                holdResultWrapperFn();
+                try {
+                    for (var outputs_1 = __values(outputs), outputs_1_1 = outputs_1.next(); !outputs_1_1.done; outputs_1_1 = outputs_1.next()) {
+                        var output = outputs_1_1.value;
+                        output.dataSync();
+                    }
+                }
+                catch (e_1_1) { e_1 = { error: e_1_1 }; }
+                finally {
+                    try {
+                        if (outputs_1_1 && !outputs_1_1.done && (_a = outputs_1.return)) _a.call(outputs_1);
+                    }
+                    finally { if (e_1) throw e_1.error; }
+                }
+                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);
+        }
+    }
+
+    // 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, __spread(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, __spread(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, __spread(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;
+    }
+
+    /**
+     * 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 e_1, _b;
+            var locs = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                locs[_i] = arguments[_i];
+            }
+            if (locs.length === 0) {
+                locs = [0];
+            }
+            var i = 0;
+            try {
+                for (var locs_1 = __values(locs), locs_1_1 = locs_1.next(); !locs_1_1.done; locs_1_1 = locs_1.next()) {
+                    var loc = locs_1_1.value;
+                    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++;
+                }
+            }
+            catch (e_1_1) { e_1 = { error: e_1_1 }; }
+            finally {
+                try {
+                    if (locs_1_1 && !locs_1_1.done && (_b = locs_1.return)) _b.call(locs_1);
+                }
+                finally { if (e_1) throw e_1.error; }
+            }
+            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$1 = 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$1 = 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 (_b) {
+                    switch (_b.label) {
+                        case 0: return [4 /*yield*/, this.data()];
+                        case 1:
+                            vals = _b.sent();
+                            return [2 /*return*/, opHandler$1.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$1.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 (_b) {
+                    switch (_b.label) {
+                        case 0: return [4 /*yield*/, this.data()];
+                        case 1:
+                            vals = _b.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 (_b) {
+                    switch (_b.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 = _b.sent();
+                            try {
+                                return [2 /*return*/, bytes.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().');
+                            }
+                            _b.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 (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            this.throwIfDisposed();
+                            return [4 /*yield*/, trackerFn().read(this.dataId)];
+                        case 1:
+                            data = _b.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$1.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$1.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$1.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.
+     * =============================================================================
+     */
+    exports.Rank = void 0;
+    (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
+    };
+
+    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 = _a.name, asyncInit = _a.asyncInit;
+                    if (asyncInit) {
+                        throw new Error("The highest priority backend '" + name + "' has not yet been " +
+                            "initialized. Make sure to await tf.ready() or " +
+                            "await tf.setBackend() before calling other methods");
+                    }
+                    this.setBackend(name);
+                }
+                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) {
+                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;
+                        warn("Initialization of backend " + backendName + " failed");
+                        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) {
+                warn("Initialization of backend " + backendName + " failed");
+                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) {
+            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 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 dataId = outInfo.dataId, shape = outInfo.shape, dtype = outInfo.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, _b, _c, kernel, _d, _e, e_1_1;
+                var e_1, _f;
+                return __generator(this, function (_g) {
+                    switch (_g.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 = _g.sent();
+                            this.state.profiling = false;
+                            this.state.activeProfile.peakBytes = Math.max.apply(Math, __spread(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;
+                            _g.label = 2;
+                        case 2:
+                            _g.trys.push([2, 8, 9, 10]);
+                            _b = __values(this.state.activeProfile.kernels), _c = _b.next();
+                            _g.label = 3;
+                        case 3:
+                            if (!!_c.done) return [3 /*break*/, 7];
+                            kernel = _c.value;
+                            _d = kernel;
+                            return [4 /*yield*/, kernel.kernelTimeMs];
+                        case 4:
+                            _d.kernelTimeMs = _g.sent();
+                            _e = kernel;
+                            return [4 /*yield*/, kernel.extraInfo];
+                        case 5:
+                            _e.extraInfo = _g.sent();
+                            _g.label = 6;
+                        case 6:
+                            _c = _b.next();
+                            return [3 /*break*/, 3];
+                        case 7: return [3 /*break*/, 10];
+                        case 8:
+                            e_1_1 = _g.sent();
+                            e_1 = { error: e_1_1 };
+                            return [3 /*break*/, 10];
+                        case 9:
+                            try {
+                                if (_c && !_c.done && (_f = _b.return)) _f.call(_b);
+                            }
+                            finally { if (e_1) throw e_1.error; }
+                            return [7 /*endfinally*/];
+                        case 10: 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$1(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$1);
+                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) {
+                        var e_2, _a;
+                        try {
+                            for (var _b = __values(node.saved), _c = _b.next(); !_c.done; _c = _b.next()) {
+                                var tensor = _c.value;
+                                tensor.dispose();
+                            }
+                        }
+                        catch (e_2_1) { e_2 = { error: e_2_1 }; }
+                        finally {
+                            try {
+                                if (_c && !_c.done && (_a = _b.return)) _a.call(_b);
+                            }
+                            finally { if (e_2) throw e_2.error; }
+                        }
+                    });
+                    _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, __spread(inputs, [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;
+        };
+        return Engine;
+    }());
+    Engine.nextTensorId = 0;
+    Engine.nextVariableId = 0;
+    function ones$1(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$1(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;
+    }
+    var isMobileMockValue;
+    function mockIsMobile(value) {
+        isMobileMockValue = value;
+    }
+    function isMobile(nav) {
+        if (isMobileMockValue !== undefined) {
+            return isMobileMockValue;
+        }
+        if (nav || _isNavigatorDefined()) {
+            if (!nav) {
+                nav = navigator;
+            }
+            if (nav.product === 'ReactNative') {
+                return true;
+            }
+            var a = nav.userAgent || nav.vendor ||
+                // tslint:disable-next-line:no-any
+                (typeof window !== 'undefined' ? window.opera : '');
+            // Use `navigator.userAgentData.mobile` as fallback.
+            if (!a) {
+                // tslint:disable-next-line:no-any
+                var navAny = nav;
+                return navAny.userAgentData && navAny.userAgentData.mobile;
+            }
+            // 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,
+        mockIsMobile: mockIsMobile,
+        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); });
+    }
+
+    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, __spread(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
+    };
+
+    /** 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 = names[i];
+                            var t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];
+                            if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&
+                                t.dtype !== 'string' && t.dtype !== 'complex64') {
+                                throw new Error("Unsupported dtype in weight '" + name + "': " + t.dtype);
+                            }
+                            var spec = { name: name, 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) {
+        var e_1, _a;
+        // TODO(adarob, cais): Support quantization.
+        var out = {};
+        var float16Decode;
+        var offset = 0;
+        try {
+            for (var specs_1 = __values(specs), specs_1_1 = specs_1.next(); !specs_1_1.done; specs_1_1 = specs_1.next()) {
+                var spec = specs_1_1.value;
+                var name = 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 + "': " + 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] = complex(realTensor, imageTensor);
+                        realTensor.dispose();
+                        imageTensor.dispose();
+                    }
+                    else {
+                        throw new Error("Unsupported dtype in weight '" + name + "': " + dtype);
+                    }
+                    offset += size * dtypeFactor;
+                }
+                if (dtype !== 'complex64') {
+                    out[name] = tensor(values, shape, dtype);
+                }
+            }
+        }
+        catch (e_1_1) { e_1 = { error: e_1_1 }; }
+        finally {
+            try {
+                if (specs_1_1 && !specs_1_1.done && (_a = specs_1.return)) _a.call(specs_1);
+            }
+            finally { if (e_1) throw e_1.error; }
+        }
+        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];
+    }
+    /**
+     * Create `ModelJSON` from `ModelArtifacts`.
+     *
+     * @param artifacts Model artifacts, describing the model and its weights.
+     * @param manifest Weight manifest, describing where the weights of the
+     *     `ModelArtifacts` are stored, and some metadata about them.
+     * @returns Object representing the `model.json` file describing the model
+     *     artifacts and weights
+     */
+    function getModelJSONForModelArtifacts(artifacts, manifest) {
+        var result = {
+            modelTopology: artifacts.modelTopology,
+            format: artifacts.format,
+            generatedBy: artifacts.generatedBy,
+            convertedBy: artifacts.convertedBy,
+            weightsManifest: manifest
+        };
+        if (artifacts.signature != null) {
+            result.signature = artifacts.signature;
+        }
+        if (artifacts.userDefinedMetadata != null) {
+            result.userDefinedMetadata = artifacts.userDefinedMetadata;
+        }
+        if (artifacts.modelInitializer != null) {
+            result.modelInitializer = artifacts.modelInitializer;
+        }
+        if (artifacts.trainingConfig != null) {
+            result.trainingConfig = artifacts.trainingConfig;
+        }
+        return result;
+    }
+    /**
+     * Create `ModelArtifacts` from a JSON file.
+     *
+     * @param modelJSON Object containing the parsed JSON of `model.json`
+     * @param loadWeights Function that takes the JSON file's weights manifest,
+     *     reads weights from the listed path(s), and returns a Promise of the
+     *     weight manifest entries along with the weights data.
+     * @returns A Promise of the `ModelArtifacts`, as described by the JSON file.
+     */
+    function getModelArtifactsForJSON(modelJSON, loadWeights) {
+        return __awaiter(this, void 0, void 0, function () {
+            var modelArtifacts, _a, weightSpecs, weightData;
+            return __generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        modelArtifacts = {
+                            modelTopology: modelJSON.modelTopology,
+                            format: modelJSON.format,
+                            generatedBy: modelJSON.generatedBy,
+                            convertedBy: modelJSON.convertedBy
+                        };
+                        if (modelJSON.trainingConfig != null) {
+                            modelArtifacts.trainingConfig = modelJSON.trainingConfig;
+                        }
+                        if (!(modelJSON.weightsManifest != null)) return [3 /*break*/, 2];
+                        return [4 /*yield*/, loadWeights(modelJSON.weightsManifest)];
+                    case 1:
+                        _a = __read.apply(void 0, [_b.sent(), 2]), weightSpecs = _a[0], weightData = _a[1];
+                        modelArtifacts.weightSpecs = weightSpecs;
+                        modelArtifacts.weightData = weightData;
+                        _b.label = 2;
+                    case 2:
+                        if (modelJSON.signature != null) {
+                            modelArtifacts.signature = modelJSON.signature;
+                        }
+                        if (modelJSON.userDefinedMetadata != null) {
+                            modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata;
+                        }
+                        if (modelJSON.modelInitializer != null) {
+                            modelArtifacts.modelInitializer = modelJSON.modelInitializer;
+                        }
+                        return [2 /*return*/, modelArtifacts];
+                }
+            });
+        });
+    }
+    /**
+     * 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); };
+
+    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); };
+            });
+        };
+        return BrowserIndexedDB;
+    }());
+    BrowserIndexedDB.URL_SCHEME = 'indexeddb://';
+    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$1(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 e_1, _a;
+                                    var out = {};
+                                    try {
+                                        for (var _b = __values(getAllInfoRequest.result), _c = _b.next(); !_c.done; _c = _b.next()) {
+                                            var item = _c.value;
+                                            out[item.modelPath] = item.modelArtifactsInfo;
+                                        }
+                                    }
+                                    catch (e_1_1) { e_1 = { error: e_1_1 }; }
+                                    finally {
+                                        try {
+                                            if (_c && !_c.done && (_a = _b.return)) _a.call(_b);
+                                        }
+                                        finally { if (e_1) throw e_1.error; }
+                                    }
+                                    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$1(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;
+    }());
+
+    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)
+        };
+    }
+    function removeItems(keys) {
+        var e_1, _a;
+        try {
+            for (var _b = __values(Object.values(keys)), _c = _b.next(); !_c.done; _c = _b.next()) {
+                var key = _c.value;
+                window.localStorage.removeItem(key);
+            }
+        }
+        catch (e_1_1) { e_1 = { error: e_1_1 }; }
+        finally {
+            try {
+                if (_c && !_c.done && (_a = _b.return)) _a.call(_b);
+            }
+            finally { if (e_1) throw e_1.error; }
+        }
+    }
+    /**
+     * 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(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, metadata;
+                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));
+                            metadata = {
+                                format: modelArtifacts.format,
+                                generatedBy: modelArtifacts.generatedBy,
+                                convertedBy: modelArtifacts.convertedBy,
+                                signature: modelArtifacts.signature != null ?
+                                    modelArtifacts.signature :
+                                    undefined,
+                                userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ?
+                                    modelArtifacts.userDefinedMetadata :
+                                    undefined,
+                                modelInitializer: modelArtifacts.modelInitializer != null ?
+                                    modelArtifacts.modelInitializer :
+                                    undefined,
+                                trainingConfig: modelArtifacts.trainingConfig != null ?
+                                    modelArtifacts.trainingConfig :
+                                    undefined
+                            };
+                            this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata));
+                            return [2 /*return*/, { modelArtifactsInfo: modelArtifactsInfo }];
+                        }
+                        catch (err) {
+                            // If saving failed, clean up all items saved so far.
+                            removeItems(this.keys);
+                            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;
+                        }
+                        if (metadata.trainingConfig != null) {
+                            out.trainingConfig = metadata.trainingConfig;
+                        }
+                    }
+                    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];
+                });
+            });
+        };
+        return BrowserLocalStorage;
+    }());
+    BrowserLocalStorage.URL_SCHEME = 'localstorage://';
+    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(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));
+                    removeItems(keys);
+                    return [2 /*return*/, info];
+                });
+            });
+        };
+        return BrowserLocalStorageManager;
+    }());
+
+    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, schemes_1, schemes_1_1, scheme, schemeOut, path, url, e_1_1;
+            var e_1, _a;
+            return __generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        schemes = ModelStoreManagerRegistry.getSchemes();
+                        out = {};
+                        _b.label = 1;
+                    case 1:
+                        _b.trys.push([1, 6, 7, 8]);
+                        schemes_1 = __values(schemes), schemes_1_1 = schemes_1.next();
+                        _b.label = 2;
+                    case 2:
+                        if (!!schemes_1_1.done) return [3 /*break*/, 5];
+                        scheme = schemes_1_1.value;
+                        return [4 /*yield*/, ModelStoreManagerRegistry.getManager(scheme).listModels()];
+                    case 3:
+                        schemeOut = _b.sent();
+                        for (path in schemeOut) {
+                            url = scheme + URL_SCHEME_SUFFIX + path;
+                            out[url] = schemeOut[path];
+                        }
+                        _b.label = 4;
+                    case 4:
+                        schemes_1_1 = schemes_1.next();
+                        return [3 /*break*/, 2];
+                    case 5: return [3 /*break*/, 8];
+                    case 6:
+                        e_1_1 = _b.sent();
+                        e_1 = { error: e_1_1 };
+                        return [3 /*break*/, 8];
+                    case 7:
+                        try {
+                            if (schemes_1_1 && !schemes_1_1.done && (_a = schemes_1.return)) _a.call(schemes_1);
+                        }
+                        finally { if (e_1) throw e_1.error; }
+                        return [7 /*endfinally*/];
+                    case 8: 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 = {
+        buffer: buffer,
+        cast: cast,
+        clone: clone,
+        print: print
+    };
+    setOpHandler(opHandler);
+
+    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.modelJsonFileName = 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, modelJSON, modelJsonURL, 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
+                                }];
+                            modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);
+                            modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: 'application/json' }));
+                            jsonAnchor_1 = this.modelJsonAnchor == null ?
+                                document.createElement('a') :
+                                this.modelJsonAnchor;
+                            jsonAnchor_1.download = this.modelJsonFileName;
+                            jsonAnchor_1.href = modelJsonURL;
+                            // 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) }];
+                    }
+                });
+            });
+        };
+        return BrowserDownloads;
+    }());
+    BrowserDownloads.URL_SCHEME = 'downloads://';
+    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.jsonFile = files[0];
+            this.weightsFiles = files.slice(1);
+        }
+        BrowserFiles.prototype.load = 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 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 " + _this.jsonFile.name));
+                                    return;
+                                }
+                                var weightsManifest = modelJSON.weightsManifest;
+                                if (weightsManifest == null) {
+                                    reject(new Error("weightManifest field is missing from file " + _this.jsonFile.name));
+                                    return;
+                                }
+                                if (_this.weightsFiles.length === 0) {
+                                    resolve({ modelTopology: modelTopology });
+                                    return;
+                                }
+                                var modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, function (weightsManifest) { return _this.loadWeights(weightsManifest); });
+                                resolve(modelArtifactsPromise);
+                            };
+                            jsonReader.onerror = function (error) { return reject("Failed to read model topology and weights manifest JSON " +
+                                ("from file '" + _this.jsonFile.name + "'. BrowserFiles supports loading ") +
+                                "Keras-style tf.Model artifacts only."); };
+                            jsonReader.readAsText(_this.jsonFile);
+                        })];
+                });
+            });
+        };
+        BrowserFiles.prototype.loadWeights = function (weightsManifest) {
+            var e_1, _a;
+            var _this = this;
+            var weightSpecs = [];
+            var paths = [];
+            try {
+                for (var weightsManifest_1 = __values(weightsManifest), weightsManifest_1_1 = weightsManifest_1.next(); !weightsManifest_1_1.done; weightsManifest_1_1 = weightsManifest_1.next()) {
+                    var entry = weightsManifest_1_1.value;
+                    weightSpecs.push.apply(weightSpecs, __spread(entry.weights));
+                    paths.push.apply(paths, __spread(entry.paths));
+                }
+            }
+            catch (e_1_1) { e_1 = { error: e_1_1 }; }
+            finally {
+                try {
+                    if (weightsManifest_1_1 && !weightsManifest_1_1.done && (_a = weightsManifest_1.return)) _a.call(weightsManifest_1);
+                }
+                finally { if (e_1) throw e_1.error; }
+            }
+            var pathToFile = this.checkManifestAndWeightFiles(weightsManifest);
+            var promises = paths.map(function (path) { return _this.loadWeightsFile(path, pathToFile[path]); });
+            return Promise.all(promises).then(function (buffers) { return [weightSpecs, concatenateArrayBuffers(buffers)]; });
+        };
+        BrowserFiles.prototype.loadWeightsFile = function (path, file) {
+            return new Promise(function (resolve, reject) {
+                var weightFileReader = new FileReader();
+                weightFileReader.onload = function (event) {
+                    // tslint:disable-next-line:no-any
+                    var weightData = event.target.result;
+                    resolve(weightData);
+                };
+                weightFileReader.onerror = function (error) { return reject("Failed to weights data from file of path '" + path + "'."); };
+                weightFileReader.readAsArrayBuffer(file);
+            });
+        };
+        /**
+         * Check the compatibility between weights manifest and weight files.
+         */
+        BrowserFiles.prototype.checkManifestAndWeightFiles = function (manifest) {
+            var e_2, _a;
+            var _this = this;
+            var basenames = [];
+            var fileNames = this.weightsFiles.map(function (file) { return basename(file.name); });
+            var pathToFile = {};
+            try {
+                for (var manifest_1 = __values(manifest), manifest_1_1 = manifest_1.next(); !manifest_1_1.done; manifest_1_1 = manifest_1.next()) {
+                    var group = manifest_1_1.value;
+                    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] = _this.weightsFiles[fileNames.indexOf(pathBasename)];
+                        }
+                    });
+                }
+            }
+            catch (e_2_1) { e_2 = { error: e_2_1 }; }
+            finally {
+                try {
+                    if (manifest_1_1 && !manifest_1_1.done && (_a = manifest_1.return)) _a.call(manifest_1);
+                }
+                finally { if (e_2) throw e_2.error; }
+            }
+            if (basenames.length !== this.weightsFiles.length) {
+                throw new Error("Mismatch in the number of files in weights manifest " +
+                    ("(" + basenames.length + ") and the number of weight files provided ") +
+                    ("(" + this.weightsFiles.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));
+    }
+
+    /**
+     * 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 in nameToTensorMap) {
+                                        weightsTensorMap[name] = nameToTensorMap[name];
+                                    }
+                                });
+                                bufferIndexOffset += numBuffers;
+                            });
+                            return [2 /*return*/, weightsTensorMap];
+                    }
+                });
+            });
+        };
+    }
+
+    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 = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);
+                            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, modelJSON, message, modelTopology, weightsManifest;
+                var _this = this;
+                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:
+                            modelJSON = _a.sent();
+                            return [3 /*break*/, 5];
+                        case 4:
+                            _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 = modelJSON.modelTopology;
+                            weightsManifest = modelJSON.weightsManifest;
+                            if (modelTopology == null && weightsManifest == null) {
+                                throw new Error("The JSON from HTTP path " + this.path + " contains neither model " +
+                                    "topology or manifest for weights.");
+                            }
+                            return [2 /*return*/, getModelArtifactsForJSON(modelJSON, function (weightsManifest) { return _this.loadWeights(weightsManifest); })];
+                    }
+                });
+            });
+        };
+        HTTPRequest.prototype.loadWeights = function (weightsManifest) {
+            return __awaiter(this, void 0, void 0, function () {
+                var weightPath, _a, prefix, suffix, pathPrefix, weightSpecs, weightsManifest_1, weightsManifest_1_1, entry, fetchURLs, urlPromises, weightsManifest_2, weightsManifest_2_1, weightsGroup, _b, _c, path, _d, _e, _f, buffers;
+                var e_2, _g, e_3, _h, e_4, _j;
+                return __generator(this, function (_k) {
+                    switch (_k.label) {
+                        case 0:
+                            weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
+                            _a = __read(parseUrl(weightPath), 2), prefix = _a[0], suffix = _a[1];
+                            pathPrefix = this.weightPathPrefix || prefix;
+                            weightSpecs = [];
+                            try {
+                                for (weightsManifest_1 = __values(weightsManifest), weightsManifest_1_1 = weightsManifest_1.next(); !weightsManifest_1_1.done; weightsManifest_1_1 = weightsManifest_1.next()) {
+                                    entry = weightsManifest_1_1.value;
+                                    weightSpecs.push.apply(weightSpecs, __spread(entry.weights));
+                                }
+                            }
+                            catch (e_2_1) { e_2 = { error: e_2_1 }; }
+                            finally {
+                                try {
+                                    if (weightsManifest_1_1 && !weightsManifest_1_1.done && (_g = weightsManifest_1.return)) _g.call(weightsManifest_1);
+                                }
+                                finally { if (e_2) throw e_2.error; }
+                            }
+                            fetchURLs = [];
+                            urlPromises = [];
+                            try {
+                                for (weightsManifest_2 = __values(weightsManifest), weightsManifest_2_1 = weightsManifest_2.next(); !weightsManifest_2_1.done; weightsManifest_2_1 = weightsManifest_2.next()) {
+                                    weightsGroup = weightsManifest_2_1.value;
+                                    try {
+                                        for (_b = (e_4 = void 0, __values(weightsGroup.paths)), _c = _b.next(); !_c.done; _c = _b.next()) {
+                                            path = _c.value;
+                                            if (this.weightUrlConverter != null) {
+                                                urlPromises.push(this.weightUrlConverter(path));
+                                            }
+                                            else {
+                                                fetchURLs.push(pathPrefix + path + suffix);
+                                            }
+                                        }
+                                    }
+                                    catch (e_4_1) { e_4 = { error: e_4_1 }; }
+                                    finally {
+                                        try {
+                                            if (_c && !_c.done && (_j = _b.return)) _j.call(_b);
+                                        }
+                                        finally { if (e_4) throw e_4.error; }
+                                    }
+                                }
+                            }
+                            catch (e_3_1) { e_3 = { error: e_3_1 }; }
+                            finally {
+                                try {
+                                    if (weightsManifest_2_1 && !weightsManifest_2_1.done && (_h = weightsManifest_2.return)) _h.call(weightsManifest_2);
+                                }
+                                finally { if (e_3) throw e_3.error; }
+                            }
+                            if (!this.weightUrlConverter) return [3 /*break*/, 2];
+                            _e = (_d = fetchURLs.push).apply;
+                            _f = [fetchURLs];
+                            return [4 /*yield*/, Promise.all(urlPromises)];
+                        case 1:
+                            _e.apply(_d, _f.concat([__spread.apply(void 0, [_k.sent()])]));
+                            _k.label = 2;
+                        case 2: return [4 /*yield*/, loadWeightsAsArrayBuffer(fetchURLs, {
+                                requestInit: this.requestInit,
+                                fetchFunc: this.fetch,
+                                onProgress: this.onProgress
+                            })];
+                        case 3:
+                            buffers = _k.sent();
+                            return [2 /*return*/, [weightSpecs, concatenateArrayBuffers(buffers)]];
+                    }
+                });
+            });
+        };
+        return HTTPRequest;
+    }());
+    HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
+    /**
+     * 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,
+        getModelArtifactsForJSON: getModelArtifactsForJSON,
+        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
+    };
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'matMul');
+        _a = __read(makeTypesMatch($a, $b), 2), $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$1 = 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$1(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 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;
+    }
+
+    var broadcast_util = {
+        __proto__: null,
+        getBroadcastDims: getBroadcastDims,
+        getReductionAxes: getReductionAxes,
+        assertAndGetBroadcastShape: assertAndGetBroadcastShape
+    };
+
+    /**
+     * @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);
+    }
+
+    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]`.
+     *
+     * Note: fromPixels can be lossy in some cases, same image may result in
+     * slightly different tensor values, if rendered by different rendering
+     * engines. This means that results from different browsers, or even same
+     * browser with CPU and GPU rendering engines can be different. See discussion
+     * in details:
+     * https://github.com/tensorflow/tfjs/issues/5482
+     *
+     * @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 = __read(isVideo ?
+            [
+                pixels.videoWidth,
+                pixels.videoHeight
+            ] :
+            [pixels.width, pixels.height], 2), 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) {
+                if (typeof document === 'undefined') {
+                    if (typeof OffscreenCanvas !== 'undefined' &&
+                        typeof OffscreenCanvasRenderingContext2D !== 'undefined') {
+                        // @ts-ignore
+                        fromPixels2DContext = new OffscreenCanvas(1, 1).getContext('2d');
+                    }
+                    else {
+                        throw new Error('Cannot parse input in current context. ' +
+                            'Reason: OffscreenCanvas Context2D rendering is not supported.');
+                    }
+                }
+                else {
+                    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;
+            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:
+                        _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 " + $img.rank + ".");
+                        }
+                        _a = __read($img.shape.slice(0, 2), 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 = __spread(computeStrides(tensor.shape).map(function (stride) { return stride / sliceSize; }), [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$1(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 = __spread(computeStrides(shape.slice(0, sliceRank)), [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$1,
+        calculateShapes: calculateShapes
+    };
+
+    var NEW_AXIS = -2;
+    var SHRINK_AXIS = -1;
+    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$2(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 = __spread(strides);
+        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 = __spread(inputShape);
+        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 = __spread(inputShape);
+        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_ = __spread([begin], 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_ = __spread([size], 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_];
+    }
+    // Convert the slicing specification from a sparse representation to a dense
+    // representation. This means that all ellipses and newaxis are expanded out.
+    function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+        var stridesNonNull;
+        if (strides == null) {
+            stridesNonNull = new Array(begin.length);
+            stridesNonNull.fill(1);
+        }
+        else {
+            stridesNonNull = strides;
+        }
+        // Only one non-zero bit is allowed in ellipsisMask, which means ellipsisMask
+        // is a power of 2. Use bit compares to ensure ellipsisMask is 0 or a power
+        // of 2. When i is a power of 2, i & (i - 1) is always 0.
+        // Also ref:
+        // https://stackoverflow.com/questions/600293/how-to-check-if-a-number-is-a-power-of-2
+        if (ellipsisMask != null && (ellipsisMask & (ellipsisMask - 1)) !== 0) {
+            throw new Error('Multiple ellipses in slice is not allowed.');
+        }
+        // Step 1: Account for ellipsis and new axis.
+        // Check for ellipsis and count how many non-newaxis there are after.
+        var ellipsisSeen = false;
+        var sparseSpec = {
+            dims: stridesNonNull.length,
+            numAddAxisAfterEllipsis: 0,
+            begin: begin.slice(),
+            end: end.slice(),
+            strides: stridesNonNull.slice(),
+            beginMask: beginMask,
+            endMask: endMask,
+            ellipsisMask: ellipsisMask,
+            newAxisMask: newAxisMask,
+            shrinkAxisMask: shrinkAxisMask
+        };
+        for (var i = 0; i < sparseSpec.dims; i++) {
+            if (ellipsisSeen && ((1 << i) & newAxisMask) !== 0) {
+                sparseSpec.numAddAxisAfterEllipsis++;
+            }
+            if ((1 << i) & ellipsisMask) {
+                ellipsisSeen = true;
+            }
+        }
+        // If no ellipsis insert one at the end.
+        if (!ellipsisSeen) {
+            sparseSpec.ellipsisMask |= (1 << sparseSpec.dims);
+            sparseSpec.dims++; // this effects loop iteration below
+        }
+        // Step 2: Make a sparse spec into a full index spec.
+        //
+        // The sparse spec deos not correspond to the number of dimensions.
+        // Make a dense spec that cooresponds to the number of dimensions.
+        //
+        // For example suppose foo[...,3:] on foo.shape = [2, 2, 3] then we need to
+        // produce the missing beginMask for the first two dimensions i.e. from
+        // beginMaskSpec = 0, endMaskSpec = 2, we achieve beginMask = 6 (110),
+        // endMask = 7 (111).
+        var denseSpec = {
+            dims: xShape.length,
+            beginMask: 0,
+            endMask: 0,
+            beginValid: false,
+            endValid: false
+        };
+        buildDenseSpec(sparseSpec, denseSpec);
+        // Step 3: Make implicit ranges (non-zero beginMasks and endMasks) explicit
+        // and bounds check.
+        var isIdentity = true;
+        var sliceDim0 = true;
+        var isSimpleSlice = true;
+        var processingShape = [];
+        var finalShape = [];
+        for (var i = 0; i < xShape.length; ++i) {
+            if (denseSpec.strides[i] === 0) {
+                throw Error("strides[" + i + "] must be non-zero");
+            }
+            var shrinkI = !!(denseSpec.shrinkAxisMask & (1 << i));
+            var dimI = xShape[i];
+            if (dimI === -1) {
+                processingShape.push(shrinkI ? 1 : -1);
+                continue;
+            }
+            var masks = [denseSpec.beginMask & (1 << i), denseSpec.endMask & (1 << i)];
+            var validRange = [
+                denseSpec.strides[i] > 0 ? 0 : -1,
+                denseSpec.strides[i] > 0 ? dimI : dimI - 1
+            ];
+            if (shrinkI && denseSpec.strides[i] <= 0) {
+                throw Error('only stride 1 allowed on non-range indexing.');
+            }
+            isSimpleSlice = isSimpleSlice && (denseSpec.strides[i] === 1);
+            var beginAndEndMasked = !!((denseSpec.beginMask & (1 << i)) && (denseSpec.endMask & (1 << i)));
+            if (denseSpec.beginValid && denseSpec.endValid) {
+                if (shrinkI) {
+                    // If we are shrinking, the end index is now possibly incorrect. In
+                    // particular foo[-1] produces sparseBegin = -1, sparseEnd = 0.
+                    // and canonical puts these to n-1 and 0, which implies a degenerate
+                    // interval. Fortunately, it is now safe to re-create end as begin + 1.
+                    var xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] :
+                        denseSpec.begin[i];
+                    denseSpec.begin[i] = xFwd;
+                    denseSpec.end[i] = denseSpec.begin[i] + 1;
+                    if (xFwd < 0 || xFwd >= dimI) {
+                        throw Error("slice index " + denseSpec.begin[i] + " of dimension " + i + " out of bounds.");
+                    }
+                }
+                else {
+                    denseSpec.begin[i] = canonical(denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks, validRange);
+                    denseSpec.end[i] = canonical(denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange);
+                }
+                // Update optimization values
+                var takeAllInDimension = denseSpec.strides[i] === 1 &&
+                    denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI;
+                isIdentity = isIdentity && takeAllInDimension;
+                sliceDim0 = sliceDim0 &&
+                    ((i === 0 && denseSpec.strides[i] === 1) || takeAllInDimension);
+            }
+            else {
+                isIdentity =
+                    isIdentity && ((denseSpec.strides[i] === 1) && beginAndEndMasked);
+                sliceDim0 = sliceDim0 &&
+                    ((i === 0 && denseSpec.strides[i] === 1) || beginAndEndMasked);
+            }
+            // Compute the processing shape (the intermediate Eigen will produce)
+            var intervalLength = void 0;
+            var knownInterval = false;
+            if (denseSpec.beginValid && denseSpec.endValid) {
+                intervalLength = denseSpec.end[i] - denseSpec.begin[i];
+                knownInterval = true;
+            }
+            else if (shrinkI) {
+                // The dimension is still known as 1 for the processingShape, but will be
+                // discarded for the final shape.
+                intervalLength = 1;
+                knownInterval = true;
+            }
+            else if (beginAndEndMasked) {
+                // Even if we don't have values for begin or end, we do know that this
+                // dimension covers the whole interval. If we have shape information for
+                // this dimension, that tells us the interval length.
+                if (dimI >= 0) {
+                    if (denseSpec.strides[i] < 0) {
+                        intervalLength = -dimI;
+                    }
+                    else {
+                        intervalLength = dimI;
+                    }
+                    knownInterval = true;
+                }
+            }
+            if (knownInterval) {
+                var sizeI = void 0;
+                // Hold zero if the interval is degenerate, otherwise account for
+                // remainder
+                if (intervalLength === 0 ||
+                    ((intervalLength < 0) !== (denseSpec.strides[i] < 0))) {
+                    sizeI = 0;
+                }
+                else {
+                    sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) +
+                        (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0);
+                }
+                processingShape.push(sizeI);
+            }
+            else {
+                processingShape.push(-1);
+            }
+        }
+        // Step 4: Compute the final shape
+        //
+        // newAxis will increase dimension by 1 (with a one-size dimension)
+        // slices like foo[3, ...] will reduce dimension by 1.
+        // This cannot be done earlier, because it depends on Step 3.
+        for (var denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim) {
+            var gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];
+            if (gatherIndex >= 0) {
+                finalShape.push(processingShape[gatherIndex]);
+            }
+            else if (gatherIndex === NEW_AXIS) {
+                finalShape.push(1);
+            }
+        }
+        var finalShapeSparse = finalShape.filter(function (dim, i) { return denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS; });
+        return {
+            finalShapeSparse: finalShapeSparse,
+            finalShape: finalShape,
+            isIdentity: isIdentity,
+            sliceDim0: sliceDim0,
+            isSimpleSlice: isSimpleSlice,
+            begin: denseSpec.begin,
+            end: denseSpec.end,
+            strides: denseSpec.strides
+        };
+    }
+    function buildDenseSpec(sparse, dense) {
+        dense.beginMask = 0;
+        dense.endMask = 0;
+        dense.shrinkAxisMask = 0;
+        var fullIndex = 0;
+        dense.beginValid = sparse.begin != null;
+        dense.endValid = sparse.end != null;
+        dense.begin = new Array(dense.dims);
+        dense.end = new Array(dense.dims);
+        dense.strides = new Array(dense.dims);
+        dense.finalShapeGatherIndices = [];
+        dense.finalShapeGatherIndicesSparse = [];
+        dense.inputShapeGatherIndicesSparse = new Array(dense.dims);
+        for (var i = 0; i < sparse.dims; i++) {
+            if ((1 << i) & sparse.ellipsisMask) {
+                // Only the bit that has ellipsis will fall in this condition.
+                // Expand the ellipsis into the appropriate indices
+                // Note: this only works because we guaranteed one ellipsis.
+                var nextIndex = Math.min(dense.dims - (sparse.dims - i) + 1 + sparse.numAddAxisAfterEllipsis, dense.dims);
+                for (; fullIndex < nextIndex; fullIndex++) {
+                    // newAxis aren't real axis so you have to skip.
+                    dense.begin[fullIndex] = 0;
+                    dense.end[fullIndex] = 0;
+                    dense.strides[fullIndex] = 1;
+                    dense.beginMask |= (1 << fullIndex);
+                    dense.endMask |= (1 << fullIndex);
+                    dense.finalShapeGatherIndices.push(fullIndex);
+                    dense.finalShapeGatherIndicesSparse.push(-1);
+                    dense.inputShapeGatherIndicesSparse[fullIndex] = i;
+                }
+            }
+            else if ((1 << i) & sparse.newAxisMask) {
+                // Only the bit that has newAxis will fall in this condition.
+                dense.finalShapeGatherIndices.push(NEW_AXIS);
+                dense.finalShapeGatherIndicesSparse.push(-1);
+            }
+            else {
+                if (fullIndex === dense.begin.length) {
+                    throw Error("Index out of range using input dim " + fullIndex + "; input " +
+                        ("has only " + dense.dims + " dims, " + dense.begin.length + "."));
+                }
+                // Gather slicing spec into appropriate index.
+                if (sparse.begin != null) {
+                    dense.begin[fullIndex] = sparse.begin[i];
+                }
+                if (sparse.end != null) {
+                    dense.end[fullIndex] = sparse.end[i];
+                }
+                dense.strides[fullIndex] = sparse.strides[i];
+                if (sparse.beginMask & (1 << i)) {
+                    dense.beginMask |= (1 << fullIndex);
+                }
+                if (sparse.endMask & (1 << i)) {
+                    dense.endMask |= (1 << fullIndex);
+                }
+                // If shrink, record where to get the dimensionality from (i.e. newAxis)
+                // creates a fake 1 size dimension. Also remember shrink axis (now in
+                // dense form) so we can ignore dense.end below.
+                if (sparse.shrinkAxisMask & (1 << i)) {
+                    dense.finalShapeGatherIndices.push(SHRINK_AXIS);
+                    dense.finalShapeGatherIndicesSparse.push(-1);
+                    dense.shrinkAxisMask |= (1 << fullIndex);
+                }
+                else {
+                    dense.finalShapeGatherIndices.push(fullIndex);
+                    // Remember that where in the sparse shape the dense dim comes from.
+                    dense.finalShapeGatherIndicesSparse.push(i);
+                }
+                dense.inputShapeGatherIndicesSparse[fullIndex] = i;
+                fullIndex++;
+            }
+        }
+    }
+    function canonical(x, c, strideI, dimI, masks, validRange) {
+        if (masks[c]) {
+            return strideI > 0 ? validRange[c] : validRange[(c + 1) & 1];
+        }
+        else {
+            var xFwd = x < 0 ? dimI + x : x; // make negative indices positive
+            return xFwd < validRange[0] ? validRange[0] :
+                xFwd > validRange[1] ? validRange[1] : xFwd;
+        }
+    }
+
+    var slice_util = {
+        __proto__: null,
+        assertParamsValid: assertParamsValid,
+        maskToAxes: maskToAxes,
+        computeOutShape: computeOutShape$2,
+        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 = '3.12.0';
+
+    /**
+     * @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);
+    }
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'add');
+        _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1];
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(Add, inputs);
+    }
+    var add = op({ add_: add_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'floorDiv');
+        _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1];
+        var inputs = { a: $a, b: $b };
+        return ENGINE.runKernel(FloorDiv, inputs);
+    }
+    var floorDiv = op({ floorDiv_: floorDiv_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'div');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'mul');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'atan2');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     *
+     * @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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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 = __spread(filterShape, [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 = __read(parseTupleParam(filterSize), 2), 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 = __read(parse3TupleParam(filterSize), 3), 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) {
+        var _a, _b;
+        if (depthwise === void 0) { depthwise = false; }
+        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+        var _c = __read([-1, -1, -1, -1], 4), batchSize = _c[0], inHeight = _c[1], inWidth = _c[2], inChannels = _c[3];
+        if (dataFormat === 'channelsLast') {
+            _a = __read(inShape, 4), batchSize = _a[0], inHeight = _a[1], inWidth = _a[2], inChannels = _a[3];
+        }
+        else if (dataFormat === 'channelsFirst') {
+            _b = __read(inShape, 4), batchSize = _b[0], inChannels = _b[1], inHeight = _b[2], inWidth = _b[3];
+        }
+        else {
+            throw new Error("Unknown dataFormat " + dataFormat);
+        }
+        var _d = __read(filterShape, 4), filterHeight = _d[0], filterWidth = _d[1], filterChannels = _d[3];
+        var _e = __read(parseTupleParam(strides), 2), strideHeight = _e[0], strideWidth = _e[1];
+        var _f = __read(parseTupleParam(dilations), 2), dilationHeight = _f[0], dilationWidth = _f[1];
+        var effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+        var effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+        var _g = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat), padInfo = _g.padInfo, outHeight = _g.outHeight, outWidth = _g.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) {
+        var _a, _b;
+        if (depthwise === void 0) { depthwise = false; }
+        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+        var _c = __read([-1, -1, -1, -1, -1], 5), batchSize = _c[0], inDepth = _c[1], inHeight = _c[2], inWidth = _c[3], inChannels = _c[4];
+        if (dataFormat === 'channelsLast') {
+            _a = __read(inShape, 5), batchSize = _a[0], inDepth = _a[1], inHeight = _a[2], inWidth = _a[3], inChannels = _a[4];
+        }
+        else if (dataFormat === 'channelsFirst') {
+            _b = __read(inShape, 5), batchSize = _b[0], inChannels = _b[1], inDepth = _b[2], inHeight = _b[3], inWidth = _b[4];
+        }
+        else {
+            throw new Error("Unknown dataFormat " + dataFormat);
+        }
+        var _d = __read(filterShape, 5), filterDepth = _d[0], filterHeight = _d[1], filterWidth = _d[2], filterChannels = _d[4];
+        var _e = __read(parse3TupleParam(strides), 3), strideDepth = _e[0], strideHeight = _e[1], strideWidth = _e[2];
+        var _f = __read(parse3TupleParam(dilations), 3), dilationDepth = _f[0], dilationHeight = _f[1], dilationWidth = _f[2];
+        var effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
+        var effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+        var effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+        var _g = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode), padInfo = _g.padInfo, outDepth = _g.outDepth, outHeight = _g.outHeight, outWidth = _g.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$1((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+        var outputCols = round$1((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$1((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+        var outputRows = round$1((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+        var outputCols = round$1((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 = Math.floor(padAlongHeight / 2);
+            var bottom = padAlongHeight - top;
+            var left = Math.floor(padAlongWidth / 2);
+            var right = padAlongWidth - left;
+            padInfo = { top: top, 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 = 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 === 0 && bottom === 0 && left === 0 && right === 0) ?
+                'VALID' :
+                'EXPLICIT';
+            padInfo = { top: top, bottom: bottom, left: left, right: right, type: padType };
+            outHeight = round$1((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);
+            outWidth = round$1((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 = Math.floor(padAlongHeight / 2);
+            var bottom = padAlongHeight - top;
+            var left = Math.floor(padAlongWidth / 2);
+            var right = padAlongWidth - left;
+            padInfo = { top: top, 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$1(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 = __read(parseTupleParam(param), 3), 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);
+        }
+    }
+    /**
+     * Check validity of pad when using dimRoundingMode.
+     * @param opDesc A string of op description
+     * @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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     * @throws unknown padding parameter
+     */
+    function checkPadOnDimRoundingMode(opDesc, pad, dimRoundingMode) {
+        if (dimRoundingMode != null) {
+            if (typeof pad === 'string') {
+                throw Error("Error in " + opDesc + ": pad must be an integer when using " +
+                    ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+            }
+            else if (typeof pad === 'number') {
+                assert(isInt(pad), function () { return "Error in " + opDesc + ": pad must be an integer when using " +
+                    ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
+            }
+            else if (typeof pad === 'object') {
+                pad.forEach(function (p) {
+                    p.forEach(function (v) {
+                        assert(isInt(v), function () { return "Error in " + opDesc + ": pad must be an integer when using " +
+                            ("dimRoundingMode " + dimRoundingMode + " but got pad " + v + "."); });
+                    });
+                });
+            }
+            else {
+                throw Error("Error in " + opDesc + ": Unknown padding parameter: " + pad);
+            }
+        }
+    }
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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_docs/python/tf/nn/convolution](
+     *         https://www.tensorflow.org/api_docs/python/tf/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 + "."; });
+        checkPadOnDimRoundingMode('avgPool', pad, dimRoundingMode);
+        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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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); });
+        checkPadOnDimRoundingMode('avgPool3d', pad, dimRoundingMode);
+        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', 'float32');
+        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', 'float32');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Tanh, inputs);
+    }
+    var tanh = 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$1(combined, $lstmKernel);
+        var res = add(weighted, $lstmBias);
+        // i = input_gate, j = new_input, f = forget_gate, o = output_gate
+        var batchSize = res.shape[0];
+        var sliceCols = res.shape[1] / 4;
+        var sliceSize = [batchSize, sliceCols];
+        var i = slice(res, [0, 0], sliceSize);
+        var j = slice(res, [0, sliceCols], sliceSize);
+        var f = slice(res, [0, sliceCols * 2], sliceSize);
+        var o = slice(res, [0, sliceCols * 3], sliceSize);
+        var newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f))));
+        var newH = mul(tanh(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 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.
+     * =============================================================================
+     */
+    /**
+     * Return the shape of s0 op s1 with broadcast.
+     *
+     * compute r0, the broadcasted shape as a tensor.
+     * s0, s1 and r0 are all integer vectors.
+     *
+     * This function returns the shape of the result of an operation between
+     * two tensors of size s0 and s1 performed with broadcast.
+     *
+     * @param s0 A tensor representing a shape
+     * @param s1 A tensor representing a shape
+     *
+     * @doc {heading: 'Tensors', subheading: 'Transformations'}
+     */
+    function broadcastArgs_(s0, s1) {
+        var shape1Input = convertToTensor(s0, 's0', 'broadcastArgs', 'int32');
+        var shape2Input = convertToTensor(s1, 's1', 'broadcastArgs', 'int32');
+        if (shape1Input.rank !== 1) {
+            throw new Error('broadcastArgs(): first input must be a vector (rank=1). ' +
+                ("Has rank " + shape1Input.rank));
+        }
+        if (shape2Input.rank !== 1) {
+            throw new Error('broadcastArgs(): second input must be a vector (rank=1). ' +
+                ("Has rank " + shape2Input.rank));
+        }
+        var inputs = { s0: shape1Input, s1: shape2Input };
+        return ENGINE.runKernel(BroadcastArgs, inputs);
+    }
+    var broadcastArgs = op({ broadcastArgs_: broadcastArgs_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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', 'float32');
+        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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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', 'float32');
+        var $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');
+        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 + "."); });
+        checkPadOnDimRoundingMode('conv2d', pad, dimRoundingMode);
+        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$1 = 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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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 + "."); });
+        checkPadOnDimRoundingMode('conv1d', pad, dimRoundingMode);
+        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$1(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] + "."); });
+        checkPadOnDimRoundingMode('conv2dDerInput', pad, dimRoundingMode);
+        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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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. Must be float32 type.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function cos_(x) {
+        var $x = convertToTensor(x, 'x', 'cos', 'float32');
+        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. Must be float32 type.
+     *
+     * @doc {heading: 'Operations', subheading: 'Basic math'}
+     */
+    function cosh_(x) {
+        var $x = convertToTensor(x, 'x', 'cosh', 'float32');
+        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', 'float32');
+        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(blockSize > 1, function () { return "blockSize should be > 1 for depthToSpace, but was: " + blockSize; });
+        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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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', 'float32');
+        var $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');
+        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] + "."); });
+        checkPadOnDimRoundingMode('depthwiseConv2d', pad, dimRoundingMode);
+        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$1 = 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.tensor2d([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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'equal', 'string_or_numeric');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'div');
+        _a = __read(makeTypesMatch($a, $b), 2), $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$1(t12D, t22D);
+            return reshape(t1t2, []);
+        }
+        else if ($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$1(t12D, t22D);
+            return reshape(t1t2, [t1t2.size]);
+        }
+        else if ($t1.rank === 2 && $t2.rank === 1) {
+            var t22D = reshape($t2, [-1, 1]);
+            var t1t2 = matMul$1($t1, t22D);
+            return reshape(t1t2, [t1t2.size]);
+        }
+        else {
+            var t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
+            var t1t2 = matMul$1($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 ? x : (e ^ x) - 1`.
+     *
+     * ```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', 'float32');
+        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', 'float32');
+        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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'greater', 'string_or_numeric');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'greaterEqual', 'string_or_numeric');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'less', 'string_or_numeric');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'lessEqual', 'string_or_numeric');
+        _a = __read(makeTypesMatch($a, $b), 2), $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', 'float32');
+        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_ });
+
+    /**
+     * 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 $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;
+            return ENGINE.tidy(function () {
+                var _a = ENGINE.gradients(function () { return f.apply(void 0, __spread($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, __spread(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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'sub');
+        _a = __read(makeTypesMatch($a, $b), 2), $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 = op({ sum_: sum_ });
+
+    /**
+     * 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 " + $logits.rank + " and axis was " + axis));
+        }
+        // const forward: ForwardFunc<Tensor> = (backend, save) => {
+        //   const keepDims = true;
+        //   const xMax = max(logits, axis, true);
+        //   const shifted = sub(logits, xMax);
+        //   const value =
+        //       sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis,
+        //       keepDims)));
+        //   save([value]);
+        //   return value;
+        // };
+        // Use a custom gradient for numerical stability.
+        var customOp = customGrad(function (logits, save) {
+            var keepDims = true;
+            var xMax = max(logits, axis, true);
+            var shifted = sub(logits, xMax);
+            var value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));
+            save([value]);
+            var gradFunc = function (dy, saved) {
+                var _a = __read(saved, 1), value = _a[0];
+                var keepDims = true;
+                var softmax = exp(value);
+                return sub(dy, mul(sum(dy, axis, keepDims), softmax));
+            };
+            return { value: value, gradFunc: gradFunc };
+        });
+        return customOp($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(b, axes);
+        var d = log(c);
+        var res = add(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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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 + "'"); });
+        checkPadOnDimRoundingMode('maxPool', pad, dimRoundingMode);
+        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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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); });
+        checkPadOnDimRoundingMode('maxPool3d', pad, dimRoundingMode);
+        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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'maximum');
+        _a = __read(makeTypesMatch($a, $b), 2), $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(shape, dtype) {
+        if (dtype === void 0) { dtype = 'float32'; }
+        if (dtype === 'complex64') {
+            var real = ones(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$1(ones([h, 1], $x.dtype), $x),
+                matMul$1($y, ones([1, w], $y.dtype)),
+            ];
+        }
+        $x = reshape($x, [-1, 1]);
+        $y = reshape($y, [1, -1]);
+        return [
+            matMul$1($x, ones([1, h], $x.dtype)),
+            matMul$1(ones([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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'minimum');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'mod');
+        _a = __read(makeTypesMatch($a, $b), 2), $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_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'notEqual', 'string_or_numeric');
+        _a = __read(makeTypesMatch($a, $b), 2), $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$1(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_ });
+
+    /**
+     * 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`.
+     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
+     *     provided, it will default to truncate.
+     *
+     * @doc {heading: 'Operations', subheading: 'Convolution'}
+     */
+    function pool_(input, windowShape, poolingType, pad, dilations, strides, dimRoundingMode) {
+        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 = __read(requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding), 2), 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, dimRoundingMode); } :
+            function () { return maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode); };
+        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_ });
+
+    /**
+     * 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 $exp = convertToTensor(exp, 'exp', 'pow');
+        _a = __read(makeTypesMatch($base, $exp), 2), $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) {
+        var module = { exports: {} };
+        return fn(module, module.exports), module.exports;
+    }
+
+    var alea = createCommonjsModule(function (module) {
+        // A port of an algorithm by Johannes Baagøe <[email protected]>, 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 <[email protected]>
+        //
+        // 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 = String(data);
+                    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, X = [];
+                    if (seed === (seed | 0)) {
+                        // Seed state array using a 32-bit integer.
+                        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)
+                        X[7] = -1;
+                    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
+        );
+    });
+
+    /*
+    Copyright 2019 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.
+
+    */
+    var seedrandom$1 = createCommonjsModule(function (module) {
+        (function (global, pool, math) {
+            //
+            // The following constants are related to IEEE 754 limits.
+            //
+            var 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);
+            }
+            //
+            // 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$$0__default['default'];
+                }
+                catch (ex) { }
+            }
+            else {
+                // When included as a plain script, set up Math.seedrandom global.
+                math['seed' + rngname] = seedrandom;
+            }
+            // End anonymous scope, and pass initial values.
+        })(
+        // global: `self` in browsers (including strict mode and web workers),
+        // otherwise `this` in Node and other environments
+        (typeof self !== 'undefined') ? self : commonjsGlobal, [], // 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$1.alea = alea;
+    seedrandom$1.xor128 = xor128;
+    seedrandom$1.xorwow = xorwow;
+    seedrandom$1.xorshift7 = xorshift7;
+    seedrandom$1.xor4096 = xor4096;
+    seedrandom$1.tychei = tychei;
+    var seedrandom = seedrandom$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.
+     * =============================================================================
+     */
+    // 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.alea(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.alea(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.alea(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 = 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', 'float32');
+        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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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$1(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);
+        var pointwiseStride = 1;
+        var res = conv2d$1(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', 'float32');
+        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', 'float32');
+        var inputs = { x: $x };
+        return ENGINE.runKernel(Sqrt, inputs);
+    }
+    var sqrt = op({ sqrt_: sqrt_ });
+
+    /**
+     * 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 $b = convertToTensor(b, 'b', 'squaredDifference');
+        _a = __read(makeTypesMatch($a, $b), 2), $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', 'float32');
+        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);
+    }
+
+    /**
+     * 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 < 0) {
+            throw new Error("'k' passed to topk() must be >= 0 but got " + k);
+        }
+        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 = __read(ENGINE.runKernel(TopK, inputs, attrs), 2), 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_ });
+
+    /**
+     * 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 = __read(ENGINE.runKernel(Unique, inputs, attrs), 2), 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_;
+
+    /**
+     * 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(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(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(abs(x), axis[0]), axis[1] - 1);
+            }
+            if (p === Infinity) {
+                return max(sum(abs(x), axis[1]), axis[0]);
+            }
+            if (p === -Infinity) {
+                return min(sum(abs(x), axis[1]), axis[0]);
+            }
+            if (p === 'fro' || p === 'euclidean') {
+                // norm(x) = sqrt(sum(pow(x, 2)))
+                return sqrt(sum(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, $step)));
+        }
+        return add($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$1($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(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', $sparseValues.dtype);
+        validateInput($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(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');
+    }
+
+    /**
+     * 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 = __read([predictionsVals.length / lastDim, lastDim], 2), 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] + ")."); });
+        checkPadOnDimRoundingMode('conv2dDerFilter', pad, dimRoundingMode);
+        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(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';
+    };
+
+    /**
+     * 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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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 _b;
+        var x = _a.x, filter = _a.filter, strides = _a.strides, pad = _a.pad, _c = _a.dataFormat, dataFormat = _c === void 0 ? 'NHWC' : _c, _d = _a.dilations, dilations = _d === void 0 ? [1, 1] : _d, dimRoundingMode = _a.dimRoundingMode, bias = _a.bias, _e = _a.activation, activation = _e === void 0 ? 'linear' : _e, preluActivationWeights = _a.preluActivationWeights, leakyreluAlpha = _a.leakyreluAlpha;
+        activation = activation || 'linear';
+        if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
+            var result = conv2d$1(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
+            if (bias != null) {
+                result = add(result, bias);
+            }
+            return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
+        }
+        var $x = convertToTensor(x, 'x', 'conv2d', 'float32');
+        var $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');
+        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 + "."); });
+        checkPadOnDimRoundingMode('fused conv2d', pad, dimRoundingMode);
+        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) {
+            $bias = convertToTensor(bias, 'bias', 'fused conv2d');
+            _b = __read(makeTypesMatch($bias, $x), 1), $bias = _b[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 = __read(saved, 4), $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 = 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_ });
+
+    /**
+     * 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_docs/python/tf/nn/convolution](
+     *          https://www.tensorflow.org/api_docs/python/tf/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 _b;
+        var x = _a.x, filter = _a.filter, strides = _a.strides, pad = _a.pad, _c = _a.dataFormat, dataFormat = _c === void 0 ? 'NHWC' : _c, _d = _a.dilations, dilations = _d === void 0 ? [1, 1] : _d, dimRoundingMode = _a.dimRoundingMode, 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 = depthwiseConv2d$1(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
+            if (bias != null) {
+                result = add(result, bias);
+            }
+            return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
+        }
+        var $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32');
+        var $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');
+        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 + "'"); });
+        checkPadOnDimRoundingMode('fused depthwiseConv2d', pad, dimRoundingMode);
+        var convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
+        var $bias;
+        if (bias != null) {
+            $bias = convertToTensor(bias, 'bias', 'fused conv2d');
+            _b = __read(makeTypesMatch($bias, $x), 1), $bias = _b[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 _a = __read(saved, 4), $filter = _a[0], x4D = _a[1], y = _a[2], bias = _a[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 = op({ fusedDepthwiseConv2d_: fusedDepthwiseConv2d_ });
+
+    /**
+     * 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, _c;
+        var a = _a.a, b = _a.b, _d = _a.transposeA, transposeA = _d === void 0 ? false : _d, _e = _a.transposeB, transposeB = _e === void 0 ? false : _e, bias = _a.bias, _f = _a.activation, activation = _f === void 0 ? 'linear' : _f, preluActivationWeights = _a.preluActivationWeights, leakyreluAlpha = _a.leakyreluAlpha;
+        if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
+            var result = matMul$1(a, b, transposeA, transposeB);
+            if (bias != null) {
+                result = add(result, bias);
+            }
+            return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
+        }
+        var $a = convertToTensor(a, 'a', 'fused matMul');
+        var $b = convertToTensor(b, 'b', 'fused matMul');
+        _b = __read(makeTypesMatch($a, $b), 2), $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(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 outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));
+        var outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+        var a3D = transposeA ?
+            reshape($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) {
+            $bias = convertToTensor(bias, 'bias', 'fused matMul');
+            _c = __read(makeTypesMatch($bias, $a), 1), $bias = _c[0];
+            assertAndGetBroadcastShape(outShape, $bias.shape);
+        }
+        var $preluActivationWeights;
+        if (preluActivationWeights != null) {
+            $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');
+        }
+        var grad = function (dy, saved) {
+            var _a = __read(saved, 4), a3D = _a[0], b3D = _a[1], y = _a[2], $bias = _a[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$1(dyActivation, b3D, false, true);
+                bDer = matMul$1(a3D, dyActivation, true, false);
+            }
+            else if (!transposeA && transposeB) {
+                aDer = matMul$1(dyActivation, b3D, false, false);
+                bDer = matMul$1(dyActivation, a3D, true, false);
+            }
+            else if (transposeA && !transposeB) {
+                aDer = matMul$1(b3D, dyActivation, false, true);
+                bDer = matMul$1(a3D, dyActivation, false, false);
+            }
+            else {
+                aDer = matMul$1(b3D, dyActivation, true, true);
+                bDer = matMul$1(dyActivation, a3D, true, true);
+            }
+            if (bias != null) {
+                var biasDer = getFusedBiasGradient($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 = 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,
+        depthwiseConv2d: depthwiseConv2d,
+        matMul: matMul
+    };
+
+    /**
+     * @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 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 images from grayscale to RGB format.
+     *
+     * @param image A grayscale tensor to convert. The `image`'s last dimension must
+     *     be size 1 with at least a two-dimensional shape.
+     *
+     * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
+     */
+    function grayscaleToRGB_(image) {
+        var $image = convertToTensor(image, 'image', 'grayscaleToRGB');
+        var lastDimsIdx = $image.rank - 1;
+        var lastDims = $image.shape[lastDimsIdx];
+        assert($image.rank >= 2, function () { return 'Error in grayscaleToRGB: images must be at least rank 2, ' +
+            ("but got rank " + $image.rank + "."); });
+        assert(lastDims === 1, function () { return 'Error in grayscaleToRGB: last dimension of a grayscale image ' +
+            ("should be size 1, but got size " + lastDims + "."); });
+        var reps = new Array($image.rank);
+        reps.fill(1, 0, lastDimsIdx);
+        reps[lastDimsIdx] = 3;
+        return tile($image, reps);
+    }
+    var grayscaleToRGB = op({ grayscaleToRGB_: grayscaleToRGB_ });
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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', 'float32');
+        var $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression', 'float32');
+        var inputs = nonMaxSuppSanityCheck($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;
+    }
+
+    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, __spread(new Array(elemsToPad).fill(0)));
+            selectedScores.push.apply(selectedScores, __spread(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));
+    }
+
+    /**
+     * 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 = __read.apply(void 0, [_c.sent(), 2]), 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_;
+
+    /**
+     * 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]]);
+        }
+        __read(size, 0);
+        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_ });
+
+    /**
+     * 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]]);
+        }
+        __read(size, 0);
+        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_ });
+
+    /**
+     * 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 = __read(split($image, [1, 1, 1], -1), 3), r = _a[0], g = _a[1], b = _a[2];
+            var $r = mul(r, RED_INTENCITY_COEF);
+            var $g = mul(g, GREEN_INTENCITY_COEF);
+            var $b = mul(b, BLUE_INTENCITY_COEF);
+            grayscale = add(add($r, $g), $b);
+        }
+        else {
+            grayscale = image;
+        }
+        if (method === 'otsu') {
+            var $histogram = bincount(cast(round(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(classFirst), total);
+            weightBack = div(sum(classSecond), total);
+            var meanFirstDivA = sum(mul(classFirst, range(0, classFirst.size)));
+            meanFirst = div(meanFirstDivA, sum(classFirst));
+            var meanSecFill = fill(classSecond.shape, classFirst.size);
+            var meanSecAdd = add(range(0, classSecond.size), meanSecFill);
+            var meanSecMul = mul(classSecond, (meanSecAdd));
+            meanSec = div(sum(meanSecMul), sum(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_ });
+
+    /**
+     * 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 = $a.shape;
+        var _a = __read($a.shape.slice(-2), 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(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_ });
+
+    /**
+     * 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 = __read(qr2d(x2d, fullMatrices), 2), 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 = __read(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$1(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$1(tauTimesW, matMul$1(wT, rjEndAll)));
+                    }
+                    else {
+                        var rTimesTau = sub(rjEndAll, matMul$1(tauTimesW, matMul$1(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$1(matMul$1(qAllJEnd, w), tawTimesWT));
+                    }
+                    else {
+                        var qTimesTau = sub(qAllJEnd, matMul$1(matMul$1(qAllJEnd, w), tawTimesWT));
+                        q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
+                    }
+                    return [w, r, q];
+                }), 3), 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.
+     * =============================================================================
+     */
+    exports.Reduction = void 0;
+    (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, $weights);
+        if (reduction === exports.Reduction.NONE) {
+            return weightedLoss;
+        }
+        if (reduction === exports.Reduction.SUM) {
+            return sum(weightedLoss);
+        }
+        if (reduction === exports.Reduction.MEAN) {
+            if ($weights == null) {
+                return mean(weightedLoss);
+            }
+            else {
+                var broadcastFactor = $losses.size / $weights.size;
+                var result = div(sum(weightedLoss), sum($weights));
+                return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :
+                    result;
+            }
+        }
+        if (reduction === exports.Reduction.SUM_BY_NONZERO_WEIGHTS) {
+            if ($weights == null) {
+                return div(sum(weightedLoss), scalar($losses.size));
+            }
+            else {
+                var broadcastedWeights = mul($weights, ones($losses.shape));
+                var numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');
+                return div(sum(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, $predictions.shape, 'Error in cosineDistance: ');
+        var one = scalar(1);
+        var losses = sub(one, sum(mul($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(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($predictions, epsilonScalar))));
+        var l2 = mul(sub(one, $labels), log(add(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(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);
+            $multiClassLabels =
+                add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));
+        }
+        var losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    var sigmoidCrossEntropy = op({ sigmoidCrossEntropy_: sigmoidCrossEntropy_ });
+
+    /**
+     * 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(costVector, [dim]);
+            var gradFunc = function (dy, saved) {
+                var _a = __read(saved, 2), labels = _a[0], logResult = _a[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]);
+            $onehotLabels =
+                add(mul($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', 'int32');
+        var $values = convertToTensor(values, 'values', 'sparseFillEmptyRows');
+        var $denseShape = convertToTensor(denseShape, 'denseShape', 'sparseFillEmptyRows', 'int32');
+        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', 'int32');
+        var $inputShape = convertToTensor(inputShape, 'inputShape', 'sparseReshape', 'int32');
+        var $newShape = convertToTensor(newShape, 'newShape', 'sparseReshape', 'int32');
+        if ($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', 'int32');
+        var $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentMean', 'int32');
+        if ($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', 'int32');
+        var $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentSum', 'int32');
+        if ($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,
+        grayscaleToRGB: grayscaleToRGB,
+        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
+    };
+
+    /** @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;
+        }
+    });
+
+    /** @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(mul(accumulatedGrad, _this.rho), mul(square(gradient), 1 - _this.rho));
+                    var updates = mul(div(sqrt(add(accumulatedUpdate, _this.epsilon)), sqrt(add(accumulatedGrad, _this.epsilon))), gradient);
+                    var newAccumulatedUpdate = add(mul(accumulatedUpdate, _this.rho), mul(square(updates), 1 - _this.rho));
+                    accumulatedGrad.assign(newAccumulatedGrad);
+                    accumulatedUpdate.assign(newAccumulatedUpdate);
+                    var newValue = add(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 = __spread(this.accumulatedGrads, 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']);
+        };
+        return AdadeltaOptimizer;
+    }(Optimizer));
+    /** @nocollapse */
+    AdadeltaOptimizer.className = 'Adadelta'; // Name matters for Python compatibility.
+    registerClass(AdadeltaOptimizer);
+
+    /** @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(accumulatedGrad, square(gradient));
+                    accumulatedGrad.assign(newAccumulatedGrad);
+                    var newValue = add(mul(div(gradient, sqrt(add(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']);
+        };
+        return AdagradOptimizer;
+    }(Optimizer));
+    /** @nocollapse */
+    AdagradOptimizer.className = 'Adagrad'; // Note: Name matters for Python compatibility.
+    registerClass(AdagradOptimizer);
+
+    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(mul(firstMoment, _this.beta1), mul(gradient, 1 - _this.beta1));
+                    var newSecondMoment = add(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(mul(div(biasCorrectedFirstMoment, add(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 = __spread(this.accumulatedFirstMoment, 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']);
+        };
+        return AdamOptimizer;
+    }(Optimizer));
+    /** @nocollapse */
+    AdamOptimizer.className = 'Adam'; // Note: Name matters for Python compatibility.
+    registerClass(AdamOptimizer);
+
+    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(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(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(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add(newWeightedInfNorm, _this.epsilon))), value);
+                    value.assign(newValue);
+                });
+                _this.iteration.assign(add(_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']);
+        };
+        return AdamaxOptimizer;
+    }(Optimizer));
+    /** @nocollapse */
+    AdamaxOptimizer.className = 'Adamax'; // Note: Name matters for Python compatbility.
+    registerClass(AdamaxOptimizer);
+
+    /** @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(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']);
+        };
+        return SGDOptimizer;
+    }(Optimizer));
+    /** @nocollapse */
+    SGDOptimizer.className = 'SGD'; // Note: Name matters for Python compatibility.
+    registerClass(SGDOptimizer);
+
+    /** @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(mul(_this.m, accumulation), gradient);
+                    if (_this.useNesterov) {
+                        newValue = add(mul(_this.c, add(gradient, mul(newAccumulation, _this.m))), value);
+                    }
+                    else {
+                        newValue = add(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']);
+        };
+        return MomentumOptimizer;
+    }(SGDOptimizer));
+    /** @nocollapse */
+    MomentumOptimizer.className = 'Momentum'; // Name matters for Python compatibility.
+    registerClass(MomentumOptimizer);
+
+    /** @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(mul(accumulatedMeanSquare, _this.decay), mul(square(gradient), 1 - _this.decay));
+                    if (_this.centered) {
+                        var accumulatedMeanGrad = _this.accumulatedMeanGrads[i].variable;
+                        // Centered gradient
+                        var newAccumulatedMeanGrad = add(mul(accumulatedMeanGrad, _this.decay), mul(gradient, 1 - _this.decay));
+                        var gradContribution = div(mul(gradient, _this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add(square(newAccumulatedMeanGrad), _this.epsilon))));
+                        var newAccumulatedMoments = add(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(mul(accumulatedMeanSquare, _this.decay), mul(square(gradient), 1 - _this.decay));
+                        var newAccumulatedMoments = add(mul(accumulatedMoments, _this.momentum), div(mul(gradient, _this.learningRate), sqrt(add(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 = __spread(this.accumulatedMeanSquares, this.accumulatedMoments);
+                            if (this.centered) {
+                                variables.push.apply(variables, __spread(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']);
+        };
+        return RMSPropOptimizer;
+    }(Optimizer));
+    /** @nocollapse */
+    RMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.
+    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, __spread(permutedBeforeBatch));
+            permuted.push(0);
+            permuted.push.apply(permuted, __spread(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.
+     * =============================================================================
+     */
+    /**
+     * 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 };
+    }
+
+    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 = __read(equation.split(ARROW), 2), 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 e_1, _a;
+        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);
+            try {
+                for (var termIndices_1 = (e_1 = void 0, __values(termIndices)), termIndices_1_1 = termIndices_1.next(); !termIndices_1_1.done; termIndices_1_1 = termIndices_1.next()) {
+                    var termIndex = termIndices_1_1.value;
+                    if (computedTermIndices.indexOf(termIndex) === -1) {
+                        steps[i].push(termIndex);
+                        computedTermIndices.push(termIndex);
+                    }
+                }
+            }
+            catch (e_1_1) { e_1 = { error: e_1_1 }; }
+            finally {
+                try {
+                    if (termIndices_1_1 && !termIndices_1_1.done && (_a = termIndices_1.return)) _a.call(termIndices_1);
+                }
+                finally { if (e_1) throw e_1.error; }
+            }
+        }
+        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 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.
+     * =============================================================================
+     */
+    /**
+     * Generates sparse fill empty rows indices, dense shape mismatch error message.
+     *
+     * @param indicesLength The first dimension of indices.
+     */
+    function getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesLength) {
+        return "Received SparseTensor with denseShape[0] = 0 but\n  indices.shape[0] = " + indicesLength;
+    }
+    /**
+     * Generates sparse fill empty rows negative index error message.
+     *
+     * @param index The index with a negative value.
+     * @param value The negative value.
+     */
+    function getSparseFillEmptyRowsNegativeIndexErrorMessage(index, value) {
+        return "indices(" + index + ", 0) is invalid: " + value + " < 0";
+    }
+    /**
+     * Generates sparse fill empty rows out of range index error message.
+     *
+     * @param index The index with an out of range value.
+     * @param value The out of range value.
+     * @param limit The upper limit for indices.
+     */
+    function getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(index, value, limit) {
+        return "indices(" + index + ", 0) is invalid: " + value + " >= " + limit;
+    }
+
+    /**
+     * @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.
+     * =============================================================================
+     */
+    /**
+     * Generates sparse reshape multiple negative 1 output dimension error message.
+     *
+     * @param dim1 The first dimension with a negative 1 value.
+     * @param dim2 The second dimension with a negative 1 value.
+     */
+    function getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(dim1, dim2) {
+        return "only one output dimension may be -1, not both " + dim1 + " and " + dim2;
+    }
+    /**
+     * Generates sparse reshape negative output dimension error message.
+     *
+     * @param dim The dimension with a negative value.
+     * @param value The negative value.
+     */
+    function getSparseReshapeNegativeOutputDimErrorMessage(dim, value) {
+        return "size " + dim + " must be non-negative, not " + value;
+    }
+    /**
+     * Generates sparse reshape empty tensor zero output dimension error message.
+     *
+     */
+    function getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() {
+        return 'reshape cannot infer the missing input size for an empty tensor ' +
+            'unless all specified input sizes are non-zero';
+    }
+    /**
+     * Generates sparse reshape input output multiple mismatch error message.
+     *
+     * @param inputShape the input shape.
+     * @param outputShape the requested output shape.
+     */
+    function getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape) {
+        var inputSize = sizeFromShape(inputShape);
+        var outputSize = sizeFromShape(outputShape);
+        return "Input to reshape is a SparseTensor with " + inputSize + "\n  dense values, but the requested shape requires a multiple of " + outputSize + ". inputShape=" + inputShape + " outputShape= " + outputShape;
+    }
+    /**
+     * Generates sparse reshape input output inequality error message.
+     *
+     * @param inputShape the input shape.
+     * @param outputShape the requested output shape.
+     */
+    function getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape) {
+        var inputSize = sizeFromShape(inputShape);
+        var outputSize = sizeFromShape(outputShape);
+        return "Input to reshape is a tensor with " + inputSize + " dense values, but the requested shape has " + outputSize + ". inputShape=" + inputShape + " outputShape=" + 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.
+     * =============================================================================
+     */
+    /**
+     * Generates sparse segment reduction negative segment ids error message.
+     *
+     */
+    function getSparseSegmentReductionNegativeSegmentIdsErrorMessage() {
+        return "segment ids must be >= 0";
+    }
+    /**
+     * Generates sparse segment reduction non increasing segment ids error message.
+     *
+     */
+    function getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() {
+        return "segment ids are not increasing";
+    }
+    /**
+     * Generates sparse segment reduction segment id out of range error message.
+     *
+     * @param segmentId The segment id index that is out of range.
+     * @param outputRows Upper bound of valid segment id values.
+     */
+    function getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(segmentId, outputRows) {
+        return "Segment id " + segmentId + " out of range [0, " + outputRows + "), possibly because segmentIds input is not sorted.";
+    }
+    /**
+     * Generates sparse segment reduction input indice out of range error message.
+     *
+     * @param index The index that holds the out of range value.
+     * @param indexValue The value that is out of range.
+     * @param inputRows Upper bound of valid index values.
+     */
+    function getSparseSegmentReductionIndicesOutOfRangeErrorMessage(index, indexValue, inputRows) {
+        return "Bad: indices[" + index + "] == " + indexValue + " out of range [0, " + inputRows + ")";
+    }
+
+    /**
+     * @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(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,
+        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,
+        checkPadOnDimRoundingMode: checkPadOnDimRoundingMode,
+        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$1,
+        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,
+        getSparseFillEmptyRowsIndicesDenseShapeMismatch: getSparseFillEmptyRowsIndicesDenseShapeMismatch,
+        getSparseFillEmptyRowsNegativeIndexErrorMessage: getSparseFillEmptyRowsNegativeIndexErrorMessage,
+        getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: getSparseFillEmptyRowsOutOfRangeIndexErrorMessage,
+        getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: getSparseReshapeMultipleNegativeOneOutputDimErrorMessage,
+        getSparseReshapeNegativeOutputDimErrorMessage: getSparseReshapeNegativeOutputDimErrorMessage,
+        getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: getSparseReshapeEmptyTensorZeroOutputDimErrorMessage,
+        getSparseReshapeInputOutputMultipleErrorMessage: getSparseReshapeInputOutputMultipleErrorMessage,
+        getSparseReshapeInputOutputMismatchErrorMessage: getSparseReshapeInputOutputMismatchErrorMessage,
+        getSparseSegmentReductionNegativeSegmentIdsErrorMessage: getSparseSegmentReductionNegativeSegmentIdsErrorMessage,
+        getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage,
+        getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage,
+        getSparseSegmentReductionIndicesOutOfRangeErrorMessage: getSparseSegmentReductionIndicesOutOfRangeErrorMessage
+    };
+
+    /**
+     * @license
+     * Copyright 2020 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT 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.BroadcastArgs = BroadcastArgs;
+    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.OptimizerConstructors = OptimizerConstructors;
+    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;
+    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.broadcastArgs = broadcastArgs;
+    exports.broadcastTo = broadcastTo;
+    exports.broadcast_util = broadcast_util;
+    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$1;
+    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$1;
+    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$1;
+    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;
+    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;
+    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;
+    exports.sumOutType = sumOutType;
+    exports.tan = tan;
+    exports.tanh = tanh;
+    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